Hierarchical Q-learning network for online simultaneous optimization of energy efficiency and battery life of the battery/ultracapacitor electric vehicle
نویسندگان
چکیده
Reinforcement learning has been gaining attention in energy management of hybrid power systems for its low computation cost and great saving performance. However, the potential reinforcement (RL) not fully explored electric vehicle (EV) applications because most studies on RL only focused single design targets. This paper studied online optimization supervisory control system an EV (powered by battery ultracapacitor) with two targets, maximizing efficiency life. Based a widely used method, Q-learning, hierarchical network is proposed. Within Q-learning network, independent Q tables, Q1 Q2, are allocated layers. In addition to baseline power-split layer, which determines split ratio between ultracapacitor based knowledge stored Q1, upper layer developed trigger engagement Q2. process, Q2 updated during real driving using measured signals states, actions, rewards. The evaluated following full propulsion model. By introducing single-layer method rule-based as baselines, performance three methods (i.e., one proposed) simulated under different cycles. results show that reduces capacity loss 12%. proposed shown superior reducing 8% loss. range slightly extended along life extension. Moreover, strategy validated considering cycle measurement noise. can be adapted applied systems.
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ژورنال
عنوان ژورنال: Journal of energy storage
سال: 2022
ISSN: ['2352-1538', '2352-152X']
DOI: https://doi.org/10.1016/j.est.2021.103925