Lithium-ion battery state of health estimation using intelligent methods
In electric vehicle applications, detecting Li-ion battery degradation is essential to ensure safety and reliability. A key approach to assessing battery health is monitoring the internal impedance and capacity over the battery's lifetime, which provides insight into the State of Health (SOH) a...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000271 |
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| author | Hemavathi S |
| author_facet | Hemavathi S |
| author_sort | Hemavathi S |
| collection | DOAJ |
| description | In electric vehicle applications, detecting Li-ion battery degradation is essential to ensure safety and reliability. A key approach to assessing battery health is monitoring the internal impedance and capacity over the battery's lifetime, which provides insight into the State of Health (SOH) and indicates whether the battery has reached its End of Life (EOL). This study proposes an intelligent SOH estimation algorithm utilizing Feed-forward and Recurrent Neural Networks, trained with the Levenberg-Marquardt function, to predict battery SOH under various aging conditions. The methodology begins with life cycle and Electrochemical Impedance Spectroscopy (EIS) tests to establish the charge-discharge characteristics and create an Equivalent Circuit Model that represents the dynamic properties and degradation indicators of an 18650 Li-ion battery. Key model parameters, such as internal resistance, are extracted per cycle to track aging progression. Finally, the SOH estimation models, developed in SIMULINK, utilize internal impedance and capacity metrics to predict SOH under various aging scenarios. Results in SIMULINK demonstrate that both networks provide accurate SOH estimations; however, the Recurrent Neural Network achieves faster convergence, reaching accurate predictions within 10 epochs. This improved convergence speed, along with high measurement accuracy and reliability, underscores the Recurrent Neural Network's suitability for real-time SOH monitoring in electric vehicle applications. |
| format | Article |
| id | doaj-art-2947bf7464bc43b089184078f6f0d20f |
| institution | DOAJ |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-2947bf7464bc43b089184078f6f0d20f2025-08-20T03:06:00ZengElsevierFranklin Open2773-18632025-03-011010023710.1016/j.fraope.2025.100237Lithium-ion battery state of health estimation using intelligent methodsHemavathi S0CSIR-Central Electrochemical Research Institute (CECRI), CSIR-Madras Complex, Chennai 600 113, Tamil Nadu, India; Academy of Scientific & Innovative Research, Ghaziabad 201 002, Uttar Pradesh, IndiaIn electric vehicle applications, detecting Li-ion battery degradation is essential to ensure safety and reliability. A key approach to assessing battery health is monitoring the internal impedance and capacity over the battery's lifetime, which provides insight into the State of Health (SOH) and indicates whether the battery has reached its End of Life (EOL). This study proposes an intelligent SOH estimation algorithm utilizing Feed-forward and Recurrent Neural Networks, trained with the Levenberg-Marquardt function, to predict battery SOH under various aging conditions. The methodology begins with life cycle and Electrochemical Impedance Spectroscopy (EIS) tests to establish the charge-discharge characteristics and create an Equivalent Circuit Model that represents the dynamic properties and degradation indicators of an 18650 Li-ion battery. Key model parameters, such as internal resistance, are extracted per cycle to track aging progression. Finally, the SOH estimation models, developed in SIMULINK, utilize internal impedance and capacity metrics to predict SOH under various aging scenarios. Results in SIMULINK demonstrate that both networks provide accurate SOH estimations; however, the Recurrent Neural Network achieves faster convergence, reaching accurate predictions within 10 epochs. This improved convergence speed, along with high measurement accuracy and reliability, underscores the Recurrent Neural Network's suitability for real-time SOH monitoring in electric vehicle applications.http://www.sciencedirect.com/science/article/pii/S2773186325000271End of lifeNeural networkState of healthElectrochemical impedance spectroscopyEquivalent circuit model |
| spellingShingle | Hemavathi S Lithium-ion battery state of health estimation using intelligent methods Franklin Open End of life Neural network State of health Electrochemical impedance spectroscopy Equivalent circuit model |
| title | Lithium-ion battery state of health estimation using intelligent methods |
| title_full | Lithium-ion battery state of health estimation using intelligent methods |
| title_fullStr | Lithium-ion battery state of health estimation using intelligent methods |
| title_full_unstemmed | Lithium-ion battery state of health estimation using intelligent methods |
| title_short | Lithium-ion battery state of health estimation using intelligent methods |
| title_sort | lithium ion battery state of health estimation using intelligent methods |
| topic | End of life Neural network State of health Electrochemical impedance spectroscopy Equivalent circuit model |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000271 |
| work_keys_str_mv | AT hemavathis lithiumionbatterystateofhealthestimationusingintelligentmethods |