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The power-split hybrid electric vehicle achieves excellent fuel economy because both the engine speed and the torque of this system are decoupled from the road load. However, for a power-split hybrid electric vehicle with multiple power sources, the inconsistency of the response characteristic of each power source seriously affects the stability control of the power system and riding comfort, so the coordinated control of the power system is particularly important. This article proposed a dynamic coordinated control strategy. First, extended Kalman filter is applied to realize robust online estimation of the engine dynamics. Then, an extended Kalman filter–based and model predictive control–based dynamic coordinated control strategy is designed to achieve accurate reference tracking in hybrid electric mode. Considering the real-time performance for the online application of the dynamic coordinated control strategy, a fast model predictive control solver is formed based on a reasonable assumption. Offline simulation results show that accurate reference tracking is achieved in hybrid electric mode. Hardware-in-the-loop simulation is also conducted to validate the real-time performance of the proposed dynamic coordinated control strategy. This study is expected to improve the performance and robustness of the dynamic coordinated control strategy in hybrid electric mode while reducing the calibration load.

A model predictive feedback control strategy based on time-varying efficiency is investigated and applied to a hydraulic hub-motor auxiliary system (HHMAS) in this paper. Adding HHMAS to a traditional heavy commercial vehicle can improve fuel economy and traction performance on roads with low adhesion coefficients. However, the hydraulic drive system experiences serious disturbance imposed by time-varying parameters and external conditions. Model predictive feedback control based on time-varying efficiency offers a solution for HHMAS to cope with the disadvantage of the hydraulic drive system and improve the environmental adaptability of the vehicle controller. In this study, the control law of hydraulic variable pump (HVP) target displacement is established based on temperature compensation in consideration of the influence of multiple factors on pump target displacement. For coordinated power distribution of HHMAS, the minimum wheel speed difference and the reduction in system impact are regarded as optimal control targets in adjusting the engine torque and HVP displacement and designing the model predictive controller. Simulation results show that the proposed model predictive control method can reduce the speed difference between front and rear wheels by up to 64% and can achieve the wheel speed following effect faster than the traditional proportional-integral-derivative algorithm. Given that the control parameters do not need to be calibrated in the proposed method, the calibration time is saved, and the actual development process of the hydraulic hub-motor driving vehicle is remarkably improved.

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