Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms
State of Charge (SoC) estimation plays a crucial role in battery management systems for electric vehicles, directly impacting their operational efficiency and reliability. This study presents a hybrid approach combining the CatBoost algorithm with metaheuristic optimization techniques to enhance SoC...
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| Format: | Article |
| Language: | English |
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Elsevier
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44970/ http://umpir.ump.edu.my/id/eprint/44970/1/1-s2.0-S2773186325000830-main.pdf |
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| author | Mohd Herwan, Sulaiman Zuriani, Mustaffa Ahmad Salihin, Samsudin Amir Izzani, Mohamed Mohd Mawardi, Saari |
| author_facet | Mohd Herwan, Sulaiman Zuriani, Mustaffa Ahmad Salihin, Samsudin Amir Izzani, Mohamed Mohd Mawardi, Saari |
| author_sort | Mohd Herwan, Sulaiman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | State of Charge (SoC) estimation plays a crucial role in battery management systems for electric vehicles, directly impacting their operational efficiency and reliability. This study presents a hybrid approach combining the CatBoost algorithm with metaheuristic optimization techniques to enhance SoC estimation accuracy and robustness. The methodology was validated using an extensive dataset collected from 72 real-world driving trips of a BMW i3 (60 Ah), comprising 1053,910 instances of battery and vehicle operation metrics. A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. Experimental results demonstrate that the BMOCatBoost approach achieved superior performance with best-case metrics of RMSE = 6.1031, MAE = 4.1303, and R² = 0.8211, outperforming both PSOCatBoost, GA-CatBoost, and WOA-CatBoost implementations. The framework's effectiveness was validated through rigorous testing, establishing its potential for real-world electric vehicle applications. This research contributes to the advancement of battery management technology, offering promising implications for electric vehicle energy management and broader energy storage applications. |
| first_indexed | 2025-11-15T03:57:20Z |
| format | Article |
| id | ump-44970 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:20Z |
| publishDate | 2025 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-449702025-07-08T08:11:25Z http://umpir.ump.edu.my/id/eprint/44970/ Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms Mohd Herwan, Sulaiman Zuriani, Mustaffa Ahmad Salihin, Samsudin Amir Izzani, Mohamed Mohd Mawardi, Saari TK Electrical engineering. Electronics Nuclear engineering State of Charge (SoC) estimation plays a crucial role in battery management systems for electric vehicles, directly impacting their operational efficiency and reliability. This study presents a hybrid approach combining the CatBoost algorithm with metaheuristic optimization techniques to enhance SoC estimation accuracy and robustness. The methodology was validated using an extensive dataset collected from 72 real-world driving trips of a BMW i3 (60 Ah), comprising 1053,910 instances of battery and vehicle operation metrics. A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. Experimental results demonstrate that the BMOCatBoost approach achieved superior performance with best-case metrics of RMSE = 6.1031, MAE = 4.1303, and R² = 0.8211, outperforming both PSOCatBoost, GA-CatBoost, and WOA-CatBoost implementations. The framework's effectiveness was validated through rigorous testing, establishing its potential for real-world electric vehicle applications. This research contributes to the advancement of battery management technology, offering promising implications for electric vehicle energy management and broader energy storage applications. Elsevier 2025 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/44970/1/1-s2.0-S2773186325000830-main.pdf Mohd Herwan, Sulaiman and Zuriani, Mustaffa and Ahmad Salihin, Samsudin and Amir Izzani, Mohamed and Mohd Mawardi, Saari (2025) Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms. Franklin Open, 11 (100293). pp. 1-12. ISSN 2773-1863. (Published) https://doi.org/10.1016/j.fraope.2025.100293 10.1016/j.fraope.2025.100293 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Herwan, Sulaiman Zuriani, Mustaffa Ahmad Salihin, Samsudin Amir Izzani, Mohamed Mohd Mawardi, Saari Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms |
| title | Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms |
| title_full | Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms |
| title_fullStr | Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms |
| title_full_unstemmed | Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms |
| title_short | Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms |
| title_sort | electric vehicle battery state of charge estimation using metaheuristic-optimized catboost algorithms |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/44970/ http://umpir.ump.edu.my/id/eprint/44970/ http://umpir.ump.edu.my/id/eprint/44970/ http://umpir.ump.edu.my/id/eprint/44970/1/1-s2.0-S2773186325000830-main.pdf |