Optimizing Lithium-Ion Battery Performance: Integrating Machine Learning and Explainable AI for Enhanced Energy Management

Saadin Oyucu (), Betül Ersöz, Şeref Sağıroğlu, Ahmet Aksöz and Emre Biçer
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Saadin Oyucu: Faculty of Engineering, Department of Computer Engineering, Adıyaman University, 02040 Adıyaman, Türkiye
Betül Ersöz: Artificial Intelligence and Big Data Analytics Security R&D Center, Gazi University, 06570 Ankara, Türkiye
Şeref Sağıroğlu: Artificial Intelligence and Big Data Analytics Security R&D Center, Gazi University, 06570 Ankara, Türkiye
Ahmet Aksöz: Mobilers Team, Sivas Cumhuriyet University, 58050 Sivas, Türkiye
Emre Biçer: Battery Research Laboratory, Faculty of Engineering and Natural Sciences, Sivas University of Science and Technology, 58010 Sivas, Türkiye

Sustainability, 2024, vol. 16, issue 11, 1-15

Abstract: Managing the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale applications, enhances the cost-effectiveness of energy storage systems. Less frequent replacement or maintenance of LiBs results in cost savings in the long term. Therefore, in this study, AdaBoost, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble learning models were employed to predict the discharge capacity of LiBs. The prediction performances of each model were compared based on mean absolute error (MAE), mean squared error (MSE), and R-squared values. The research findings reveal that the LightGBM model exhibited the lowest MAE (0.103) and MSE (0.019) values and the highest R-squared (0.887) value, thus demonstrating the strongest correlation in predictions. Gradient boosting and XGBoost models showed similar performance levels but ranked just below LightGBM. The competitive performance of the ensemble model indicates that combining multiple models could lead to an overall performance improvement. Furthermore, the study incorporates an analysis of key features affecting model predictions using SHAP (Shapley additive explanations) values within the framework of explainable artificial intelligence (XAI). This analysis evaluates the impact of features such as temperature, cycle index, voltage, and current on predictions, revealing a significant effect of temperature on discharge capacity. The results of this study emphasize the potential of machine learning models in LiB management within the XAI framework and demonstrate how these technologies could play a strategic role in optimizing energy storage systems.

Keywords: Li-ion; BMS; SoH estimation; ensemble learning; explainable AI; SHAP (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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