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

• Published in: Energy (Oxford) 2022-05, Vol.247, p.123357, Article 123357
• Main Authors: Liu, Hanwu, Lei, Yulong, Fu, Yao, Li, Xingzhong
• Format: Article
• Language: English
• Subjects: Barebones multi-objective particle swarm optimization; Dynamic programming; Energy management strategy; Multi-objective optimization; Multi-performance balance; Range-extended electric vehicle
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2022.123357

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. [Display omitted] •Development a novel hybrid-point-line control strategy for R-EEV.•A general framework is proposed to solve the MOO problem.•The adaptive module considering the battery state is designed.•Optimized method based on traffic condition is applied and implemented.•Investigation of energy management effects on performance from a multi-scale view.