Utilizing Machine Learning Algorithms in a Data-Driven Approach to the Prediction of Vehicle Battery State of Charge with BMW i3 Datasets 2022-01-5088

The state of charge (SoC) in an electric vehicle must be assessed and projected for any scenario, using the array of data points that can be extracted from a vehicle. In this paper, we explored the utility of data-driven approaches to SoC prediction that do not rely upon any internal or equation-based understanding of the device operation. We leveraged three unique machine learning algorithms to predict the battery SoC using data from other features of electric vehicles. We used a publicly available dataset describing vehicle parameters and trip details for 70 trips in EV BMW™ i3 (60 ah) vehicles and evaluated aforementioned machine learning algorithms for predicting SoC percentage. We utilized a data processing technique (delta and stagger) to extract different perspectives from each trip record and demonstrated that machine learning techniques can be effectively used to predict battery SoC for a wide range of driving conditions and trip parameters.