Title: Prediction of battery critical parameters using machine learning algorithms for electric vehicles

Authors: Vasudha Hegde; Jaskaran Singh Sohal; Gopi Balaraman; Aayush Karn; Kumar Bhaskar Pandey

Addresses: Department of E&EE, Nitte Meenakshi Institute of Technology, Bengaluru-560064, India ' Department of E&EE, Nitte Meenakshi Institute of Technology, Bengaluru-560064, India ' Department of E&EE, Nitte Meenakshi Institute of Technology, Bengaluru-560064, India ' Department of E&EE, Nitte Meenakshi Institute of Technology, Bengaluru-560064, India ' Department of E&EE, Nitte Meenakshi Institute of Technology, Bengaluru-560064, India

Abstract: To enhance the adaptability of electric vehicles (EVs) and mitigate the intermittent nature of renewable energy sources, energy storage via batteries is imperative. Accurate forecasting of battery performance parameters is vital for optimal utilisation. This study introduces a machine learning algorithm for electric vehicle battery management systems (BMS), focusing on predicting state of charge (SoC) efficiently and precisely. Utilising linear regression and long short-term memory (LSTM) models, the algorithm constructs and deploys predictions. Training data, obtained from Li-ion battery packs during charge-discharge cycles via smart BMS, enables precise modelling. Predicted values are validated against empirical results, and the resultant error guides algorithm refinement for enhanced accuracy. The algorithm, integrated into a web application using Streamlit, achieved a remarkable 99% R2_score, indicating its robust performance. This framework advances EV battery management, facilitating informed decision-making and optimising energy utilisation in conjunction with renewable sources.

Keywords: battery management system; BMS; long short-term memory; LSTM; linear regression; machine learning algorithm; state of charge; SoC.

DOI: 10.1504/IJEHV.2024.140023

International Journal of Electric and Hybrid Vehicles, 2024 Vol.16 No.3, pp.247 - 260

Received: 12 Jul 2023
Accepted: 19 Nov 2023
Published online: 15 Jul 2024 *

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