Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning

Abstract

Today's drivers of battery electric vehicles must deal with limited driving range in a sparse charging infrastructure. An accurate prediction of energy demand and driving range is therefore important and enables reliable routing and charge planning applications. Predictions of energy demand entail uncertainty, which can be considered directly with the use of probabilistic prediction algorithms. Machine learning algorithms are frequently applied in this context, but data used to train these algorithms are often distributed over a fleet of connected vehicles. Federated learning can be applied in this setting, but predictive uncertainty is typically not considered. We apply an extension of the federated averaging algorithm to learn probabilistic neural networks and linear regression models in a communication-efficient and privacy-preserving manner. We demonstrate the performance advantage of probabilistic prediction models over deterministic prediction models using proper scoring rules. Furthermore, we show that federated learning can improve the standard, driver-individual learning. Using probabilistic predictions, variable safety margins based on destination attainability can be applied, leading to increased effective driving range and reduced travel time.