基于行驶工况分类的混合动力车辆速度预测方法与能量管理策略

摘要

为有效地改善双模式混合动力车辆的性能,设计了基于预测控制的能量管理策略,通过实时优化进行功率在线分配,提出了未来车速预测方法.通过K均值聚类算法将工况分为平稳工况与快变工况两类,实时判断车辆当前所处工况类别,并在平稳工况下,基于马尔科夫链预测车速,在快变工况下,基于径向基神经网络预测车速,以获得最优的预测精度.仿真结果的对比验证了所提出的车速预测方法的准确性和能量管理策略的有效性.%In order to effectively improve the performance of a dual-mode hybrid electric vehicle, a predic-tive-control-based energy management strategy is devised to conduct online power distribution through real-time opti-mization, and a vehicle upcoming speed prediction method is proposed. Driving conditions are classified into station-ary condition and quickly-changing condition though K-means clustering algorithm. Then current vehicle driving con-dition is determined real-time, and for obtaining best prediction accuracy, vehicle speed is predicted based on Markov-chain for stationary condition, while vehicle speed is predicted based on radial basis neural network for quickly-changing condition,. The comparison of simulation results verifies the correctness of vehicle speed prediction method proposed and the effectiveness of energy management strategy.