Two-Speed Transmission Gear Shift Process Analysis and Optimization Using Genetic Algorithm

Electric Vehicle (EV) equipped with two-speed transmission has benefit in improving dynamic performance and saving battery consumption. However, during gear shift process, torque interruption and shift impact may lead to a bad shift quality. This work investigates gear shift process in an Automated Manual Transmission (AMT) configuration-based two-speed transmission. First of all, a typical gear shift process is analyzed. Parameters like motor speed, shift force, motor torque change rate, and speed difference between synchronizer and target engage gear are all included to find the relationships with shift duration. Then vehicle jerk is introduced as a criterion to evaluate shift impact. Besides, a comprehensive shift control strategy is developed. While keeping the output torque at wheels unchanged, the shift strategy also improved motor working efficiency after gear shift. Therefore, to determine an optimum shift strategy and achieve a balance among different parameters, Genetic Algorithm (GA) multi-objective optimization method is implemented. Through GA optimization, several solutions are presented and discussed. The final results can well satisfy the requirements of different objectives. This work provides a novel and efficient method for shift strategy development.