基于ECMS混联式混合动力客车工况识别控制策略

由于城市特殊的运行工况,单纯的基于规则控制策略很难从城市复杂的工况中获取最佳燃油经济性.以提高一款新型混联式混合动力客车燃油经济性为目的,为了更好地适应城市复杂的行驶工况,制定了一种工况自适应实时优化控制策略.根据等效燃油最小控制策略思想结合新型混联式混合动力客车的结构特点构建发动机与电池间的功率分配实时优化算法,针对城市循环工况的特点选定了4种典型的工况类型,并获得不同行驶工况和等效燃油转换系数及油耗的关系,经分析发现每一行驶工况都存在相应的等效燃油系数使得其油耗最低,因此采用LVQ神经网络模型对各工况特征参数进行学习训练以进行实时工况识别,利用工况识别的方法获取当前运行的工况类型并选择相对应的等效系数进行周期性更新以期达到最佳燃油经济性,从而实现对不同工况的适应性.仿真结果表明:其燃油经济性比单纯的能量管理优化控制策略提高了8.55%,同时电池SOC能够控制在预定的范围内运行.

Driving Pattern Recognition Based on ECMS and its Application to Control Strategy for a Series-parallel Hybrid Electric Bus

Due to complex driving conditions in cities,it is hard to obtain the optimal economic performance with the rule-based strategy alone.An adaptive real time control strategy was proposed to adapt the various driving conditions and to improve fuel economy of a new series-parallel hybrid electric bus(SPHEB).This method consists of the Equivalent fuel Consumption Minimization Strategy and algorithm of driving pattern recognition in essence.The key role of ECMS is the equivalence factor,which is used to convert electrical power used into an equivalent fuel quantity.Four types of roadways were selected to present the characteristics of city driving cycle,and a driving pattern recognition approach was employed to obtain better estimation of the equivalence factor under different roadway types.The main idea of the adaptive real time control strategy is periodically updating the equivalence factor dependent on the corresponding driving condition.To validate the proposed strategy,a forward model was built on the basis of simulink.The simulation results demonstrate that the roadway types can be successfully recognized and the improvement of fuel economy is up to 8.55%,while the battery SOC is limited in the desired range.