LightGBM Based Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Battery under Driving Conditions
Xiao Qian1, Jiao Zhipeng2, Mu Yunfei1, Lu Wenbiao1, Jia Hongjie1
1. Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China; 2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China
Abstract：The degradation of the remaining useful life (RUL) for EV lithium-ion battery under driving conditions is complicated. The appropriate prediction of RUL can provide guidance for the periodic maintenance and stable operation to avoid the risks. Therefore, a RUL prediction method for driving conditions is proposed in this paper. Firstly, a light gradient boosting machine (LightGBM) based RUL prediction model is constructed, and the coefficients are obtained by the hyper parameter optimization (Hyperopt). Secondly, the experimental bench of battery cycle life capacity is established to simulate the vibration stress and charge-discharge stress, and the RUL degradation of battery under driving conditions is measured. Then, based on the dynamic time warping (DTW), the similarity of RUL degradation between driving conditions and static conditions is analyzed, and a new capacity sequence can be generated by the generative adversarial networks (GAN). Finally, experimental results verify the effectiveness of the proposed model and the generated capacity sequence.
肖迁, 焦志鹏, 穆云飞, 陆文标, 贾宏杰. 基于LightGBM的电动汽车行驶工况下电池剩余使用寿命预测[J]. 电工技术学报, 2021, 36(24): 5176-5185.
Xiao Qian, Jiao Zhipeng, Mu Yunfei, Lu Wenbiao, Jia Hongjie. LightGBM Based Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Battery under Driving Conditions. Transactions of China Electrotechnical Society, 2021, 36(24): 5176-5185.
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2021, Vol. 36 
