A Novel Approach for a Predictive Online ECMS Applied in Electrified Vehicles Using Real Driving Data

Abstract

To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system’s theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh.