A health indicator extraction and optimization for capacity estimation of Li-ion battery using incremental capacity curves

Abstract

Accurate lithium battery online capacity and remaining useful life (RUL) estimation are critical to increasing penetration of electric vehicles. Motivated by this, health indicators (HIs) extraction and optimization using incremental capacity curves are proposed. This paper reports a straightforward approach to smooth the noise on IC curves, thereby capturing accurate and reliable HIs. To prevent overfitting in machine learning, a combined weighting method is emphasized to reduce the dimensionality of HIs. It is then used in the modeling of battery capacity estimation as the improved Gaussian process regression is applied. In this framework, results show that the correlation between the battery capacity and dimension-reducing HIs is desirable. Analysis results reveal the above measures' trustworthiness, with the average error of the six batteries is 2.3% under the cross-validation test. What's more, a set of different types of batteries are used to verify the robustness of this method.