Estimation of State-of-Energy for Electric Vehicles Based on the Identification and Prediction of Driving Condition
Liu Weilong1,2,Wang Lifang1,Wang Liye1
1. Key Laboratory of Power Electronics and Electric Drives Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190 China; 2. University of Chinese Academy of Sciences Beijing 100049 China
Abstract：State-of-energy (SOE) is an important index of the internal state of electric vehicle traction batteries that determines the range of electric vehicles directly and which is influenced by the driving condition significantly. In order to estimate SOE based on the driving condition, the SOE estimation algorithm, driving condition identification algorithm, driving condition prediction algorithm were studied in this paper. A battery state of residual energy (SOR) estimation algorithm based on battery model was established. A driving condition identification algorithm based on the informational entropy theory was built. A driving condition prediction algorithm was proposed with Markov chain theory. The battery predicted working condition schedule was achieved by modeling the electric vehicle system. In the end, the SOE estimation algorithm based on the identification and prediction of driving condition was achieved. Validation results show that the proposed SOE estimation algorithm was efficient.
刘伟龙,王丽芳,王立业. 基于电动汽车工况识别预测的锂离子电池SOE估计[J]. 电工技术学报, 2018, 33(1): 17-25.
Liu Weilong,Wang Lifang,Wang Liye. Estimation of State-of-Energy for Electric Vehicles Based on the Identification and Prediction of Driving Condition. Transactions of China Electrotechnical Society, 2018, 33(1): 17-25.
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2018, Vol. 33 
