Bayesian optimization algorithm‐based Gaussian process regression for in situ state of health prediction of minorly deformed lithium‐ion battery

Abstract

AbstractAccurate on‐board state‐of‐health (SOH) prediction is crucial for lithium‐ion battery applications. This study presents an in situ prediction technique for minorly deformed battery SOH, utilizing a Gaussian process regression (GPR) model tuned by a Bayesian optimization algorithm. Unlike previous methods that interpret voltage–time data as incremental capacitance curves, our approach directly operates on raw voltage–time data. We apply gray relational analysis to select feature variables as inputs and train the Bayesian Gaussian process regression (BGPR) model using experimental data from batteries under different working conditions. To demonstrate the performance of the BGPR model, we compare it with stepwise linear regression, neural network, and Bayesian support vector machine (BSVM) models. The performance of these four models is evaluated using different performance indicators: mean absolute percentage error (MAPE), root‐mean‐squared percentage error (RMSPE), and coefficient of determination (R²). The results demonstrate that the BGPR model exhibits superior prediction performance with the lowest MAPE (0.11%), RMSPE (0.12%), and the highest R² (0.9915) for minorly deformed batteries. Furthermore, the BGPR model exhibits excellent robustness for SOH prediction of normal batteries under different conditions. This study provides an effective and robust method for accurate on‐board SOH prediction in lithium‐ion battery applications.