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Predictive energy management of hybrid vehicle
Abstract
Hybrid vehicles use two energy sources for their propelling. Usually an internal combustion engine (ICE) is used with one or more electric machine(s) (EM). The problem is then to split the driver power demand between the ICE and the EM in order to minimize a criterion, usually the fuel consumption. A global optimization algorithm based on optimal control theory is recalled. The obtained results are optimal but can only be obtained in simulation. For real time control purpose, this optimization algorithm is applied on a receding horizon. The main problem is then to choose the variables to be predicted on this horizon. By analyzing the optimization algorithm, it is shown that the prediction of the future driving conditions (vehicle speed and driver torque demand) is not necessary. Therefore, under some assumptions, a real time control is possible.
... In recent years, a new category of real time algorithms based on the exploitation of optimal control algorithms has been widely investigated [28][29][30][31][32]. Another popular approach is Equivalent Consumption Minimization Strategy [33][34][35][36][37][38]. These strategies are derived from the minimum principle, and always focus on a chargesustaining (CS) task for the ESS with a specific optimal object. ...
... The PEMFC polarization curve is obtained from the sum of Nernst's voltage, activation overvoltage, and ohmic overvoltage. Assuming a constant temperature and oxygen concentration, the output voltage of the FC stack could be expressed as [20,37]: ...
... To minimize hydrogen consumption, a SOC constraint is often considered. These proposed control strategies [33][34][35][36][37] are aimed towards fuel savings for a charge-sustaining operation in hybrid electric vehicles, i.e. DSOC = SOC(N) À SOC(0) = 0. ...
In this study, a test station of fuel cell-battery hybrid powertrain is established for validating the control strategy and system components as a Hardware-in-the-Loop test platform. Firstly, a fuel cell and LiFeO 4 battery pack full hybrid powertrain is presented and the structure and methods of the module-based test station are described. Secondly, a power management strategy is proposed for the hybrid powertrain, aiming to minimize the hydrogen consumption of the fuel cell stack with a limited power rising rate and meanwhile to obtain a given depleting value for the state of charge (SOC) of the battery pack over the ECE driving cycle. The strategy has been implemented in the Matlab/Simulink software and its effectiveness is evaluated by the simulation results and experimental data from the test station. Finally, it is deduced that the proposed fuel cell-battery full hybrid powertrain can bring about greater improvements in driving range than pure battery electric vehicle. Thus, it is confirmed that the full hybrid structure and optimal control scheme can be used to achieve specific objectives for fuel cell-battery hybrid powertrains.
... The majority of existing work focuses on optimal controllers that seek to minimize fuel consumption. Such controllers, which ignore other important drivetrain behavior, can be undesirable in practice because they will use high levels of powertrain activity like shifting and staring the engine in order to save miniscule amounts of fuel [17,19,43,56,60,76]. These powertrain behaviors are known as "drivability." ...
... The real-world robustness testing addresses a common customer complaint that the fuel economy shown on the "window sticker" does not match the vehicle performance obtained in practice [24,37,69,86,93]. To provide a realistic bench- There are relatively few results that test energy management controllers in real hardware [13,35,42,43,61,77,78], or that address the many practical considerations required to do so. One major goal was to sufficiently develop the algorithms so that they could be tested in a real vehicle. ...
... These include controllers based on neural networks [85], game theory [21], and fuzzy logic [25,48,65,88,92,102], and numerical optimization [44,87,103]. Perhaps the most realistic of these "other" methods is model predictive control (MPC) [11,31,42,43] There are several variants of the dynamic programming algorithm, including both deterministic and stochastic versions. The general optimization goal is the minimization of a performance metric J which is an additive cost function ...
Hybrid Vehicle fuel economy performance is highly sensitive to the "Energy Management" strategy used to regulate power flow among the various energy sources and sinks. Optimal solutions are easy to specify if the drive cycle is known a priori. It is very challenging to compute controllers that yield good fuel economy for a class of drive cycles representative of typical driver behavior. Additional challenges come in the form of constraints on powertrain activity, like shifting and starting the engine, which are commonly called "drivability" metrics. These constraints can adversely affect fuel economy. In this dissertation, drivability restrictions are included in a Shortest Path Stochastic Dynamic Programming (SPSDP) formulation of the energy management problem to directly address this tradeoff and generate optimal, causal controllers. The controllers are evaluated on Ford Motor Company's highly accurate proprietary vehicle model and compared to a controller developed by Ford for a prototype vehicle. The SPSDP-based controllers improve fuel economy more
than 15% compared to the industrial controller on government test cycles. In addition, the
SPSDP-based controllers can directly quantify tradeoffs between fuel economy and drivability. Hundreds of thousands of simulations are conducted using real-world drive cycles to evaluate performance and robustness in the real world, demonstrating 10% improvement compared to the baseline. Finally, the controllers are tested in a real vehicle.
... Figure 1 outlines some of the major ICE types along with the different types of EMCs used in HEVs. In the first category, a CI engine has been used in different HEV architectures [8] [9] [10] [11] [21] [12] [13] [14]. The CI engines have been mostly used in Sport Utility Vehicles (SUVs) and trucks [21] [13] [15] [10] [13]. ...
... However, they offer only limited optimization and may fail to completely exploit the HEV benefits [26] [25]; 2) Offline global optimization such as Dynamic Programming (DP) [14] [27], and Pontryagin Minimum Principle (PMP) [16] [17]; the DP method is a global optimization method that assumes the information of an entire driving cycle is available and numerically finds the global optimal solution. This method is not implementable in real applications due to the necessity of knowing a priori of the driving cycle [26]; 3) Real time optimization controllers such as Model Predictive Controllers (MPC) [8] [29], Equivalent Consumption Minimization Strategy (ECMS) [12] [28], and Stochastic Dynamic Programming (SDP) [13]. The MPC method is a local optimization method that is implementable online but requires high A. Solouk et al. Figure 1 Different types of ICEs and EMCs used in HEVs in previous studies. ...
... Previous studies on different types of ICE utilisation in HEVs are divided into three main groups: CI engines, SI engines, and LTC engines.Figure 1 outlines some of the major ICE types along with the different types of energy management controls (EMCs) used in HEVs. In the first category, a CI engine has been used in different HEV architectures (Kermani et al., 2012; Opila et al., 2012; Johri et al., 2011; Jalil and Salman, 1997; Albert et al., 2004; Sciarretta et al., 2004; Lin et al., 2004; Brahma et al., 2000). The CI engines have been mostly used in sport utility vehicles (SUVs) and trucks (Albert et al., 2004; Lin et al., 2004; Brahma et al., 1999; v et al., 2011). ...
Low temperature combustion (LTC) engines show promise for fuel
economy improvement, however, these engines suffer from limited operating
range. In this study a specific type of LTC engine, known as homogeneous
charge compression ignition (HCCI), is used in a series hybrid and extended
range electric vehicle (E-REV) powertrain to investigate the ultimate fuel
saving of a purely dedicated HCCI mode powertrain. Three types of
energy management control (EMC) strategies including rule-based, dynamic
programing (DP), and model predictive control (MPC) are designed and
implemented. The simulation results show 17.7% fuel economy improvement
in series HEV and 18.0% fuel economy improvement in E-REV compared to a
conventional HEV using an SI engine in urban dynamometer driving schedule
(UDDS) driving cycle. In addition, simulation results show that the HCCI fuel
economy improvement increases in the driving cycles with greater base average
power.
... On the basis of realtime information provided by historical driving data, mathematical models or intelligent transportation systems, MPCcan predict the torque requirements of vehicles and optimize the energy allocation ratio to achieve low fuel consumption and emission [25,26]. However, the MPC control effect dependson the future driving information prediction accuracy, whichremainedto be an open question for now [27][28][29]. Based on Pontryagin's minimum principle, the ECMS simplifies the dynamic optimization problem into an equivalent instantaneous optimization problem, which reduces the computational complexity of the optimal algorithm and is suitable for real-time controllers. ...
... Then, the s(t) can be obtained as: − (27) where si is the initial s(t), k is the coordination variable, SOCn is the current SOC. ...
Plug-in hybrid electric buses (PHEBs) is some of the most promising products to address air pollution and the energy crisis. Considering the switching between different working modes often bring aboutsudden changes of the torque and the speed of different power sources, which may lead to the instability of the power output and affect the driving performance and ride comfort, it is of great significance to develop a real-time optimal energy management strategy for PHEBs to achieve the optimization of fuel economy and drivability. In this study, the proposed strategy includes an offline part and an online part. In the offline part, firstly, the energy conversion coefficient s(t) is optimized by linear weight particle swarm optimization algorithm (LinWPSO), then, the optimization results of s(t) are converted into a 2-dimensional look-up table. Secondly, combined with three typical driving cycle conditions, the gear-shifting correction and mode switching boundary parameters that affect the drivabilityof the vehicle are extracted by dynamic programming (DP) algorithm. In the online part, combined with the s(t), the gear-shifting correction and mode switching boundary parameters which are obtained through offline optimization, the real-time energy management strategy is proposed to solve the trade-off problem between minimizing the fuel consumption and improving the drivability and riding comfort. Finally, the proposed strategy is verified with simulation, the results show that the proposed strategy can guarantee the engine and the electric motor (EM) work in the high-efficiency area with optimal energy distribution while keeping drivability in the variation of driving circle. The overall performance is improved by 18.54% compared with the rule-based control strategy. The proposed strategy may provide theoretical support for the optimal control of PHEB.
... Previous studies based on types of utilized ICEs are divided into three main groups: Compression Ignition (CI) engines, SI engines, and LTC engines. In the first group, CI engine studies have been used in different HEV configurations [7,8,10,11,12]. CI engines mostly have been used in trucks or Sport Utility Vehicles (SUVs) [11,8,13]. In another group, conventional SI engines are integrated with HEV architecture [16]. ...
