Abstract:

Accurate estimation of the battery state of health (SOH) is a critical technology in battery management systems, which is crucial for ensuring the safe and reliable operation of electric vehicles. To solve the problem of low SOH estimation accuracy due to insufficient generalization performance of a single kernel function in Gaussian process regression (GPR) and the tendency of hyperparameter selection to fall into local optimality, an SOH estimation method based on the grey wolf optimization algorithm (GWO) and a combined kernel function was proposed. First, the characteristics of battery aging were extracted using incremental capacity analysis (ICA) method. The capacity-voltage curve of constant-current charging of the battery was interpolated and the increment capacity (IC) curve was calculated using the difference method. The IC curve was smoothed using Savitzky-Golay filtering, and the peak height, voltage, and area were extracted as health features. Second, multidimensional scaling (MDS) was presented to eliminate feature redundancy and reduce the computational complexity of the model. The Pearson coefficient was used to verify the correlation between the proposed health features and SOH. Then, considering the nonlinearity of the SOH degradation trajectory and the quasi-periodicity of battery capacity regeneration, the combination of the neural network kernel function and periodic kernel function was used as the covariance kernel function of GPR, and the initial hyperparameters of the combined kernel function were optimized by the GWO method. Finally, the proposed method was compared with SVR, ELM, and GPR models based on the NASA battery data set to verify the accuracy of the GWO-GPR model. The 60^th, 80^th, and 100^th cycles were used as estimation starting points to verify the robustness of the model.

Key words: lithium-ion battery, state of health, increment capacity curve, gaussian process regression, grey wolf optimization algorithm