State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression

Gaussian process regression (GPR) is extensively employed in lithium-ion battery state-of-health (SOH) estimation, which ensures the safe, reliable operation of electric vehicles (EVs). However, a single GPR can produce performance discrepancies across different fast-charge batteries, as well as high time consumption. Therefore, we propose an SOH estimation method for fast-charging batteries based on stacking ensemble sparse Gaussian process regression (SGPR). First, health factors are extracted in partial discharge fragments to reflect battery degradation. Then, SGPRs based on the fully independent training condition (FITC) are developed with different kernel functions as level-1 learners, and a genetic algorithm (GA) is used to optimize the parameters of the kernel function. Further, the level-2 learner integrates the features produced by the level-1 learner based on cross validation. Finally, the accuracy, robustness, and reliability of the proposed method were evaluated under various fast-charging experiments. The results show that the mean absolute error (MAE) and root mean square error (RMSE) of SOH estimation were within 1.0852% and 1.2123%, respectively, and that the average relative time consumption was reduced by 85.68% compared with stacking ensemble GPR. Thus, the proposed method has broad application prospects in processing vast datasets from numerous batteries in monitoring platforms or cloud data centers.