A New Method of Battery State of Charge Prediction in the Hybrid Electric Vehicle
Wang Qi1,3, Sun Yukun2,3, Ni Fuyin1,3, Luo Yinsheng1
1.School of Electrical and Information Engineering Jiangsu University of Technology Changzhou 213001 China 2.School of Electrical Engineering Nanjing Institute of Technology Nanjing 211167 China 3.Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu University Zhenjiang 212013 China
Abstract：In order to predict the battery’s state of charge (SOC) in the hybrid electric vehicles (HEV),the Bayesian extreme learning machine (BELM) is utilized.The basic principles of the extreme learning machine and the Bayesian linear regression are introduced in detail.To improve the abilities of fitting and generalization of the ELM,the Bayesian linear regression is used to optimize the weights of the output layer.The working voltage,the current,and the surface temperature of the battery are chosen to predict the real-time value of SOC under the driving cycle.At the same time,the energy feedback process is taken into account when the HEV is under regenerative braking model.Both the simulation results under ADVISOR and the experimental results indicate that the proposed prediction model has higher predicted accuracy and can achieve real-time and accurate SOC prediction.
王琪, 孙玉坤, 倪福银, 罗印升. 一种混合动力电动汽车电池荷电状态[J]. 电工技术学报, 2016, 31(9): 189-196.
Wang Qi, Sun Yukun, Ni Fuyin, Luo Yinsheng. A New Method of Battery State of Charge Prediction in the Hybrid Electric Vehicle. Transactions of China Electrotechnical Society, 2016, 31(9): 189-196.
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2016, Vol. 31 
