scholarly journals Application of Driving Style Recognition in the Shift Control of a Two-speed DCT for Pure Electric Vehicles

2021 ◽  
Vol 1922 (1) ◽  
pp. 012002
Author(s):  
Jianjun Zhang ◽  
Xinbo Chen
Keyword(s):  
Electric Vehicles ◽  
Driving Style ◽  
Shift Control

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.

Control Strategy of Hybrid Electric Vehicles Based on Driving Style Identification

2014 ◽  
Vol 945-949 ◽  
pp. 1587-1596
Author(s):  
Xian Zhi Tang ◽  
Shu Jun Yang ◽  
Huai Cheng Xia
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Entropy Theory ◽  
Driving Style ◽  
Identification Error ◽  
Identification Method ◽  
Hybrid Electric

The driving style comprehensive identification method based on the entropy theory is presented. The error and error proportion of each identification result are calculated. The entropy and the variation degree of the identification error of each identification method are calculated based on the definition of information entropy. According to the entropy and the variation degree of the identification error, the weight of each kind of identification method can be determined in the comprehensive identification method, and the driving style comprehensive identification algorithm is derived. The control strategy of hybrid electric vehicles based on the driving style identification is proposed. The economic control strategy and dynamic control strategy are established. Depending on the results of driving style identification, aiming at different kinds of drivers, the mode of control strategies can be adjusted, so the demands of different kinds of drivers can be satisfied. The hybrid electric vehicle simulation model and control strategy model are built, and the simulations have been done. Due to the simulation results, the drivers’ intention comprehensive identification method based on the entropy theory is proved to represent the driver’s driving style systematically and comprehensively, and the hybrid electric vehicle control strategy based on the driving style identification can make the vehicles satisfy different drivers’ demands.

Adaptive Shift Control Strategy Based On Driving Style Recognition

2013 ◽  
Author(s):  
Wang Jun ◽  
Qingnian Wang ◽  
Peng-yu Wang ◽  
Li li
Keyword(s):  
Control Strategy ◽  
Driving Style ◽  
Shift Control ◽  
Adaptive Shift

scholarly journals A novel shift control concept for multi-speed electric vehicles

2018 ◽  
Vol 112 ◽  
pp. 171-193 ◽  
Author(s):  
Wenwei Mo ◽  
Paul D. Walker ◽  
Yuhong Fang ◽  
Jinglai Wu ◽  
Jiageng Ruan ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Shift Control ◽  
Control Concept

Driving situation and driving style dependent charging strategy in hybrid electric vehicles

ATZ worldwide ◽  
2008 ◽  
Vol 110 (5) ◽  
pp. 18-24 ◽  
Author(s):  
Andreas Wilde ◽  
Jörg Schneider ◽  
Hans-Georg Herzog
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Style ◽  
Driving Situation ◽  
Hybrid Electric ◽  
Charging Strategy

scholarly journals Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3951
Author(s):  
Haksu Kim
Keyword(s):  
Electric Vehicles ◽  
High Performance ◽  
Internal Combustion Engines ◽  
Accelerator Pedal ◽  
Quantitative Characterization ◽  
Driving Style ◽  
Motor Torque ◽  
Propulsion Performance ◽  
Emission Regulations ◽  
The Individual

As worldwide vehicle CO2 emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input.

A Study on Shift Process Control of AMT for Battery Electric Bus

2011 ◽  
Vol 88-89 ◽  
pp. 128-133 ◽  
Author(s):  
Hong Bo Liu ◽  
Yu Long Lei ◽  
Yu Zhang ◽  
Xiao Lin Zhang ◽  
You De Li
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Mechanical Transmission ◽  
Environment Protection ◽  
Battery Electric Vehicles ◽  
Shift Control ◽  
Shift Performance ◽  
Electric Bus ◽  
Dynamics Models ◽  
Energy Shortage

With ever increasing concerns on energy shortage and environment protection, the development of the battery electric vehicles (BEVs) has taken on an accelerated pace. In this paper, firstly, an AMT (Automatic Mechanical Transmission) without clutch and synchronizer used in battery electric bus is introduced. Then, the dynamics models of the shift process are created, and the factors that affect the shift performance are discussed. Finally, the AMT shift control strategy is designed and applied in the field buses. The on-road tests results show that the shift control strategy is valid and reliable, which meets the vehicle comfort and power requirements.

Energy Consumption Estimation in Electric Vehicles Considering Driving Style

2015 ◽  
Author(s):  
Jimenez Felipe ◽  
Juan Carlos Amarillo ◽  
Jose Eugenio Naranjo ◽  
Francisco Serradilla ◽  
Alberto Diaz
Keyword(s):  
Energy Consumption ◽  
Electric Vehicles ◽  
Driving Style
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