Energy management strategy for fuel cell vehicles via soft actor-critic-based deep reinforcement learning considering powertrain thermal and durability characteristics

Zhang Yuanzhi; Zhang Caizhi; Fan RuijiaDeng ChenghaoWan SongChaoui Hicham

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Energy management strategy for fuel cell vehicles via soft actor-critic-based deep reinforcement learning considering powertrain thermal and durability characteristics

机译：Energy management strategy for fuel cell vehicles via soft actor-critic-based deep reinforcement learning considering powertrain thermal and durability characteristics

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摘要

Temperature can significantly affect the water equilibrium, electrochemical kinetics and mass transmission in a proton exchange membrane fuel cell (PEMFC) stack, meanwhile it also impacts the lifespan and safety of a lithium-ion battery (LIB). Yet, energy management strategy (EMS) is rarely to synchronously study the durability performances of the LIB and PEMFC stack with their thermal effects in fuel cell vehicles (FCVs) under real-world driving scenarios. Thus, this study proposes a deep reinforcement learning (DRL)-based EMS to minimize tran-sient costs of the LIB and PEMFC stack, which include their state-of-health (SOH) descents and overtemperature penalties. Meanwhile, the transient costs are incorporated into the overall cost, which comprises the hydrogen consumption rate of the PEMFC stack, and penalty of maintaining the LIB state-of-charge (SOC). Moreover, the soft actor-critic (SAC) is applied to the DRL-based EMS due to its advantage of stability across different random environments and no meticulous hyperparameter calibration. Specifically, the proposed EMS intelligently allo-cates the direct current (DC) bus power of FCVs in real time to maximize a multi-objective reward in accordance with FCV states, in which the reward is the negative overall cost. Then, long-term real-world driving scenarios in Chongqing city, China, are used for off-line training and real-time control to advance the adaptability of the proposed EMS. The results show that in comparison with the deep Q-network (DQN)-based EMS considering the powertrain temperature and durability, and the SAC-based EMS neglecting the powertrain temperature and durability, the proposed strategy can actualize overall SOH increments of the powertrain up to 14.01 % and 3.45 %, respectively, and restrict the maximum temperatures of the PEMFC stack and LIB. In addition, the general-ization of the proposed EMS is verified, in which the trained model of the proposed EMS is tested in other FCV and driving cycles, and it can acquire similar effectiveness. Thus, the proposed strategy can enforce the lifespan durability and thermal stability of the powertrain system.

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来源
《Energy conversion & management》 |2023年第5期|116921.1-116921.18|共18页
作者
Zhang Yuanzhi; Zhang Caizhi; Fan RuijiaDeng ChenghaoWan SongChaoui Hicham;
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作者单位

Chongqing Univ;

Tongji Univ;

Chongqing Changan New Energy Vehicle Technol Co LtCarleton Univ||Texas Tech Univ;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
Energy management strategy; Fuel cell vehicles; Deep reinforcement learning; Soft actor -critic; Lifespan durability; Thermal stability;

Energy management strategy for fuel cell vehicles via soft actor-critic-based deep reinforcement learning considering powertrain thermal and durability characteristics

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