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This paper proposes a composite stability control strategy for tire blowout electric vehicle with explicit consideration of vertical load redistribution, subject to multi-constraints, uncertainty and redundant actuation. To readily implement system state and actuator increment constraints, a model predictive controller is designed to enhance vehicle stability performance and original lane keeping. Meanwhile, a sliding mode control-based longitudinal velocity controller is collaboratively proposed for maintaining the original longitudinal velocity after tire blowout in the freeway, which can reduce considerably the rear-end collision accidents. After that, a constrained weighting least square-based reconfigurable torque distributor is developed to realize the tracking of the virtual resultant yaw-moment and longitudinal tire force signals generated by the above controllers, considering system physical constraints and actuator capability simultaneously. Simulations with different tire blowout scenarios are conducted on the developed dynamic simulation platform to demonstrate the effectiveness of the designed control methods; furthermore, the influence of different tire blowout on the vehicle movement behaviors is discussed. Statistical results based on the system transient performance and control effort evaluating indictors highlight the considerable superiority of the developed model predictive controller compared with the linear quadratic regulator.

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The paper analyses the importance of simulation technology for the research and development of hybrid engines in vehicle engineering, and introduces the current development status and key technologies of hybrid engine simulation technology, and puts forward the focus of future research. Simultaneously, taking the hybrid electric vehicle as a case, through the analysis of the drive structure and work requirements, to optimize the power performance and fuel economy of the vehicle, the electric assist control strategy for the hybrid power system is proposed. The control logic formulates the driving conditions. A related control model was established, and the control strategy was simulated on the Advisor software platform. The simulation results show that the proposed electric-assisted control strategy is entirely suitable for hybrid electric vehicles. Compared with traditional vehicles, the power performance and fuel economy of the whole vehicle are further improved.