This paper introduces an optimization based holistic energy management system for a battery electric vehicle. The energy management can adapt the velocity and the power consumed by the cabin heating, in order to minimize the energy consumption, while keeping total driving time and the cabin temperature within predened limits. For the optimization a hybrid genetic algorithm is used. The approach is applied to a driving cycle, which is optimized by dividing it into separate time frames. This approach is referred to as sliding window approach. The results of the sliding window approach are compared to an optimization of the whole driving cycle. The results presented in this paper demonstrate the feasibility of the sliding window approach. Moreover, they show that the sliding window approach does not lead to a signicant deterioration compared with the optimization of the whole driving cycle. At the same time the driver comfort remains well within the acceptable limits and the driving time constant.     «

This paper introduces an optimization based holistic energy management system for a battery electric vehicle. The energy management can adapt the velocity and the power consumed by the cabin heating, in order to minimize the energy consumption, while keeping total driving time and the cabin temperature within predened limits. For the optimization a hybrid genetic algorithm is used. The approach is applied to a driving cycle, which is optimized by dividing it into separate time frames. This...     »