Engine clutch control algorithm during mode change for parallel hybrid electric vehicle

2012 ◽  
Author(s):  
Minseok Song ◽  
Joseph Oh ◽  
Hyunsoo Kim
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Control Algorithm ◽  
Mode Change ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Engine Clutch ◽  
Clutch Control

Research on Dynamic Torque Control Strategy for Parallel Hybrid Electric Vehicle

2011 ◽  
Vol 228-229 ◽  
pp. 951-956 ◽  
Author(s):  
Yun Bing Yan ◽  
Fu Wu Yan ◽  
Chang Qing Du
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Control Algorithm ◽  
Dynamic Control ◽  
Torque Control ◽  
Engine Torque ◽  
Parallel Hybrid Electric Vehicle ◽  
Torque Control Strategy ◽  
Parallel Hybrid ◽  
Hybrid Electric

It is necessary for Parallel Hybrid Electric Vehicle (PHEV) to distribute energy between engine and motor and to control state-switch during work. Aimed at keeping the total torque unchanging under state-switch, the dynamic torque control algorithm is put forward, which can be expressed as motor torque compensation for engine after torque pre-distribution, engine speed regulation and dynamic engine torque estimation. Taking Matlab as the platform, the vehicle control simulation model is built, based on which the fundamental control algorithm is verified by simulation testing. The results demonstrate that the dynamic control algorithm can effectively dampen torque fluctuations and ensures power transfer smoothly under various state-switches.

Speed and Throttle Position Forecasting on a Parallel Hybrid Electric Vehicle

2003 ◽  
Author(s):  
Christian M. Muehlfeld ◽  
Sudhakar M. Pandit
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Control Algorithm ◽  
Feedforward Control ◽  
Vehicle Simulation ◽  
The Road ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

Included in this paper is the forecasting of the speed and throttle position on a thru-the-road parallel hybrid electric vehicle (HEV). This thru-the-road parallel hybrid design is implemented in a 2002 model year Ford Explorer XLT, which is also the Michigan Tech Future Truck. Data Dependent Systems (DDS) forecasting is used in a feedforward control algorithm to improve the fuel economy and to improve the drivability. It provides a one step ahead forecast, thereby allowing the control algorithm to always be a step ahead, utilizing the engine and electric motor in their most efficient ranges. This control algorithm is simulated in PSAT, a hybrid vehicle simulation package, which can estimate the fuel economy and certain performance characteristics of the vehicle. In this paper a fuel economy savings of 2.2% is shown through simulation. Charge sustainability was achieved along with drivability being improved as indicated by the reduction in number of deviations from the speed profile in the driving cycle.

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