Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases...
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MDPI AG
2025-01-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/11/1/31 |
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| author | Mohamed Ahwiadi Wilson Wang |
| author_facet | Mohamed Ahwiadi Wilson Wang |
| author_sort | Mohamed Ahwiadi |
| collection | DOAJ |
| description | Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system failures. To ensure the reliability and longevity of Li-ion batteries in applications, various methods have been proposed for battery health monitoring and remaining useful life (RUL) prediction. This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. Hybrid approaches integrate multiple methods to enhance predictive accuracy by combining the physical insights of model-based methods with the statistical and analytical strengths of data-driven techniques. This paper thoroughly evaluates these methodologies, focusing on recent advancements along with their respective strengths and limitations. By consolidating current findings and highlighting potential pathways for advancement, this review paper serves as a foundational resource for researchers and practitioners working to advance battery health monitoring and RUL prediction methods across both academic and industrial fields. |
| format | Article |
| id | doaj-art-6cd35c3ecb5b4947b6fa0869be7d2d09 |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Batteries |
| spelling | doaj-art-6cd35c3ecb5b4947b6fa0869be7d2d092025-01-24T13:22:28ZengMDPI AGBatteries2313-01052025-01-011113110.3390/batteries11010031Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of TechnologiesMohamed Ahwiadi0Wilson Wang1Department of Mechanical and Mechatronics Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, CanadaDepartment of Mechanical and Mechatronics Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, CanadaLithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system failures. To ensure the reliability and longevity of Li-ion batteries in applications, various methods have been proposed for battery health monitoring and remaining useful life (RUL) prediction. This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. Hybrid approaches integrate multiple methods to enhance predictive accuracy by combining the physical insights of model-based methods with the statistical and analytical strengths of data-driven techniques. This paper thoroughly evaluates these methodologies, focusing on recent advancements along with their respective strengths and limitations. By consolidating current findings and highlighting potential pathways for advancement, this review paper serves as a foundational resource for researchers and practitioners working to advance battery health monitoring and RUL prediction methods across both academic and industrial fields.https://www.mdpi.com/2313-0105/11/1/31lithium-ion batteriesbattery health managementbattery degradationstate of health estimationremaining useful life predictiondata-driven techniques |
| spellingShingle | Mohamed Ahwiadi Wilson Wang Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies Batteries lithium-ion batteries battery health management battery degradation state of health estimation remaining useful life prediction data-driven techniques |
| title | Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies |
| title_full | Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies |
| title_fullStr | Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies |
| title_full_unstemmed | Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies |
| title_short | Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies |
| title_sort | battery health monitoring and remaining useful life prediction techniques a review of technologies |
| topic | lithium-ion batteries battery health management battery degradation state of health estimation remaining useful life prediction data-driven techniques |
| url | https://www.mdpi.com/2313-0105/11/1/31 |
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