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Energy management strategy influences the power performance and fuel economy of plug-in hybrid electric vehicles greatly. To explore the fuel-saving potential of a plug-in hybrid electric bus (PHEB), this paper searched the global optimal energy management strategy using dynamic programming (DP) algorithm. Firstly, the simplified backward model of...
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The speed of a hybrid electric vehicle is a critical factor that affects its energy management performance. In this study, we focus on the importance of solving the problem of inaccurate speed prediction in the energy management strategy (EMS) and application of dynamic programming (DP) needs to know the entire driving cycle. A gated recurrent unit...
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Citations
... La obtención de los mapas de eficiencia de consumo de combustible en MEP o MEC es mediante datos experimentales, determinando las variables de entrada como es el par, el régimen del motor y la variable de salida es el CEC, en la figura 5 se muestra un ejemplo de un mapa de eficiencia de consumo de combustible en un MEC (He, Tang, & Wang, 2013). ...
... Mapa de consumo específico de combustible en un motor de encendido por compresión(He et al., 2013). ...
The objective of this work was to calculate fuel consumption using the engine map. Speed, engine load, torque, and power were measured to identify the working zones of the engine in its operating range. The fuel consumption was measured by the gravimetric method and the onboard diagnostic system. To determine the relationship between engine speed, mean effective pressure or effective torque, and specific fuel consumption, an algorithm was developed that relates engine condition to fuel consumption. Fuel consumption was measured in three scenarios. In the extra-urban route, an efficiency of 4.9 L/100 km was obtained, increasing 29% compared to the manufacturer's data. It was concluded that the factors that substantially affect engine performance and cause an increase in fuel consumption are: fuel and altitude above sea level. Thus, the fuel consumption zones on the engine map determine the engine's behavior in different performance locations of the vehicle.
... To make the proposed framework more generic and applicable for different use-cases, Dynamic Programming (DP) has been proposed to solve the control problem. Based on Bellman's "Principle of Optimality" (Bellman, 1966), it is widely used in optimal control problems across disciplines, especially for automotive applications (Guzzella and Sciarretta, 2007;He et al., 2013;Ke and Song, 2018), and is one of the useful methods for performance benchmarking of systems executing a task in non-real-time applications. In a DP formulation, the state variables are discretized into a "grid" that allows the system to switch between conceivable states. ...
... This work also uses their pseudo-code as a skin for finding the optimal control strategy of the hauler. A similar procedure can also be found in He et al. (2013), however, their purpose was optimal energy management, whereas the purpose in this work is to find the optimal velocity for the hauler. As explained previously, the operation needs to be discretized into several instances in spatial or time domain, and for each instance in deterministic DP problems, the successive state of the system ( + 1) is a function of the initial state ( ) and the control inputs ( ) as shown in Equation 2: ...
... Having system states as responses is common practice in DO (Martins and Lambe, 2013), but usually these states are a function of nominal or averaged system control. Control problems often consider the system to be well-established, and the goal becomes turning the right knobs to achieve the desired performance output, see, for example, (Ghandriz et al., 2021;He et al., 2013;Ke and Song, 2018). The proposed framework attempts the confluence of these two domains, enabling making decisions about the configuration and the control of the system simultaneously, especially early in the design phase for relatively mature systems. ...
The paper presents a framework for the integration of the system's design variables, state variables, control strategies, and contextual variables into a design optimization problem to assist early-stage design decisions. The framework is based on a global optimizer incorporating Dynamic Programming, and its applicability is demonstrated by the conceptual design of an electrical hauler. Pareto front of optimal design solutions, in terms of time and cost, together with optimal velocity profiles and battery state-of-charge is visualized for the given mining scenario.
... For example, for a single-axis series-parallel plug-in hybrid electric bus, the battery state of charge (SOC) is chosen as the only state variable, and engine torque (T e ) and motor torque (T m ) are chosen as the independent control variables in Ref. [22]. However, both in Ref. [23] and Ref. [24], the DP model has three control variables: T e , n e (engine speed), and T m . ...
