插电式混合动力汽车模型预测控制策略研究

针对传统插电式混合动力汽车智能控制策略计算量大,难以实现实时最优控制的问题,提出了基于蓄电池充放电管理的插电式混合动力汽车预测控制策略.利用实测通勤插电式混合动力汽车车速信息,以蓄电池荷电状态为系统状态变量,以蓄电池充放电功率为系统控制变量,插电式混合动力汽车燃油消耗量最低为系统性能指标,设计了插电式混合动力汽车的模型预测控制智能优化算法,运用连续广义最小残量方法求解最优控制问题.在Matlab/Simulink与GT-POWER联合仿真平台上进行仿真,实验结果验证了所设计的模型预测控制算法不仅可以大幅度提高混合动力汽车的燃油经济性,而且能够满足实时控制的要求.

Research on Model Predictive Control Strategies for Plug-In Hybrid Electric Vehicles

Model predictive control strategies are proposed based on battery charge/discharge management for plug-in hybrid electric vehicles to deal with computation burden and on-line optimization problems in conventional control strategies.Based on real commuting vehicle speed,the model predictive control intelligent optimization algorithm for plug-in hybrid electric vehicles is designed.The battery state of charge is selected as system state.The battery charge/discharge power is selected as system control input.The performance index is minimization of the fuel consumption.The optimal control problem is solved by using continuation/generalized minimum residual method.The simulation is conducted by using Matlab/Simulink and GT-POWER co-simulation platform.The results show that the proposed model predictive control method can improve fuel economy significantly and can be implemented in real-time.