车用复合电源系统在线自适应能量管理

doi:10.19595/j.cnki.1000-6753.tces.191597

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Abstract Aiming at improving the online performance for a battery/ultracapacitor hybrid energy storage system (HESS) in electric vehicles, an online adaptive energy management strategy (EMS) was proposed. Firstly, the second-order Markov chain model was employed to improve the speed prediction accuracy of vehicle driving cycles. Then, the objective function of the HESS was established, and the framework of model predictive control (MPC) algorithm was designed to optimize the power distribution of the system online. To improve system efficiency under different driving cycles, an adaptive correction factor was employed in the optimization objective function. Compared with the rule-based strategy, the energy consumption of the HESS based on the proposed adaptive EMS is significantly reduced. Compared to the rule strategy and the MPC strategy with a first-order Markov chain model and a fixed reference factor, the system efficiency under the proposed method in the 5s prediction horizon is improved by 3.3% and 0.9%, respectively.

Key words： Hybrid energy storage system energy management strategy second-order Markov chain model predictive control online adaptive optimization

Received: 22 November 2019

PACS:

TM912

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	Chen Huan
	Lin Cheng
	Xiong Rui

Cite this article:

Chen Huan,Lin Cheng,Xiong Rui. Online Adaptive Energy Management Strategy for a Hybrid Energy Storage System in Electric Vehicles[J]. Transactions of China Electrotechnical Society, 2020, 35(zk2): 644-651.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191597 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/Izk2/644

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