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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Elsevier
2025-06-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000830 |
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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 |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-087dcb4e70614e4bb51831f55534c13e |
| institution | Kabale University |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-087dcb4e70614e4bb51831f55534c13e2025-08-20T03:24:44ZengElsevierFranklin Open2773-18632025-06-011110029310.1016/j.fraope.2025.100293Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithmsMohd Herwan Sulaiman0Zuriani Mustaffa1Ahmad Salihin Samsudin2Amir Izzani Mohamed3Mohd Mawardi Saari4Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600 Pekan, Pahang, Malaysia; Center for Advanced Industrial Technology (AIT), Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600 Pekan, Pahang, Malaysia; Corresponding author.Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600 Pekan, Pahang, MalaysiaIonic Materials Team, Faculty of Industrial Sciences & Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26300 Gambang, Pahang, MalaysiaFaculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600 Pekan, Pahang, MalaysiaFaculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600 Pekan, Pahang, MalaysiaState 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.http://www.sciencedirect.com/science/article/pii/S2773186325000830Battery state of chargeCatBoost algorithmMachine learningMetaheuristic algorithms |
| spellingShingle | 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 Franklin Open Battery state of charge CatBoost algorithm Machine learning Metaheuristic 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 | Battery state of charge CatBoost algorithm Machine learning Metaheuristic algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000830 |
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