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.