Development of Real-Time Control for a Hydrogen Powered Hybrid Electric Vehicle

2005 ◽  
Author(s):  
Xiaolai He ◽  
Andrew Leslie ◽  
Jaakko Halmari ◽  
Aaron Cordaway ◽  
Tim Maxwell ◽  
...  
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Real Time Control ◽  
Time Control ◽  
Hybrid Electric

scholarly journals Real time control of hybrid electric vehicle on a prescribed road

2008 ◽  
Vol 41 (2) ◽  
pp. 3356-3361
Author(s):  
S. Kermani ◽  
S. Delprat ◽  
T.M. Guerra ◽  
R. Trigui
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Real Time Control ◽  
Time Control ◽  
Hybrid Electric

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

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Global Optimization ◽  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Real Time Control ◽  
Switching Strategy ◽  
Time Control ◽  
Hybrid Electric

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.

Multirate Integration in Hybrid Electric Vehicle Virtual Proving Grounds

1998 ◽  
Author(s):  
Dario Solis ◽  
Chris Schwarz
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Time Scales ◽  
Electric Vehicles ◽  
Time Scale ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Differential Algebraic Equations ◽  
Vehicle Systems ◽  
Hybrid Electric

Abstract In recent years technology development for the design of electric and hybrid-electric vehicle systems has reached a peak, due to ever increasing restrictions on fuel economy and reduced vehicle emissions. An international race among car manufacturers to bring production hybrid-electric vehicles to market has generated a great deal of interest in the scientific community. The design of these systems requires development of new simulation and optimization tools. In this paper, a description of a real-time numerical environment for Virtual Proving Grounds studies for hybrid-electric vehicles is presented. Within this environment, vehicle models are developed using a recursive multibody dynamics formulation that results in a set of Differential-Algebraic Equations (DAE), and vehicle subsystem models are created using Ordinary Differential Equations (ODE). Based on engineering knowledge of vehicle systems, two time scales are identified. The first time scale, referred to as slow time scale, contains generalized coordinates describing the mechanical vehicle system that includs the chassis, steering rack, and suspension assemblies. The second time scale, referred to as fast time scale, contains the hybrid-electric powertrain components and vehicle tires. Multirate techniques to integrate the combined set of DAE and ODE in two time scales are used to obtain computational gains that will allow solution of the system’s governing equations for state derivatives, and efficient numerical integration in real time.

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