Due to the high-dimensionality of the operating conditions space (input), relationships with endof-life metrics (outputs) are not obvious. To deconvolute the impacts of individual protocol parameters, we applied explainable ML models. We used the discharge profile parameters as input features in an XGBoost ML model (using xgboost's XGBRegressor with a wrapper from the scikit-learn library ) to predict selected target outputs. The XGBoost model was chosen due to its high performance and flexibility. The outputs calculated for this analysis were EFCs, Q pe , Q ne , Q Li , R 0s , R 5s , and R 10s at EoL (degradation modes). The resistances were extracted from the HPPCs at 50 % SOC during the discharge pulse.