Road attributes, traffic conditions and the individual driving behavior have an impact on the energy consumption of electric vehicles (EV) and lead to a variation of the speed profile of EVs. With the increasing number of connected vehicles, speed profiles are collected in a backend. Models for the prediction of the remaining driving range of EVs use statistical features of the collected speed profiles, vehicle parameters and attributes of digital maps. Regression models and machine learning methods are compared and evaluated by recorded measurements from existing EVs.
«
Road attributes, traffic conditions and the individual driving behavior have an impact on the energy consumption of electric vehicles (EV) and lead to a variation of the speed profile of EVs. With the increasing number of connected vehicles, speed profiles are collected in a backend. Models for the prediction of the remaining driving range of EVs use statistical features of the collected speed profiles, vehicle parameters and attributes of digital maps. Regression models and machine learning met...
»