基于道路工况预测的混合动力汽车自适应等效燃油消耗最小策略

Adaptive Equivalent Fuel Consumption Minimization Strategy for Hybrid Electric Vehicle Based on Road Condition Prediction

为解决传统自适应等效燃油消耗最小策略(A-ECMS)SOC波动频繁和存在坡度时控制效果不佳的问题,提出基于改进型灰色预测的车速预测算法和基于卡尔曼滤波估计坡度的线性拟合函数的坡度预测算法,实现未来短期道路工况(即车速和道路坡度)的预测,从而提出基于道路工况预测的A-ECMS。仿真结果表明,该算法能很好地预测未来短期道路工况,所提出的能量管理策略相比基于SOC反馈的A-ECMS燃油经济性提高了6.54%,电池SOC更稳定且更好地维持在初始值附近。

In order to solve the problems of SOC trajectory’s frequent fluctuation and poor control effect on slope- road condition of traditional Adaptive fuel Equivalent Consumption Minimization Strategy (A- ECMS), speed prediction algorithm based on improved grey prediction and slope prediction algorithm based on linear fitting function of the slope estimated by Kalman filter are proposed to predict the future short-term road conditions (i.e. vehicle speed and road slope prediction), and an A-ECMS based on road condition prediction is proposed. The simulation results show that the algorithm can accurately predict the future short-term road condition. Compared with A-ECMS based on SOC feedback, the fuel economy of the proposed energy management strategy is improved by 6.54%, and the battery SOC is more stable and maintained near the initial value.