Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin's Minimal Principle

Li, Xiyun; Wang, Yujie; Yang, Dandan; Chen, Zonghai

doi:10.1016/j.jpowsour.2019.227105

Cited by 132 publications

(42 citation statements)

References 39 publications

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“…They search the maximum efficiency point, taking into account the hydrogen consumption. The adaptive EMS proposed by Li et al 39 for the FCHVs is using online adaptation. Their results validate the solution through the identification of different driving paths and can predict the driver's behavior, it reduces with 4% the hydrogen consumption, and it sustains the optimal method.…”

Section: Methodsmentioning

confidence: 99%

“…Several types of classifications of energy management strategies have been suggested in the revised literature considering the criteria of taxonomy, advantages and disadvantages, the natural‐inspired algorithm used, performance obtained, etc. In the following sections, we built two classifications: the first is based on the type of algorithm, and the second is based on the goal they seek to optimize. Rule‐based strategies Fuzzy control strategy 42,76,80,82 State machine control strategy 84 Classical PI control strategy 37,43,47,48,52‐55,58,60,65,67,69,79 Power prediction 24,30,47,87,89,91 Unscented Kalman filter 61 Optimisation‐based strategies Pontryagin's minimum principle (PMP) 39,64 Quadratic programming (QPo) 38,56,86 Stochastic dynamic programming (SDP) 71,92 Multi‐mode predictive 68,90 Dynamic particle swarm optimization 28,83 Equivalent consumption minimization strategy (ECMS) 31,38,41,98 Dynamic programming (DP) 25,59,64 Genetic algorithm (GA) 40,43,76 Efficiency optimization strategy 27,29,36,57,85,93,96 Learning‐based strategies Reinforcement learning 33,62,74 Hybrid 32,46,49,63,66,70,72,73,75,77,78,81,88,95,97 …”

Section: Classification Of Strategiesmentioning

confidence: 99%

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Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

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Summary The object of this research came from the continuing need to analyze and identify effective methods of reducing pollution and optimizing the functions of electric vehicles. So, a classic analysis of the management strategies proposed in the literature was performed to identify the main research directions that can be used to design an optimal strategy. The performed analysis was validated using different counting and representation methods in VOSviewer and Excel. VOSviewer is a program that visually presents the best research approaches for understanding and developing applications. Thus, analyzing the results obtained, it is possible to identify where the research of energy management strategies for electric vehicles is focused. For example, the most important factors for increasing the performance of a strategy are identified as related to optimization, efficiency, degradation, fuel economy, and systemic development. We identified 127 energy management strategies in the studied articles, which were analyzed and classified by multiple criteria. For example, 21.59% of the strategies have as main goal to increase the vehicles efficiency, followed by the 10% on fuel economy, 7.8% hydrogen consumption strategy, 6.29% work for performance optimization and emission reduction, and so on. So, using this tool, researchers can explore their field of research to identify challenging topics to address in the coming years.

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Section: Methodsmentioning

confidence: 99%

Section: Classification Of Strategiesmentioning

confidence: 99%

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

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“…15,16 However, this kind of optimization method brings a heavy computation burden to the controller so that it is not suitable for real-time applications. 17 On the contrary, the instantaneous optimization method conducts real-time power distribution according to optimization objectives. As one of the representative real-time optimization EMSs, equivalent consumption minimum strategy (ECMS) is widely used due to its near-optimal performance and it does not depend on prior knowledge of driving cycles.…”

Section: Introductionmentioning

confidence: 99%

“…The global optimization method can obtain theoretical optimal power distribution on the condition that the entire driving cycle is known in advance, for example, the dynamic programming (DP) 15,16 . However, this kind of optimization method brings a heavy computation burden to the controller so that it is not suitable for real‐time applications 17 . On the contrary, the instantaneous optimization method conducts real‐time power distribution according to optimization objectives.…”

Section: Introductionmentioning

confidence: 99%

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

Lin

Wang

2020

Int J Energy Res

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Summary The stochastic driving cycles pose a challenge to the actual implementation of the control strategy for fuel cell electric vehicles (FCEVs). Considering the problem, this research proposes a predictive control strategy based on velocity prediction. Firstly, a novel velocity prediction method is developed, which considers the prediction error of back propagation neural network (BPNN)‐based method. Then, it is incorporated into the predictive control strategy for a plug‐in FCEV. Finally, simulation studies are conducted to verify the effectiveness and superiority of the proposed predictive control strategy. Simulation results show that the proposed velocity prediction method can adaptive to different driving cycles with high accuracy. In another case, with the velocity prediction used in the predictive control strategy, hydrogen consumption reduces by 17.07% when compared with the traditional rule‐based strategy. All the results indicate that the designed velocity prediction approach made it possible to forecast vehicle velocity with relatively high precision and promising to promote the energy management strategy (EMS) to reduce the hydrogen consumption of the plug‐in FCEV.

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“…It has the advantages of strong robustness and easy application in practice 10, 11. Optimization‐based strategies are used to solve the global optimization problem, dynamic programming (DP) 12, 13, linear programming 14, genetic algorithm 15, 16, and Pontryagin's minimum principle (PMP) 17, 18 can be utilized in this area. DP can find the optimal solution of a global optimization problem, however, a large amount of computation was generated in the process of solving energy management problem.…”

Section: Introductionmentioning

confidence: 99%

A Long‐term Energy Management Strategy for Fuel Cell Electric Vehicles Using Reinforcement Learning

et al. 2020

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The two power sources of a fuel cell electric vehicle (FCEV) are proton electrolyte membrane fuel cell (PEMFC) and Liion battery (LIB). The health status of PEMFC and LIB decreases with the use of FCEV, so the energy management strategy (EMS) needs to give an optimal power distribution based on the health status of power sources throughout the lifetime. However, rule-based control strategies cannot achieve this. To prolong the service lifetime of two power sources by optimizing power distribution, this article proposes a long-term energy management strategy (LTEMS) for FCEV, which contains a reinforcement learning module and an improved thermostat controller. By designing a reward function, the reinforcement learning module outputted various LIB state of charge (SOC) boundary which changes with power source attenuation. Based on SOC boundary, the improved thermostat controller will control the fuel cell current under specific driving conditions. Simulation was carried out based on different LIB state of health (SOH) and external temperature, and the simulation results were compared with the data collected from FCEV under rule-based (RB) strategies. It can be found that the proposed LTEMS can effectively reduce fuel cell and LIB attenuation, and meet the FCEV power demand.

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Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin's Minimal Principle

Cited by 132 publications

References 39 publications

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

A Long‐term Energy Management Strategy for Fuel Cell Electric Vehicles Using Reinforcement Learning

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