An online state of health estimation technique for lithium-ion battery using artificial neural network and linear interpolation

Abstract

To accurately estimate the battery state of health (SOH) is crucial to enhance the performance of a battery-powered system. This paper proposes a SOH estimation method that can predict the battery current SOH when the battery is fully charged. When the state of charge (SOC) is 100%, the AC impedance of the Li-ion battery at each frequency can be obtained by electrochemical impedance spectroscopy (EIS) and used as the training data of the neural network. The result has been validated by another aging battery and it shows that with the best performance neural network, the maximum relative error of the estimated SOH is only 1.31 %. Besides, compared to the linear interpolation method, the maximum relative error has improved by 1.74 %, MRE by 0.50 %, MAE by 0.42 %, and MSE by 0.86 %. Furthermore, this paper discusses a technique that can estimate the battery SOH under different temperatures without collecting the AC impedance through the whole cycle life test.