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The highest control layer of a (hybrid) vehicular drive train is termed the Energy Management Strategy (EMS). In this paper an overview of different control methods is given and a new rule-based EMS is introduced based on the combination of Rule-Based and Equivalent Consumption Minimization Strategies (RB-ECMS). The RB-ECMS uses only one main desig...
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This paper addresses the energy management problem of a Mild hybrid electric vehicle (HEV) provided with a singular dual electric storage system. The latter is the combination of a Li-ion battery, a double layer capacitor (DLC) pack and a DC/DC converter. The energy management of the hybrid powertrain is formulated as an optimal control problem wit...
![Figure 2. Economy line on ICE Torque-Speed map [18]](profile/S-Baslamisli-2/publication/320710548/figure/fig2/AS:555100302655488@1509357633567/Economy-line-on-ICE-Torque-Speed-map-18_Q320.jpg)
Road vehicles using electric power sources have become
increasingly popular in the last decade. Meanwhile,
battery technology is still not mature enough to meet expected vehicle range; thus
the transition from ICE vehicle to fully electric vehicle is not imminent.
Therefore the concept of hybrid drivetrain technology was introduced. The
hybrid powe...
In this paper, an energy management strategy (EMS) based on optimal control theory is applied to a hybrid vehicle propelled by fuel cell and supercapacitors pack. In order to show the effectiveness of this strategy, another EMS based on thermostat method is proposed and compared with the first one. Firstly, the power sources used in this study are...
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... The instantaneous optimization strategy 1 represented by the equivalent fuel consumption minimum control strategy has strong real-time performance, but poor robustness and versatility. 5,6 The local optimization strategy represented by model predictive control 7,8 can realize theoretical optimal control in the time domain. It has strong fuel-saving capacity and good generalization, but requires high real-time computing performance of the vehicle controller. ...
SDP (Stochastic Dynamic Programming) control strategy can mine the travel data of drivers, so that the energy-saving potential of vehicles could be improved. However, there are two problems need to be solved in the current SDP: firstly, the analysis and construction method of driver’s typical driving cycle is not clear; secondly, the adaptability of SDP algorithm to a typical driving cycle is insufficient. To solve the above problems, a driving cycle construction method for off-line SDP solution is proposed, which is based on “analysis, dimension reduction and clustering” process. In addition, a coupled control strategy (ECMS-SDP) based on driving conditions identification is developed. Because it is difficult to predict the driving conditions in real time, the working part of the coupled control strategy is calculated by the method of improved random forest. The simulation results show that ECMS-SDP control strategy can save 8%–15% fuel on average compared with CD-CS control strategy, and can save 4%–7% fuel on average compared with ECMS control strategy. The results prove that the ECMS-SDP coupled control strategy can respond well to the changing driving environment, and the fuel economy of vehicles is enhanced.
... In [8], an exhaustive chronological review of the most famous real-time capable control methods for (P)HEVs is reported. Starting from the 1990s, more and more sophisticated techniques have been developed, from very simple rule-based EMSs [8], passing through equivalent consumption minimization strategies (ECMS) [9,10] and dynamic programming (DP) [11] related optimizers, until the latest findings of model predictive control (MPC) [12] and learning algorithms [13,14]. Among the latter, Reinforcement Learning (RL) has started to be effectively exploited for control problems from 2015 onwards [8,15]; hence, it can be considered as an up-to-date approach. ...
Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered.
... Therefore, for aircraft, it is better that the SoC is held within a range instead of at a specific value. The most intuitive way is to combine the ECMS with a rule-based control 139,140 , since the desired SoC requirement can be achieved using logic rules. This means, the control performance will not be heavily dependent on the selection of equivalence factor. ...
