杜常清,杨贤诚,郭孔辉,等.混合动力货车能量管理策略硬件在环仿真研究[J].内燃机工程,2022,43(3):19-26.
混合动力货车能量管理策略硬件在环仿真研究
Hardware in the Loop Simulation of Hybrid Electric Truck Energy Management Strategy
DOI：10.13949/j.cnki.nrjgc.2022.03.003
关键词:能量管理  粒子群算法  等效燃油消耗最小控制策略  硬件在环仿真
Key Words:energy management  particle swarm optimization  equivalent fuel consumption minimization control strategy  hardware in the loop simulation
基金项目:国家自然科学基金项目（51775393）；新能源汽车科学与关键技术学科创新引智基地项目（B17034）；教育部创新团队发展计划项目（IRT_17R83）；现代汽车零部件技术湖北省重点实验室开放基金项目（XDQCKF2021010）
作者单位E-mail
杜常清* 武汉理工大学 汽车工程学院，武汉 430070 cq_du@whut.edu.cn 
杨贤诚 武汉理工大学 汽车工程学院，武汉 430070  
郭孔辉 武汉理工大学 汽车工程学院，武汉 430070  
何隽逸 武汉理工大学 汽车工程学院，武汉 430070  
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摘要:针对等效燃油消耗最小策略（equivalent consumption minimum strategy， ECMS）获得优化等效因子的途径提出了一种基于粒子群寻优算法的重型混合动力卡车等效油耗最小控制策略。该策略采用粒子群算法并以整车油耗与终止电池组荷电状态（state of charge， SOC）偏离目标SOC程度的绝对值加权之和作为适应性函数，对等效油耗最小策略的关键参数进行选取。根据该重型卡车的传动系统构型，基于Simulink建立了车辆的纵向动力学模型，并基于此模型分别设计了基于动态规划（dynamic programming， DP）算法优化规则的能量管理策略和基于等效油耗最小的控制策略。在选取的测试工况下验证优化后的策略，硬件在环测试结果表明，在保持SOC稳定的情况下相比于基于DP算法优化规则的能量管理策略，所提出的基于粒子群算法优化的等效油耗最小策略使发动机油耗降低3.63%及1.36%。
Abstract:In order to get the optimized equivalent fuel consumption factor for the equivalent consumption minimum strategy（ECMS）， a control strategy based on particle swarm optimization（PSO） was proposed to minimize the equivalent fuel consumption of heavy-duty hybrid electric truck. The sum of the absolute weighted values of the vehicle fuel consumption and the deviation of the end state of charge（SOC） from the target SOC was taken as the adaptability function， and the key parameters of the strategy were selected with PSO. According to the characteristics of the power system of a heavy-duty truck， a Simulink- based longitudinal dynamic model of the vehicle was established. Based on this model， the energy management strategy based on DP algorithm optimization rules and the control strategy based on ECMS were designed respectively. The optimized strategy was verified under the specific driving cycles. The hardware in the loop（HIL） test results indicated that the proposed strategy could reduce the engine fuel consumption by 3.63% and 1.36% compared with the energy management strategy based on DP algorithm while maintaining SOC stability.
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