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Function approximation reinforcement learning of energy management with the fuzzy REINFORCE for fuel cell hybrid electric vehicles

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Energy and AI 2023年第3期13卷 76-87页

作者： Liang Guo Zhongliang Li Rachid Outbib Fei Gao Aix-Marseille University LIS UMR CNRS 7020MarseilleFrance Universite de Franche-Comte UTBMCNRSinstitut FEMTO-STF-90000 BelfortFrance UTBM CNRSinstitut FEMTO-STF-90000 BelfortFrance

In the paper,a novel self-learning energy management strategy(EMS)is proposed for fuel cell hybrid electric vehicles(FCHEV)to achieve the hydrogen saving and maintain the battery *** the EMS,it is proposed to approximate the EMS policy function with fuzzy inference system(FIS)and learn the policy parameters through policy gradient reinforcement learning(PGRL).Thus,a so-called Fuzzy REINFORCE algorithm is first proposed and studied for EMS problem in the *** REINFORCE is a model-free method that the EMS agent can learn itself through interactions with environment,which makes it independent of model accuracy,prior knowledge,and expert ***,to stabilize the training process,a fuzzy baseline function is adopted to approximate the value function based on FIS without affecting the policy gradient ***-over,the drawbacks of traditional reinforcement learning such as high computation burden,long convergence time,can also be *** effectiveness of the proposed methods were verified by Hardware-in-Loop *** adaptability of the proposed method to the changes of driving conditions and system states is also verified.

关键词： Energy management strategy fuel cell hybrid electric vehicle Reinforcement learning Fuzzy inference system Fuzzy policy gradient Hardware-in-loop

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A Novel Method for the Application of the ECMS(Equivalent Consumption Minimization Strategy)to Reduce Hydrogen Consumption in fuel cell hybrid electric vehicles

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Fluid Dynamics & Materials Processing 2022年第4期18卷 867-882页

作者： Wen Sun Hao Liu Ming Han Ke Sun Shuzhan Bai Guoxiang Li School of Energy and Power Engineering Shandong UniversityJinan250061China

fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s *** this context,this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor(determined in the framework of the equivalent consumption minimum strategy-ECMS)on the working *** simulation results show that under typical conditions(some representative cities being considered),the proposed strategy can maintain the power balance;for different initial battery’s states of charge(SOC),after the SOC stabilizes,the fuel consumption is 5.25 L/100 km.

关键词： Energy management fuel cell hybrid electric vehicle dynamic programming adaptive equivalent consumption minimum strategy

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An online energy management strategy for fuel cell hybrid vehicles

An online energy management strategy for fuel cell hybrid ve...

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第40届中国控制会议

作者： Yu Zhang Ming Chen Shuo Cai Shengyan Hou Hai Yin Jinwu Gao State Key Laboratory of Automotive Simulation and Control Jilin University Department of Control Science and Engineering Jilin UniversityCampus Nanling Advanced Technology Center Dongfeng Motor Corporation

As a new energy vehicle, fuel cell hybrid electric vehicle（FCHEV） has a broad development prospect and provides a new way of thinking to solve the energy crisis. Energy management strategy is one of the key issues in the research of *** are many power sources of the driveline system. Effective management of energy flows in power sources is the key to improving vehicle performance. Aiming at the FCHEV, an equivalent fuel consumption minimum control strategy（ECMS） was proposed to achieve the optimal fuel economy and real-time ***, the fuel cell hybrid vehicle is modeled based on ADVISOR. Secondly, the online real-time energy management strategy ECMS is designed. Finally, the results of the NEDC and UDDS cycle are compared with the global optimal results of offline dynamic programming（DP）. The results show that compared with the global optimal DP hydrogen consumption, the ECMS control strategy in this paper is only 0.59% and 2.10% different,but 13.31% and 12.28% higher than that of the rule-based CD-CS control strategy. In the following hardware loop experiment,the ECMS energy management strategy also improved by 5.00% and 5.64% compared to the CD-CS results. It is proved that the ECMS strategy in this paper has good control effects.

关键词： fuel cell hybrid electric vehicle Energy management strategy ADVISOR ECMS Optimization

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