Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM

Abstract

Predictive Maintenance (PdM) of lithium-ion batteries has garnered significant attention in recent years due to their widespread application as energy supplies in various industrial equipment, including automated guided vehicles and battery Electric Vehicles (EVs). Accurately estimating these batteries’ Remaining Useful Life (RUL) is crucial for ensuring optimal performance, preempting unexpected failures, and minimizing maintenance costs. This article focuses on the importance of RUL prediction for lithium-ion batteries and its implications in predictive maintenance. We suggest a novel method based on machine learning techniques using optimization parameters to accurately predict the RUL of lithium-ion batteries. Our approach uses several battery performance variables, such as voltage, current, and temperature, to build a prediction model to anticipate the battery’s RUL precisely. We compare the performance of our suggested process with existing models for battery RUL prediction, incorporating Harris Hawks Optimization (HHO) for hyperparameter tuning. We evaluate the performance of our approach on a dataset of lithium-ion batteries and compare it with other related methods. On a dataset of lithium-ion batteries, we assess our method’s effectiveness and contrast it with other relevant techniques. The proposed method achieves high accuracy in predicting RUL, as evidenced by low values of metrics such as MAE, MSE, RMSE, MAPE, $R^{2}$ , and NMRSE. Also, it achieves high $R^{2}$ scores of 0.979 and 0.971 for the training and testing data, suggesting the model’s high effectiveness in predicting the RUL of batteries.

Keywords

• RUL prediction
• random forest
• LightGBM
• Harris Hawk optimization
• predictive maintenance
• lithium-ion batteries