Stochastic Model Predictive Energy Management of Electric Trucks in Connected Traffic

This paper proposes a cost-effective power management strategy utilizing the data provided by V2I communication techniques for dual electric machine coupling propulsion trucks. We formulate a bilevel program where the high-level optimizes operation mode implicitly, while the low-level computes an explicit policy for power distribution of two electric machines. Stochastic model predictive control (SMPC) strategy is employed at the highlevel, the performance of which highly depends on the prediction accuracy of future driving information. To establish a position dependent stochastic velocity predictor using limited amount of historical data, two improved approaches are developed: 1) Predictor using multiple features; 2) Predictor combining data and model. Simulations are performed to validate the performance of the proposed predictors compared with a benchmark. The results show that the controllers using the proposed predictors can reduce driving cost by 3.36 % and 4.26 %, respectively.

Automated summary

This paper proposes a cost-effective power management strategy for dual electric machine coupling propulsion trucks using V2I communication data. A bilevel program is formulated where the high-level optimizes operation mode implicitly, and the low-level computes an explicit power distribution policy. Stochastic model predictive control (SMPC) strategy is employed at the high level, with position dependent stochastic velocity predictors developed using limited historical data. The proposed predictors are compared with a benchmark in simulations, showing a reduction in driving cost by 3.36% and 4.26% respectively.