An Experimental Analysis on Optimizing Lithium Ion Battery Lifespan Predictions with XGBoost and Bayesian Techniques

This study seeks to evaluate the effectiveness of the XGBoost algorithm in estimating lithium-ion batteries Remaining Useful Life (RUL).With the increasing demand for reliable and efficient battery health monitoring systems, accurately forecasting the RUL of batteries has become crucial, especially in applications involving electric vehicles, renewable energy storage, and portable electronics. This study focuses on comparing different optimization techniques applied to XGBoost, including parameter grid search and Bayesian optimization, to determine the most effective approach for improving prediction accuracy.The dataset employed in this research was acquired from Kaggle, containing approximately 6500 records of lithium-ion battery data.The findings from this study emphasize the effectiveness of XGBoost in RUL prediction, with optimization techniques playing a crucial role in improving its performance. The base XGBoost model, while reasonably accurate, benefited significantly from hyperparameter tuning.In terms of MSE, the Bayesian-optimized XGBoost model reduced the error from 0.00266 to 0.00141, a significant reduction that demonstrates its efficiency in minimizing prediction errors. Similarly, RMSE was reduced from 0.052 to 0.0375, and MAE decreased from 0.00219 to 0.00178, indicating that the Bayesian optimization technique was able to fine-tune the model more effectively than grid search.By achieving an R2 score of 0.9997 and minimizing MSE, RMSE, and MAE to extremely low values, the Bayesian-optimized XGBoost model offers a promising solution for applications requiring precise battery health predictions.