Model reference control strategy for the mode transition of electro-mechanical transmission

Wen, Boxuan; Wang, Weida; Chen, Xiang; Du, Feng

doi:10.1109/icma.2014.6885853

Cited by 1 publication

(3 citation statements)

References 8 publications

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“…EVT2 mode reference model. The combined vertical (5), ( 6), ( 7), (16), and (25) eliminate the intermediate variables to obtain the reference model of dual-mode power split hybrid vehicle in EVT2 mode, as follows:…”

Section: Torque Coordinated Control Reference Modelsmentioning

confidence: 99%

“…Based on the model reference adaptive control algorithm, Wen et al 16 designed the dynamic coordination controller for the power split hybrid vehicle. Firstly, the switching system method is used to simplify the mode switching process and the whole complex mode switching problem is decomposed into several simple problems.…”

Section: Introductionmentioning

confidence: 99%

“…Zeng et al 15 puts forward a dynamic coordinated control strategy based on predictive model, according to the principle of nonlinear observer design for engine torque estimation algorithm, and based on model predictive control theory to establish the forecast model of the engine impact degree, combining feedback control and prediction model, implements the dynamic control system with complicated working condition of driving cycle adaptability. Based on the model reference adaptive control algorithm, Wen et al 16 designed the dynamic coordination controller for the power split hybrid vehicle. Firstly, the switching system method is used to simplify the mode switching process and the whole complex mode switching problem is decomposed into several simple problems.…”

Section: Introductionmentioning

confidence: 99%

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Research on real-time control strategy of multi-power flow of dual-mode power-split hybrid electric vehicle

Liu

Li²,

Han

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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With the continuous development of hybrid vehicle control technology, great progress has been made in the research of multi-power flow collaborative control. Due to the internal delay link of each power component, the role of energy storage element, and the limitation of electric power in the whole system, the inevitable delay characteristic of state transfer is caused. Therefore, the speed of multi-power flow control torque coordinated response of hybrid vehicles needs to be improved. The dual-mode power-split hybrid electric vehicle (DMPS-HEV) overall structure and working modes are analyzed, by adopting the combination of theory and experiment method. In order to solve the problem that the power components of dual-mode power-split hybrid electric vehicle cannot follow the optimal control command of the upper energy management strategy quickly due to the engine response delay, thus affecting the control effect of the upper energy management strategy. The research on torque coordination control strategy is carried out, the reference model of electromechanical composite drive is established, and the model reference adaptive coordination control strategy based on Lyapunov stability theory is proposed. The results show that the proposed model reference adaptive torque coordinated control strategy significantly improves the effect of engine response delay on the optimization effect of energy management strategy, and can achieve the control effect of the optimal control strategy of 93.58%. The test platform of the dual-mode power-split hybrid electric vehicle was built. The control system was built based on the rapid control prototype, and the data acquisition system was built based on the NI data acquisition module. The coordinated control strategy of the dual-mode power-split hybrid electric vehicle power system proposed in this paper was verified through the bench test to significantly improve the vehicle fuel economy and the real-time performance of the control strategy, which has a good practical value

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Section: Torque Coordinated Control Reference Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%