基于负荷跟随阈值变化规则的并联混合动力汽车能量管理策略
2021-10-31陈致西周金应龙军张明德高越
陈致西 周金应 龙军 张明德 高越



摘 要:针对并联混合动力汽车的能量管理问题,提出了负载跟随阈值改变的控制策略(LTS)。LTS控制策略基于阈值变化机制和负载跟随方法,可以与电池电荷状态(SOC)保持成比例的微小偏差,能够有效确保电池持续稳定运行。本文设计LTS控制策略的阈值通过电池荷电状态(SOC)和发动机转速来综合调整动力输出方式,其能量管理的精细化程度更高。将该策略应用于混合动力汽车进行仿真测试,并与传统的等效燃油消耗率最小化策略(ECMS)和电动辅助控制策略(EACS)进行性能对比。结果表明,在燃油经济性方面,LTS控制策略优于EACS控制策略3.1%~10.4%,LTS控制策略优于ECMS控制策略2.5%~5.7%。在电池荷电状态(SOC)方面,LTS控制策略可以使SOC值大于60%。
关键词:混合动力汽车;负载跟随;阈值变化;能量管理;控制策略
中图分类号:U469.7文献标识码:A文章编号:1003-5168(2021)15-0077-05
Abstract: Aiming at the energy management problem of parallel hybrid electric vehicle, a load-following threshold change control strategy (LTS) was proposed. Based on the threshold change mechanism and load following method, the LTS control strategy can maintain a small deviation proportional to the battery charge state (SOC), which can effectively ensure the battery to run continuously and stably. In this paper, the threshold value of the LTS control strategy is designed to adjust the power output mode comprehensively through the battery state of charge (SOC) and the engine speed, and the energy management is more refined. The proposed strategy was applied to the simulation test of hybrid electric vehicle, and the performance was compared with the traditional equivalent fuel consumption minimization strategy (ECMS) and the electric assisted control strategy (EACS).The results show that the LTS control strategy is superior to the EACS control strategy by 3.1%~10.4% in fuel economy, and the LTS control strategy is superior to the ECMS control strategy by 2.5%~5.7%.In terms of battery state of charge (SOC), the LTS control strategy can make the SOC value greater than 60%.
Keywords: hybrid electric vehicle;load following; threshold change;energy management;control strategy
混合動力汽车(Hybrid Electric Vehicle,HEV)具有节能、低污染、技术成熟等优势,现已成为车辆电动化转型升级的解决方案之一[1]。与传统的燃油汽车相比,混合动力汽车的动力系统具有多个动力源,对其动力系统进行科学合理的管理极为重要,一般是通过混合动力汽车的监控系统(SCS)来对多个动力源进行功率分配[2]。因此,对监控系统(SCS)进行深入研究,以提高车辆燃油经济性并降低排放,具有重要的实际应用价值。
当前的监控系统(SCS)控制策略主要分为基于优化和基于规则两种方法[3]。基于优化的控制策略是通过求解复杂的优化问题来实现能量分……