... In addition, different types of EMCs have been used for HEV control. These include: 1) Real Time Optimization controllers such as Model Predictive Controllers (MPC) [21,7], Equivalent Consumption Minimization Strategy (ECMS) [10,22], and Stochastic Dynamic Programming (SDP) [11]; 2) Offline global optimization such as Dynamic Programming (DP) [12,23], Pontriyagen's Minimum Principal (PMP) [14,15]; 3) Rule-Based Controller (RBC) such as Thermostatic [9] and Fuzzy [13] strategies. The RBC strategies are robust and have low computational costs. ...
Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.
... • Pontryagin's maximum principle (PMP): In a deterministic framework, so-called indirect method, based on PMP, can also be implemented in order to find the optimal control, see [52,51,91]. ...
The focus of this PhD thesis is to design an optimal Energy Management System (EMS) for a Hybrid Electric Vehicle (HEV) following traffic constraints.In the current state of the art, EMS are typically divided between real-time designs relying on local optimization methods, and global optimization that is only suitable for off-line use due to computational constraints.The starting point of the thesis is that in terms of energy consumption, the stochastic aspect of the traffic conditions can be accurately modelled thanks to (speed,acceleration) probability distributions.In order to reduce the data size of the model, we use clustering techniques based on the Wasserstein distance, the corresponding barycenters being computed by either a Sinkhorn or Stochastic Alternate Gradient method.Thanks to this stochastic traffic model, an off-line optimization can be performed to determine the optimal control (electric motor torque) that minimizes the fuel consumption of the HEV over a certain road segment.Then, a bi-level algorithm takes advantage of this information to optimize the consumption over a whole travel, the upper level optimization being deterministic and therefore fast enough for real-time implementation.We illustrate the relevance of the traffic model and the bi-level optimization, using both traffic data generated by a simulator, as well as some actual traffic data recorded near Lyon (France).Finally, we investigate the extension of the bi-level algorithm to the eco-routing problem, using an augmented graph to track the state of charge information over the road network.
... Over the past decade, the transportation system has generated increased fuel usage and air emissions [1,2]. Electric vehicles (EVs) have been regarded as a potential solution to significantly reduce transport emissions, thus helping to reduce road traffic pollution, particularly in densely populated urban areas [3]. ...
Although electric vehicles (EVs) have been regarded as promising to reduce tailpipe emissions and energy consumption, a mixed traffic flow of EVs and internal combustion engine vehicles (ICEVs) makes the energy/emissions reduction objective more difficult because EVs and ICEVs have various general characteristics. This paper proposes a low-emission-oriented speed guidance model to address the energy/emission reduction issue under a mixed traffic flow at an isolated signalized intersection to achieve the objective of reducing emissions and total energy consumption while reducing vehicle delay and travel time. The total energy/emissions under different market penetration rates of EVs with various traffic volumes are analyzed and compared. Numerical examples demonstrate that the proposed speed guidance model has better performance than those without considering the impact of queues. For a certain traffic volume, the energy/emission reduction effects under speed guidance will increase with an increasing share of EVs. This paper also explores the impact of the time interval for guidance renewal on vehicle emissions in practice.
... In general, the performance and robustness of the predictive strategy is related to the quality of the prediction itself [3]. It is challenging to predict the future driving conditions with a sufficient accuracy [16]. The accuracy of the power prediction is the key issue to save fuel within a predictive strategy [17]. ...
Fuel economy is a key aspect to reduce operating costs and improve efficiency of freight traffic, thus increasing truck competitiveness. The main objective of the IMPERIUM project (IMplementation of Powertrain Control for Economic and Clean Real driving EmIssion and ConsUMption) is to achieve fuel consumption reduction by 20% (diesel and urea) whilst keeping the vehicle within the legal limits for pollutant emissions. The approach relies on three stages targeting the improvement of the control strategy: (a) direct optimisation of the control of the main components (engine, exhaust after-treatment, transmission, waste heat recovery, e-drive) to maximize their performances, (b) global powertrain energy manager to coordinate the different energy sources and optimize their use depending on the current driving situation, (c) providing a more comprehensive understanding of the mission (eHorizon, mission-based learning) such that the different energy sources can be planned and optimized on a long term. The IMPERIUM consortium consists of major European actors and is able to provide a 100% European value chain for the development of future powertrain control strategies for trucks. This paper addresses the opportunities for powertrain optimization from the control strategy point of view, by modeling the physical behaviour of the truck, presenting the existing control strategies, and finally identifying the opportunities for additional, look-ahead mission-related information.
... Note that the NMPC still requires a few predictive partitions within the optimization window (preview) of the driving profile, but not as extensively as the dynamic programming approach. The problem underlying the NMPC strategy for PMCP in [18] is a mixed integer optimization problem, e.g., [19], which is computationally expensive. ...
This paper presents a nonlinear-model based hybrid optimal control technique to compute a suboptimal power-split strategy for power/energy management in a parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained as model predictive control solution to the power management control problem (PMCP) of the PHEV, i.e., to decide upon the power distribution among the internal combustion engine, an electric drive, and other subsystems. A hierarchical control structure of the hybrid vehicle, i.e., supervisory level and local or subsystem level is assumed in this study. The PMCP consists of a dynamical nonlinear model, and a performance index, both of which are formulated for power flows at the supervisory level. The model is described as a bi-modal switched system, consistent with the operating mode of the electric ED. The performance index prescribing the desired behavior penalizes vehicle tracking errors, fuel consumption, and frictional losses, as well as sustaining the battery state of charge (SOC). The power-split strategy is obtained by first creating the embedded optimal control problem (EOCP) from the original bi-modal switched system model with the performance index. Direct collocation is applied to transform the problem into a nonlinear programming problem. A nonlinear predictive control technique (NMPC) in conjunction with a sequential quadratic programming solver is used to compute suboptimal numerical solutions to the PMCP. Methods for approximating the numerical solution to the EOCP with trajectories of the original bi-modal PHEV are also presented in this paper. The usefulness of the approach is illustrated via simulation results on several case studies.
... The optimization model, able to assess the effect of the vehicle to grid as a contribution to the management of energy resources of the small electric energy system. Kermani et al.[46]used hybrid vehicle model for energetic studies and the energy management problem with an offline optimization algorithm based on the minimum principle to predict control scheme. The proposed model provided a comparison between fuel consumption and battery state of charge monitor and allowed guarantying bounds on the state of charge error while providing adequate fuel consumption. ...
... DP usually depends on a model to provide a provably optimal control strategy by searching all state and control grids exhaustively [11,31,32]. However, DP is not applicable for real-time problems since the exact future driving information is seldom known in the real world [33]. Nonetheless, the DP-based strategy can provide a good benchmark for evaluating the optimality of other algorithms and contribute to improving the real-time strategies [34,35]. ...
This paper presents a comprehensive review of power management strategy (PMS) utilized in hybrid electric vehicles (HEVs) with an emphasis on model predictive control (MPC) based strategies for the first time. Research on MPC-based power management systems for HEVs has intensified recently due to its many inherent merits. The categories of the existing PMSs are identified from the latest literature, and a brief study of each type is conducted. Then, the MPC approach is introduced and its advantages are discussed. Based on the acquisition method of driver behavior used for state prediction and the dynamic model used, the MPC is classified and elaborated. Factors that affect the performance of the MPC are put forward, including prediction accuracy, design parameters, and solvers. Finally, several important issues in the application of MPC-based power management strategies and latest developing trends are discussed. This paper not only provides a comprehensive analysis of MPC-based power management strategies for HEVs but also puts forward the future and emphasis of future study, which will promote the development of energy management controller with high performance and low cost for HEVs.
... In the design process of MPCs, the drive demand torque was assumed to be exponentially decreasing over the prediction horizon. Literature[109] developed an MPC which does not require the time-ordered prediction of the driving condition but a prediction of their distribution under a constant costate assumption. A drive cycle prediction algorithm was designed for the optimization-based PMS development in ...
As people have begun to pay more attention to energy conservation and emission reduction in recent
years, anti-idling has become a growing concern for automobile engineers due to the low efficiency
and high emissions caused by engine idling, i.e., the engine is running when the vehicle is not
moving. Currently, different technologies and products have emerged in an effort to minimize engine
idling. By studying and comparing most of these methods, the conclusion can be drawn that there is
still much room to improve existing anti-idling technologies and products. As a result, the optimized
Regenerative Auxiliary Power System (RAPS) is proposed.
Service vehicles usually refer to a class of vehicles that are used for special purposes, such as
public buses, delivery trucks, and long-haul trucks. Among them, there are vehicles with auxiliary
devices such as air conditioning or refrigeration (A/C-R) systems that are essential to be kept running
regardless of the vehicle motion. In addition, such auxiliary systems usually account for a large
portion of fuel from the tank. Food delivery trucks, tourist buses, and cement trucks are examples of
such service vehicles. As a leading contributor to greenhouse gas emissions, these vehicles sometimes
have to frequently idle to for example keep people comfortable, and keep food fresh on loading and
unloading stops. This research is intended to develop and implement a novel RAPS for such service
vehicles with the A/C-R system as the main auxiliary device. The proposed RAPS can not only
electrify the auxiliary systems to achieve anti-idling but also use regenerative braking energy to
power them.
As the main power consuming device, the A/C-R system should be treated carefully in terms of its
efficiency and performance. Thus, the developments of an advanced controller for A/C-R system to
minimize energy consumption and an optimum power management system to maximize the overall
efficiency of the RAPS are the primary objectives of this thesis. In this thesis, a model predictive
controller (MPC) is designed based on a new A/C-R simplified model to minimize the power
consumption while meeting the temperature requirements. The controller is extensively validated
under both common and frosting conditions. Meanwhile, after integrating the RAPS into a service
vehicle, its powertrain turns into a parallel hybrid system due to the addition of an energy storage
system (ESS). For the sake of maximizing the overall efficiency, RAPS requires a power
management controller to determine the power flow between different energy sources. As a result, a
predictive power management controller is developed to achieve this objective, where a regenerative
iv
braking control strategy is developed to meet the driver’s braking demand while recovering the
maximum braking energy when vehicles brake. For the implementation of the above controllers, a
holistic controller of the RAPS is designed to deal with the auxiliary power minimization and power
management simultanously so as to maximize the overall energy efficiency and meet the high
nonlinearities and wide operating conditions.
