State-of-charge estimation of two-wheeler electric vehicle battery pack by long-short term memory model trained using cell-level data

Abstract

The rising global pollution and the urgent shift towards sustainable transportation have accelerated the adoption of electric vehicles (EVs). EV efficiency and performance is majorly dependent on the lithium-ion battery pack, which requires accurate State of Charge (SoC) estimation to ensure effective battery management and longevity. Traditional methods like Coulomb counting and model-based approaches often struggle with cumulative errors, parameter sensitivity, and modelling inaccuracies. This study explores the applicability of deep-learning based model for estimation of pack-level SoC when trained using cell-level data, even when battery chemistry and ambient temperature are unknown. Long short-term memory (LSTM) networks are initially trained on individual cell-level datasets and tested on pack-level data to assess the applicability. In order to investigate the feasibility of a generalized model, LSTM model, further, is trained on multiple cell-level datasets consisting of two different chemistries and different temperatures and tested on pack-level dataset. The findings indicate that models trained with individual or multiple cell-level datasets can effectively estimate pack-level SoC. Results confirm the model’s robustness in estimating SoC without prior knowledge of battery chemistry.