A Comparative Machine Learning-based State-of-Charge Estimation of Lithium-Ion Batteries using OCV-Corrected Reference Modeling and Multi-Domain Feature Engineering

Abstract

State of Charge (SoC) estimation is an indispensable feature in Battery Management Systems (BMS) and is an important function to be implemented for Electric Vehicles (EVs), Renewable Energy Storage (RES) and Portable electronics. For the nonlinearity of lithium-ion Batteries, the current model-based ones generally suffer from uncertain parameters and lose accuracy under various operating conditions. This paper discusses four traditional machine learning algorithms: Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extra Trees (ET), Support Vector Regression (SVR) and explores them in the context of a common evaluation methodology with regard to SoC estimation. In order to make a fair comparison between the machine learning algorithms, a set of common methodology, which involves preprocessing, feature engineering, splitting into train and test set, and hyperparameters tuning are evaluated in all these machine learning techniques. Various performance parameters have been taken into account for assessing the performance of the actual prediction and to decide the extent to which the selected models are appropriate to be utilized in actual BMS applications.