... Figure 1 divides prior HEV studies for the major ICE types. In the first category, a CI engine has been used in different HEV architectures [6][7][8][9][10][11][12][13]. The CI engines have been mostly used in Sport Utility Vehicles (SUVs) and trucks [8,10,12,12,14]. ...
Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. Extended range electric vehicles (EREVs), by decoupling the engine from the drivetrain, allows the engine to operate in a limited operating range; thus, EREVs offer an ideal platform for realizing the advantages of LTC engines. In this study, the global optimum fuel economy improvement of an experimentally developed 2-liter multi-mode LTC engine in a series EREV is investigated. The engine operation modes include Homogeneous-Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The
simulation results show in the city driving cycle, the single-mode HCCI and RCCI engines offer 12% and 9% fuel economy improvement, respectively over a single-mode SI engine in the EREV. These improvements increase to 13.1% and 10.3% in the highway driving cycles. In addition, the mode-switching fuel penalty is included in the optimization problem and the results are used to determine number of LTC modes. The results show that the multi-mode LTC engine offers 2% more fuel economy improvement over the best single-mode LTC engine operation. These results depend on the type of driving cycle and mode-switching fuel penalty. HCCI and RCCI engine modes can be the dominant optimal engine operating modes depending on the
mode-switching fuel penalty value.
... Le couple embrayage est aussi utilisé dans les applications de contrôle de la chaîne cinématique [15] [20]. Généralement, le couple moteur est donné par une cartographie dépendante du régime et de la position pédale [11] [1] [21] [19], mais ces cartographies sont obtenues à l'aide de nombreux essais sur banc, elles n'intègrent pas l'aspect dynamique du couple moteur et ne sont pas robuste à la dispersion que l'on peut avoir entre deux moteurs théoriquement identiques. Afin d'eviter se problème des méthodes d'estimation du couple moteur basées sur des modèles dynamiques ont été développées [14] [8]. ...
L'estimation du couple moteur et du couple embrayage présente un grand intérêt pour beaucoup d'applications de l'industrie automobile telles que les véhicules hybrides, les transmissions automatisée, le contrôle moteur, etc... Une méthode d'estimation du couple moteur et du couple embrayage basée sur un observateur flou discrétisé dans le domaine angulaire utilisant un calcul de torsion angulaire est proposée dans cet article. Cette méthode est appliquée au double volant amortisseur ; un dispositif de filtration des acyclismes moteur. La méthode proposée a permis d'obtenir de bon résultats expérimentaux d'estimation de couple moteur instantané et de couple embrayage. Abstract: The interest in engine torque and clutch torque estimation is important in the automotive industry. They are determinant variables in engine and powertrain management like hybrid engine vehicle, automated transmission and energy saving strategies. An instantaneous brake engine torque and transmitted clutch torque estimation method based on discrete angular domain fuzzy observer using a torsion angle calculation is proposed in this paper. It is applied on a filtration device; the dual mass flywheel. The observer permits to obtain good experimental results of the engine torque and clutch torque estimation.
... A real-time predictive strategy to reduce fuel consumption using global optimisation has been presented by Kermani et al. [Kerm08]. This approach does not require predicting the temporal evolution of the driving conditions, contrary, previous values are used within a predictive control scheme. ...
One of the effects of the globalization of our society is that people travel more covering longer distances, live far from their work place and consume goods from all around the world. Therefore, it si no coincidence that the transport of people and goods represents more than 25% of the energy consumption and is one of the principal sources of pollution worldwide. Several efforts must be done to reduce the oil dependence, the energy consumption and the environmental impact of transport systems. In this perspective, the French Army (DGA) has designed and constructed the Electrical Chain Components Evaluation vehicle (ECCE). It is a mobile laboratory to evaluate under real conditions the electric components of Hybrid Electrical Vehicles (HEVs) that reduce the energy consumption and the pollution emission of conventional vehicles. ECCE permits evaluating different energy sources such as batteries, fuel cells, internal combustion engines, ultracapacitors or flywheels. The ECCE project, nowadays in a second phase/footnote {The first phase of the ECCE project is explained in Chapter 1} is developed in joint cooperation with the FEMTO-ST laboratory of the University of Franche-Comté and two industrial partners, HELION and PANHARD General Defense. It aims to study the implementation, control and energy management of different hybrid sources. As a research developed along the second phase of the ECCE project, the principal objective of the thesis is to design, to implement and to evaluate an energy management supervision system in the ECCE HEV. This thesis proposes an original energy management strategy based on expert knowledge and type-2 fuzzy logic. The design of the fuzzy logic controller is done by using knowledge engineering. This technique allows extracting knowledge from several experts using surveys. The considering of type-2 fuzzy logic systems enables modelling the uncertainty in the answers of the experts. The thesis presents a second application of type-2 fuzzy logic : the voltage regulation of a DC/DC power converter. The principal motivation for developing this application is that it is easier to implement in laboratory at a relatively low cost and it permits a viability evaluation of type-2 fuzzy logic before an implementation in the ECCE mobile laboratory. This is useful because one of the main challenges of this thesis is to reduce the time to experimentally validate the energy management system. This is required to respect the time schedule constraints and to reduce the costs associated to gather the partners of the project at PANHARD locations in Saint-Germain Laval.
... In order to guarantee these constraints, a lot of studies have been done to optimize the powertrain management for energy saving and to improve the performances. The engine torque is needed in almost all of these applications like in hybrid vehicles management [1] [2] [3], engine [4] [5]. The clutch torque is also used for driveline control applications [6][7]. ...
The interest in engine torque and clutch torque estimation is important in the automotive industry. They are determinant variables in engine and powertrain management like hybrid engine vehicle, automated transmission and energy saving strategies. An instantaneous brake engine torque and transmitted clutch torque estimation method based on discrete angular domain fuzzy observer using a torsion angle calculation is proposed in this paper. It is applied on a filtration device; the dual mass flywheel. The observer permits to obtain good experimental results of the engine torque and clutch torque estimation.
... Applying DP in PHEVs consists of finding optimal control sequences to obtain the optimal battery state of charge (SoC) trajectory and to minimize fuel consumption over a given driving schedule. The DP-based energy management strategy belongs to the category of off-line energy management techniques, which are not suitable for online control [11]. However, this approach provides a benchmark for assessing the optimality of other energy management strategies and helps to improve the online strategy [12][13][14][15][16]. ...
To explore the problems associated with applying dynamic programming (DP) in the energy management strategies of plug-in hybrid electric vehicles (PHEVs), a plug-in hybrid bus powertrain is introduced and its dynamic control model is constructed. The numerical issues, including the discretization resolution of the relevant variables and the boundary issue of their feasible regions, were considered when implementing DP to solve the optimal control problem of PHEVs. The tradeoff between the optimization accuracy when using the DP algorithm and the computational burden was systematically investigated. As a result of overcoming the numerical issues, the DP-based approach has the potential to improve the fuel-savings potential of PHEVs. The results from comparing the DP-based strategy and the traditional control strategy indicate that there is an approximately 20% improvement in fuel economy.
... However, both the rule-based strategies and FLC strategies fail to reach the optimal performance due to lack of an optimization process. Another online available methodology of EMS design are local optimization strategies, like model predictive control (MPC)[20][21][22][23]and stochastic dynamic programming[24,25]. These strategies are based on a period of predicted driving cycle in the future and employ the optimal control policy on this short-term time horizon. ...
Plug-in hybrid electric vehicles (PHEVs) have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO) algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity) from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment.
... In most situations, the maximal velocity of city buses is less than 50 km/h. Naturally the hybrid system should be able to emulate the engine driving in low speed, to recover the brake energy, and to stop the engine when the bus is in stops or waiting for the traffic lights [3,4]. ...
A design methodology which uses the regenerative brake model is introduced to determine the major system parameters of a parallel electric hybrid bus drive train. Hybrid system parameters mainly include the power rating of internal combustion engine (ICE), gear ratios of transmission, power rating, and maximal torque of motor, power, and capacity of battery. The regenerative model is built in the vehicle model to estimate the regenerative energy in the real road conditions. The design target is to ensure that the vehicle meets the specified vehicle performance, such as speed and acceleration, and at the same time, operates the ICE within an expected speed range. Several pairs of parameters are selected from the result analysis, and the fuel saving result in the road test shows that a 25% reduction is achieved in fuel consumption.
... Due to the weakness of MPC based on navigation technology or vehiclemounted sensors above mentioned, obtaining prediction information by mathematical model has drawn much attention of relevant scholars, thus formulating MPC based on mathematical prediction model. In this kind of MPC, mathematical prediction models consist of two types: one provides deterministic torque demand over prediction horizon, for example, future torque demand can be assumed to be exponentially decreasing in the prediction horizon [161], the other describes probability distributions of future torque demand based on current driving cycle or historical data, e.g., various standard driving cycles are used to acquire probability distributions of future torque demand in [162]. 4.2.2.3. ...
... Refs. [16][17][18][19] emphasise the effect of the driving route on the optimal performance of a hybrid electric vehicle and its energy consumption and battery state of charge. ...
... Recently more advanced control techniques based on optimal control has emerged [19,20]. In the paper [21] a model predictive control strategy (MPC) has been applied to energy management for hybrid vehicles. At each sampling time the powertrain operation point is chosen minimizing a desired criterion , in this case the fuel consumption. ...
This paper presents the development of an energy management strategy of a Plug-in Hybrid Electric Vehicle (PHEV). In this case, a rule-based optimal controller selects the appropriate operation mode. Furthermore, advantages and drawbacks of such vehicles are compared with respect to other vehicles powered by the most popular powertrain architectures. Simulations are carried out for three predefined drive cycles repeated over different geographic regions with varying CO2 intensities. The results reveal that the proposed controller is able to switch the vehicle’s operating mode according to previous established criteria.
