Traffic Condition-Based Gain Scheduling of Electric Assist Control Strategy for Parallel Hybrid Electric Vehicles

2007 ◽  
Author(s):  
Amir Poursamad
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Gain Scheduling ◽  
Driving Cycle ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Parallel Hybrid ◽  
Fuel Consumption And Emissions

This paper presents gain scheduling of control strategy for parallel hybrid electric vehicles based on the traffic condition. Electric assist control strategy (EACS) is employed with different parameters for different traffic conditions. The parameters of the EACS are optimized and scheduled for different traffic conditions of TEH-CAR driving cycle. TEH-CAR is a driving cycle which is developed based on the experimental data collected from the real traffic condition in the city of Tehran. The objective of the optimization is to minimize the fuel consumption and emissions over the driving cycle, while enhancing or maintaining the driving performance characteristics of the vehicle. Genetic algorithm (GA) is used to solve the optimization problem and the constraints are handled by using penalty functions. The results from the computer simulation show the effectiveness of the approach and reduction in fuel consumption and emissions, while ensuring that the vehicle performance is not sacrificed.

Optimal integrated energy management and shift control in parallel hybrid electric vehicles with dual-clutch transmission

2019 ◽  
Vol 234 (2-3) ◽  
pp. 599-609
Author(s):  
Guoqiang Li ◽  
Daniel Görges
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Weighting Factor ◽  
Engine Torque ◽  
Shift Control ◽  
Parallel Hybrid ◽  
Power Split ◽  
Hc Emissions

This paper addresses the integration of the energy management and the shift control in parallel hybrid electric vehicles with dual-clutch transmission to reduce the fuel consumption, decrease the pollutant emissions, and improve the driving comfort simultaneously. Dynamic programming with a varying weighting factor in the cost function is proposed to balance the shift frequency and the fuel consumption for the power-split control and gear schedule design. Simulation results present that the drivability can be improved with a varying weighting factor due to fewer shift events while the fuel consumption is only slightly increased compared to dynamic programming with a constant weighting factor. A shift-energy-management strategy integrating the upshift and power-split control based on a multi-objective optimization is presented where model predictive control is employed to ensure engine load rate constraints. The strategy can smoothen the engine torque through torque compensation from the electric motor to prevent engine transient emissions resulting from a sudden load change. In a simulation study, the NOx and HC emissions could be reduced by 1.4% and 2.6% with 2% increase of the overall fuel consumption for the Federal Test Procedure 75 by smoothening the engine torque. For the New European Driving Cycle, 0.9% and 1.1% reduction of NOx and HC emissions could be achieved with only 0.3% more fuel consumption.

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