A Machine Learning Approach to Lithium-Ion State of Charge (SoC) Time Series Forecasting in Battery Management Systems (BMS)

Abstract

State of charge (SoC) is a key indicator that represents the proportion of energy remaining in a battery relative to its full capacity. This parameter is continuously tracked by the battery management system (BMS) to maintain safe and efficient operation. Precise determination of SoC is essential, as errors in estimation can negatively affect device performance, accelerate battery wear, and in severe cases lead to system malfunction. As a result, achieving reliable and accurate SoC estimation is a critical aspect that requires careful attention and advanced monitoring techniques. This study aims to design a method for estimating state of charge (SoC) levels through the application of time series forecasting techniques. In this paper five models are used: Deep Neural Networks (DNN), Long Short Time Memory (LSTM), Convolutional Neural Network (CNN), Combination of CNN with LSTM and autoregressive LSTM. For this time-series analysis, battery voltage, battery discharge current, and the core temperature are used as the inputs and the battery SoC used as the output. The investigation covers multi-sample single-output time series forecasting method. The models are evaluated based on performance indices and training time with forecast diagrams graphically represented for each of the models under investigation. Dataset used for this study is based on the public LG® 18650HG2 lithium ion (Li-ion) battery dataset covering series of tests under six operating temperatures. This paper concludes that combining CNN with LSTM is the most accurate model with its lowest mean average error (MAE) on both the validation and test sets. While DNN model has the shortest training time out of the five models.