Fast State of Health Prediction of Lithium-Ion Battery Based on Terminal Voltage Drop During Rest for Short Time
Guo Yongfang1, Huang Kai2, Li Zhigang2
1. School of Artificial Intelligence Hebei University of Technology Tianjin 300130 China; 2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China
Abstract The maximum available capacity is an important basis for indicating the State of Healthy (SOH) of lithium-ion battery. Upon analyzing the experiments results of the cycle life and the open circuit voltage, a novel healthy factor could represent the maximum available capacity was proposed in this paper to predict the SOH of the battery. The healthy factor of the paper mainly used the data relative with the terminal voltage drop during the battery rest for 10min after the battery was charged or discharge. Comparing with the traditional methods, the method of the paper does not limit the working conditions of the battery, and it can predict the SOH quickly. Besides, it does not need to discharge the battery to obtain the health factor. Furthermore, four regression prediction methods were used to model the relationship between health factors and the battery available capacity. Upon analyzing the performance and results of the regression neural networks, an improved weighted hybrid neural network was proposed. The experimental results show that the proposed health factor can be used to characterize SOH of the battery and it is robust to the working conditions. The weighted hybrid neural network of the paper can achieve high precision for SOH prediction.
Guo Yongfang,Huang Kai,Li Zhigang. Fast State of Health Prediction of Lithium-Ion Battery Based on Terminal Voltage Drop During Rest for Short Time[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 3968-3978.
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2019, Vol. 34 
