Online optimum velocity calculation under V2X for smart new energy vehicles

In this paper, a vector data net solver is proposed, which can reduce bivariate discrete-time dynamic programming (DP) computation time by 98.0% without losing accuracy. Therefore, for the first time, bivariate discrete-time DP can operate under model predictive control rolling optimization to calculate future optimum vehicle velocity in real time considering future road altitude and instant traffic information. Simulation results indicate that with the solution presented in this paper, front vehicles and the proper windows to pass through front intersections can be constantly considered. Meanwhile, the calculated optimum vehicle velocity almost remains the same as the global optimum solutions. Simulation results are validated by real-car tests, and the test new energy vehicle (NEV) electricity consumption is reduced by up to 48.6%. A comparison experiment is performed between the solution presented in this paper and commonly used adaptive dynamic programming (ADP), and the results indicate that the former has better performance and stability. This paper describes a novel solution for online optimum velocity calculation under vehicle to everything (V2X) environment and can be used by all smart NEVs with autonomous driving or active cruise control functions for lower electricity consumption and better riding comfort.