计及行驶工况影响的混合动力汽车控制策略

Hybrid Electric Vehicle Control Strategy with Consideration of the Effects of Driving Cycle

为减小行驶工况波动对汽车性能的影响，引入行驶工况比例系数。应用遗传优化算法，以汽车燃油经济性和排放性为优化目标，在不同比例系数的行驶工况下对控制策略参数进行优化，分析并总结了行驶工况比例系数与控制策略参数之间的关系。通过采用模糊神经网络对行驶工况的比例系数进行识别，建立了一个计及行驶工况影响的混合动力汽车智能控制策略。实车验证结果表明，所建立的控制策略减小了行驶工况波动对电动汽车性能的影响，使其在不同的行驶工况下都具有较好的燃油经济性和排放性。

For reducing the influence of driving cycle variation on the performance of vehicle, a scaling factor of driving cycle is introduced. By using genetic optimization algorithm with the fuel economy and emission of vehicle as objectives, the control strategy parameters under different scaling factors of driving cycle are optimized, and the relation between the scaling factors of driving cycle and the parameters of control strategy is analyzed and summarized. By making use of a fuzzy neural network to identify the scaling factors of driving cycle, an intelligent control strategy for hybrid electric vehicle （HEV）, taking into account the effects of driving cycle is created. The resuits of real vehicle validation show that the control strategy created reduces the effects of driving cycle variation on the performance of HEV, leading to better fuel economy and emission performance in different driving cycles.