The paper overviews the state-of-art of aircraft powered by hybrid electric propulsion systems. The research status of the design and energy management of hybrid aircraft and hybrid propulsion systems are further reviewed. The first contribution of the review is to demonstrate that, in the context of relatively underdeveloped electrical storage technologies, the study of mid-scale hybrid aircraft can contribute the most to both theoretical and practical knowledge. Meanwhile, the profits and potential drawbacks of applying hybrid propulsion to mid-scale hybrid airplanes have not been thoroughly illustrated. Secondly, as summed in the overview of design methodologies, the multi-objective optimization transcends the single-objective one. The potential of the hybrid propulsion system can be thoroughly evaluated in only one optimization run, if several objectives optimized simultaneously. Yet there are few researches covering the conceptual design of hybrid aircraft using multi-objective optimization. The review of the most popular energy management strategies discloses the third research gap—current methodologies favoured in hybrid ground vehicles do not consider the aircraft safety. Additionally, both non-causal and causal energy management are needed for performing a complicated flight mission with several sub-tasks.
... These approaches can be roughly classified into four groups: rule-based control strategy, fuzzy logic control strategy, offline optimization control strategy, and online optimization control strategy [20] . The rule-based control strategy is widely used for it is simple and practical, with fast computing speed, low configuration requirements, and relatively stable control quality [21,22] . Despite the simplicity of rule-based control, the disadvantage is that the overall efficiency is compromised. ...
A dual input clutchless transmission system based on automated manual transmission (AMT) structure is developed for pure electric vehicles. An energy management strategy (EMS) is proposed to determine the power distribution between two motors and the optimal gear state. A mathematical model is built to minimize the energy consumption of the motors at each instant based on the motor efficiency maps. However, the proposed EMS in line with other energy-oriented strategies often result in excessive gear shifts and compromised drivability. To avoid the undesired gear shift, a shifting stabilizer is built in the EMS objective function to improve the shift quality. Accordingly, to achieve a balance between the energy consumption and the drivability, a multi-objective optimization method is adopted to reduce the unnecessary shift events while minimizing energy consumption. Two driving cycles representing typical daily driving conditions are used to demonstrate the effectiveness of the proposed system in terms of energy efficiency and shifting stability.
... To improve the fuel economy as much as possible, numerous effort s in the research areas on optimal energy management have been made in the last decade. The equivalent consumption minimization strategy (ECMS) [3,4] , the Pontryagin's minimum principle (PMP)-based strategy [5] , and Dynamic programming (DP) [6,7] all could obtain the global or approximate global optimal control strategy if all of the future driving information is known in advance. Thus, these methods all suffer from their own difficulties in being put to practice. ...
The series-parallel architecture of the plug-in hybrid electric powertrain has attracted wide attentions in recent years for its flexible and highly efficient operating modes. However, despite the improvement of the vehicle fuel economy, it has been gradually facing more challenges on the design of the optimal controller due to the complex structure and the fast depletion of the battery. In this paper, a two-step optimal energy management strategy is proposed for a novel single-shaft series-parallel powertrain. In the first step, an equivalent method is adopted, in which two motors are equivalently regarded as one. After detailed analysis of the operating modes, various objective functions are established to pre-optimize the power split between engine and motor or two motors. In the second step, a stochastic dynamic programming (SDP) is adopted to optimize the power split between the engine and the equivalent motor, and the optimal combination of the operating modes. Then coupled with the pre-optimized results of the first step, the optimal power split among the engine and two motors could be obtained and then constructed as simple lookup-tables, which have great potential for practical applications. Finally, the preliminary test about the real-time performance of the optimal results is developed on the hardware-in-the-loop (HIL) system.
... In applications, the EMC strategies are typically of rule-based type including SoC feedback control [26,27]. On the other hand, many research papers deal with design of strategies based on instantaneous minimization of fuel consumption (so-called Equivalent Consumption Minimization Strategy, ECMS; [10,28,29,30]). The equivalent fuel consumption to be minimized consists of real fuel consumption and the "virtual" fuel consumption related to the actual battery power since the battery energy was obtained from the fuel in the past (if the hybrid/CS mode is considered). ...
