Design of an Electromagnetic Variable Valve Train with a Magnetorheological Buffer

Guo, He; Liu, Liang; Zhu, Xiangbin; Chang, Siqin; Xu, Zhaoping

doi:10.3390/en12203999

Cited by 8 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applications in vehicle mainly include intake and exhaust valve, electronic injection device, suspension systems, gearshift hydraulic valve, etc. [12]- [14]. In this paper, DAMT as a new direct-drive transmission form is formed by integrating an electromagnetic linear actuator with AMT.…”

Section: Built Gearshift Model Of Amt Bymentioning

confidence: 99%

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Direct-Driving Automated Mechanical Transmission (DAMT) gearshift actuator is a kind of electromagnetic linear actuator. In practical application, the actual gearshift force in each steady states is significantly different from the electromagnetic force model. For the optimization of gear shifting actuator with undetermined volume constraint, this paper presents a variable weight coefficient optimization method of electromagnetic linear actuator which equates the output forces under different steady states in the fitness function. These are intended to adjust the relationships among the optimized objects by using the fitness function to improve the overall optimization effect. In order to obtain the fitness value of each combination parameter accurately, finite element analysis (FEA) is adopted in this paper, to improve the efficiency of the algorithm, and a more efficient simulated annealing-particle swarm optimization algorithm (SA-PSO) is used by the proposed optimization method. Finally, experiment is carried out based on prototype of gearshift actuator to verify the optimization results. The results show that the maximum electromagnetic force and terminal electromagnetic force are both increased by more than 20%, and the volatility of gearshift force is reduced 26.4%, which ensures the structure compactness and fast response. The proposed method decreases the volatility by 27.5% compared with the constant weight coefficients and improves the gearshift force in each stage. INDEX TERMS DAMT, gearshift actuator, variable weight coefficient optimization, experiment.

show abstract

Section: Built Gearshift Model Of Amt Bymentioning

confidence: 99%

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Figure 1 shows the structure of the electromagnetic fully variable valve. 10 From the figure it can be observed that the system is composed of an EMLA, a MR buffer, and valve components. The EMLA mainly consists of outer and inner magnetic yokes, an electromagnetic coil, a coil bobbin, and seven permanent magnets.…”

Section: System Overviewmentioning

confidence: 99%

“…Simulation and experiment results showed that valve seating characteristics become stable and soft when the engine is at the speed of 5000 r/min. Guo et al 10 and Zhu et al 11 have studied a fully variable valve system that uses the magnetorheological buffer (MR buffer) to realize seat cushioning, which could minimize the seating velocity and rebound height of the valve. In the system, the electromagnetic linear actuator (EMLA) is a moving coil linear motor that the axial driving force is generated by the current-carrying coil in the permanent magnetic field to drive the valve moving.…”

Section: Introductionmentioning

confidence: 99%

Optimization design of a fully variable valve system based on Nelder-Mead algorithm

Zheng

Liu

Guo

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

Self Cite

View full text Add to dashboard Cite

This paper presents an optimal design for the soft-landing of the fully variable valve system in internal combustion engines based on Nelder-Mead algorithm and integrated simulation. The system is used to enhance the engine performance in terms of emissions and fuel economy. The electromagnetic linear actuator (EMLA) and the magnetorheological (MR) buffer are modelled with COMSOL Multiphysics, and a co-simulation platform is constructed with MATLAB/Simulink. The prototype test results are basically consistent with the integrated simulation, indicating the feasibility of the integrated simulation platform. After that, the structure of the magnetorheological buffer is optimized based on the Nelder-Mead algorithm. The optimization objective is the adjustable coefficient and the optimized parameters are the effective length, the annular gap width, the piston diameter, the groove depth and length of the coil. The adjustable coefficient of the optimized MR buffer was increased from 8.8 to 25.46. And the co-simulation results showed that the optimized system has a great improvement in soft-landing performance, with the valve seat velocity reduced from 0.51 m/s to 0.03 m/s and the bounce height reduced from 0.6 mm to nearly 0 when the lift is 8 mm.

show abstract

“…There were eight features (X k ) in our work, as well as labels C. The dimension of X k and C were both N × 1. We calculated the score of each feature using the MATLAB(2018a) software, based on Equation (5). The score of each feature is shown in Table 3.…”

Section: Feature Selectionmentioning

confidence: 99%

“…In addition, the manufacturing costs of aeroengines are quite high, and the replacement of an engine requires large investments, in terms of both manpower and financial resources. It is, therefore, necessary to find an effective method to monitor the condition of and predict the faults in aeroengines [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning

Wang

Jiang

et al. 2019

Energies

View full text Add to dashboard Cite

An aircraft engine (aeroengine) operates in an extremely harsh environment, causing the working state of the engine to constantly change. As a result, the engine is prone to various kinds of wear faults. This paper proposes a new intelligent method for the diagnosis of aeroengine wear faults based on oil analysis, in which broad learning system (BLS) and ensemble learning models are introduced and integrated into the bagging-BLS model, in which 100 sub-BLS models are established, which are further optimized by ensemble learning. Experiments are conducted to verify the proposed method, based on the analysis of oil data, in which the random forest and single BLS algorithms are used for comparison. The results show that the output accuracy of the proposed method is stable (at 0.988), showing that the bagging-BLS model can improve the accuracy and reliability of engine wear fault diagnosis, reflecting the development trend of fault diagnosis in implementing intelligent technology.

show abstract

Design of an Electromagnetic Variable Valve Train with a Magnetorheological Buffer

Cited by 8 publications

References 23 publications

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Optimization design of a fully variable valve system based on Nelder-Mead algorithm

Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning

Contact Info

Product

Resources

About