scholarly journals Research and Simulation of the Electrical Vehicle Based Dynamical System

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Ko-Chun Chen ◽  
Hsin-Yi Shih
Keyword(s):  
Dynamical System ◽  
Graphical User Interface ◽  
Suspension System ◽  
Model Parameters ◽  
Electrical System ◽  
Vehicle Model ◽  
System Parameters ◽  
Vehicle System ◽  
System Components ◽  
Electrical Vehicle

This study developed a dynamic model of electric vehicle system by using the MATLAB/Simulink tool. The vehicle model comprises two system components: an electrical system and a suspension system. This study also designed various road conditions for simulating the motion of vehicle traveling along a road. The results show that the electrical and suspension system parameters can be adjusted immediately to enhance passenger comfort. The findings of this research have practical teaching applications. Students can modify the vehicle model parameters byes using the MATLAB graphical user interface, allowing them to observe the motion of vehicle under various road conditions.

Multi-objective optimization of the suspension system parameters of a full vehicle model

2018 ◽  
Vol 20 (1) ◽  
pp. 151-177 ◽  
Author(s):  
Giovani Gaiardo Fossati ◽  
Letícia Fleck Fadel Miguel ◽  
Walter Jesus Paucar Casas
Keyword(s):  
Suspension System ◽  
Multi Objective Optimization ◽  
Vehicle Model ◽  
System Parameters ◽  
Multi Objective ◽  
Full Vehicle

scholarly journals A Driver Modeling Based on the Preview-Follower Theory and the Jerky Dynamics

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jianyong Cao ◽  
Hui Lu ◽  
Konghui Guo ◽  
Jianwen Zhang
Keyword(s):  
Path Following ◽  
Driver Model ◽  
Test Results ◽  
Vehicle Model ◽  
System Parameters ◽  
Vehicle System ◽  
Jerky Dynamics ◽  
Weighting Coefficients ◽  
Drive Model ◽  
The Relationship

Based on the preview optimal simple artificial neural network driver model (POSANN), a new driver model, considering jerky dynamics and the tracing error between the real track and the planned path, is established. In this paper, the modeling for the driver-vehicle system is firstly described, and the relationship between weighting coefficients of driver model and system parameters is examined through test data. Secondly, the corresponding road test results are presented in order to verify the vehicle model and obtain the information on drive model and vehicle parameters. Finally, the simulations are carried out via CarSim. Simulation results indicate that the jerky dynamics need to be considered and the proposed new driver model can achieve a better path-following performance compared with the POSANN driver model.

scholarly journals Design, Simulation and Performance Analysis of a Battery Electric Vehicle Model

2019 ◽  
Vol 9 (1) ◽  
pp. 3115-3118
Keyword(s):  
Performance Analysis ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Battery Electric Vehicle ◽  
Electrical System ◽  
Vehicle Model ◽  
System Components ◽  
Mode Of Transportation ◽  
Simulation Results ◽  
And Performance

Electric vehicles (EVs), in today’s scenario have become a replacement of conventional mode of transportation as they have shown an ability to minimize the carbon and sulfur emitting fuel operating vehicles. In this study, the components of the battery operated EV (BEV) systems are discussed and a model of BEV on the MATLAB-Simulink platform is simulated. Moreover, the relevant electrical system components as well as its corresponding equations for verification are identified. Furthermore, all simulation results were considered. Thus this study presents a foundation for higher researches in the field of EVs.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Application of the dynamic Lorentz model to modeling the motion of a rigid body

2021 ◽  
pp. 32-36
Author(s):  
Nikolay Makeyev ◽  
Keyword(s):  
Dynamical System ◽  
Qualitative Research ◽  
Rigid Body ◽  
Body Motion ◽  
Dynamic Equations ◽  
Lorentz Model ◽  
Dissipative Forces ◽  
System Parameters ◽  
Phase Trajectories ◽  
Inertia Forces

A qualitative research of the field of phase trajectories of the system of dynamic equations of an absolutely rigid body was carried out, moving around the selected pole under the influence of gyroscopic, dissipative forces and Coriolis inertia forces. The equations of body motion are reduced to a dynamical system generating a Lorentz attractor. Under parametric constraints imposed on the equations of a dynamical system, the structure of its phase trajectories is described depending on the values of the system parameters.

