Abstract：State of health (SOH) of Lithium-ion battery describes the current aging degree of the battery. The difficulty of its estimation lies in the lack of a clear definition, the inability to directly measure, and the difficulty in determining the appropriate number and high correlation of the estimation input. In order to overcome the above problems, this article defines SOH from the perspective of capacity, and takes the voltage, current, and temperature curves of the lithium-ion battery constant current-constant voltage charging process as input, and proposes to use a one-dimensional deep convolutional neural network (CNN) to achieve lithium-ion battery capacity estimation to obtain SOH. Experimental results on NASA's lithium-ion battery random use data set and Oxford battery aging data set show that this method can achieve accurate SOH estimation, and has the advantages of fewer network parameters and less memory. In addition, the influences of network input, model structures and data augmentation on the proposed SOH estimation method are discussed through experiments.
李超然, 肖飞, 樊亚翔, 杨国润, 唐欣. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
Li Chaoran, Xiao Fei, Fan Yaxiang, Yang Guorun, Tang Xin. An Approach to Lithium-Ion Battery SOH Estimation Based on Convolutional Neural Network. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
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2020, Vol. 35 
