基于多工况优化算法的混合电动汽车参数优化

Parameter Optimization of Hybrid Electric Vehicle Based on Multi-cycle Optimization Algorithm

协调优化混合动力电动汽车动力系统与能量控制策略的参数是提高燃油经济性的关键。以汽车的动力性能为约束条件,把电池SOC平衡油耗引入目标函数,提出一种采用模拟退火粒子群算法的基于多工况优化混合动力汽车动力系统与控制策略参数的方法。利用MATLAB/Simulink仿真软件进行建模仿真,结果表明,利用该方法可以获取一组参数,在保持汽车动力性、电池SOC平衡的基础上,相对于优化前,在综合工况下油耗降低了5.49%,分工况HWFET、FTP、LA92、US06_HWY、UDDS与SC03下油耗分别降低了8.46%、4.62%、3.84%、8.43%、5.56%、4.31%。为了证明本方法优化出的参数在其他工况的通用性,选取NEDC工况进行仿真验证,结果发现,在NEDC工况下油耗降低了8.36%。并进行了实车道路试验,试验结果较好的验证了所提出的基本控制策略的有效性。

Coordinated optimization for parameters of hybrid electrical vehicle（HEV） powertrain and control strategy is the key to improving fuel economy. In order to reduce fuel consumption, a methodology using simulated annealing particle swarm optimization（SAPSO） and multi-cycle is presented to achieve parameter optimization for both the powertrain and the control strategy based on the constraint of the dynamic performance requirements. The simulation have been carried out based on the MATLAB/Simulink software, and the result show that the proposed approach can find a set of control parameters to reduce fuel consumption with keeping power performance and balance of battery SOC. After optimization, the fuel consumption of vehicle will be reduced 5.49% at comprehensive driving cycle and 8.46%, 4.62%, 3.84%, 8.34%, 5.56% and 4.31% at cycles of HWFET, FTP, LA92, US06_HWY, UDDS, SC03, respectively. Without lose generality, the cycle of NEDC have been chose to validate the optimization control parameters and the fuel consumption will be reduced 8.36%. The road test is developed, and demonstrated the effectiveness of the energy management strategy in this work.