Fuzzy Control Strategy Based on Mode Identification Used in Semi-Active Control

2011 ◽  
Vol 71-78 ◽  
pp. 3975-3982 ◽  
Author(s):  
Qing Li Meng ◽  
Min Zheng Zhang ◽  
Jian Ye
Keyword(s):  
Numerical Simulation ◽  
Fuzzy Control ◽  
Active Control ◽  
Drift Velocity ◽  
Control Strategy ◽  
Passive Control ◽  
Control Law ◽  
Mode Identification ◽  
Control Arithmetic ◽  
Fuzzy Control Strategy

In this paper, a new fuzzy control arithmetic based on mode identification used in semi-active control is put forward, the mode is differentiated by the eigenvectors constituted with drift, velocity and their resultant, and then different fuzzy control strategy is applied. Moreover, by numerical simulation, the control efficiency of the new control arithmetic is compared with semi-active control using sign control law and some passive control.

scholarly journals Decentralised coordinated energy management for hybrid AC/DC microgrid by using fuzzy control strategy

2020 ◽  
Vol 14 (14) ◽  
pp. 2649-2656
Author(s):  
Zeyan Lv ◽  
Yong Zhang ◽  
Miao Yu ◽  
Yanghong Xia ◽  
Wei Wei
Keyword(s):  
Fuzzy Control ◽  
Energy Management ◽  
Control Strategy ◽  
Dc Microgrid ◽  
Fuzzy Control Strategy

scholarly journals Research on Genetic-fuzzy Control Strategy for Parallel Hybrid Electric Vehicle

2010 ◽  
Vol 4 (1) ◽  
pp. 224-231 ◽  
Author(s):  
Shichun Yang ◽  
Ming Li ◽  
Haoyu Weng ◽  
Bao Liu ◽  
Qiang Li ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Fuzzy Control Strategy

scholarly journals Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3642 ◽  
Author(s):  
Ming Ye ◽  
Yitao Long ◽  
Yi Sui ◽  
Yonggang Liu ◽  
Qiao Li
Keyword(s):  
Active Control ◽  
Control Strategy ◽  
Passive Control ◽  
Prediction Algorithm ◽  
Control Effect ◽  
Demand Forecast ◽  
Powertrain System ◽  
Cluster Environment ◽  
Active Control Strategy ◽  
Vehicle Powertrain

With the development of intelligent vehicle technologies, vehicles can obtain more and more information from various sensors. Many researchers have focused on the vertical and horizontal relationships between vehicles in a vehicle cluster environment and control of the vehicle power system. When the vehicle is driving in the cluster environment, the powertrain system should quickly respond to the driver’s dynamic demand, so as to achieve the purpose of quickly passing through the cluster environment. The vehicle powertrain system should be regarded as a separate individual to research its active control strategy in a vehicle cluster environment to improve the control effect. In this study, the driving characteristics of vehicles in a cluster environment have been analyzed, and a vehicle power-demanded prediction algorithm based on a vehicle-following model has been proposed in a cluster environment. Based on the vehicle power demand forecast and driver operation, an active control strategy of the vehicle powertrain system has been designed considering the passive control strategy of the powertrain system. The results show that the vehicle powertrain system can ensure a sufficient backup power with the active control proposed in the paper, and the motor efficiency is improved by 0.61% compared with that of the passive control strategy. Moreover, the overall efficiency of the powertrain system is increased by 0.6% and the effectiveness of the active control is validated using the vehicle cluster environment.

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