State of Charge Estimation for Electric Vehicles Using LSTM and FNN: A Deep Learning Approach

L. Anand, Riya Ranjan, Aditi Arun Patil, Anish Patil, Sayyad Abid

2025

Abstract

Dependence on fossil fuel is one of the major contributing factors to climate change. While it does provide energy, it also presents significant problems. To mitigate this, the transportation industry is transitioning to battery-powered systems for a more sustainable future. This calls for a system that could manage the batteries for safe and efficient operation. This requires to accurately predict features such as State of Charge of a battery (SOC). Traditional estimation methods, such as Kalman filters and equivalent circuit models, often struggle with nonlinearities and uncertainties in battery behaviour. The aim of this study is to propose a hybrid model which utilises Feedforward Neural Network (FNN) and Long short-term memory (LSTM) FNN is employed as it possesses the ability to deal with complex nonlinear features that a battery management system would have to deal with while LSTM is used for modelling temporal dependencies., improving prediction accuracy over time. Experimental battery datasets are used to train and validate the model, and its results are compared to those of traditional techniques. The results show that even with different load and temperature circumstances, the suggested method delivers improved accuracy and robustness. This contributes to advancement in systems such as BMS by demonstrating the potential of deep learning models.

Paper Citation

in Harvard Style

Anand L., Ranjan R., Patil A., Patil A. and Abid S. (2025). State of Charge Estimation for Electric Vehicles Using LSTM and FNN: A Deep Learning Approach. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 548-553. DOI: 10.5220/0013916500004919

in Bibtex Style

@conference{icrdicct`2525,
author={L. Anand and Riya Ranjan and Aditi Patil and Anish Patil and Sayyad Abid},
title={State of Charge Estimation for Electric Vehicles Using LSTM and FNN: A Deep Learning Approach},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={548-553},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013916500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - State of Charge Estimation for Electric Vehicles Using LSTM and FNN: A Deep Learning Approach
SN - 978-989-758-777-1
AU - Anand L.
AU - Ranjan R.
AU - Patil A.
AU - Patil A.
AU - Abid S.
PY - 2025
SP - 548
EP - 553
DO - 10.5220/0013916500004919
PB - SciTePress