State of health estimation method for lithium-ion batteries based on multiple dynamic operating conditions

Abstract

Lithium-ion battery state of health estimation is an important task for electric vehicles. However, the uncertainty and complexity of operating conditions pose significant challenges for state of health estimation. In this paper, a state of health estimation method with dynamic operating conditions generalization is developed. The equivalent circuit model parameters are kept unchanged, and the voltage is predicted based on the equivalent circuit model of initial aging point. The integral voltage error is extracted as an indicator of battery aging, and feature fusion is achieved by combining the feature of dynamic operating conditions. By cross-validation under dynamic operating conditions, the fused features are input into a back propagation neural network to achieve accurate estimation of state of health. At the same time, the voltage error reference baseline and pseudo-state of charge interval that affect the accuracy of the model estimation are discussed. After selecting the appropriate voltage error reference baseline and pseudo-state of charge interval, the mean absolute errors of the state of health estimation obtained on the dynamic operating conditions are all around 1%. The proposed state of health estimation method has low computational requirements, eliminates the dependence on the accuracy of state of charge , and can achieve generalization of multiple dynamic operating conditions, making it potentially applicable in actual vehicle applications.