scholarly journals Fuzzy Logic Controller for Parallel Plug-in Hybrid Vehicle

2019 ◽  
Author(s):  
Sorush Niknamian

Control strategies for hybrid electric vehicles are usually aimed at several simultaneous objectives. The primary one is usually the minimization of the vehicle fuel consumption, while also attempting to minimize engine emissions and maintaining or enhancing drivability. Regardless of the topology of the vehicle, the essence of the HEV control problem is the instantaneous management of the power flows from more devices to achieve the overall control objectives. One important characteristic of this generic problem is that the control objectives are mostly integral in nature (fuel consumption and emission per mile of travel), or semi-local in time like drivability, while the control actions are local in time. Furthermore, the control objectives are often subject to integral constraints, such as nominally maintaining the battery state-of-charge (SOC). The global nature of both objectives and constraints do not lend itself to traditional global optimization technique, because the main problem with global optimization index is whole of driving cycle should be predetermined and real time control strategy is not implemented simply. A common method to control of the complex dynamic systems with many uncertainties is designing some different of local controllers each for a specific operating area or determined objects and then designing of a switching strategy through the subsystems to achieve the global objectives of the system. In this research, the control structure has been investigated due to the complexity of hybrid electric vehicle powertrain. From the view point of hierarchy, the switching strategy relates to upper hierarchy and plays the key role in systems operating. Then for each subsystems of hybrid electric vehicle, itself local controller has been designed and after that in order to achieve the operating objectives, switching strategy through subsystems for the real time control strategy has been designed.

2008 ◽  
Vol 41 (2) ◽  
pp. 3356-3361
Author(s):  
S. Kermani ◽  
S. Delprat ◽  
T.M. Guerra ◽  
R. Trigui

2005 ◽  
Author(s):  
Xiaolai He ◽  
Andrew Leslie ◽  
Jaakko Halmari ◽  
Aaron Cordaway ◽  
Tim Maxwell ◽  
...  

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1493-1503
Author(s):  
Yang Li ◽  
Jili Tao ◽  
Liang Xie ◽  
Ridong Zhang ◽  
Longhua Ma ◽  
...  

Power allocation plays an important and challenging role in fuel cell and supercapacitor hybrid electric vehicle because it influences the fuel economy significantly. We present a novel Q-learning strategy with deterministic rule for real-time hybrid electric vehicle energy management between the fuel cell and the supercapacitor. The Q-learning controller (agent) observes the state of charge of the supercapacitor, provides the energy split coefficient satisfying the power demand, and obtains the corresponding rewards of these actions. By processing the accumulated experience, the agent learns an optimal energy control policy by iterative learning and maintains the best Q-table with minimal fuel consumption. To enhance the adaptability to different driving cycles, the deterministic rule is utilized as a complement to the control policy so that the hybrid electric vehicle can achieve better real-time power allocation. Simulation experiments have been carried out using MATLAB and Advanced Vehicle Simulator, and the results prove that the proposed method minimizes the fuel consumption while ensuring less and current fluctuations of the fuel cell.


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