基于LVQ工况识别的混合动力汽车自适应能量管理控制策略

为提高混合动力汽车的燃油经济性，选取6种典型行驶工况代表“市区”、“郊区”和“高速公路”3类主要工况，采用基于规则的模糊能量管理控制策略，以整车燃油经济性为目标，在3类主要工况下用改进型粒子群优化算法优化发动机联合工作曲线与发动机关闭曲线系数，得到相应的优化后的隶属度函数的参数;运用学习向量量化(LVQ)算法识别车辆运行工况，动态选择相应的模糊控制策略，使混合动力汽车控制策略对选定的几种代表性工况具有自适应性，从而提高整车的燃油经济性。仿真对比结果表明，相比于传统混合动力汽车，燃油经济性提高了3.4%。

Adaptive Energy Control Strategy of HEV Based on Driving Cycle Recognition by LVQ Algorithm

In order to reduce fuel consumption, six kinds of typical driving cycles were chosen to represent the “urban”, “suburban” and “highway”. And for vehicle fuel economy, based on fuzzy control strategy with rules,an improved PSO algorithm was adopted to optimize the engine working with motor curve factor and the engine shutting off curve factor, then the optimized parameters of membership function could be achieved under the above typical driving cycles. Furthermore, LVQ algorithm was adopted to recognize real-time driving cycle, the corresponding fuzzy control strategy could be chosen according to the recognition results, which maintained the adaptability for those driving cycles, and improved HEV's fuel economy. Simulation results show that fuel economy adopted with this control strategy is improved by 3.4% comparing to the traditional methods without cycle recognition.hybrid electric vehicle(HEV); driving cycle recognition; fuel economy;