A suspension system with quasi-zero stiffness characteristics and inerter nonlinear energy sink

2020 ◽  
pp. 107754632097290
Author(s):  
You-cheng Zeng ◽  
Hu Ding ◽  
Rong-Hua Du ◽  
Li-Qun Chen
Keyword(s):  
Integrated Control ◽  
Harmonic Approximation ◽  
Nonlinear Energy Sink ◽  
Suspension System ◽  
Degree Of Freedom ◽  
System Parameters ◽  
Suspension Systems ◽  
Control Scheme ◽  
Energy Sink ◽  
Nonlinear Energy

In this article, a novel vibration control scheme of suspension systems is proposed. It combines the advantages of quasi-zero stiffness isolator, nonlinear energy sink absorber, and inerter. This proposed scheme can achieve low transmissibility, low amplitude, and low additional weight and resolve the conflict between riding comfort and handling stability. Strong nonlinear vibration equations of a quarter-vehicle suspension system are established. It also presents the detailed process of high-order harmonic approximation to obtain steady-state responses. Moreover, approximate solutions are validated by a numerical method. Furthermore, based on riding comfort and handling stability, the following four suspension systems are evaluated and compared, namely, 2-degree-of-freedom quarter-vehicle model, 2-degree-of-freedom quarter-vehicle with quasi-zero stiffness isolator, 2-degree-of-freedom quarter-vehicle with inerter-nonlinear energy sink absorber, and 2-degree-of-freedom quarter-vehicle integrated control scheme with quasi-zero stiffness and inerter-nonlinear energy sink. It is found that the integrated control scheme with quasi-zero stiffness and inerter-nonlinear energy sink can significantly improve the riding comfort and handling stability at the same time. In addition, the effects of system parameters are studied carefully. The results show that based on the reasonable design of the control system parameters, better riding comfort and handling stability can be obtained. In short, this article provides a theoretical basis for integrating quasi-zero stiffness isolators and inerter-nonlinear energy sink absorbers to improve the riding comfort and handling stability.

Electrical Vehicle Modeling and Backstepping Control

2016 ◽  
Vol 1 (1) ◽  
pp. 60
Author(s):  
Mekkaoui Mohammed ◽  
Zemalache Meguenni Kada ◽  
Omari Abdel Hafid ◽  
Lotfi Motefai
Keyword(s):  
Simple Structure ◽  
Backstepping Control ◽  
Control Law ◽  
Parameter Adaptation ◽  
Vehicle Modeling ◽  
Vehicle System ◽  
Electrical Vehicles ◽  
Electrical Vehicle ◽  
Domestic Use ◽  
Adaptation Law

Nowadays, the development of electric vehicles has become a general trend. Electrical vehicles have improved their performance, and have been made suitable for commercial and domestic use during the last decades. The proportional–integral–differential (PID) controller has been widely used in the industrial field. It has a simple structure, and can be easily realized. The recursive backstepping design methodology is originally introduced inadaptive control theory to systematically construct the feedback control law, the parameter adaptation law and the associated Lyapunov function for a class of nonlinear systems satisfying certain structured properties. the backstepping control (BKC)  is used to improve the robustness and real-time performance of the electrical vehicle system. Numerical simulation results show the effectiveness of this approach.

Impaired Driver Assistance Control With Gain-Scheduling Composite Nonlinear Feedback for Vehicle Trajectory Tracking

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Yimin Chen ◽  
Chuan Hu ◽  
Junmin Wang
Keyword(s):  
Trajectory Tracking ◽  
Controller Design ◽  
Gain Scheduling ◽  
Driver Model ◽  
Model Parameters ◽  
Nonlinear Feedback ◽  
Composite Nonlinear Feedback ◽  
The Road ◽  
Vehicle System ◽  
Feedback Algorithm

Abstract Impaired drivers have deteriorated driving performances that may greatly endanger the road safety. It is challenging to design assistance controllers for the impaired drivers because the impaired driver behaviors are difficult to be modeled and considered in the controller design. To this end, this paper proposes a gain-scheduling composite nonlinear feedback (GCNF) controller to assist the impaired drivers. A driver-vehicle system containing the impaired driver model is developed. The steering behaviors of the impaired drivers are described by deteriorating the driver model parameters and including the driver uncertainties. Based on the driver-vehicle system, a GCNF controller integrating the gain-scheduling technique, the weighted H∞ performance, and the composite nonlinear feedback algorithm is designed to handle the declined driving performances and improve the transient performances. The designed GCNF controller is validated in the carsim simulations. The simulation results show that the GCNF controller can effectively assist the impaired drivers of different impaired levels to reduce the trajectory tracking errors and improve the driving performances.

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