A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle

To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range extended electric vehicle, a novel hybrid-point-line energy management strategy (H-P-LEMS) has been proposed from a multi-scale view. First, a multi-objective optimization (MOO) model is established which takes into account energy consumption, emissions and battery life. The barebones multi-objective particle swarm optimization is applied for solving the MOO problem. And a dynamic programming optimized algorithm is applied to obtain the optimal curve/area of APU to establish objective function of MOO. Then, an adaptive approach uses a fuzzy logic controller with the battery consideration to adjust parameters in real time. Simulation results show that there is a clear conflict that three optimization objectives cannot be optimal at the same time and the final optimization solution with optimal comprehensive evaluation index is selected to evaluate the performance of the proposed methodology. Finally, the simulation and experimental results thoroughly indicate that the proposed H-P-LEMS has better balance than conventional rule-based energy management strategy (EMS). As expected, economy improvement, emission reduction and prolonging the battery service life are kept in balance effectively. And this result can be used to develop EMS to improve comprehensive performance levels. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

In this study, a novel hybrid-point-line energy management strategy (H-P-LEMS) is proposed from a multi-scale view for achieving optimal energy allocation in a range extended electric vehicle. The strategy considers multiple objectives including energy consumption, emissions, and battery life. It utilizes a multi-objective optimization model and a dynamic programming optimized algorithm to obtain the optimal solution. Additionally, an adaptive approach with a fuzzy logic controller is employed for real-time parameter adjustment. Simulation and experimental results demonstrate that the H-P-LEMS outperforms conventional rule-based energy management strategies in maintaining a balance between economy improvement, emission reduction, and battery service life extension.