In this paper, Bouc–Wen type magnetorheological fluid damper has been used to monitor the ride quality of a prevailing rail vehicle in lateral vibrations. Modelling of the rail vehicle is done in such a manner that it has an entire 9 degrees of freedom by significant considerations of lateral, roll and yaw motions of the car body, rear, and the front chassis. 200 km/h is considered as train speed for tracks with two varying disturbances. A system consisting of multibody in VI-rail software is provided by a track input and ergo, wheel response it obtained. SIMULINK (software) is responsible for the representation of the motions of the wheel as mathematical models. Two different types of analysis are done firstly with conventional passive lateral damper and secondly with semi-active MR lateral damper in subordinate suspension. To diminish lateral vibrations, the disturbance refusal and non-stop state controller algorithms were executed to manage the damper force. Results acquired are in the form of acceleration and displacement of the center of mass of the body under consideration is done by comparing in terms of reduction indices of their vibrations. A significant improvement in the index is seen in which a semi-active lateral damper is mounted. The results show that the proposed system significantly improves both, the vibration attenuation ability and the ride quality of the vehicle.

Semi-active Control to Reduce Lateral Vibration of Passenger Rail Vehicle Using Disturbance Rejection and Continuous State Damper Controllers

Non-linear analysis of a quarter-car model with stroke-dependent twin-tube shock absorber

Recently, automotive industry has adopted semi-active damper systems to improve handling and comfort properties of vehicles. Nowadays, Magneto-Rheological (MR) dampers are among the most effective solutions; with the control algorithm used for their operation being a key element. While basic controllers do not require mathematical damper models, improved performance can be achieved if these are available. Usually, the accuracy of a particular set of models can be assessed by evaluating standard quantitative metrics. However, two models with similar error-metrics can still have widely different qualitative properties. In this context, the main aim of this paper is to study the effects that may appear in the closed-loop performance of an automotive suspension system when the damper model is unable to represent crucial nonlinear MR phenomena. To highlight the model influence on the controller synthesis and subsequently on the suspension performance, two damper models with different accuracy levels were chosen: an ANN-based model is compared with the classical Bingham model. First, their accuracy is experimentally validated using typical error-metrics. Afterwards, the same suspension control strategy is designed using both models. Frequency-Estimation-Based control was selected because it better exploits available model data than other typical strategies such as sky-hook. The resulting performance is assessed with a software-in-the-loop approach using CarSim and complemented with a hardware-in-the-loop implementation using a CAN-bus, both closed-loop control cases use a Simulation-Oriented ANN model as benchmark to represent the MR damper nonlinearities. Results show that although the difference in error-metrics between models can be small using typical identification methods (e.g. 16% in one scenario), suspension performance in comfort and road-holding are significantly different. Error-metrics can be deceptive for assessing the effectiveness of MR damper models during the controller design phase. Accurate qualitative modeling in the pre/post-yield regions are the main factors which determine the resulting controller performance.

https://iopscience.iop.org/article/10.1088/1361-665X/ab39f2/pdf

Magneto-rheological dampers—model influence on the semi-active suspension performance

Robust semiactive control of a half‐car vehicle suspension system with magnetorheological dampers: Quantitative feedback theory approach with dynamic decoupler

As there is no clutch or hydraulic torque converter in electric vehicles to buffer and absorb torsional vibrations. Oscillation will occur in electric vehicle drivetrains when drivers tip in/out or are shifting. In order to improve vehicle response to transients, reduce vehicle jerk and reduce wear of drivetrain parts, torque step changes should be avoided. This article mainly focuses on drivetrain oscillations caused by torque interruption for shifting in a Motor-Transmission Integrated System. It takes advantage of the motor responsiveness, an optimal active control method is presented to reduce oscillations by adjusting motor torque output dynamically. A rear-wheel-drive electric vehicle with a two gear automated manual transmission is considered to set up dynamic differential equations based on Newton’s law of motion. By linearization of the affine system, a joint genetic algorithm and linear quadratic regulator method is applied to calculate the real optimal motor torque. In order to improve immediacy of the control system, time consuming optimization process of parameters is completed off-line. The active control system is tested in AMEsim® and limitation of motor external characteristics are considered. The results demonstrate that, compared with the open-loop system, the proposed algorithm can reduce motion oscillation to a satisfied extent when unloading torque for shifting.

/pdf/off-line-optimization-based-active-control-of-torsional-33spdc3z2x.pdf

Off-Line Optimization Based Active Control of Torsional Oscillation for Electric Vehicle Drivetrain

In this paper, control of the stationary response of a half-car model with a magnetorheological (MR) damper moving over a random road is considered. The MR damper is characterized using Bingham and...

Sadagopan Narayanan

Papers

Response of a half-car model with optimal magnetorheological damper parameters