A Data-driven Energy Estimation based on the Mixture of Experts Method for Battery Electric Vehicles

Battery electric vehicles (BEVs) are an immediate solution to the reduction of greenhouse gas emissions. However, BEVs are limited in their range by the battery capacity. An accurate estimation of BEV's range and its energy consumption have become a significant factor in eliminating customers "range anxiety". To overcome range anxiety, advanced algorithms can predict the remaining capacity, estimate the range and inform the driver. Algorithms need to consider various influencing factors for their range estimation. A crucial part for an accurate range estimation is the energy consumption modeling itself. Thus, machine learning-based approaches are highly investigated which are able to learn nonlinear relations between relevant features and the energy consumption. In this paper, we propose a data-driven approach for the energy estimation of BEVs by utilizing ensemble learning to achieve a feature-specific estimation. In this paper, we trained neural networks on different road types independently. We improve the overall estimation by combining models via the mixture of experts method compared to a monolithic trained neural network. The results demonstrate that specialized neural networks for the energy estimation of BEVs are beneficial for the energy estimation. This approach contributes to reducing range anxiety and therefore helping toward elevated adoption of BEVs.