A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data. (July 2022)

• Record Type: Journal Article
• Authors: Hu, Lin; Tian, Qingtao; Zou, Changfu; Huang, Jing; Ye, Yao; Wu, Xianhui
• Is Part Of: Renewable & sustainable energy reviews. Volume 162(2022)
• Publication Date: 2022-07
• Subjects: Electric vehicles -- Hybrid energy storage system -- Energy management strategy -- Driving style -- Optimal analysis; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2022.112416
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: This paper proposes a novel energy distribution optimization method of hybrid energy storage system (HESS) and its improved semi-active topology for electric vehicles (EVs) to further reduce battery capacity degradation and energy loss. Compared with the traditional HESS semi-active topology, the proposed improved topology reduces the energy loss when the battery charges the supercapacitor (SC) to further enhance the efficiency of the system. The real urban driving data of electric vehicles are collected through experiments and divided into aggressive type, cautious type and standard type according to driving style. Based on the mature multi-mode control (MMC), different weight coefficients are assigned to the two optimization objectives of battery capacity degradation and energy loss based on different driving styles, and gray wolf optimization (GWO) is used to optimize the battery output power upper limit and SC charging upper limit of MMC. The simulation results show that compared with the traditional MMC and semi-active topology, the battery capacity degradation and energy loss are improved under different driving styles. In addition, by further analyzing the simulation results, the research direction of HESS energy distribution strategy in the future is discussed. Highlights: An improved semi-active topology of HESS is proposed. The real urban driving data of EV is taken as the research object and classified according to driving style. GWO is used to optimize the energy loss and LIB capacity degradation of HESS. Assign different weights to the two optimization objectives according to different driving styles.