Real-time Eco-Driving Control with Mode Switching Decisions for Electric Trucks with Dual Electric Machine Coupling Propulsion

Artikel i vetenskaplig tidskrift, 2023

This paper proposes a locally convergent, computationally efficient model predictive controller with mode switching decisions for the eco-driving problem of electric trucks. The problem is formulated as a bi-level program where the high-level optimises the speed trajectory and operation mode implicitly, while the low-level computes an explicit policy for power distribution of two electric machines. The alternating direction method of multipliers (ADMM) is employed at the high-level to obtain a locally optimal solution considering both speed optimisation and integer switching decisions. Simulation results indicate that the ADMM operates the powertrain with 0.9% higher total cost and 0.86% higher energy consumption than the global optimum obtained by dynamic programming for a quantised version of the same problem. Compared to a benchmark solution that maintains a constant velocity, the ADMM, running in a model predictive control framework (ADMM_MPC), operates the powertrain with a 8.77% lower total cost and 10.3% lower energy consumption, respectively. The average time for each ADMM_MPC update is 4.6ms on a standard PC, indicating its suitability for real-time control. Simulation results also show that the prediction errors of speed limits and road slope in ADMM_MPC cause only 0.12%-0.56% performance degradation.