Stochastically predictive co-optimization of the speed planning and powertrain controls for electric vehicles driving in random traffic environment safely and efficiently.

With inevitable random disturbance in traffic scenarios, electric vehicles (EVs) may face the driving safety issue, while, if operated over cautiously, the frequent speed variation deteriorates the energy economy of the EV. This conflict provokes a desire to understand the energy consumption behavior of EVs in a stochastic driving environment and reveal the corresponding energy optimal control policy. For addressing these issues, this paper develops a chance constraint stochastic model predictive control (CC-MPC) method for simultaneously optimizing the speed planning and the powertrain energy management strategy, which cooperates with a bi-level prediction model for estimating the future driving environment. Validated by massive car-following cases in the urban traffic flow, the proposed CC-MPC increases the success rate (no constraint violation) to 87%, while the deterministic MPC methods only achieve a success rate of 27%. Although the proposed CC-MPC method generates a sensitive driving style to variations of the driving environment, the conflict between energy economy and driving safety has been successfully removed. Validations suggest that when safety probability is 0.9, the success rate is 84% with only 0.8% deterioration in energy economy compared with the energy consumption resulting from the MPC with perfect knowledge of the leading vehicle speed. ● Individual speed prediction is corrected by the traffic state ● Driving safety in random environment is significantly improved by the proposed CC-MPC ● The CC-MPC removes the conflict between driving safety and the energy economy ● The novel solver improves optimality by 12% and computation efficiency by 25 times. [ABSTRACT FROM AUTHOR]