Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation
Abstract Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation....
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82778-w |
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| author | Yara A. Sultan Abdelfattah A. Eladl Mohamed A. Hassan Samah A. Gamel |
| author_facet | Yara A. Sultan Abdelfattah A. Eladl Mohamed A. Hassan Samah A. Gamel |
| author_sort | Yara A. Sultan |
| collection | DOAJ |
| description | Abstract Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance. The proposed system includes two balancing strategies: a charging balance that redistributes excess charge from high-SOC cells to maximize capacity, and a discharging balance that addresses low-SOC cells to extend discharge duration. Experimental results confirm that this method effectively reduces SOC disparities, enhancing both charging and discharging capacities. Additionally, to accurately predict battery lifespan and remaining useful life (RUL), seven machine learning models are evaluated using R-squared (R2) and Mean Absolute Error (MAE) metrics. Among these, k-nearest Neighbors and Random Forest models deliver the highest accuracy, achieving R2 values of 0.996 and above with low MAE, demonstrating strong predictive capability. The integration of active balancing and RUL prediction enables a feedback loop where balanced SOC levels promote battery health, and RUL predictions inform optimal balancing strategies. This comprehensive approach advances EV battery management, enhancing lifespan and reliability through proactive balancing and predictive insights. |
| format | Article |
| id | doaj-art-b988ba538f4f465c993be555f1d0975e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b988ba538f4f465c993be555f1d0975e2025-01-05T12:19:55ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-82778-wEnhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimationYara A. Sultan0Abdelfattah A. Eladl1Mohamed A. Hassan2Samah A. Gamel3Mechatronics Department, Faculty of Engineering, Horus University-EgyptElectrical Engineering Department, Faculty of Engineering, Mansoura UniversityElectrical Engineering Department, Faculty of Engineering, Mansoura UniversityElectronics and Communication Engineering Dept. Faculty of Engineering, Horus UniversityAbstract Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance. The proposed system includes two balancing strategies: a charging balance that redistributes excess charge from high-SOC cells to maximize capacity, and a discharging balance that addresses low-SOC cells to extend discharge duration. Experimental results confirm that this method effectively reduces SOC disparities, enhancing both charging and discharging capacities. Additionally, to accurately predict battery lifespan and remaining useful life (RUL), seven machine learning models are evaluated using R-squared (R2) and Mean Absolute Error (MAE) metrics. Among these, k-nearest Neighbors and Random Forest models deliver the highest accuracy, achieving R2 values of 0.996 and above with low MAE, demonstrating strong predictive capability. The integration of active balancing and RUL prediction enables a feedback loop where balanced SOC levels promote battery health, and RUL predictions inform optimal balancing strategies. This comprehensive approach advances EV battery management, enhancing lifespan and reliability through proactive balancing and predictive insights.https://doi.org/10.1038/s41598-024-82778-wActive balanceLithium‐ion battery packRemaining useful life estimationMachine learning |
| spellingShingle | Yara A. Sultan Abdelfattah A. Eladl Mohamed A. Hassan Samah A. Gamel Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation Scientific Reports Active balance Lithium‐ion battery pack Remaining useful life estimation Machine learning |
| title | Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation |
| title_full | Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation |
| title_fullStr | Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation |
| title_full_unstemmed | Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation |
| title_short | Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation |
| title_sort | enhancing electric vehicle battery lifespan integrating active balancing and machine learning for precise rul estimation |
| topic | Active balance Lithium‐ion battery pack Remaining useful life estimation Machine learning |
| url | https://doi.org/10.1038/s41598-024-82778-w |
| work_keys_str_mv | AT yaraasultan enhancingelectricvehiclebatterylifespanintegratingactivebalancingandmachinelearningforpreciserulestimation AT abdelfattahaeladl enhancingelectricvehiclebatterylifespanintegratingactivebalancingandmachinelearningforpreciserulestimation AT mohamedahassan enhancingelectricvehiclebatterylifespanintegratingactivebalancingandmachinelearningforpreciserulestimation AT samahagamel enhancingelectricvehiclebatterylifespanintegratingactivebalancingandmachinelearningforpreciserulestimation |