... Also, a Pareto-optimal fronts were proposed for an electrical vehicle by reference [19], to obtain an optimal speeds by solving a multiobjective optimization problem that maximize electric motor efficiency and minimized power consumption. In addition, a model predictive control scheme is proposed to predict the future driving conditions [20]. In reference [21], a rule-based energy management strategy and a model predictive control (MPC) strategy for supercapacitor assisted powertrains is proposed and evaluated. ...
... Pontryagin Maximum Principle (PMP) has been used before to address similar supervisory control problems in [5,11,[18][19][20], etc. In these articles, in order to consider the state constraint, the optimality conditions are formulated following the "indirect adjoining approach''. ...
In the optimization of power management of hybrid electric vehicles, the equivalent consumption factor is often used. This parameter represents a way of penalizing the use of power from the batteries, taking into account the fuel consumption that such use eventually hides. If the problem of determining the power split between the energy sources of the vehicle that minimizes fuel consumption is stated as a non linear constrained optimal control problem, and is solved using Pontryagin Maximum Principle (PMP), the equivalent consumption factor may be computed from the adjoint state. Following this approach we compute the trajectory of the adjoint state in the case where state constraints are taken into account. The optimality conditions from PMP are a boundary value problem (BVP), which is solved numerically using a code named PASVA4 . Numerical examples are compared with dynamic programming solutions of the same problem. It is found that the adjoint state is continuous and its trajectory is described. The approach may be generalized to similar optimal control problems.
... 、动态规划 (dynamic programming，DP) [51] 、 随 机 动 态 规 划 (stochastic dynamic programming，SDP) [52] 、 最 优 控 制 理 论 [53] ， 以及遗传算法 (genetic algorithm， GA) [ [55][56] ，模型预测控制算法 (model predictive control，MPC) [57][58] ，基于 Pontryagin 最小原理 (Pontryagin's minimum principle，PMP) 的 控 制 算 法 [59][60] ， 基于鲁棒控制 [61] 、以及解耦算法 [ Cooperative control of regenerative braking and hydraulic braking of an electrified passenger car [J]. and key technologies of the hybrid powertrain system, the regenerative braking system, the electric vehicle dynamics, and the experimental equipments for hybrid propulsion and braking dynamical tests. ...
The performances of hybrid propulsions and hybrid brakes of various electric vehicles (EVs) significantly affect their energy efficiency and their safety. The development statuses were worldwide reviewed for the hybrid propulsion and hybrid braking technologies from the aspects of the parameter matching and optimization, the blending energy management, and the dynamical cooperative control to conclude and analyze the scientific topics and generic technologies. Further researches that need to be carried out in the hybrid propulsion and the hybrid braking to improve EV performances include the parameter matching and optimization when vehicle dynamics considered, the construction of cyber-physical system which can provide a platform for online management of vehicle multi-source and dual-way driving and braking energy, and the investigation of dynamic characteristics, blended mechanisms, and cooperative control for dynamical-process of the hybrid propulsion and the braking systems under critical driving situations.
... The ICE model is experimentally validated in this study for different throttle openings and various engine loads during both transient and steady-state operations. The dynamic ICE model is integrated into a parallel HEV model and will be used as a test bed to evaluate optimal torque split control strategies in this work.Figure 1 also shows different types of controllers used for the torque split strategies including: deterministic rulebased controllers [2, 5,78910 , fuzzy-logic rule-based con- trollers [6,11121314 18], global optimization-based controllers such as Dynamic Programming (DP)15161720212223, and local optimization-based or Model Predictive Control (MPC)242526. This paper centers on application of MPC for the parallel HEV torque split management. ...
Energy management strategies in a parallel Hybrid Electric Vehicle (HEV) greatly depend on the accuracy of internal combustion engine (ICE) data. It is a common practice to rely on static maps for required engine torque-fuel efficiency data. The engine dynamics are ignored in these static maps and it is uncertain how neglecting these dynamics can affect fuel economy of a parallel HEV. This paper presents the impact of ICE dynamics on the performance of the torque split management strategy. A parallel HEV torque split strategy is developed using a method of model predictive control. The control strategy is implemented on a HEV model with an experimentally validated, dynamic ICE model. Simulation results show that the ICE dynamics can degrade performance of the HEV control strategy during the transient periods of the vehicle operation by more than 20 % for city driving conditions in a common North American drive cycle. This also leads to substantial fuel penalty which is often overlooked in conventional HEV energy management strategies.
... The majority of existing work focuses on optimal controllers that seek to minimize fuel consumption. These controllers can be undesirable in practice because of excessive powertrain activity like shifting and staring the engine [27], [28], [29], [30]. These powertrain behaviors are known as "drivability." ...
Hybrid Vehicle fuel economy performance is highly sensitive to the "Energy Management" strategy used to regulate power flow among the various energy sources and sinks. Optimal solutions are easy to specify if the drive cycle is known a priori. It is very challenging to compute controllers that yield good fuel economy for a class of drive cycles representative of typical driver behavior. Additional challenges come in the form of constraints on powertrain activity, like shifting and starting the engine, which are commonly called "drivability" metrics. These constraints can adversely affect fuel economy. In this paper, drivability restrictions are included in a Shortest Path Stochastic Dynamic Programming (SPSDP) formulation of the energy management problem to directly address this tradeoff and generate optimal, causal controllers. The controllers are evaluated on Ford Motor Company's highly accurate proprietary vehicle model on the FTP and NEDC government drive cycles, and compared to a controller developed by Ford for a prototype vehicle. The SPSDP-based controllers improve fuel economy more than 15% compared to the industrial controller on government test cycles. In addition, the SPSDP-based controllers can directly quantify tradeoffs between fuel economy and drivability. The theoretical basis of the SPSDP method is related to the popular Equivalent Consumption Minimization Strategy (ECMS). This paper is the first of two parts and focuses on methods and results on government test cycles, while the second part studies this method in a broader and more practical sense, including simulation on large numbers of real-world drive cycles.
Hybridization of automotive powertrains by using more than one type of energy converter is considered as an important step towards reducing fuel consumption and air pollutants. Specifically, the development of energy efficient, highly complex, alternative drive-train systems, in which the interactions of different energy converters play an important role, requires new design methods and processes. This paper discusses the inclusion of an alternative hybrid power train into an existing vehicle platform for maximum energy efficiency. The new proposed integrated Vehicle Hardware In-the-loop (VHiL) and Model Based Design (MBD) approach is utilized to evaluate the energy efficiency of electrified powertrain. In VHiL, a complete chassis system becomes an integrated part of the vehicle test bed. A complete conventional Internal Combustion Engine (ICE) powered vehicle is tested in roller bench test for the integration of energy efficient hybrid electric power train modules in closed-loop, real-time, feedback configuration. A model that is a replica of the test vehicle is executed – in real-time- where all hybrid power train modules are included. While the VHiL platform is controlling the signal exchange between the test bed automation software and the vehicle on-board controller, the road load exerted on the driving wheels is manipulated in closed –loop real-time manner in order to reflect all hybrid driving modes including: All Electric Range (AER), Electric Power Assist (EPA) and blended Modes (BM). Upon successful implementation of VHiL, a comparative study between Rule Based (RB) energy management strategy (EMS) and Equivalent Consumption Minimization Strategy (ECMS) to Control Parallel Through-The-Road Hybrid Electric Vehicle (PTTR-HEV) is performed. The study shows that the actual fuel efficiency of the tested vehicle under both control strategies can be used in order to evaluate the effectiveness of energy conversion efficiency of the powertrain system. The fuel consumption of hybridized powertrain is compared with the conventional powertrain equipped in an actual vehicle to help comprehend the degree of efficiency attained by the hybridization. This process is developed in order to enable effective tuning/validation of advanced energy management strategies utilized in hybrid electric powertrain through an evaluation of a complete real chassis system subject to electric hybridization. The VHiL is considered as new evolution for the utilization of vehicle test bed as a predictive mechatronic platform for the development of energy efficient electrified propulsion systems and thus reduce cost and time.
By integrating at least one additional energy converter into the drive train, parallel hybrid
vehicles gain an additional degree of freedom compared to conventional vehicles. In addition
to the design and efficiency of the individual drive train components, especially the
use of this additional degree of freedom is the key responsible to achieve the desired
goals in the operation of a hybrid vehicle, such as minimizing fuel consumption and exhaust
emissions. Responsible for this are so-called supervisory strategies.
In a first step, the present thesis provides an overview of current supervisory control
strategies for vehicles with a parallel hybrid architecture and compares selected approaches.
In a second step, a promising Equivalent Consumption Minimization Strategy
(ECMS) is chosen and implemented in a MATLAB/Simulink-longitudinal dynamics model.
This approach relates on the determination of the equivalence factor which is carried out
without the use of prediction data. A comparison of the fuel consumption, obtained for a
rule-based supervisory strategy, shows the advantages of the implemented ECMS approach.
To consider the different states of charge at the end of the trip, a chargedependent
fuel correction will be presented.
This paper presents an anti-idling regenerative auxiliary power system for service vehicles. The energy storage system in the regenerative auxiliary power system is able to electrify the auxiliary systems so as to achieve anti-idling. Service vehicles (e.g. delivery trucks or public buses) generally have predetermined routes, thus it is feasible and profitable to utilize a model predictive control strategy to improve the fuel economy of the new powertrain. However, the mass/load of such service vehicles is time-varying during a drive cycle. Therefore, an adaptive model predictive controller should be designed to account for this variation. Although the drive cycle is preset, it would experience uncertainties or disturbances caused by traffic or weather conditions in real situations. To deal with this problem, a large step size prediction method is used in the adaptive model predictive algorithm to enhance its robustness. The proposed algorithm is compared to a prescient model predictive controller in different scenarios to demonstrate its applicability and optimality (more than 7% fuel savings). The proposed approach is independent of the powertrain topology such that it is able to be directly extended to other types of hybrid electric vehicles.
