基于滚动优化和能量回收的V2I电动汽车决策

通过对标准新欧洲汽车法规循环(NEDC)工况的分析,提取出NEDC工况中的实时交通信息,分析不同驾驶状态对于车辆能耗的影响,提出一种新的基于实时交通信息的适用于V2I车辆的测试工况;结合电动汽车能量回收的优点,考虑电池-电机-制动特性约束,设计多源信息融合框架下的制动力分配策略;结合模型预测控制(MPC)的滚动优化思想提出MPC软约束框架下的电动汽车V2I最优控制策略;在AMESim & Simulink联合仿真平台上进行高精度纯电动车整车建模和MPC最优控制器的设计;对优化后车辆和未优化的车辆进行仿真对比验证,结果表明:结合道路交通信息进行最优决策的V2I纯电动车辆可有效降低车辆运行中的启停频率,减少整车能耗、车辆加速度和冲击度幅度,并显著提高整车经济性和舒适性.

Decision-making research of V2I electric vehicle based on rolling optimization and energy recovery

By analyzing the standard new european driving cycle(NEDC), this paper extractes the real-time traffic data in NEDC and analyses the influence of different driving behaviour for vehicle energy consumption, and proposes a new cycle with real-time traffic information for the V2I vehicle test. In order to develope the regenerative braking potential in electric vehicles and considering the battery constraints, motor constraints and braking characterics, the braking force distribution strategy under the framework of multi-source information fusion is designed. Then the optimal control strategy of V2I for the electric vehicles under the soft constraint framework of model predictive control (MPC) is proposed based on the rolling optimization theory of MPC. The dynamic modeling of high precision vehicles and the design of MPC optimal controllers are carried out on the AMESim & Simulink co-simulation platform. Finally, the comparison between the optimized vehicle and the unoptimized vehicle is simulated, and results show that, after the combination of road traffic information and the optimization strategy, the V2I intelligent vehicle can effectively reduce the stop&go frequency, energy consumption as well as the acceleration and jerk, thus significantly improving the economic and comfortable performance.