When vehicles equipped with automated manual transmission (AMT) are in the gear engagement process (GEP), the problems of longitudinal shift jerk and synchronizer wear deterioration is prominent, which seriously affects the GEP quality. This paper develops and evaluates a hierarchical mode optimization strategy (HMOS) for GEP. Firstly, the models of gear-shift actuator and three-stage GEP are established, and the correlation between sleeve displacement and cone torque is revealed based on the established models. Next, the HMOS is proposed for cone torque optimization and sleeve trajectory tracking during GEP. At the top layer of the HMOS, a novel sliding-mode predictive controller (SMPC) with constraints is proposed for the cone torque decision during the synchronization stage of the GEP. At the bottom layer of the HMOS, an MPC-based sleeve trajectory tracking controller integrated with a load torque disturbance observer (L-DOB) is presented. Finally, the simulation and hardware-in-the-loop (HIL) test are carried out in a driving condition of power upshift from the first gear to the second gear to verify the performance of the optimization strategy. The verification results show that the proposed optimization strategy achieves satisfactory longitudinal shift jerk, synchronizer sliding work, and thermal stress.

Torque tracking is an important control target of a dry clutch. At present, the torque tracking control method of a dry clutch generally uses the relationship between the clutch torque and release bearing position obtained via experiment to convert the torque tracking control of the clutch into the position tracking control of the release bearing. However, due to the nonlinearity and time-varying parameters of the dry clutch, it is difficult to obtain an accurate and fixed relationship between torque and position. At the same time, there are also the nonlinearity and interference problems in the position tracking control process. In order to solve the above problems, this paper takes the torque tracking control process of the dry clutch in a three-speed automatic mechanical transmission of an electric vehicle as an example and proposes a double-loop control controller including torque loop and position loop. Firstly, the drive system dynamic model of electric vehicles and clutch actuator dynamic models is established. Secondly, the torque loop controller is established with the adaptive feedforward-feedback control method. The time-varying parameters are adaptively adjusted with adaptive feedforward control to solve the problem of the time-varying relationship between torque and position. In addition, the feedback control loop is added to improve the robustness of the controller. Thirdly, the position loop controller is established with the backstepping-based active disturbance rejection control method to solve the nonlinear and disturbance problems of the position tracking control. In the end, the torque loop and position loop are cascaded to form a double-loop controller, and the torque tracking control simulations of the dry clutch are carried out. The simulation results show that the double-loop controller has good control performance, and the effectiveness of the controller has been preliminarily verified.

Accurate clutch torque control is essential in improving vehicle comfort, drivability, and fuel economy. In this article, for automatic clutch system with electro-hydraulic clutch actuator, a clutch torque control strategy with a robust predefined-time stability guarantee based on position closed-loop impedance control is proposed and evaluated. For this purpose, a general second order model of electro-hydraulic clutch actuator with a disturbance term is established. Based on this model, a reference position compensator based on force control in the outer loop and a predefined-time backstepping position controller in the inner loop are developed. In addition, to obtain the release load of the release bearing, a virtual sensor is designed, composed of an online adaptive sliding mode disturbance observer with predefined-time stability and an offline Gaussian process model of uncertain disturbance based on the Gaussian process machine learning method. Finally, simulations and experiments are completed to verify the proposed control strategy. The results show that the control strategy can realize the precise clutch torque tracking control and achieve satisfactory performance.

Multi-speed transmission can greatly improve the power and economic performance of electric vehicles (EVs) compared with single-speed transmission. Gear ratio is the key design parameter of multi-speed transmission. Optimizing gear ratios can further improve vehicle performance. Most of the existing optimization methods of gear ratios take the power and economy of vehicles in gear as the optimization objectives, but rarely consider the shift performance of the transmission, such as shift time, friction, and shift jerk. Considering the shift performance in the process of gear ratio optimization can not only optimize the vehicle performance in gear, but also improve the shift performance of the transmission. Therefore, this paper proposes a multi-objective optimization method of gear ratios considering the shift performance. Firstly, a seamless three-speed automated manual transmission (AMT) of EVs is selected as the research object, the structure and the shift process without power interruption of the three-speed AMT are introduced, and the detailed EV simulation model is established. Then, the multi-objective optimization method of gear ratios considering shifting performance is described. Specifically, the acceleration time, energy consumption, and jerk of the vehicle in gear are taken as the objective functions, and the shift time, clutch friction, and the shift jerk are added to the corresponding objective functions, respectively. Finally, the multi-objective optimization algorithm is used to solve the gear ratio optimization problem. The simulation results show optimization of the gear ratios significantly improves the power, economy, and comfort of the vehicle compared with the original. More importantly, compared with the optimization method without shift performance, gear ratios optimized by the proposed optimization method has better shift performance, and the feasibility of the proposed method is verified by simulations.

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