При проектировании получающих распространение комбинированных энергетических установок транспортных средств возникает новая задача определения характеристик источников энергии в ее составе. С использованием предложенной авторами методики определена энергоемкость накопителя энергии автобуса с комбинированной энергетической установкой в зависимости от мощности первичного источника энергии. Расчет произведена основе зависимости скорости движения транспортного средства от времени. Выполнен анализ возможных источников исходных данных для ее расчета. Рассмотрено использование расчетных кривых движения, стандартных циклов, применяемых для исследования топливной экономичности транспортных средств, а также экспериментальных записей скорости движения транспортного средства. Показана необходимость использования экспериментальных записей при проектировании транспортных средств, имеющих комбинированную энергетическую установку. Установлено, что в исследованных условиях энергоемкость буферного накопителя энергии, определенная на основе экспериментальных данных, приблизительно в 2 раза превосходит энергоемкость, полученную на основе расчетных циклов движения. Показано, что в этом случае при заданной мощности первичного источника энергии величина энергоемкости буферного накопителя слабо зависит от длины перегона и ускорения транспортного средства, но определяется максимальной скоростью,достигнутой за цикл разгона и торможения.
The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters (e.g. length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one.
This study designed an efficient, easily implementable online optimal control strategy for three-power-source hybrid electric powertrains. The energy improvement of optimal energy management and integrated optimal energy management/mode switch timing relative to the energy consumption in rule-based control was evaluated. First, a control-oriented vehicle model with seven subsystems was developed. For achieving rule-based control, the torque distribution among the engine, motor, and generator was designed according to performance maps of power sources. To conduct power allocation of three sources, two power-split ratios were obtained. Furthermore, for switching between three operation modes (hybrid, electric vehicle, and range extension modes), two hysteresis zones based on the required power and battery state-of-charge were used with four designed variables (boundaries). A global search method was used for the optimization. A cost function with a physical-constraint penalty was used to maximize the travel distance. A simulation performed using nested-structure for-loop programs showed that the mileage extension (energy improvement) for the optimal energy management and integrated optimal energy management/mode switch timing relative to the mileage in rule-based control for two driving cycles, NEDC and FTP-75, were [26.32%, 30.52%] and [17.22%, 20.68%], respectively. The improvements of CO2 reduction were [26.34%, 27.10%] and [23.47%, 24.12%], respectively, thus proving that this study significantly reduced energy consumption and pollutant emission by employing an easily designed control strategy. Online parameter tuning and implementation of optimal energy management in a real vehicle will be conducted in the future.
An appropriate energy management strategy is able to further improve the fuel economy of PHEVs. The rule-based energy management algorithms are dominated in industry due to their fast computation and ease of establishment potentials, however, their performance differ a lot from improper setting of parameters and control actions. This paper employs the dynamic programming (DP) to locate the optimal actions for the engine in PHEVs, and more importantly, proposes a recalibration method to improve the performance of the rule-based energy management through the results calculated by DP algorithm. Eventually, an optimization-based rule development procedure is presented and further validated by hardware-in-loop (HIL) simulation experiments. The HIL simulation results show that, the improved rule-based energy management strategy reduces fuel consumption per 100 km from 25.46 L diesel to 22.80 L diesel. The main contribution of this study is to explore a novel way to calibrate the existed heuristic control strategy with the global optimization result through advanced intelligent algorithms.
The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. These represent substantial opportunities considering that they only require software adjustments to implement.
This paper proposes a predictive real time energy management strategy for plug-in-hybrid electric vehicles (PHEV) based on an adaptation of Dynamic Programming (DP). The computational load of predictive real time strategies increases with the trip length. Therefore, for online computation by the onboard computer, they strongly depend on an efficient implementation. To reduce computation cost, current approaches for predictive strategies rely on strongly simplified intern vehicle models. The here proposed energy management strategy (EMS) uses a different approach, which is based on the use of precalculated lookup tables for the different operating points of the powertrain. This precalculation make the use of more exact vehicle models possible by using more detailed loss models of the powertrain components. The proposed EMS separates the optimization process, i.e. the calculation of the power distribution to engine and electric motor and gear in two calculation steps. The first step, which is computationally more intensive, has only to be executed once for a certain vehicle configuration. The obtained results are saved in lookup tables to avoid a later recomputation. In the second step, which is done online in the vehicle, a shortest path search algorithm is employed which is based on the predicted vehicle speed and rode slope of the trip. Techniques are integrated which decrease the rounding error caused by the use of lookup tables. The resulting difference of the consumed fuel mass between the lookup table based DP and standard DP is smaller than 0.03% by an approximately 50 times faster calculation. Using the proposed algorithm, even complex intern vehicle models do not affect the online computation cost and can be implemented by real time strategies.
In this study, the hybrid power system of the fuel cell ship combining fuel cell, as the main power source, and battery, as the energy storage unit is developed. A fuel consumption optimal energy management strategy was proposed for the fuel cell hybrid ship, aiming to minimize the hydrogen consumption of the fuel cell stack. For ensuring the battery working at the optimum region, a charge-sustaining strategy for the battery was introduced. The simulation model of the fuel cell hybrid power system was established in the MATLAB/SIMULINK simulation environment. The optimal energy management strategy was verified by simulation according to the typical drive cycle of ship compared to power following energy management strategy. The simulation results show that the proposed optimization can improve fuel economy while the state of charge (SOC) of the battery is maintained at reasonable range. ©, 2014, Acta Simulata Systematica Sinica. All right reserved.
In automation control system applications, the transmitted engine and clutch torque are unavoidable information for the control strategies. For example, these information are required to engine control, hybrid vehicle and automated transmission management. An angular domain discrete observer is proposed to estimate the transmitted torque from incremental encoders which are already implemented on the vehicle. Some simulation results of transmitted clutch torque estimation are presented as instantaneous brake engine torque and clutch torque estimation on real data. This proposed torque estimation method based on shaft angular deformation permits to obtain accurate results in different automotive applications.
This work investigates an event-based electric vehicle mass and grade estimation using a Recursive Least Squares (RSL) with variable forgetting factors method. Given the vehicle speed and electric power consumption, we proposed a two-layer identification architecture in which the first layer provides acceleration and cruise motion periods, whereas the second layer is responsible for mass and grade parameter estimations. The forgetting factors are updated based on the vehicle acceleration values. The proposed method does not require torque measurements from the propulsion system. Therefore, it can be used for different type of vehicles. The preliminary comparative study suggests that the proposed method is efficient and can provide satisfactory results even in presence of noisy measurements.
An energy management controller based on shortest path stochastic dynamic programming (SP-SDP) is implemented and tested in a prototype vehicle. The controller simultaneously optimizes fuel economy and powertrain activity, namely gear shifts and engine on-off events. Previous work reported on the controller's design and its extensive simulation based evaluation. This paper focuses on implementation of the controller algorithm in hardware. Practical issues concerning real-time computability, driver perception, and command timing are highlighted and addressed. The SP-SDP controllers are shown to run in real-time, gracefully handle variations in engine start and gear-shift-completion times, and operate in a manner that is transparent to the driver. A hardware problem with the test vehicle restricted its maximum engine torque, which prevented a reliable fuel economy assessment of the SP-SDP controller. The data that were collected indicated that SP-SDP controllers could be straightforwardly designed to operate at different points of the fuel economy tradeoff curve and that their fuel economy may equal or exceed that of a baseline industrial controller designed for the vehicle.
Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.
This paper presents a new numerical simulation method to calculate transient voltage profiles of lithium-ion secondary batteries. The method employs circuit analysis of an internal equivalent electric circuit composed of an electromotive force, an LR parallel circuit, and eight CR parallel circuits. To demonstrate the accuracy and versatility of this approach, the authors measured the transient voltage responses of three types of test batteries with different output power densities, and compared these experimental data with simulation results. Battery performance was tested using different charge/discharge current patterns and a range of values for state of charge (SOC) and operating temperature. The accuracy of the proposed simulation method was confirmed for all test cases using the three different batteries and charge/discharge current patterns, demonstrating that the method is versatile and applicable to various lithium-ion secondary batteries regardless of type. Since the employed internal equivalent electric circuit is composed of only DC voltage source and linear and C elements, all of general purpose software for electric circuit simulations can easily deal with the circuit. This advantage and the obtained results indicate that the proposed simulation method is a useful technique and offers a powerful tool to develop sophisticated battery control systems for various applications.
This paper presents the design and implementation of the power management system utilizing supercapacitors for the hybrid vehicle. The researchers develops a system that has monitoring and cell balancing using a microcontroller board which controls, monitors, and charges the battery efficiently to avoid overcharging. The system uses a generator to charge the supercapacitor utilizing it for the electric motor. The supply was in single phase form and it uses step-up transformer with 48V volts switch-mode power supply. Before storing the energy gathered from the generator, the battery will undergo for cell balancing to ensure that all battery pair are equal to each other. The power management system in the project balances the supercapacitors that are connected in series.
The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.
This paper addresses the coordinated control of the internal combustion engine and the electric motor in a parallel hybrid electric vehicle, when both of them are running. In deciding how much torque each motor contributes, both long term energy oriented and short term drivability goals must be considered. The first contribution in this paper consists of proposing an architecture in which three main functional blocks are present, namely a steady-state performance generator, providing an energy oriented torque contribution, a transient performance generator providing a drivability oriented torque contribution, and a dynamic input allocator blending the outputs of the other two blocks in such a way as to satisfy both the short and the long term goals. The second contribution consists in showing how the input allocator must be designed. The other two blocks can be designed following any of several recipes already described in the literature. Experimental validation of the proposed approach confirms the relevance of accounting for the different motor dynamics in the allocator design.
This paper describes control applications in automobiles. Many aspects of automotive applications of advanced control methods, which include suspension systems, stability control systems, engines, hybrid vehicle control systems, electric vehicle controls systems, advanced driver assistance systems and automated driving control systems, are reviewed. The control methods used in each area are briefly reviewed to help readers understand the applicability and effectiveness of these methods. In addition, some new trends in the research of automotive applications are described.
