Efficient Adaptive Power Coordination Control for Heavy-Duty Series Hybrid Electric Vehicles With Model and Weight Transfer Awareness

To fully explore the potential of dynamic and economic performance in series hybrid electric vehicles (SHEVs), efficient coordinated control of power flow is essential. On the one hand, the variability in vehicle characteristics and driving modes complicates the stability of power output in hybrid electric powertrains. On the other hand, improving fuel economy while ensuring sufficient power output remains a significant challenge. To address this issue, this article proposes an efficient adaptive power coordination approach for SHEVs. To effectively capture changes in vehicle status, an enhanced least-squares parameter estimator is implemented to facilitate the adaptation of control-oriented model parameter. Moreover, a fuzzy adaptive weight method is proposed to enhance the interpretability of the cost function by adjusting the target weight based on inferred driving behavior. Furthermore, a modified continuation/generalized minimal residual (MC/GMRES) algorithm is developed to alleviate the significant computational burden of online nonlinear model predictive control (NMPC) controller, thereby enhancing real-time control performance. Finally, simulation and hardware-in-the-loop (HIL) test results demonstrate that the proposed control strategy can effectively optimize fuel economy while maintaining dynamic performance under complex driving conditions. Compared to the benchmark strategy, the proposed strategy achieves the fuel savings of 4.75% and 5.81% under two test driving cycles.