Comprehensive machine learning approaches for modelling the state of charge of lithium-ion batteries

The advancement of lithium-ion batteries (LIBs) is vital for achieving net-zero emissions because it enables renewable energy integration, supports electric vehicle (EV) adoption, and promotes cost-effective and sustainable solutions. The growing demand for EVs and portable electronics has amplified the need for reliable battery management systems to ensure safety and performance. Machine learning (ML) methods for modelling the state of charge (SOC) in batteries are gaining traction owing to their adaptability to diverse datasets and lower computational demands. However, the challenge lies in selecting the most suitable ML architecture for a specific application. This study evaluates three ML approaches for SOC modelling in LIBs: multilayer perceptron (MLP), long short-term memory (LSTM), and nonlinear autoregressive with exogenous input (NARX) neural networks. The models were tested using an experimental dataset with multiple input variables, including electrochemical impedance spectroscopy data, voltage, and capacity from commercial LIB cells. The results show that MLP and LSTM perform effectively with smaller training datasets (14 samples), whereas the NARX model requires more extensive data (34 out of 67 samples) for accuracy. Additionally, the NARX model showed greater sensitivity to learning rate adjustments and hidden layer configurations, whereas MLP and LSTM maintained robust performance across varying parameters. • MLP, LSTM and NARX are used to model SOC in LiBs with coarse datasets. • MLP and LSTM are more adaptable with a smaller training dataset. • Higher correlation between SOC and EIS data is found for EIS measured at 0% SOC. • Strengths and weaknesses of the MLP, LSTM and NARX are discussed. • Effects of training size, hidden layers and learning rates of ANNs are discussed.