Energy management strategy is an important factor in determining the fuel economy of hybrid electric vehicles; thus, much research on how to distribute the required power to engines and motors of hybrid vehicles is required. Recently, various studies have been conducted based on reinforcement learning to optimally control the hybrid electric vehicle. In fact, the fundamental control approach of reinforcement learning shares many control frameworks with the control approach by using deterministic dynamic programming or stochastic dynamic programming. In this study, we compare the reinforcement learning based strategy by using these dynamic programming-based control approaches. For optimal control of hybrid electric vehicle, each control method was compared in terms of fuel efficiency by performing simulation by using various driving cycles. Based on our simulations, we showed the reinforcement learning-based strategy can obtain global optimality in the optimal control problem with an infinite horizon, which can also be obtained by stochastic dynamic programming. We also showed that the reinforcement learning-based strategy can present a solution close to the optimal one using deterministic dynamic programming, while a reinforcement learning-based strategy is more appropriate for a time variant controller with boundary value constraints. In addition, we verified the convergence characteristics of the control strategy based on reinforcement learning, when transfer learning was performed through value initialization using stochastic dynamic programming.
This paper presents a comparison of energy management strategies based on Pon-tryagin’s Minimums Principle (PMP). The fuel consumption of Plug-in-Hybrid Electric Vehicles is minimized by considering the power split and electrical driving decision as optimization variables. This paper discusses the trade-off between optimality and computational efficiency of analytical and map-based PMP methods. Thereby a decision guidance for further research is presented which evaluates the discussed methods by contrasting fuel consumption, state of charge trajectory, power split and engine on/off decision and computational efficiency.
The strategy for energy management (EM) of a hybrid electric vehicle (HEV) has a considerable impact on the vehicle fuel economy. One well-known EM strategy is the equivalent consumption minimization strategy (ECMS) that is a form of Pontryagin's minimum principle (PMP). PMP proves under certain conditions that ECMS yields the maximum fuel economy. However, even if the required conditions are met, the optimal value of the costate still has to be estimated. Many approaches have been suggested for estimating the optimal value of the costate, or the equivalent factor for using battery power in the ECMS cost function. Instead of direct estimation of ECMS optimal equivalent factor, this brief derives estimations for the upper and lower bounds of the optimal equivalent factor. The derived bounds are functions of the HEV configuration and independent of the drivecycle, verified by simulation results. The knowledge about these bounds can be employed in designing new types of adaptive ECMSs (A-ECMSs). To demonstrate the application of the bounds, this brief introduces a new A-ECMS. Finally, the simulation results are presented comparing the fuel economy of the introduced A-ECMS with the fuel economies of an existing A-ECMS and global optimal controller.
The Cooperative Adaptive Cruise Control (CACC) is the enabling technology for vehicle platooning. It has been shown that platooning can improve driving safety, fuel economy, traffic flow, and the comfort of the passengers. To achieve a safe platooning system, it is essential to guarantee the string stability of the vehicle platoon while handling the safety, comfort, and performance constraints. This study presents a distributed reference governor (RG) approach to the constraint handling of vehicle platoons equipped with CACC. First, a string stable platoon is designed based on a frequency domain approach. Second, an RG is designed that sits behind the controlled system and keeps the output inside the defined constraints. RG does not change the behavior of the controlled system; therefore, the platoon remains string stable based on the frequency domain design. Only when there is a possibility of violating the defined contrarians, the RG would intervene to push the system back into the constraints. Third, to improve the platoon`s energy economy, a controller is presented for the leader`s control using Nonlinear Model Predictive Control (NMPC) method, assuming it is a Plug-in Hybrid Electric Vehicle (PHEV). Evaluations performed with a platoon model constructed using high-fidelity models of the baseline PHEV, namely Toyota Prius plug-in hybrid, show that the proposed method is able to simultaneously maintain the string stability and platooning constraints, while improving the total energy economy of the entire platoon. Moreover, the results of the hardware-in-the-loop testing demonstrate the
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between predicted and assumed trajectories. Together with this penalization, an equality based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of proposed DMPC.
The energy management strategy is crucial in improving the fuel economy of hybrid electric vehicles (HEVs). This paper targets at evaluating the role of velocity forecasting in the adaptive equivalent consumption minimization strategies (ECMS) of HEVs. By predicting the short-term future velocity through a data-driven approach, the energy management controller is able to optimize the equivalence factor online and adapt to current driving situations intelligently. Compared with basic adaptive ECMS approach without velocity forecasting abilities, the proposed strategy is able to foresee the change of the driving behaviors and adjust the equivalence factor more reasonably. Simulation results show that the adaptive ECMS with velocity forecast ability is more sensitive to the driving profiles, and the resultant fuel economy is improved by over 3%.
Hybrid electric vehicle (HEV) improvements in fuel economy and emissions strongly depend on the energy management strategy. The control of an HEV with minimum fuel consumption and emissions is a global problem and the control action taken at each time instant affects the following. Thus, dynamic programming (DP) is a well-suited technique to find the optimal solution to the control problem. Unfortunately, this approach to solving the optimal control problem requires a priori knowledge of the driving conditions (necessary to implement the DP backward algorithm) and is therefore not suitable for HEV real-time control.
It is shown that it is possible to obtain the global optimal control policy using the instantaneous minimization of a “well-defined” cost function dependent only on the system variables at the current time. The definition of such a cost function requires an equivalence factor for comparing the electrical energy with the fuel energy. This approach is known in literature as equivalent consumption minimization strategy (ECMS). The optimal value of the equivalence factor can be found through a systematic optimization only if the driving cycle is known.
In this paper a new control strategy called adaptive ECMS (A-ECMS) is presented. This real-time energy management for HEV is obtained adding to the ECMS framework an on-the-fly algorithm for the estimation of the equivalence factor according to the driving conditions. The main idea is to periodically refresh the control parameter according to the current road load, so that the battery state of charge is maintained within the boundaries and the fuel consumption is minimized. The results obtained with A-ECMS show that the fuel economy that can be achieved is only slightly suboptimal and the operations are charge-sustaining.
The aim of this paper is to propose a power control strategy for hybrid electrical vehicles. This strategy uses a fuel consumption criterion with battery charge sustaining. It is based on an instantaneous minimization of the equivalent fuel flow. Two comparisons are performed to evaluate the proposed strategy. The first one uses the loss minimization strategy of Seiler and Schröder [1], which appears to be realistic and efficient for real-time control. This strategy is also based on an instantaneous optimization and allows the battery state of charge to be taken into account. The second comparison is made with an optimal solution found for a given driving schedule. Although not realistic for real-time control, this solution is derived through a global optimization algorithm, the well-known simulated annealing method.
Electric vehicles (EVs) are considered to relieve energy crisis and environmental problems due to their high efficiency and low emissions, and energy management strategies (EMSs) have been extensively studied to improve the performance of hybrid energy storage systems (HESSs) for EVs. To effectively reduce HESS energy loss and extend battery life, this paper proposes a predictive EMS (PEMS) for the battery/supercapacitor HESSs. First, the pattern sequence-based velocity predictor is presented to accurately predict the future short-term velocity profile. Second, the PEMS is proposed by formulating an HESS power split optimization problem, where the HESS energy loss and the battery capacity loss are considered. Third, an improved chaotic particle swarm optimization algorithm is presented to solve the formulated optimization problem. Simulation results demonstrate that, compared with the benchmark, the proposed PEMS can effectively reduce the HESS energy loss and extend the battery lifetime at the same time.
This paper develops a model predictive multi-objective control framework for HEVs in car-following scenarios to investigate the interplay between fuel economy, vehicle exhaust emissions, and inter-vehicle safety. Specifically, an MPC-based controller is developed to optimize the vehicle speed and engine torque for better fuel economy and fewer exhaust emissions while ensuring inter-vehicle safety. The engine-out emission model and its impact on energy management are considered in the optimization. The proposed controller is evaluated at different driving conditions, such as urban driving and highway driving. The proposed controller is compared with conventional controllers used in ADVISOR. The comparison results demonstrate that the proposed controller can reduce fuel consumption by 10.49%, CO by 48.02%, HC by 55.38%, and NOx by 22.79% in the UDDS driving cycle.
In this paper we present models and optimization algorithms to rapidly compute the fuel-optimal energy management strategies of a hybrid electric powertrain for a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear problem and demonstrate its effectiveness by assessing the impact of different battery charge targets on the achievable fuel consumption. Numerical results show that the proposed algorithm can assess fuel-optimal control strategies in a few seconds, paving the way for extensive parameter studies and real-time implementations.
This article details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior-point method is proposed, where the size and complexity of the Newton step matrix inversion is reduced by applying inequality constraints on the control input as a projection, and its properties are demonstrated through simulation in comparison with an alternating direction method of multipliers (ADMM) algorithm and a general purpose convex optimization software CVX. It is found that the ADMM algorithm has favorable properties when a solution with modest accuracy is required, whereas the projected interior-point method is favorable when high accuracy is required, and that both are significantly faster than CVX.
Energy management in hybrid and electric vehicles is a key factor to improve the operational performance and meet the efficiency objectives defined in the transport sector. Thus, optimized energy management strategies (EMS), before being integrated in a real system, need to be experimentally validated in test-bench platforms in order to identify the possible deviations from the expected simulation-based performance. This way, minimizing the time needed for implementation and field-test on the real application. An economical and flexible mean of validating these strategies is the Hardware-in-the-loop (HIL) simulation. Therefore, this work aims to present the design approach and comparison, by means of experimental tests, of two optimized (simulation-based) EMSs proposed for a hybrid electric bus (HEB) focusing on the real-time operational performance. Both EMSs handle the proper power split behaviour of the vehicle demand among a genset (internal combustion engine + electric generator) and a hybrid energy storage system (combining Li-ion batteries with super-capacitors). The experimental platform consists in a scaled test-bench emulating the electrical DC grid of a HEB. This test-bench, combined with software models to control the power electronic devices, allows to emulate the real behaviour of the genset, battery, super-capacitor, traction and auxiliary loads.
Higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made due to public concerns about energy crisis and environmental issues. By organizing a group of automated vehicles into a platoon at a short inter-vehicular distance, the overall fuel consumption and greenhouse gas emissions of vehicle platoon can be decreased due to reduced aerodynamic drag, which is called the platooning technology. In addition, the eco-driving technology can help further increase the fuel efficiency of vehicle platoon by optimizing speed trajectories of vehicles. However, little research has been done into the combination of the eco-driving and platooning technologies. Based on distributed model predictive control (DMPC), this paper proposes an ecological cooperative look-ahead control strategy for a platoon of automated vehicles travelling on a freeway with varying slopes, where both the eco-driving and platooning technologies are used. To maximize the fuel efficiency of vehicle platoon, an ecological cooperative look-ahead control problem (Eco-CLC) is first formulated based on DMPC, where rotational inertia coefficient related to reduction ratio of gear box, aerodynamic drag related to spacing and model constraints are considered. Since the Eco-CLC problem is a nonconvex and nonlinear optimization problem with hard state constraints, it is very difficult to quickly obtain its optimal solution. To enhance real-time control performance, after the hard state constraints of the Eco-CLC problem are transformed into parts of the multi-objective function using the band-stop function, the improved ecological cooperative look-ahead control (iEco-CLC) based on DMPC is given. A particle swarm optimization algorithm with multiple dynamic populations is further presented to quickly solve the iEco-CLC problem online. Simulation results demonstrate, compared with benchmarks, the proposed strategy can save more than 21% of fuel for vehicle platoon.
In order to mitigate the power density shortage of current energy storage systems (ESSs) in pure electric vehicles (PEVs or EVs), a hybrid ESS (HESS), which consists of a battery and a super-capacitor, is considered in this research. Duo to the use of the two ESSs, an energy management should be carried out for the HESS. An optimal energy management strategy is proposed based on the Pontryagin's Minimum Principle (PMP) in this research, which instantaneously distributes the required propulsion power to the two ESSs during the vehicle's propulsion and also instantaneously allocates the regenerative braking energy to the two ESSs during the vehicle's braking. The objective of the proposed energy management strategy is to minimize the electricity usage of the EV and meanwhile to maximize the battery lifetime. A simulation study is conducted for the proposed energy management strategy and also for a rule-based energy management strategy. The simulation results show that the proposed strategy saves electricity compared to the rule-based strategy and the single ESS case for the three typical driving cycles studied in this research. Meantime, the proposed strategy has the effect of prolonging the battery lifetime compared to the other two cases.
Automobiles are responsible for a substantial part of the world's consumption of primary energy, mostly fossil liquid hydrocarbons. The reduction of the fuel consumption of these vehicles has become a top priority. Many ideas to reach that objective have been presented. In most cases these systems are more complex than the traditional approaches. For such complex systems a heuristic design approach fails. The only way to deal with this situation is to employ model-based methods. This text provides an introduction to the mathematical modeling and subsequent optimization of vehicle propulsion systems and their supervisory control algorithms.
An analytical method is proposed to solve the optimization problem of energy management for a parallel hybrid electric vehicle. This method is based on Pontryagin’s Maximum Principle (PMP) for a class of Hybrid Dynamic Systems (HDS). Therefore, the analytical models are used, which are an approximation of the reference models. A numerical method based on the reference models is also used in order to validate the analytical solution by comparing their results. In this paper, two types of optimization variables are considered: continuous and discrete. The first type is the power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM). The second one is the transmission ratio, which includes the ICE On/Off decision. The results show that the analytical and the numerical solutions are almost the same. In addition, the analytical approach requires less computing time and requires less memory space than the numerical method.
Hybrid vehicle energy management is often studied in simulation as an optimal control problem. Under strict convexity assumptions, a solution can be developed using Pontryagin's Minimum Principle. In practice, however, many engineers do not formally check these assumptions resulting in the possible occurrence of so-called unexplained “numerical issues”. The present work intends to explain and solve these issues. Due to the binary controlled-state variable considered (e.g., switching on/off internal combustion engine) and the use of a lookup table with linear interpolation (e.g., engine fuel consumption map), the corresponding Hamiltonian function can have multiple minima. Optimal control is not unique. Moreover, it is defined as being singular. Consequently, an infinite number of optimal state trajectories can be obtained. In this work, a control law is proposed to easily construct a few of them.
A nonlinear model predictive control (NMPC) strategy has been presented as the energy management strategy (EMS) of a battery-supercapacitor (SC) hybrid energy storage system (H-ESS) in a Toyota Rav4EV. For the first time, the NMPC has been shown to be real-time implementable for these fast systems. The performance of the proposed controller has been demonstrated against a linear model predictive control (LMPC) and a rule-based control (RBC) strategy. The NMPC shows to outperform the RBC even with no prior knowledge of the future trip available. The NMPC also shows performance improvement over the LMPC by compensating for the error accompanied by linearization in LMPCs. Hardware-in-the-Loop (HiL) testing has been performed to demonstrate the NMPC capability for real-time implementation in a battery-SC H-ESS. Upon carefully choosing the prediction horizon and control horizon size, as well as the maximum number of iterations, the turn-around time for the control update is shown to fall far below the necessary sampling time of 10 milliseconds in vehicle control. IEEE
This paper aims at proposing an efficient and versatile application of Petri nets (PNs) either alone without global positioning system (GPS) as in (GPS-free) system or together with the navigation system (GPS-registered) to conveniently provide a proper energy management strategy for hybrid electric vehicles (HEVs) of high hybridization level and serial architecture. A comparison between the PN strategy and two fuzzy logic strategies is performed in terms of fuel consumption and convergence time. In this paper, short and long trip types of 30 km mainly urban and 240 km mostly highway are considered with an initial state of charge (SoC) of 50% and different daily driving cycles or various standard the New York City Cycle, the New European Driving Cycle, US06 driving cycles. Both kinds of battery management strategies, GPS-free and GPS registered, are demonstrated and compared through simulation studies using the MTCsim software. Dealing with both types of trips, the simulation results significantly illustrate the superiority of the novel GPS-registered methodology's efficiency toward improving the HEV's energy management and reducing its fuel consumption besides the relative economic feasibility and structural simplicity features. Over one week duration, the GPS allows reaching the desired final SoC with acceptable errors and reducing the fuel consumption for both daily short and weekend long trips. The originality of this paper is proposing a hybrid GPS/rule-based approach to reduce the fuel consumption during daily driving trips that present about half of the professional travels in 2008 according to the French Sustainable Development Division. This novel strategy is developed on the basis of the recorded GPS data from past trips and the batteries' final recharging capacities.
Previously, an equivalent consumption minimization strategy (ECMS) was developed that provides near-optimal performance of hybrid vehicles based on an adaptation of equivalence factor from state of charge feedback. However, under real-world driving conditions with uncertainties such as hilly roads, ECMS requires a predictive scheme utilizing future driving information in order to prevent a loss of optimality. In this paper, we synthesize predictive ECMS in a feed-forward way to adjust the equivalence factor based on its theoretical connection with future driving statistics, in a systematic manner. First, a useful non-causal adaptation strategy is extracted from dynamic programming results. Then, the inverse problem is formulated and solved to derive an explicit representation of the constant optimal equivalence factor with justified assumptions. Finally, a causal, predictive adaptation strategy using this closed-form solution is synthesized to mimic the non-causal one, and its effectiveness is evaluated for fuel cell hybrid electric vehicles. Results show that if the predicted statistical information reflects well the future driving conditions, the proposed strategy accurately estimates the constant optimal equivalence factor, including the jump behavior, thereby yielding less than 1.5% loss of fuel optimality. Moreover, this approach is extendible to other configurations.
This paper presents a model predictive power management strategy for a novel anti-idling system, regenerative auxiliary power system (RAPS), designed for service vehicles. RAPS is able to utilize recovered braking energy for electrified auxiliary systems; this feature distinguishes it from its counterparts - auxiliary power unit (APU) and auxiliary battery powered unit (ABP). To efficiently operate the RAPS, a power management strategy is required to coordinate power flow between different energy sources. Thus, a model predictive controller (MPC) is developed to improve the overall efficiency of the RAPS. As an optimization-based approach, the MPC-based power management strategy usually requires the drive cycle or the drivers’ command to be known a priori. However, in this study, an average concept based MPC is developed without such knowledge. MPC parameters are tuned over an urban drive cycle; whereas, the robustness of this MPC is tested under different drive cycles (e.g. highway and combined). Analysis shows that, the presented MPC has a comparable performance as the prescient MPC regarding fuel consumption, which assumes knows the drive cycle beforehand. Meanwhile, with the help of the proposed MPC and RAPS, the service vehicle saves up to 9% of the total fuel consumption. The proposed MPC is independent of powertrain topology such that it can be directly extended to other types of hybrid electric vehicles (HEVs), and it provides a way to apply the MPC even though future driving information is unavailable.
The paper presents a novel approach for hybrid powertrain control, based on a real-time minimization of the equivalent fuel consumption. This approach is non-predictive, thus the control strategy developed requires only information on the current status of the powertrain. The control parameters are continuously estimated on-board using information deriving from static route mapping and telemetry. Simulations for a prototypical hybrid car under development show the very promising benefits in terms of fuel consumption reduction and charge sustaining that can be obtained with the controller presented.
Energy management strategy is crucial in improving the fuel economy of hybrid electric vehicles (HEVs). This paper targets at evaluating the role of velocity forecast in the adaptive equivalent consumption minimization strategies (ECMS) for HEVs. A neural network based velocity predictor is constructed to forecast the short-term future driving behaviors by learning from history data. Then the velocity predictor is combined with adaptive-ECMS to provide temporary driving information for real-time equivalence factor (EF) adaptation. Compared with traditional adaptive-ECMS, which uses historical driving profile for EF estimation, the proposed strategy is able to foresee the change of the driving behaviors and adjust the EF more reasonably. Simulation results show that, compared with traditional adaptive-ECMS, the proposed improvement with velocity forecast incorporated is able to achieve better fuel economy and more stable battery state of charge (SOC) trajectory, with a fuel consumption reduction by over 3%.