To reveal the energy-saving mechanisms of global energy management, we propose a global optimization framework of “information layer-physical layer-energy layer-dynamic programming” (IPE-DP), which can realize the unity of different information scenarios, different vehicle configurations and energy conversions. The deterministic dynamic programing (DP) and adaptive dynamic programming (ADP) are taken as the core algorithms. As a benchmark for assessing the optimality, DP strategy has four main challenges: standardization, real-time application, accuracy, and satisfactory drivability. To solve the above problems, the IPE-DP optimization framework is established, which consists of three main layers, two interface layers and an application layer. To be specific, the full-factor trip information is acquired from three scenarios in the information layer, and then the feasible work modes of the vehicle are determined in the physical layer based on the proposed conservation framework of “kinetic/potential energy & onboard energy“. The above lays a foundation for the optimal energy distribution in the energy layer. Then, a global domain-searching algorithm and action-dependent heuristic dynamic programming (ADHDP) model are developed for different information acquisition scenarios to obtain the optimal solution. To improve the computational efficiency under the deterministic information, a fast DP is developed based on the statistical rules of DP behavior, the core of which is to restrict the exploring region based on a reference SOC trajectory. Regarding the stochastic trip information, the ADHDP model is established, including determining the utility function, network design and training process. Finally, two case studies are given to compare the economic performance of the vehicle under different information acquisition scenarios, which lays a foundation for analyzing the relationship between the amount of information input and energy-saving potential of the vehicle. Simulation results demonstrate that the proposed method gains a better performance in both real-time performance and global optimality.
... where SOV ref denotes the reference SOV of the UC. β denotes a constant factor, which can keep the order of energy loss and penalty factor in a reasonable range [37]. In addition, the value β needs to be modified appropriately in different prediction horizons. ...
... To reduce the calculation burden, we set the appropriate discrete points and bounds of the control variables and state variables in the local optimization process [37], [38]. The discrete point intervals of SOC, SOV and U D are 0.01, 0.02, and 0.035, respectively. ...
An accurate driving cycle prediction is a vital function of an onboard energy management strategy (EMS) for a battery/ultracapacitor hybrid energy storage system (HESS) in electric vehicles. In this paper, we address the requirements to achieve better EMS performances for a HESS. First, a long short-term memory-based method is proposed to predict driving cycles under the framework of a model predictive control (MPC) algorithm. Secondly, the performances of three EMSs based on fuzzy logic, MPC, and dynamic programming are systematically evaluated and analyzed. For online implementation, the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS. Thirdly, the impact of battery aging on EMS performances is explored; it indicates that battery aging consciousness can slightly extend battery life. Finally, a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles. Index Terms-Energy management, hybrid energy storage system, long short-term memory, model predictive control, battery aging consciousness.
... Instantaneous fuel consumption is calculated by multiplying the specific consumption with the instantaneous engine power, and it naturally increases with the increase in the engine rotational speed and load torque. Data provided by the manufacturer in [16] contains only the specific fuel consumption value at the Forests 2020, 11, 921 3 of 19 rated engine power; therefore, the fuel consumption map shown in Figure 1a is created by adapting the fuel consumption map of a similar engine from [18]. ...
... Instantaneous fuel consumption is calculated by multiplying the specific consumption with the instantaneous engine power, and it naturally increases with the increase in the engine rotational speed and load torque. Data provided by the manufacturer in [16] contains only the specific fuel consumption value at the rated engine power; therefore, the fuel consumption map shown in Figure 1a is created by adapting the fuel consumption map of a similar engine from [18]. Engine mechanical power is transferred through a ten-speed manual transmission and transfer case to all four wheels. ...
The paper presents a hypothetical conversion of a conventional cable skidder powertrain to its hybrid version. Simulations of skidder operation were made for two existing forest paths, based on the technical characteristics of the engine, transmission system and the characteristics of the winch. Fuel and time consumption were calculated per working cycle considering the operating conditions (slope, load mass). The model was then converted to a hybrid version by adding a battery energy storage system in parallel with the electrical power generator and by employing an energy management control strategy. The dimensions of the battery and the power generator were chosen based on the characteristics of the existing winch with the aim of completely taking over its operation. The management strategy was selected using the specific fuel consumption map. All simulations were repeated for the hybrid drive under the same operating conditions. The results show that fuel savings of around 13% can be achieved with the selected hybrid drive and steering strategy.
