首页|Towards adaptive deep reinforcement learning energy management for electric vehicles: An online updating approach
Towards adaptive deep reinforcement learning energy management for electric vehicles: An online updating approach
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NETL
NSTL
Elsevier
Deep reinforcement learning is a potential method for online energy management of electric vehicles. However, deep reinforcement learning can suffer from limited training data and underfitting/overfitting characteristics, leading to a compromised vehicle economy in online applications. This paper proposes an online updating approach for deep reinforcement learning-based energy management in electric vehicles with battery/supercapacitor hybrid energy management systems. Firstly, Jensen-Shannon Divergence is introduced to compare the pre-training and online velocity transfer probability surfaces quantitatively and to trigger the online strategy updating when exceeding the threshold. Secondly, existing online driving data is optimized through dynamic programming to construct the optimal state/action dataset, which will be used for actornetwork updating. Thirdly, a soft updating method is further proposed to ensure smooth strategy updating for satisfactory generalization ability based on the weighted fusion of multiple networks. Results under Dallas realworld driving cycles validate that the proposed online updating method can reduce the battery degradation and vehicle operation cost by 5.3-10.2% and 4.5-9.0%, respectively, compared with the strategy before updating. This study provides a meaningful attempt at the online updating of deep reinforcement learning-based energy management strategies, which can be easily extended to different vehicle types and driving conditions.
Energy managementHybrid energy storage systemDeep reinforcement learningBattery durabilityOnline updatingJensen-Shannon divergence
Kaifu Guan、Zhiwu Huang、Yang Gao、Yue Wu、Fei Li、Heng Li
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School of Automation,Central South University,Changsha 410083,China
School of Electronic Information,Central South University,Changsha 410075,China
NETL
NSTL
Elsevier