There is a strong tendency of development and application of different types of electric vehicles (EV). This can clearly be beneficial for transport systems in terms of making it more efficient, cleaner, and quieter, as well as for energy systems due to the grid load leveling and renewable energy sources exploitation opportunities. The latter can be achieved only through application of smart EV charging technologies that strongly rely on application of optimization methods. For the development of both EV architectures and controls and charging optimization methods, it is important to gain the knowledge about driving cycle features of a particular EV fleet. To this end, the paper presents an overview of (i) electric vehicle architectures, modeling, and control system optimization and design; (ii) experimental characterization of vehicle fleet behaviors and synthesis of representative driving cycles; and (iii) aggregate-level modeling and charging optimization for EV fleets, with emphasis on freight transport.
... 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,[7][8][9][10], fuzzy-logic rule-based controllers [6,[11][12][13][14]18], global optimization-based controllers such as Dynamic Programming (DP) [15][16][17][20][21][22][23], and local optimization-based or Model Predictive Control (MPC) [24][25][26]. 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.
... In particular, charge-sustaining or autonomous HEV implies that the battery State Of Charge (SOC) at the end of a vehicle mission is required to be as close as possible to its initial value. A mathematical formulation of such a control problem has been posed in terms of optimal control (Ambühl et al., 2007;Hofman, Steinbuch, Serrarens, & van Druten, 2008;Kim, Cha, & Peng, 2011;Serrao, Onori, & Rizzoni, 2009; van Berkel, Hofman, Vroemen, & Steinbuch, 2012) and numerous practical implementations for various architectures such as parallel (Lin et al., 2001;Musardo & Rizzoni, 2005;Pisu & Rizzoni, 2007;Salman et al., 2000;Sciarretta et al., 2004;Sivertsson, Sundström, & Eriksson, 2011), series (Anatone, Cipollone, Donati, & Sciarretta, 2005;Pisu & Rizzoni, 2005), and combined HEV (Borhan & Vahidi, 2010;Cipollone & Sciarretta, 2006;Hofman et al., 2008;Liu & Peng, 2006) have been presented. ...
... In particular, charge-sustaining or autonomous HEV implies that the battery State Of Charge (SOC) at the end of a vehicle mission is required to be as close as possible to its initial value. A mathematical formulation of such a control problem has been posed in terms of optimal control (Ambühl et al., 2007;Hofman, Steinbuch, Serrarens, & van Druten, 2008;Kim, Cha, & Peng, 2011;Serrao, Onori, & Rizzoni, 2009; van Berkel, Hofman, Vroemen, & Steinbuch, 2012) and numerous practical implementations for various architectures such as parallel (Lin et al., 2001;Musardo & Rizzoni, 2005;Pisu & Rizzoni, 2007;Salman et al., 2000;Sciarretta et al., 2004;Sivertsson, Sundström, & Eriksson, 2011), series (Anatone, Cipollone, Donati, & Sciarretta, 2005;Pisu & Rizzoni, 2005), and combined HEV (Borhan & Vahidi, 2010;Cipollone & Sciarretta, 2006;Hofman et al., 2008;Liu & Peng, 2006) have been presented. ...
A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting–charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principle-derived optimization tool HOT.
... In all cases, the hybrid architecture to be employed is set and, on this basis, an optimization is worked out. A frequent optimization approach is the Equivalent Consumption Minimization Strategy (ECMS) [18][19][20], envisaging the minimization instant by instant of the fuel required for motion through the proper combination of the electric and thermal machines exploitation. ...
... In all cases, the hybrid architecture to be employed is set and, on this basis, an optimization is worked out. A frequent optimization approach is the Equivalent Consumption Minimization Strategy (ECMS) [18][19][20], envisaging the minimization instant by instant of the fuel required for motion through the proper combination of the electric and thermal machines exploitation. ...