Link: https://www.mdpi.com/1999-4907/11/9/921/htm
... Linear interpolation has been used to calculate engine torques for all possible throttle positions between curves shown in Fig. 1a. Data provided by the manufacturer in [13] contains only the fuel consumption value at rated power, therefore the fuel consumption map shown in Figure 1b is created by adapting the fuel consumption map of a similar engine from [15]. Note that the optimum engine operating range is from 1300 to 1800 min -1 at about 360 Nm. ...
This paper presents a hypothetical conversion of a conventional articulated forestry tractor known as skidder to its hybrid counterpart by incorporating a battery energy storage system. Starting from the basic parameters of 84 kW diesel-powered skidder currently found in the national forestry company fleet, the quasi-static model of the skidder is derived and validated. The conventional skidder model is then converted to its hybrid counterpart by adding a battery energy storage system in parallel with the electrical power generator and an adequate energy management control strategy. The hybrid skidder power-train components are also appropriately re-sized in order to meet comparable traction force and power performance. Both the conventional and hybridized skidder models are then used for the purpose of comparative analysis of main vehicle characteristics for the off-road driving scenario, which includes realistic slope and terrain limitations. The obtained simulation results are used to gain insights about the possible advantages of the proposed conversion/drive-train hybridization in terms of feasible reduction of fuel consumption and related CO2 emissions, while also considering additional hybridization costs and return of investment period.
... So, energy management research on new energy vehicles is attracting more and more scholars' attention. Taking hybrid electric vehicles (HEV) as an example, energy management strategies mainly include rule-based energy management strategies (Basma et al., 2018;Yan et al., 2018), global optimal energy management strategies (Qiu et al., 2017;He et al., 2013), equivalent consumption minimization strategies (ECMS) (Sun et al., 2017), model predictive energy management strategies (Huang et al., 2017;Xie et al., 2017), artificial intelligence-based energy management strategies (Li et al., 2019;Chaoui et al., 2018). The EMS studied in this paper is based on model predictive control (MPC), which searches the optimal control actions for a limited prediction horizon at each sampling step. ...
... The upper and lower constraints of engine speed and torque are determined by a rule-based strategy. Then, the optimizer finds the optimal solution for the rolling prediction horizon through the DP algorithm (He et al., 2013;Sun et al., 2014). Finally, the optimal control command at the first step of each rolling prediction horizon is executed on the vehicle model, and the current state variable is fed back to the optimizer. ...
To improve the energy efficiency of hybrid electric city buses, a hierarchical predictive energy management strategy (HP-EMS) based on driver behavior and type is proposed in this paper. Within the model predictive control (MPC) framework, the k-Nearest Neighbor (kNN) method is applied to identify the driver type, and the deep neural network (DNN) is adopted to predict future speed based on the historical speed, driver type, and driver behavior. Combined with the city bus driving characteristics, the hierarchical strategy aims to reduce the frequent starts of the engine. The upper-level controller implements a rule-based strategy to limit the engine start-stop frequency. The lower-level controller uses dynamic programming (DP) to search for the best control strategy in the prediction horizon. Simulation results show that, compared with speed prediction without driver information, the new method can effectively improve the accuracy of future speed prediction, and RMSE between the prediction and measurement drops from 1.58 m/s to 1.45 m/s. The HP-EMS without driver information can reduce the number of engine starts by 30% while increase only 2% energy consumption compared with predictive energy management without hierarchical control. The paper also studies the benefits of considering driver behavior and type. The same HP-EMS controller is implemented with and without driver behavior and type. The one with the additional information reduces the energy consumption by 3.34% compared to the one without the information.
