State of Health Estimation of Lithium‐Ion Batteries Based on Differential Thermal Voltammetry and Improved Gray Wolf Optimizer Optimizing Gaussian Process Regression

Abstract

Accurate estimation of the state of health (SOH) of lithium‐ion batteries (LIBs) is essential for their safe operation. Therefore, herein, a novel approach that combines Gaussian process regression (GPR) optimized using an improved gray wolf optimizer (IGWO) with differential thermal voltammetry (DTV) is introduced. In this approach, the peak and valley information of the DTV curves are used to reveal the battery‐aging mechanisms, with the slopes and durations between peaks and valleys used as health characteristics. The correlation between the characteristics and SOH of the battery is analyzed to build a health feature dataset. IGWO optimizes the GPR hyperparameters to address their dependence on the initial values and susceptibility to local optimization and employs a dimension‐learning strategy to enhance the population diversity and prevent premature convergence. DTV curves and an IGWO‐GPR model for SOH estimation using four cells from the NASA LIB public aging dataset are developed and validated. The results show root mean square errors below 0.007 and mean absolute errors under 0.006 for all cells. The coefficient of determination exceeds 0.92 for three cells, with one battery exhibiting a value of 0.866. This method provides accurate and efficient SOH estimation, essential for safe battery operation.