Electric Vehicle Charging Fault Monitoring and Warning Method Based on Battery Model

• Published in: World electric vehicle journal Vol. 12; no. 1; p. 14
• Main Authors: Zhang, Yuanxing, Li, Taoyong, Yan, Xiangwu, Wang, Ling, Zhang, Jing, Diao, Xiaohong, Li, Bin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2021
• Subjects: Accident data; Batteries; Battery chargers; battery model; CAN bus monitoring; charging fault monitoring and warning; Controller area network; Electric potential; Electric power demand; electric vehicle; Electric vehicle charging; Electric vehicles; Electromotive forces; Failure analysis; Fault detection; Fault diagnosis; Heat; Mathematical models; Methods; Monitoring; Monitoring methods; Parameter identification; Power management; Process parameters; Simulation; State of charge; Warning systems; China
• ISSN: 2032-6653; 2032-6653
• DOI: 10.3390/wevj12010014

With the development of electric vehicles in China, the fault monitoring and warning systems for the charging process of electric vehicles have received the industry’s attention. A method for the monitoring and warning of electric vehicle charging faults based on a battery model is proposed in this paper. Through online estimation of the state of charge of the power battery model and battery electromotive force, parameters such as battery state of charge, voltage, and temperature can be adjusted in real time to simulate the charging response of the power battery, which can simulate power batteries of different types, specifications, and parameters. During the charging process, CAN (Controller Area Network) bus monitoring technology is used to receive and analyze the charging information of the charger, as well as the battery charging information and battery charging demand information. The charging response information simulated by the battery model is compared with the battery charging state information, and the charging state information of the charger is compared with the battery charging demand information to determine whether the charging process is normal. When it is judged that a charging fault occurs, a fault warning signal is sent. This method can identify more than 10 types of faults, including the failure of the BMS (Battery Management System) function. The comparison and analysis of actual charging accident data and power battery model data verifies the feasibility of the charging fault monitoring method proposed in this paper.