插电式混合动力汽车加速度预测及能量管理

Acceleration prediction and energy management for a Plug⁃in Hybrid Electric Vehicle

为了减少插电式混合动力汽车(Plug-in Hybrid Electric Vehicle,PHEV)的燃油消耗,设计了基于预测控制的PHEV能量管理策略。通过对整车能量分析,建立了PHEV的纵向动力学模型,考虑电压的动态特性建立了电池组的等效电路模型。建立了基于马尔可夫链的加速度预测模型,结合纵向动力学模型进而计算出需求转矩变化。以燃油消耗量最小为目标构建PHEV能量管理的预测控制模型,在预测时域内采用动态规划(DP)求解带约束的优化问题,将控制序列的首值施加至系统完成转矩分配并更新优化问题。仿真结果表明,基于随机模型预测控制的能量管理策略的燃油消耗量与基于规则的能量管理策略相比降低22.53%,与采用Rint等效电路模型的能量管理策略相比降低13.64%。

In order to reduce the fuel consumption of Plug⁃in Hybrid Electric Vehicle(PHEV),a PHEV energy management strategy based on predictive control is designed.Through the energy analysis of the whole vehicle,the longitudinal dynamics model of the PHEV is established,and the equivalent circuit model of the battery pack is established considering the dynamic characteristics of the voltage.The acceleration prediction model based on Markov chain is established,and the demand torque change is calculated by combining with the longitudinal dynamics model.A predictive control model for PHEV energy management is built with the goal of minimizing fuel consumption,and Dynamic Programming(DP)is used to solve the optimization problem with constraints in the prediction time domain,and the first value of the control sequence is applied to the system to complete the torque distribution and update the optimization problem.The simulation results show that the fuel consumption of the energy management strategy based on stochastic model predictive control is reduced by 22.53%compared with the rule⁃based energy management strategy and 13.64%compared with the energy management strategy using the Rint equivalent circuit model.