Energy management of a parallel hybrid electric vehicle with CVT using model predictive control

Author(s):  
Jiahui Tang ◽  
Lulu Guo ◽  
Bingzhao Gao ◽  
Qifang Liu ◽  
Shuyou Yu ◽  
...  
2001 ◽  
Author(s):  
Jong-Seob Won ◽  
Reza Langari

Abstract A fuzzy torque distribution controller for energy management (and emission control) of a parallel-hybrid electric vehicle is proposed. The proposed controller is implemented in terms of a hierarchical architecture which incorporates the mode of operation of the vehicle as well as empirical knowledge of energy flow in each mode. Moreover, the rule set for each mode of operation of the vehicle is designed in view of an overall energy management strategy that ranges from maximal emphasis on battery charge sustenance to complete reliance on the electrical power source. The proposed control system is evaluated via computational simulations under the FTP75 urban drive cycle. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy as well as emissions.


2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Sun ◽  
Guojing Xing ◽  
Xudong Liu ◽  
Xiaoling Fu ◽  
Chenghui Zhang

The torque coordination control during mode transition is a very important task for hybrid electric vehicle (HEV) with a clutch serving as the key enabling actuator element. Poor coordination will deteriorate the drivability of the driver and lead to excessive wearing to the clutch friction plates. In this paper, a novel torque coordination control strategy for a single-shaft parallel hybrid electric vehicle is presented to coordinate the motor torque, engine torque, and clutch torque so that the seamless mode switching can be achieved. Different to the existing model predictive control (MPC) methods, only one model predictive controller is needed and the clutch torque is taken as an optimized variable rather than a known parameter. Furthermore, the successful idea of model reference control (MRC) is also used for reference to generate the set-point signal required by MPC. The parameter sensitivity is studied for better performance of the proposed model predictive controller. The simulation results validate that the proposed novel torque coordination control strategy has less vehicle jerk, less torque interruption, and smaller clutch frictional losses, compared with the baseline method. In addition, the sensitivity and adaptiveness of the proposed novel torque coordination control strategy are evaluated.


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