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Volume 25
Issue 15
10.3390/s25154632
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms

by
Romel Carrera
1,
Leonidas Quiroz
1,
Cesar Guevara
2 and
Patricia Acosta-Vargas
3,*
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
2
Quantitative Methods Department, CUNEF Universidad, 28040 Madrid, Spain
3
Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(15), 4632; https://doi.org/10.3390/s25154632 (registering DOI)
Submission received: 11 June 2025 / Revised: 14 July 2025 / Accepted: 23 July 2025 / Published: 26 July 2025
(This article belongs to the Section Electronic Sensors)
Versions Notes

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.
Keywords: monitoring system; genetic algorithms; SOC estimation; vehicle range monitoring system; genetic algorithms; SOC estimation; vehicle range

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MDPI and ACS Style

Carrera, R.; Quiroz, L.; Guevara, C.; Acosta-Vargas, P. State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms. Sensors 2025, 25, 4632. https://doi.org/10.3390/s25154632

AMA Style

Carrera R, Quiroz L, Guevara C, Acosta-Vargas P. State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms. Sensors. 2025; 25(15):4632. https://doi.org/10.3390/s25154632

Chicago/Turabian Style

Carrera, Romel, Leonidas Quiroz, Cesar Guevara, and Patricia Acosta-Vargas. 2025. "State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms" Sensors 25, no. 15: 4632. https://doi.org/10.3390/s25154632

APA Style

Carrera, R., Quiroz, L., Guevara, C., & Acosta-Vargas, P. (2025). State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms. Sensors, 25(15), 4632. https://doi.org/10.3390/s25154632

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