Research on adaptive energy management of extended-range electric logistics vehicle based on information of slope prediction

With the increasing intelligence and networking of new energy vehicles, the constraints and optimization objectives of energy management strategies are more diversified, and their theories and technologies are still facing great challenges. In order to optimize the fuel efficiency of the extended-range electric logistics vehicle, this paper proposes an adaptive energy management strategy based on information of slope prediction. The improved genetic algorithm is used to optimize the equivalent factor offline iteratively, and the energy saving potential of the strategy is tapped to reduce fuel consumption. The simulation results show that the equivalent fuel consumption in the three road conditions of flat slope, downhill, and uphill is reduced by 4.305%, 3.842%, and 3.782% respectively after optimization, which improves the fuel economy. The semi-physical hardware-in-the-loop test is carried out. The error between the test vehicle speed tracking and the equivalent fuel consumption and the simulation results is small, which verifies the effectiveness and feasibility of the strategy.