Capacity Estimation of Lithium-ion Batteries Based on Charging Curve Features

doi:10.3901/JME.2019.20.052

Abstract

Abstract: Accurate capacity estimation plays an important role in lithium-ion battery management. The charging curve features related to the battery capacity attenuation during the aging process are summarized through the battery cycle aging experiments. By calculating the correlation coefficient between curve features and attenuation capacity, the voltage range of curve features is further determined. A relevance vector machine with radial basis function as the kernel function is established. Five features are adopted as input and battery capacity as output for data training, and then the trained relevant sparse vector is used for online capacity estimation. The estimation results show that the accuracy of the data-driven capacity estimation algorithm is less than 2.2% and the convergence of the algorithm is rapid.

Key words: lithium-ion batteries, capacity estimation, charging curve feature, correlation coefficient, relevance vector machine

CLC Number:

TM912

DAI Haifeng, JIANG Bo, WEI Xuezhe, ZHANG Yanwei. Capacity Estimation of Lithium-ion Batteries Based on Charging Curve Features[J]. Journal of Mechanical Engineering, 2019, 55(20): 52-59.

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