Enhancing Battery management system in electric vehicles using deep learning

Vijay; Surender Singh

doi:10.36676/urr.v12.i2.1534

Authors

Vijay Department- Electrical Engineering UIET (Power system) Email- Vk762264@gmail.com
Surender Singh Assistant Professor University Institute of Engineering and Technology Maharshi Dayanand University Rohtak

DOI:

https://doi.org/10.36676/urr.v12.i2.1534

Keywords:

CNN, RNN, BMS, LSTM, SoC, EVs, Battery health, AI

Abstract

As electric vehicles (EVs) become increasingly prevalent, the demand for smarter and more efficient battery management systems (BMS) has grown significantly. Traditional BMS techniques often fall short in accurately predicting battery health, state of charge (SoC), and remaining useful life (RUL), especially under dynamic driving conditions. This paper proposes an enhanced BMS framework powered by deep learning techniques to address these challenges. By leveraging recurrent neural networks (RNNs), long short-term memory (LSTM) models, and convolutional neural networks (CNNs), the system can learn complex temporal and spatial patterns from real-time battery data. The deep learning-based BMS improves prediction accuracy, enables proactive maintenance, and optimizes energy usage, thereby extending battery life and ensuring safe vehicle operation. Simulation results and real-world datasets demonstrate the model’s superiority over traditional methods in terms of efficiency, reliability, and adaptability. This study highlights the transformative role of artificial intelligence in next-generation electric mobility systems.

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