A novel combinatorial optimization algorithm for energy management strategy of plug-in hybrid electric vehicle

Optimization design of energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV), which significantly affects the vehicle performance on fuel economy and pollutant emission, has always been a focal issue. Of various EMSs, rule-based strategies are dominant in practical applications due to their relatively low computational burden, but to obtain the optimum control parameters precisely and efficiently remains an unsolved problem. In this paper, a novel combinatorial algorithm utilizing the historical data from remote monitoring platform is proposed for the EMS optimization of PHEV. Firstly, the historical driving data are processed, and then a table which records different conditions at different time is built for reflecting the future PHEV operation schedule. Based on the historical data, a combinatorial algorithm which combines the advantages of genetic algorithm (GA) with enhanced ant colony algorithm (EACA) is proposed to optimize the control parameters. The principle of algorithm transformation from GA to EACA is when the objective function value is smaller than the default value after five generations of changing continuously in GA optimization process, and then the control parameter combinations can be regarded as the pheromone for EACA. Results show that the combinatorial algorithm successfully overcomes the low solution precision by GA and the slow resolving speed by EACA. The energy consumption of PHEV on a specific bus route can be reduced greatly by the proposed method, and it can provide a theoretical guidance for practical applications.