Design and validation of a battery management system for solar-assisted electric vehicles

Expanding the travel mileage of power batteries is of great significance for electric vehicles (EVs). The solar battery pack is considered as a promising supplement to the battery management system (BMS) of EVs but integrating solar power into EVs remains a challenge. This paper proposes a BMS that...

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Bibliographic Details
Published in:Journal of power sources Vol. 513; p. 230531
Main Authors: Wei, Hongqian, Zhong, Yan, Fan, Likang, Ai, Qiang, Zhao, Wenqiang, Jing, Rui, Zhang, Youtong
Format: Journal Article
Language:English
Published: Elsevier B.V 30.11.2021
Subjects:
Battery management system
Electric vehicles
Energy schedule
Model and optimization
Photovoltaic cell
Solar energy
Electric vehicles
Battery management system
Energy schedule
Model and optimization
Solar energy
Photovoltaic cell
ISSN:0378-7753, 1873-2755
Online Access:Get full text
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Summary:Expanding the travel mileage of power batteries is of great significance for electric vehicles (EVs). The solar battery pack is considered as a promising supplement to the battery management system (BMS) of EVs but integrating solar power into EVs remains a challenge. This paper proposes a BMS that coordinates the solar panels and the lithium battery system. The proposed BMS mainly involves three aspects. Firstly, an equivalent second-order resistance-capacitance model is established and afterwards is identified by using an improved recursive least squares algorithm. Then, the maximum power prediction strategy is developed based on the advanced state of charge (SOC) algorithm and the available solar energy estimation algorithm. Thirdly, a multi-stage constant current charging strategy based on the adaptive genetic algorithm is designed to optimize the battery temperature rise and charging time simultaneously. The proposed BMS is validated by the experiment on a real-world solar-assisted EV. The results indicate that the proposed power prediction strategy can accurately estimate the available power for EVs. Compared with the widely-used charging method, the developed optimal charging strategy reduces the charging time and temperature rise by 7%–11% and 36%–45%, respectively. •1-The BMS considering power battery and solar energy is modelled.•2-SOC estimation and SOP prediction algorithm are proposed.•3-A charging strategy is to optimize charging time and temperature rise.•4-Hardware experimental validated the executability of proposed BMS.
ISSN:0378-7753
1873-2755
DOI:10.1016/j.jpowsour.2021.230531