【作者】 刘涵； 高俊涛；

【Author】 LIU Han,GAO Jun-Tao School of Automation and Information Engineering,Xi’an University of Technology,Xi’an,710048,P.R.China

【机构】 西安理工大学自动化与信息工程学院；

【摘要】 本文研宄了并联式混合动力汽车(Parallel Hybrid E1ectric Vehicle,PHEV)的逻辑门限值控制策略,用遗传算法(GA)对控制器参数进行优化仿真,与优化前相比,在满足汽车性能的要求下能够有效的降低油耗,减少排放。同时将优化后的结果与设计的模糊控制策略进行比较,模糊控制策略的油耗和排放要优于优化后的逻辑门限值控制策略,但是由于逻辑门限值能保持电池经常充放电,电机的使用率高。而模糊控制策略使得发动机经常工作在高效区或者低排放区,因此在动力性方面要优于模糊控制策略。更多还原

【Abstract】 In this paper,based on the deterministic rule-based control strategy,controller parameters are optimized by genetic algorithms for parallel hybrid electric vehicle.Compared with previous results,this approach can effectively reduce fuel consumption and emissions without sacrificing vehicle performance.Additionally,the contrast experiments between this approach and fuzzy control strategy have also been done.The fuel consumption and emissions of fuzzy control strategy is better than that of optimized deterministic rule-based control strategy.Because the deterministic rule-based control strategy can keep the battery constantly charging or discharging and the motor often work,the fuzzy control strategy often makes the engine work in high efficiency areas or low-emission zones,so it is worse than deterministic rule-based control strategy in terms of vehicle performance.更多还原