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
Hlavní autoři: Carrera, Romel, Quiroz, Leonidas, Guevara, Cesar, Acosta-Vargas, Patricia
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 26.07.2025
MDPI
Témata:
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
SOC estimation
genetic algorithms
monitoring system
vehicle range
ISSN:1424-8220, 1424-8220
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Shrnutí: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s25154632