Transmission System Dynamics Research Articles

Dynamics modeling and analysis of the multistage planetary gear set-bearing-rotor-clutch coupling system considering the tooth impacts of clutches

Previous studies about the dynamics of the planetary gear system are mainly focused on transmission systems with a fixed transmission ratio, whereas the dynamic studies of vehicle automatic transmission with multiple transmission ratios achieved by engaging different clutches were seldom investigated. This is largely due to the lack of modeling strategies for the stochastic tooth impacts of detached clutches on the dynamics of transmission systems. Ignoring the stochastic tooth impacts on the system’s performance will result in inaccurate analysis that may overestimate the machine’s service performance. In this paper, an original dynamic model of the multistage planetary gear set-bearing-rotor-clutch coupling system with multi-shift and multi-speed transmission is established considering the tooth impacts of detached clutches. An embedded measurement method is developed to measure the in-situ torsional vibration responses of rotating parts under various shift and speed conditions, which are directly compared to simulated responses of the proposed model for validation purposes. Finally, the effects of the rotating speed and clutch’s tooth backlash on the random tooth impact characteristics of clutches are revealed. The modeling strategy of this work provides a more accurate and realistic simulation of automatic transmission’s dynamic performance and supplies theoretical guidance for the strength evaluation and early fatigue failure analysis between mating teeth of clutches.

Decentralized Charging of Plug-In Electric Vehicles and Impact on Transmission System Dynamics

This article focuses on the impact of the charge of Plug-in Electric Vehicles (PEVs) on the dynamic response of power systems and proposes an efficient solution to control electric vehicle chargers, by dynamically allocating the available power in an optimized way. The proposed approach is based on an Additive-Increase-Multiplicative-Decrease (AIMD) stochastic decentralized control strategy to efficiently and seamlessly manage the charge of a high number of PEVs with little communication efforts. A modified version of the New England network is utilized to validate the proposed control through a variety of scenarios and control setups.

Novel adaptive modified recurrent Legendre neural network control for a PMSM servo-driven electric scooter with V-belt continuously variable transmission system dynamics

Because of the unknown nonlinearity and time-varying characteristics of a V-belt continuously variable transmission (CVT) driven electric scooter system using a permanent magnet synchronous motor (PMSM) servo drive, its accurate dynamic model is difficult to establish for the design of the linear controller in the whole system. In order to conquer this difficulty and increase robustness, a novel adaptive modified recurrent Legendre neural network (NN) control system is proposed for controlling a PMSM servo-driven electric scooter with a V-belt CVT system. The novel adaptive modified recurrent Legendre NN control system consists of a modified recurrent Legendre NN control with adaptation law and a compensated control with estimation law. Additionally, the online parameter tuning methodology of the modified recurrent Legendre NN control and the estimation law of the compensated control can be derived by using the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Legendre NN are proposed according to a discrete-type Lyapunov function in order to raise the speed of convergence. Finally, comparative studies are provided by experimental results in order to show the effectiveness of the proposed control scheme.

The Gap Nonlinear Characteristics of Gear Transmission System under External Excitation

For the study of nonlinear dynamic characteristics of a pair of gears in an external torque under gear meshing error excitation, we will establish two degrees of freedom nonlinear torsional vibration model. The use of Matlab / Simulink for numerical simulation solves the nonlinear dynamic model of the gear gap. Study the dynamic characteristics of the system in a certain domain of parameters on external incentive conditions, as well as external motivation of gear transmission system dynamic characteristics influence. The results have important practical value for future engineering practice on gear transmission system's dynamic design, and have important theoretical significance for complex gear transmission system dynamics study.

The Dynamics Analysis of Comb Cotton Picker Transmission System

This article does research on 4MSC-3000 cotton picker transmission system thorough. It establishes transmission system dynamics model and vibration differential equation, ADAMS virtual prototyping technology is used to do simulation on the transmission system. Kinematics relations and dynamic characteristics of the transmission system are found out. Through the dynamic analysis of the model to solve, we can get kinetic curve of gearbox, each axis, gears and so on. Then provide the necessary theoretical basis for follow-up prototype design.

Multi-Body Dynamics Simulation of A Tracked Vehicle Power Train in Consideration of Multi-Source Excitations

This study proposed a new efficient method to investigate tracked vehicle gear transmission system dynamics applying Finite element theory and multi-body dynamics. Torsional excitation was simulated based on an engine dynamic model by considering flexibility of crankshaft, maximum fire pressure and variable inertia. Gear mesh stiffness coefficient, mesh error excitation, bearing dynamic stiffness coefficient and damping coefficient were numerically calculated. The virtual prototype model of power train system including all aforementioned excitations was constructed. Dynamic responses of transmission shaft working with multi-source excitations were analyzed. The impact of each excitation source was discussed.