Affiliations: [a] School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou, PR China | [b] Information systems Department, City University ofHong Kong, Hong Kong, PR China
Correspondence:
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Corresponding author. Qiao Zhang, School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou, PR China E-mail: [email protected].
Note: [1] This work is supported by Scientific Research Project of Liaoning Education Department (Grant No. JQL202015405) and the development grants from Shenzhen Science, Technology and Innovation Commission (Grant No. JSGG20170822145318071).
Abstract: Hybrid energy storage system supplies a feasible solution to battery peak current reduction by introducing supercapacitor as auxiliary energy source. Energy management control strategy is a key technology for guaranteeing performance. In this paper, we describe a predictive energy management strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles. To utilize the supercapacitor reasonably, Markov chain model is proposed to predict the future load power during a driving cycle. The predictive results are subsequently used by power distribution strategy, which is designed using a low-pass filter and a fuzzy logic controller. The strategy model is developed under MATLAB/Simulink software environment. To validate the performance of the proposed control strategy, a comparison test is implemented based on a 72 V rated voltage hybrid energy storage system experimental platform. The results indicate that the battery peak currents by proposed predictive control strategy are reduced by 26.32%, 28.21% and 27.12% under the UDDS, SC03 and NEDC three driving cycles respectively.
Keywords: Electric vehicle, hybrid energy storage system, predictive energy management strategy, markov chain
