Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health (SOH) estimation of e-mobilities operated in real and dynamic conditions is essential and challenging. Most of existing estimations are based on a fixed constant current charging and discharging aging profiles, which overlooked the fact that the charging and discharging profiles are random and not complete in real application. This work investigates the influence of feature engineering on the accuracy of different machine learning (ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered. Fifteen features were extracted from the battery partial recharging profiles, considering different factors such as starting voltage values, charge amount, and charging sliding windows. Then, features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection. Multiple linear regression (MLR), Gaussian process regression (GPR), and support vector regression (SVR) were applied to estimate SOH, and root mean square error (RMSE) was used to evaluate and compare the estimation performance. The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%, 2.57%, and 2.82% for MLR, GPR and SVR respectively. It was demonstrated that the estimation based on partial charging profiles with lower starting voltage, large charge, and large sliding window size is more likely to achieve higher accuracy. This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.