Powertrain Coordination Control of Electric Drive System for Heavy-Duty Vehicles with Minimal Battery Configuration Based on Adaptive Event-Triggered MPC

Power coordination strategies are crucial for enhancing the dynamic performance of heavy-duty electric drive vehicles. In electric drive system (EDS) for such vehicles with minimal battery configuration, system states fluctuate frequently due to the hybrid system’s nonlinearity, time-varying constraints, limited energy storage, and the controller complexity. To tackle the issue, this paper presents a dynamic event-triggered model predictive control (DETMPC) method with a two-parameter adaptive update strategy (TPAUS) for heavy-duty electric drive vehicles. Departing from traditional component-independent control methodologies, this study establishes an integrated control model considering front and rear power chains of EDS. The control model is updated each calculation cycle to reduce dimensionality and is subsequently reformulated into a linear optimization control problem with component time-varying constraints. To lighten the computational load and improve system stability, a dynamic event-triggered mechanism with adaptive prediction horizon and sampling interval is introduced. Finally, an EDS physical model for the heavy-duty vehicle using the Simscape-Electrical library is built, and the effectiveness of the proposed strategy is validated via hardware-in-the-loop experiments. Results show the proposed strategy effectively manages EDS state fluctuations and reduces the solving frequency by 85.13% and single-step time by approximately 56.67% compared to conventional MPC.