科研之友
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摘要
Currently, energy management control mainly focuses on single-objective optimization (SOO). Even if multi-objective optimization (MOO) problem is studied, it is often converted into an SOO problem by using the weighted sum method. Obviously, it cannot really reflect the essential strengths of MOO. In this paper, a parallel hybrid electric vehicle is taken as the research object. The fuel economy, emissions, and drivability performance are taken as optimization objectives. The parameters of energy management and driveline system are optimized. Considering the constraint conditions of the dynamic performance and charge balance, the fast non-dominated sorting differential evolution algorithm (NSDEA) is proposed to solve the multi-objective optimization problem. Then multi-group sets of Pareto solutions with good distribution and convergence are obtained. The simulation results of NSDEA show that the fuel economy is increased by 20.26% on average. The emissions evaluation index is optimized by 11.33% on average, and the maximum carbon monoxide (CO) optimization value reaches 21.9%. The average of drivability evaluation index (jerk) is up to 20.84%, and 40.32% for maximum. Obviously, the above obtained results are discrete points. They only represent some optimal solutions. Based on the above sets, the locally weighted scatter plot smoothing method is used to fit continuous curve and surfaces. Then, the multi-objective Pareto trade-off optimal control surface is established to further obtain the optimal solutions. This study can provide more reference for the optimal control strategy and lay a foundation for multi-objective energy management of the actual vehicle.
- 出版日期2018-4
- 单位重庆交通大学; 四川轻化工大学
