A novel regenerative braking energy recuperation system for electric vehicles based on driving style

Qiu Chengqun, Xinshan Wan, Na Wang, Sunjia Cao, Xinchen Ji, Kun Wu, Yaoyu Hu and Mingyu Meng

Energy, 2023, vol. 283, issue C

Abstract: The regenerative braking energy recovery system of pure electric vehicle is to recover and reuse the consumed driving energy under the premise of ensuring the braking safety. In this paper, the regenerative braking energy recovery system of pure electric vehicle was optimized based on driving style, and the driver model is constructed and the parameters that characterise driving style are determined. BLSTM (Bidirectional Long Short Term Memory) neural network model method was introduced for deep self-learning, and IDP (Iterative dynamic programming)-BLSTM based regenerative braking energy recovery management control strategy was established. Through theoretical analysis and numerical model of the system, the results of parameter representation of the energy system were preliminarily evaluated and road test was carried out. The results of real vehicle test show that IDP-BLSTM method can meet the personalized requirements of various drivers, improve driving experience and safety, and recover braking energy efficiently.

Keywords: Electric vehicles; Energy recovery; Driving style; Regenerative braking; Recovery management strategy (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s0360544223024490

DOI: 10.1016/j.energy.2023.129055

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