基于工况识别的混联式混合动力客车控制策略研究

Development of Control Strategy for a Series-Parallel Hybrid Electric City Bus Based on Roadway Type Recognition

为进一步提高新型混联式混合动力客车燃油经济性,根据城市循环工况的特点选定了四种典型的城市工况,采用学习向量量化(LVQ)神经网络模型对各工况特征参数进行训练学习以进行实时工况识别,结合基于动态规划的全局优化结果来提取功率均衡控制规则并存储于控制模块中以供不同工况选择,制定了基于工况识别的控制策略。以MATLAB/Simulink为平台建立基于工况识别的控制策略整车前向仿真模型,仿真结果表明,与普通控制策略相比,采用基于工况识别的控制策略的燃油经济性提高7%。

To improve fuel economy of a novel series-parallel hybrid electric bus（SPHEB） further,and four types of roadway were selected to present the characteristics of city driving cycle,a learning vector quantization neural network was adopted to learn and identify the driving cycle,and integrated with the power balancing strategy which was derived from the results of global optimization based on dynamic programming.So a driving cycle adaption control strategy was proposed.To validate the proposed strategy to be effective and reasonable,a forward model was built based on MATLAB Simulink,and the results were compared to that of normal strategy.The simulation results demonstrate that the proposed strategy can be more efficient and the improvement of fuel economy is up to 7%.