A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles

Abstract

Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to deploy charging stations in mostly all Moroccan cities. Furthermore, in Morocco, EVs are tax-free, and their users can charge for free their vehicles in any station. However, customers are still worried by the driving range of EVs. For instance, a new driving style is needed to increase the driving range of their EV, which is not easy in most cases. Therefore, the need for a companion system that helps in adopting a suitable driving style arise. The driving range depends mainly on the battery’s capacity. Hence, knowing in advance the battery’s state-of-charge (SoC) could help in computing the remaining driving range. In this paper, a battery SoC forecasting method is introduced and tested in a real case scenario on Rabat-Salé-Kénitra urban roads using a Twizy EV. Results show that this method is able to forecast the SoC up to 180 s ahead with minimal errors and low computational overhead, making it more suitable for deployment in in-vehicle embedded systems.

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

“…It leverages the wisdom of multiple models to make more reliable predictions. Ensemble Learning is a powerful technique that combines the predictions of multiple machine learning models to improve the accuracy and robustness of SoC estimation in EVs [101].…”