A Bond Graph-Based Method of Automated Generation of Automatic Transmission Mathematical Model

Ranogajec, Vanja; Deur, Joško

doi:10.4271/2017-01-1142

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…The first category is the software based on unified modeling language (UML), including MATLAB‐based software such as Simulink and Simscape , Modelica‐based software such as Dyloma, CyModelica, Vertex, Converge, MapleSim, MathModelica, Mworks, system‐Modeler, and SimulationX , bond graph based software such as 20 Sim and AMESim . Because of the unified model description form, the UML‐based software is excellent in the domain‐independent universal modeling, and can realize seamless integration between subsystem models in different domains.…”

Section: Related Workmentioning

confidence: 99%

HSTPNSim: An educational simulator for multi‐disciplinary modeling for hybrid system

Cao¹,

Bai²,

Hao³

et al. 2018

Comp Applic In Engineering

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Section: Related Workmentioning

confidence: 99%

HSTPNSim: An educational simulator for multi‐disciplinary modeling for hybrid system

Cao¹,

Bai²,

Hao³

et al. 2018

Comp Applic In Engineering

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“…Bond graph is a commonly used modeling method, which has been applied in the modeling of AT 4 and hybrid powertrains. 5 However, in practical AT systems, there are many nonlinear factors such as friction and inertia, which can affect the shifting process. Bond graph models have limitations in accurately describing these complex dynamic characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective parameter optimization strategy based on engine coordinated control for improving shifting quality

Shang,

Zhang,

Wang

et al. 2023

Advances in Mechanical Engineering

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In order to address the poor shifting quality issue of a certain model of heavy-duty vehicles, a multi-objective parameter optimization strategy based on engine coordinated control is proposed. This strategy aims to improve shifting quality by reducing the sliding friction work and impact during the shifting process. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to perform multi-objective optimization on the coordinated control parameters, which include external control torque of the engine, start of fuel cut-off timing, and duration of fuel cut-off. By comparing the performance of different parameter combinations in terms of sliding friction work and impact, the optimal parameter combination is determined. Through bench testing verification, it has been demonstrated that utilizing the optimized parameters for engine coordinated control during the torque phase of the shifting process can significantly enhance shifting quality. This strategy provides an effective solution for addressing shifting quality issues.

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“…In order to improve the AT shift quality, the researchers used different modeling and optimization methods to study the clutch-to-clutch control system [8][9][10][11][12]. Ranogajec et al [13,14] used the bond graph method to analyze the dynamics of a shift process for a ten-speed automatic transmission, and they proposed a solution by using an extra clutch to compensate the output torque, so as to assist the gear shift and suppress the excessive impact of the inertial phase. Zhao and Li [15] adopted subspace identification to describe the predictive equations of single-transition shift and then used the model predictive control method to optimize the shifting quality.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

et al. 2020

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In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.

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A Bond Graph-Based Method of Automated Generation of Automatic Transmission Mathematical Model

Cited by 8 publications

References 15 publications

HSTPNSim: An educational simulator for multi‐disciplinary modeling for hybrid system

HSTPNSim: An educational simulator for multi‐disciplinary modeling for hybrid system

Multi-objective parameter optimization strategy based on engine coordinated control for improving shifting quality

Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

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