Research on Control Strategy Optimization for Shifting Process of Pure Electric Vehicle Based on Multi-Objective Genetic Algorithm 2020-01-0971
With more and more countries proposing timetables for stopping selling of fuel vehicles, China has also issued a “dual-slope” policy. As electric vehicles are the most promising new energy vehicle, which is worth researching. The integration and control of the motor and gearbox have gradually become a hot research topic due to low cost with better performance.
This paper takes an electric vehicle equipped with permanent magnet synchronous motor and two-gear automatic transmission without synchronizer and clutch as the research object. Through the action of the motor, gearbox and shift actuator in the shifting process, the whole vehicle dynamics is modeled in each stage of shifting, a method for determining a short-term driving style intensity factor for decision shifting is proposed, three evaluation indexes of the shift quality of electric vehicle are put forward, the control parameters affecting the shift quality are analyzed, and the mathematical relationship between the shift control parameters, the shifting time and the shifting impact are obtain. Besides, the NSGA-II algorithm is used to carry out multi-objective global optimization of the whole process of shifting to get Pareto optimal solution. Analyze the optimization results in combination with driving style, determine some thresholds and parameters of the drive motor and shift actuator action control during the shifting process, improve the shift control strategy, and finally complete the coordinated control of the shifting process of the electric drive system based on the active synchronization of the motor. The results show that the optimization effect is good. It provides a new idea for the shifting process control of the pure electric vehicles.
Citation: Lei, Y., Zhang, J., Fu, Y., Jia, F. et al., "Research on Control Strategy Optimization for Shifting Process of Pure Electric Vehicle Based on Multi-Objective Genetic Algorithm," SAE Technical Paper 2020-01-0971, 2020, https://doi.org/10.4271/2020-01-0971. Download Citation
Yulong Lei, Jingxu Zhang, Yao Fu, Fuchun Jia, Binyu Wang