Integrated Control Optimization of Dual Clutch Transmission Gears for Electric Vehicles

AbstractThe calibration of conventional, hybrid and electric drivetrains is an important process during the development phase of any vehicle. Therefore, to optimize the comfort and dynamic behavior (known as driveability), many test drives are performed by experienced drivers during different driving maneuvers, e.g., launch, re-launch or gear shift. However, the process can be kept more consistent and independent of human-based deviations by using objective ratings. This study first introduces an objective rating system developed for the launch behavior of conventional vehicles with automatic transmission, dual-clutch transmission, and alternative drivetrains. Then, the launch behavior, namely comfort and dynamic quality, is compared between two conventional vehicles, a plug-in hybrid electric vehicle and a battery electric vehicle. Results show the benefits of pure electric drivetrains due to the lack of launch and shifting elements, as well as the usage of a highly dynamic electric motor. While the plug-in hybrid achieves a 10% higher overall rating compared to the baseline conventional vehicle, the pure electric vehicle even achieves a 21% higher overall rating. The results also highlight the optimization potential of battery electric vehicles regarding their comfort and dynamic characteristics. The transitions and the gradient of the acceleration build-up have a major influence on the launch quality.

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Hybrid Models for Efficient Control, Optimization, and Monitoring of Thermo-Chemical Processes and Plants

Processes ◽

10.3390/pr9030515 ◽

2021 ◽

Vol 9 (3) ◽

pp. 515

Author(s):

Thomas Freudenmann ◽

Hans-Joachim Gehrmann ◽

Krasimir Aleksandrov ◽

Mohanad El-Haji ◽

Dieter Stapf

Keyword(s):

Artificial Intelligence ◽

Expert Knowledge ◽

Numerical Models ◽

Integrated Control ◽

Nox Reduction ◽

Hybrid Models ◽

Control Optimization ◽

Pulverized Fuel ◽

Renewable Energy Law ◽

Institute Of Technology

This paper describes a procedure and an IT product that combine numerical models, expert knowledge, and data-based models through artificial intelligence (AI)-based hybrid models to enable the integrated control, optimization, and monitoring of processes and plants. The working principle of the hybrid model is demonstrated by NOx reduction through guided oscillating combustion at the pulverized fuel boiler pilot incineration plant at the Institute for Technical Chemistry, Karlsruhe Institute of Technology. The presented example refers to coal firing, but the approach can be easily applied to any other type of nitrogen-containing solid fuel. The need for a reduction in operation and maintenance costs for biomass-fired plants is huge, especially in the frame of emission reductions and, in the case of Germany, the potential loss of funding as a result of the Renewable Energy Law (Erneuerbare-Energien-Gesetz) for plants older than 20 years. Other social aspects, such as the departure of experienced personnel may be another reason for the increasing demand for data mining and the use of artificial intelligence (AI).

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Downshift decision and process optimal control of dual clutch transmission for hybrid electric vehicles under rapid braking condition

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2018.07.012 ◽

2019 ◽

Vol 116 ◽

pp. 943-962 ◽

Cited By ~ 6

Author(s):

Zhiguo Zhao ◽

Jiayi Chen ◽

Xueyan Li ◽

Dan Lei

Keyword(s):

Optimal Control ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Dual Clutch Transmission ◽

Hybrid Electric

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An integrated control strategy of path following and lateral motion stabilization for autonomous distributed drive electric vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019884168 ◽

2019 ◽

pp. 095440701988416

Author(s):

Yuan Zou ◽

Ningyuan Guo ◽

Xudong Zhang

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Electric Vehicles ◽

Control Strategy ◽

Integrated Control ◽

Path Following ◽

Control Technique ◽

Programming Algorithm ◽

Linear Quadratic ◽

And Control

This article proposes an integrated control strategy of autonomous distributed drive electric vehicles. First, to handle the multi-constraints and integrated problem of path following and the yaw motion control, a model predictive control technique is applied to determine optimal front wheels’ steering angle and external yaw moment synthetically and synchronously. For ensuring the desired path-tracking performance and vehicle lateral stability, a series of imperative state constraints and control references are transferred in the form of a matrix and imposed into the rolling optimization mechanism of model predictive control, where the detailed derivation is also illustrated and analyzed. Then, the quadratic programming algorithm is employed to optimize and distribute each in-wheel motor’s torque output. Finally, numerical simulation validations are carried out and analyzed in depth by comparing with a linear quadratic regulator–based strategy, proving the effectiveness and control efficacy of the proposed strategy.

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