State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms
This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-i...
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| Vydáno v: | Sensors (Basel, Switzerland) Ročník 25; číslo 15; s. 4632 |
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26.07.2025
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| Abstract | This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter α. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. |
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| AbstractList | This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48-72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter α. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications.This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48-72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter α. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter α. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. |
| Author | Quiroz, Leonidas Acosta-Vargas, Patricia Carrera, Romel Guevara, Cesar |
| AuthorAffiliation | 1 Universidad de las Fuerzas Armadas ESPE, Departamento de Ciencias de la Energía y Mecánica Sede Latacunga, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador; rdcarrera@espe.edu.ec (R.C.); laquiroz@espe.edu.ec (L.Q.) 3 Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador 2 Quantitative Methods Department, CUNEF Universidad, 28040 Madrid, Spain; cesar.guevara@cunef.edu |
| AuthorAffiliation_xml | – name: 2 Quantitative Methods Department, CUNEF Universidad, 28040 Madrid, Spain; cesar.guevara@cunef.edu – name: 3 Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador – name: 1 Universidad de las Fuerzas Armadas ESPE, Departamento de Ciencias de la Energía y Mecánica Sede Latacunga, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador; rdcarrera@espe.edu.ec (R.C.); laquiroz@espe.edu.ec (L.Q.) |
| Author_xml | – sequence: 1 givenname: Romel orcidid: 0000-0002-6936-1889 surname: Carrera fullname: Carrera, Romel – sequence: 2 givenname: Leonidas orcidid: 0000-0003-3556-4079 surname: Quiroz fullname: Quiroz, Leonidas – sequence: 3 givenname: Cesar orcidid: 0000-0003-1571-5829 surname: Guevara fullname: Guevara, Cesar – sequence: 4 givenname: Patricia orcidid: 0000-0003-4210-0117 surname: Acosta-Vargas fullname: Acosta-Vargas, Patricia |
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| SubjectTerms | Accuracy Batteries Deep learning Electric vehicles Embedded systems Genetic algorithms Lithium Machine learning monitoring system Neural networks Optimization Parameter identification SOC estimation Support vector machines vehicle range |
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| Title | State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms |
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