Influence of MPC based Driver Types on the Energy Consumption of Battery Electric Vehicles

Electric vehicles are increasingly important to reducing emissions and promoting sustainable mobility. Despite their advantages over conventional vehicles, the energy consumption of electric vehicles is heavily influenced by various factors such as driving behavior, elevation profile, and environmental conditions. In particular, the driving style plays a crucial role in determining range and energy consumption. This influence is also observed in the context of the Interreg project FreeE-Bus. This project focuses on the development of optimized charging management for electric buses in the public transport system of the Lake Constance region. Due to strict data protection regulations that prevent a detailed analysis of driver data, assessing the impact of driving styles is difficult. This paper addresses this issue by developing an innovative driver model that simulates different driver types and analyzes their effects on energy consumption. The driver model employs a Model Predictive Control approach, and two driver types, the “Predictive Driver” and the “Dynamic Driver,” are implemented and tested in the simulation and on a real vehicle. The results of this study demonstrate that it is possible to simulate different driving styles and highlight significant differences in energy consumption between them, providing valuable insights into improving driving strategies. Therefore, this approach represents a valuable addition to the existing range of driver control modeling methods.