Hierarchical optimization and nonlinear adaptive multiple input and multiple output (NAMIMO) control for two-speed dual-clutch gearshift system in electric vehicles

Abstract

One critical bottleneck impeding the widespread adoption of dual-clutch automatic transmission in pure electric vehicles (EV) is the substantial gear shifting jerk and considerable sliding friction work generated during the gear shifting process. This is due to the significant speed gap between different gears during gear shifting in EV transmissions compared to traditional internal combustion engine transmissions. To address the challenges, this study proposes a novel hierarchical gear shifting control strategy. The upper-layer strategy grounded in a global perspective, achieves non-power-interrupted shifting without power attenuation, thereby minimizing the gear shifting jerk. The middle-layer strategy integrates with the upper-layer strategy to generate gear shifting reference trajectories for torque and inertia phase, respectively, thereby minimizing clutch sliding friction work.The lower-layer strategy employs feedforward and backstepping methods based on the reference trajectories to control the torque phase and inertia phase shifting actions respectively. Additionally, it incorporates a linear quadratic regulator (LQR) for the inertia phase controller to achieve nonlinear adaptive multi-input and multi-output (NAMIMO) control, enabling the clutch to adaptively compensate for motor speed regulation. So that the motor can achieve speed adjustment quickly and accurately. Finally, the efficacy of the control strategy was validated on the MATLAB/Simulink platform under various initial torques and speeds. The effectiveness of the strategy and the accuracy of the simulation results were verified through test bench experiments.