Gear Shift Coordinated Control Strategy Based on Motor Rotary Velocity Regulation for a Novel Hybrid Electric Vehicle

This paper proposes a novel hybrid power system to improve the shift quality of a hybrid electric vehicle (HEV). After selecting a typical shift scheme, the study focused on the motor rotary velocity control algorithm and coordinated control strategy for the motor and clutch. The effects of various control algorithms on different target rotary velocities were analyzed, and a proportional-integral-derivative (PID)–bang-bang–fuzzy compound intelligent algorithm for a motor rotary velocity control system was investigated. In addition, to address the problems of the long synchronizing time required for the rotary velocity and large sliding friction work, which affect the shift quality during the process of engaging the clutch, a coordinated control strategy for the motor rotary velocity and clutch oil pressure was investigated. The research results showed that, compared with a gear shift coordinated control strategy based on a PID control algorithm, the strategy based on the PID–bang-bang–fuzzy compound intelligent control algorithm proposed here reduced the shift time and clutch slipping friction work by 35.7% and 19.2%, respectively.

Identification of drivers’ driving habits and shift schedule correction for vehicles with automatic transmission

Resource conservation has become a hot topic, and driving habits also have a significant impact on a car’s fuel consumption. In view of the different needs of drivers with different driving habits for automatic transmission and shift characteristics, a vehicle automatic transmission, and shift correction control strategy based on driving habit recognition is proposed. Based on the analysis of drivers’ driving behavior, the phase space reconstruction method is first used to reconstruct the time series of driving control signals, and the driving habit identification and gear shift correction control are conducted based on the correlation dimension and Kolmogorov entropy evaluation index. Simulation and real car test show that the identification method based on phase space reconstruction method and driving habits evaluation index can accurately identify drivers’ driving habits. The gear shift correction control strategy based on driving habit recognition fully meets the different requirements of different drivers for the gearshift performance of vehicles and improves the intelligence degree of automatic transmission of vehicles.

Transmission ratio optimization of two-speed gearbox in battery electric passenger vehicles

To optimize the power and economic performance of battery electric passenger vehicles, an 8.5-m-long battery electric passenger vehicle was selected as the research subject. The simulated-annealing particle-swarm-optimization (SAPSO) algorithm was applied to optimize the two-speed gearbox and transmission ratio of the vehicle. With the opening of the accelerator pedal and speed as variables, a comprehensive gear shift schedule that considered both the power and economic performance was established. The comprehensive gear shift schedule curve was defined in the model built in the ADVISOR software. The transmission ratio was jointly optimized using the ADVISOR and ISIGHT software, and then the ADVISOR simulation software was used to analyze the economic and dynamic performances corresponding to the optimized transmission ratio. Finally, we compared the power and economic performances of the vehicle before and after transmission ratio optimization, the results of which showed that after the optimization, the maximum speed, climbing gradient, and 0–50 km/h acceleration time of the vehicle were greatly improved, and the driving range was slightly shortened. This enabled performance advantages of the battery electric passenger vehicle by balancing the power and economic performances of the vehicle.