[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125246-en":3,"doc-seo-125246-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125246,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Optimizing Battery Charge Prediction Accuracy - Utilizing Machine Learning Methods","Energy storage systems become more cost-effective when lithium-ion batteries are managed to correctly maintain capacity, especially when deployed at large scale. This work evaluates multiple machine learning approaches—AdaBoost, XGBoost, gradient boosting, LightGBM, CatBoost, and ensemble learning—by comparing predictive accuracy using MAE, MSE, and R². LightGBM delivers the lowest MAE and MSE alongside the highest R², indicating the closest alignment with real outcomes. SHAP-based analysis supports explainable AI by identifying key factors driving predictions, notably temperature, cycle index, voltage, and power.","Optimizing Battery Charge Prediction Accuracy Utilizing Machine  \nLearning Methods  \nR. Manimegalai 1, S. Sivakumar2, Moazzam Haidari3, Dr. M. Bheemalingaiah4, Dr. P.  \nBalaramesh5, Loya Chandrajit Yadav6  \n1Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.  \n[manimegalaitec@gmail.com](manimegalaitec@gmail.com)  \n2Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India.  \n[siva.carthick@gmail.com](siva.carthick@gmail.com)  \n3Department of Electrical Engineering, Saharsa College of Engineering, Saharsa, Bihar,  \n[India. moazzam53211@gmail.com](India. moazzam53211@gmail.com)  \n4Department of Computer Science and Engineering, J.B. Institute of Engineering and Technology, Hyderabad, [India. bheemasiva2019@gmail.com](India. bheemasiva2019@gmail.com)[ ](India. bheemasiva2019@gmail.com)5Department of Science and Humanities, R.M.K.Engineering College, RSM Nagar, Kavaraipettai, Gummidipoondi (TK), Tiruvallur (DT), [India. pbr.sh@rmkec.ac.in](India. pbr.sh@rmkec.ac.in)[ ](India. pbr.sh@rmkec.ac.in)6Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, [India. yadavloya@kluniversity.in](India. yadavloya@kluniversity.in)  \n[Abstract:](Abstract: Energy storage systems are more cost-effective when they)[ Energy storage systems are more cost-effective when they](Abstract: Energy storage systems are more cost-effective when they)[ ](Abstract: Energy storage systems are more cost-effective when they)[correctly manage the capacity for lithium-ion batteries](correctly manage the capacity for lithium-ion batteries) (LiBs), especially when they are used on a big scale. The design saves money, in the long run, to repair or fix LiBs less often. To determine the amount that LiBs were capable of holding, adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), gradient boosting, light gradient boosting machine (LightGBM), category boosting (CatBoost), as well as ensemble learning models are utilized. Employing the mean absolute error (MAE), and the mean squared error (MSE) along R2 numbers, the researcher compared the accuracy with which each model could predict future outcomes.  \nFor example, the LightGBM model had the least MAE (0.102) as well as MSE (0 .018) values, as well as the greatest R-squared (0.886) value, which means that its predictions were most closely related to reality. It was about the same in terms of speed among the gradient boosting as well as XGBoost models, which came next to LightGBM. The ensemble model's efficiency suggests that integrating many models might result in an overall increase in performance. In addition, the research uses Shapley additive explanations (SHAP) values to analyze important aspects influencing model predictions within the context of explainable artificial intelligence (XAI) . This study found that discharge capacity is strongly influenced by temperature, cycle index, voltage, and power. This study demonstrates that Machine Learning (ML) methods can improve energy storage systems and regulate LiB in  \nXAI.  \nKeywords: Machine Learning, Explainable Artificial Intelligence, Shapley Additive Explanations, Lithium-Ion Batteries, Energy  \nStorage Systems.  \n1. Introduction  \nThe substantial energy density, low discharge rates, lightweight setting up, rapid charging speed, and minimal maintenance needs of LiBs make them an essential power of electric vehicles (EVs) [1]. LiBs' qualities have made them the preferred power source of EVs in several applications. With electrification reducing overall carbon footprints and greenhouse emissions compared to traditional fossil fuel automobiles, LiBs have become more appealing as alternative sources of energy [2–5] .  \nThe state of health (SoH) parameter serves as a cru","cbCaiqo2AA2V5GEU","https://ap.wps.com/l/cbCaiqo2AA2V5GEU","pdf",408028,1,11,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Lithium-ion battery benefits in EVs\n## Battery health (SoH) and its significance\n## Advanced SoH assessment with ML and diagnostics\n## Role of explainable AI (XAI) in SoH prediction","[{\"question\":\"Which machine learning models are compared for battery charge prediction accuracy?\",\"answer\":\"The study compares AdaBoost, XGBoost, gradient boosting, LightGBM, CatBoost, and ensemble learning models for predicting battery-related outcomes.\"},{\"question\":\"How is prediction performance evaluated in the research?\",\"answer\":\"Model accuracy is evaluated using MAE, MSE, and R² to compare how closely each model’s predictions match real values.\"},{\"question\":\"What does the SHAP analysis reveal about factors affecting predictions?\",\"answer\":\"SHAP-based explainability indicates discharge capacity is strongly influenced by temperature, cycle index, voltage, and power.\"}]","Optimizing Battery Charge Prediction Accuracy - 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