What question did this study set out to answer?

The aim is to improve estimation of battery state of charge (SoC) in electric vehicles using machine learning.

May 6, 2026BatteriesOpen Access

Battery State of Charge Estimation in Electric Vehicles Using Machine Learning with Feature Engineering and Seasonal Analysis Under On-Road Conditions

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Authors

FDFeriştah DalkılıçDokuz Eylül University KBKadriye Filiz BalbalDokuz Eylül University KBKökten Ulaş BirantDokuz Eylül University

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Implication

Demonstrates improved prediction accuracy for battery state of charge in electric vehicles, highlighting machine learning and feature engineering benefits.

Key Points

The aim is to improve estimation of battery state of charge (SoC) in electric vehicles using machine learning.
Utilized Light Gradient Boosting Machine for SoC estimation
Performed feature engineering to enhance model input
Conducted seasonal analysis under summer and winter conditions
Applied Explainable Artificial Intelligence techniques for model interpretation
Evaluated the model on real-world on-road data.
Achieved substantial improvements in estimation performance
Balanced prediction accuracy with computational efficiency
Enhanced learning of driving conditions and battery dynamics.

Cite This Study

Dalkılıç et al. (2026) studied this question.

synapsesocial.com/papers/69faa2b504f884e66b533571 https://doi.org/10.3390/batteries12050159

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Implication

Demonstrates improved prediction accuracy for battery state of charge in electric vehicles, highlighting machine learning and feature engineering benefits.

Battery State of Charge Estimation in Electric Vehicles Using Machine Learning with Feature Engineering and Seasonal Analysis Under On-Road Conditions

Discussion

Member takes

Implication

Key Points

Cite This Study

Battery State of Charge Estimation in Electric Vehicles Using Machine Learning with Feature Engineering and Seasonal Analysis Under On-Road Conditions

Discussion

Member takes

Implication

Key Points

Cite This Study

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