... Usually the optimization-based method can be further divided into: offline and online. Typical offline methods include: dynamic programming(DP) [9][10][11], convex programming [12,13], particle swarm optimization (PSO) [14]. And online optimization methods such as: Equivalent consumption minimization strategy (ECMS) [15,16], model predictive control (MPC), [17][18][19][20]. ...
... However, rule-based strategies require to be established in advance merely based on some empirical knowledge, which demands amount of experiment results [11,12]. Thus, research on global optimal energy management algorithm has aroused great interest recently, such as genetic algorithm (GA) [13], particle swarm optimization (PSO) [14] and dynamic programming (DP) [5,8,10,15]. The methods above can lead to the optimization results theoretically, but can neither be implemented in practical directly nor reach an efficient energy cost result virtually. ...
... Besides a proper configuration for specific EVs, the energy management strategies, especially the gear shift schedule, strongly influence the energy consumption economy and the comprehensive property. These strategies can be generally classified into rule-based control strategy and optimization-based control strategy [8,9]. The former can be easily developed through practical Figure 1 shows the powertrain structure of the DMAEB studied in this paper. ...
... According to analysis of the vehicle longitudinal dynamics shown in Figure 6, the drive force provided by the coupling propulsion system should balance the resistance force as shown in Equation (8). Thus the basic discrete-time velocity variation can be determined as Equation (9). ...
The conventional battery electric buses (BEBs) have limited potential to optimize the energy consumption and reach a better dynamic performance. A practical dual-motor equipped with 4-speed Automated Manual Transmission (AMT) propulsion system is proposed, which can eliminate the traction interruption in conventional AMT. A discrete model of the dual-motor-AMT electric bus (DMAEB) is built and used to optimize the gear shift schedule. Dynamic programming (DP) algorithm is applied to find the optimal results where the efficiency and shift time of each gear are considered to handle the application problem of global optimization. A rational penalty factor and a proper shift time delay based on bench test results are set to reduce the shift frequency by 82.5% in Chinese-World Transient Vehicle Cycle (C-WTVC). Two perspectives of applicable shift rule extraction methods, i.e., the classification method based on optimal operating points and clustering method based on optimal shifting points, are explored and compared. Eventually, the hardware-in-the-loop (HIL) simulation results demonstrate that the proposed structure and extracted shift schedule can realize a significant improvement in reducing energy loss by 20.13% compared to traditional empirical strategies.
... However, since these control strategies do not consider the effect of the present battery SOC, they cannot guarantee global optimality for the CD and CS mode. To ensure global optimality, rule-based control was proposed by implementing an optimization tool, such as dynamic programming (DP) [6,13,14] or Pontryagin's minimum principle (PMP) [15][16][17]. In a previous study [2], a rule-based mode control (RBC) strategy was obtained for the CS mode using DP with power electronics (PE) and drivetrain losses. ...
... The vehicle specifications are shown in Table 1. optimization tool, such as dynamic programming (DP) [6,13,14] or Pontryagin's minimum principle (PMP) [15][16][17]. In a previous study [2], a rule-based mode control (RBC) strategy was obtained for the CS mode using DP with power electronics (PE) and drivetrain losses. ...
This paper presents an advanced rule-based mode control strategy (ARBC) for a plug-in hybrid electric vehicle (PHEV) considering the driving cycle characteristics and present battery state of charge (SOC). Using dynamic programming (DP) results, the behavior of the optimal operating mode was investigated for city (UDDS×2, JC08 ×2) and highway (HWFET ×2, NEDC ×2) driving cycles. It was found that the operating mode selection varies according to the driving cycle characteristics and battery SOC. To consider these characteristics, a predictive mode control map was developed using the machine learning algorithm, and ARBC was proposed, which can be implemented in real-time environments. The performance of ARBC was evaluated by comparing it with rule-based mode control (RBC), which is a CD-CS mode control strategy. It was found that the equivalent fuel economy of ARBC was improved by 1.9–3.3% by selecting the proper operating mode from the viewpoint of system efficiency for the whole driving cycle, regardless of the battery SOC.