A fuel cell consumes hydrogen and oxygen to produce clean electric energy. The only byproducts are water and heat. Therefore fuel cell vehicles are considered for the long term sustainable mobility. In a fuel cell hybrid vehicle (FCHV), the electric powertrain is powered by a fuel cell system (FCS) and an energy storage system (ESS). This energy storage system can be either batteries or supercapacitors. The hybrid architecture allows defining power management strategies and offers opportunities in the powertrain sizing. Global optimisation algorithms provide a power split between the FCS and the ESS which minimizes the hydrogen consumption for a priori known driving cycles. The optimal results obtained cannot be used in real time application but they define benchmarks for control strategies evaluation. In this thesis, a sub-optimal real time strategy is proposed based on the optimal control algorithm. The hydrogen consumption also relies on the powertrain sizing. The FCS and ESS sizes should respect the vehicle dynamic capabilities (speed, acceleration) without penalizing the energetic aspects. A sizing tool which helps in the powertrain design is proposed. Finally in this thesis, the theoretical approaches concerning control strategies and sizing procedures are illustrated and validated by real applications (PAC-Car II prototype, Hy-Muve prototype, test bench).
An hybrid vehicle is a vehicle with at least two energy sources available for its displacement. In the case of a thermal-electric hybrid vehicle, one source is non-reversible (fuel tank) and the other reversible (battery).
The goal is to optimize the use of the battery energy, so as to minimize fuel consumption of the vehicle (and then CO2 emissions). This optimization is possible by using the freedom degrees in the powertrain of the vehicle (gearbox ratios for example), while satisfying power request of the driver.
This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems. The building HVAC system is modeled as a network of thermal zones controlled by a central air handling unit and local variable air volume boxes. In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components. The uncertain load forecast in each thermal zone is modeled by finitely supported probability density functions (pdfs). These pdfs are initialized using historical data and updated as new data becomes available. In the second part of this paper, we present a SMPC design that minimizes expected energy cost and bounds the probability of thermal comfort violations. SMPC uses predictive knowledge of uncertain loads in each zone during the design stage. The complexity of a commercial building requires special handling of system nonlinearities and chance constraints to enable real-time implementation, minimize energy cost, and guarantee thermal comfort. This paper focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme. The proposed SMPC scheme is compared with alternative SMPC designs, and the effectiveness of the proposed approach is demonstrated by simulation and experimental tests.
As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.
This paper presents the development of an energy management strategy of a Plug-in Hybrid Electric Vehicle (PHEV). In this case, a rule-based optimal controller selects the appropriate operation mode. Furthermore, advantages and drawbacks of such vehicles are compared with respect to other vehicles powered by the most popular powertrain architectures. Simulations are carried out for three predefined drive cycles repeated over different geographic regions with varying CO2 intensities. The results reveal that the proposed controller is able to switch the vehicle’s operating mode according to previous established criteria.
The efficiency of energy management strategies for hybrid electric vehicles depends significantly on the accuracy of the prediction of the load power which has to be provided by the hybrid drive. For vehicles in fixed-route service, measurements gained during vehicle operation can be used for the design of a predictor for the online calculation of the expected speed profile which is directly related to the load power profile. The paper describes, how the relevant information contained in measurements of the vehicle speed and position is processed and compressed in order to obtain a prediction algorithm that can cope with the limited memory space and computing power of a vehicle controller. The characteristics of the resulting algorithm are illustrated with real-life data.
This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.
This paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.
Depending on the actual battery temperature, electrical power demands in
general have a varying impact on the life span of a battery. As
electrical energy provided by the battery is needed to temper it, the
question arises at which temperature which amount of energy optimally
should be utilized for tempering. Therefore, the objective function that
has to be optimized contains both the goal to maximize life expectancy
and to minimize the amount of energy used for obtaining the first goal.
In this paper, Pontryagin's maximum principle is used to derive a causal
control strategy from such an objective function. The derivation of the
causal strategy includes the determination of major factors that rule
the optimal solution calculated with the maximum principle. The
optimization is calculated offline on a desktop computer for all
possible vehicle parameters and major factors. For the practical
implementation in the vehicle, it is sufficient to have the values of
the major factors determined only roughly in advance and the offline
calculation results available. This feature sidesteps the drawback of
several optimization strategies that require the exact knowledge of the
future power demand. The resulting strategy's application is not limited
to batteries in electric vehicles.
This paper presents a numerical solution for scalar state constrained optimal control problems. The algorithm rewrites the constrained optimal control problem as a sequence of unconstrained optimal control problems which can be solved recursively as a two point boundary value problem. The solution is obtained without quantization of the state and control space. The approach is applied to the power split control for hybrid vehicles for a predefined power and velocity trajectory and is compared with a Dynamic Programming solution. The computational time is at least one order of magnitude less than that for the Dynamic Programming algorithm for a superior accuracy.
Pollutant emissions from cars are usually measured on a test bench using driving cycles. However, the use of one unique set of driving cycles to test all cars can be seen as a weak point of emission estimation, as vehicles could conceivably be tested differently depending on their performance levels and usage characteristics. A specific study was then conducted to characterize driving conditions and vehicle usage as a function of vehicle categories, as well as to derive driving cycles specially designed for high- and low-powered cars which have significantly different driving conditions.Pollutant emissions were measured on a sample of 30 passenger cars, using on the one hand the three real-world ARTEMIS driving cycles (urban, rural road and motorway), representative of European driving, and on the other hand specific driving cycles. The comparison of the resulting aggregated emissions demonstrates that the usual test procedure (i.e. with a unique set of driving cycles) can lead to strong differences in emissions, particularly for the most recent vehicle categories.
A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by utilizing a planetary gear set to split and combine the power produced by electric machines and a combustion engine. Because of the different modes of operation, devising a near optimal energy management strategy is quite challenging and essential for these vehicles. To improve the fuel economy of a power-split HEV, we first formulate the energy management problem as a nonlinear and constrained optimal control problem. Then two different cost functions are defined and model predictive control (MPC) strategies are utilized to obtain the power split between the combustion engine and electrical machines and the system operating points at each sample time. Simulation results on a closed-loop high-fidelity model of a power-split HEV over multiple standard drive cycles and with different controllers are presented. The results of a nonlinear MPC strategy show a noticeable improvement in fuel economy with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and the other proposed methodology by the authors based on a linear time-varying MPC.
This paper presents an optimisation method based on dynamic programming, concerning the elaboration of energy management laws for hybrid electric vehicles (HEV). The objective is to minimise fuel consumption on a-known-in-advance driving schedule based on normalised or actual conditions (offline process). An optimisation tool implementing this method has been developed, called KOALA. It allows short computation times, and is flexible as it allows us to code new architectures easily. The main outcome of KOALA is the comparison of different HEVs with regard to fuel consumption, that allows us to estimate the potential consumption gain for different HEV architectures, and to forecast the best component sizing for a given vehicle architecture. Another outcome is the possibility of deriving online management laws from the optimal offline ones given by KOALA. In this paper we focus on the operating mode and validity of the optimisation process.
This paper describes a proton exchange membrane (PEM) fuel cell system model for automotive applications that includes an air compressor, cooling system, and other auxiliaries. The fuel cell system model has been integrated into a vehicle performance simulator that determines fuel economy, and allows consideration of control strategies. Significant fuel cell system efficiency improvements may be possible through control of the air compressor and other auxilia ries. Fuel cell system efficiency results are presentedfor two limiting air compressor cases: ideal control and no control. Extension of the present analysis to hybrid configurations consisting of a fuel cell system and battery is currently under study.
This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on an electric assist control strategy (EACS), the fitness function is defined so as to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. In addition, in order to reduce the number of the decision variables, a new approach is used for the battery control parameters. Finally, the optimization process is performed over three different driving cycles including ECE-EUDC, FTP and TEH-CAR. The results from the computer simulation show the effectiveness of the approach and reduction in FC and emissions while ensuring that the vehicle performance is not sacrificed.
Vehicle performance such as fuel consumption and catalyst-out emissions is affected by a driving pattern, which is defined as a driving cycle with grades in this study. To optimize the vehicle performances on a temporary driving pattern, we developed a multi-mode driving control algorithm using driving pattern recognition and applied it to a parallel hybrid electric vehicle (parallel HEV). The multi-mode driving control is defined as the control strategy which switches a current driving control algorithm to the algorithm optimized in a recognized driving pattern. For this purpose, first, we selected six representative driving patterns, which are composed of three urban driving patterns, one expressway driving pattern, and two suburban driving patterns. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, stop time/total Time, average acceleration, and average grade are chosen to characterize the driving patterns. Second, in each representative driving pattern, control parameters of a parallel HEV are optimized by Taguchi method though the fuel-consumption and emissions simulations. And these results are compared with those by parametric study. There are seven control parameters, six of them are weighting factors of performance measures for deciding the ratio of engine power to required power from driving load. And the other is the charging/discharging method of battery. Finally, in driving, a neural network (the Hamming network) decides periodically which representative driving pattern is closest to a current driving pattern by comparing the correlation related to 24 characteristic parameters. And then the current driving control algorithm is switched to the optimal one, assuming the driving pattern does not change in the next period.
Real-Time Predictive Strategies for the Optimal Energy Management of a Hybrid Electric Vehicle —The presence of at least two alternative power sources in a HEV poses the problem of determining the optimal power split among them for a given driver's request in order to achieve a minimum fuel consumption. This article will introduce two strategies capable of solving this optimisation problem both relying on predictive information about the driving conditions within a limited future time horizon. The important aspects of optimality of the determined power split and the real-time applicability of the algorithms are addressed.