Intelligent State of Charge Estimation of Lithium-ion Batteries Using Machine Learning and Deep LSTM Networks: A Comparative Study
Keywords:
Artificial neural network, Battery management system, Electric vehicle, Ensemble learning, Lithium-ion battery, LSTM, Machine learning, State of charge estimationAbstract
Accurate State‑of‑Charge (SoC) estimation is a key function of the battery management system in electric vehicles, directly influencing safety, usable range, and power‑limit decisions. This study presents a comparative data-driven framework for SoC estimation of an LG 18650HG2 lithium-ion cell using three representative machine-learning architectures: ensemble regression trees, a feedforward Artificial Neural Network (ANN), and a long short-term memory (LSTM) recurrent neural network. A publicly available high-fidelity dataset comprising dynamic automotive drive-cycle profiles and multiple temperature conditions is employed, with systematic preprocessing and feature construction. All models are implemented in a MATLAB environment and evaluated using a unified protocol based on RMSE, MAE, and R2 metrics, together with error histograms, residual analysis, cumulative error distributions, SoC‑band and temperature‑band robustness, and inference‑time measurements. The results establish a clear performance hierarchy: the LSTM achieves the highest accuracy with an RMSE of 1.44%, followed by the shallow ANN with 1.72%, while ensemble trees exhibit significantly larger error at 3.66% RMSE. The LSTM further delivers automotive‑grade reliability, with 96% of test predictions within ±2% SoC error and consistent performance across SoC and temperature ranges.
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