State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression

The performance of lithium-ion batteries(LIBs) gradually declines over time, making it critical to predict the battery’s state of health(SOH) in real-time. This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR) to predict the SOH of LIBs. Firstly, the degradation process of an LIB is analyzed through indirect health indicators(HIs) derived from voltage and temperature during discharge. Next, the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA), and the EGPR is employed to estimate the SOH of LIBs. Finally, the proposed model is tested under various experimental scenarios and compared with other machine learning models. The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration (NASA) LIB. The root mean square error(RMSE) is maintained within 0.20%, and the mean absolute error(MAE) is below 0.16%, illustrating the proposed approach’s excellent predictive accuracy and wide applicability.