High-Fidelity Modelling of an Electric Vehicle

2015 ◽  
Author(s):  
Shinhoon Kim ◽  
Nasser L. Azad ◽  
John McPhee
Keyword(s):  
Electric Vehicle ◽  
Vehicle Dynamics ◽  
Model Parameters ◽  
High Fidelity ◽  
Area Network ◽  
Dynamics Model ◽  
Pwm Inverter ◽  
Motor Torque ◽  
Vehicle System ◽  
Simulation Results

The development and validation of a high-fidelity dynamics model of an electric vehicle is presented. The developed model is comprised of two subsystems: i) the vehicle dynamics model, and ii) the electrical powertrain subsystem consists of the alternating-current (AC) induction motor, the 3-phase pulse-width-modulation (PWM) inverter, and the motor controllers. At each stage of the development, the developed models are verified by studying their simulation results. Also, vehicle testing is performed using a reference electric vehicle and experimental powertrain data is measured from the vehicle’s electrical powertrain controller area network (CAN) bus. The experimental motor torque-speed curves are used to tune the AC electric motor model parameters. Once the individual components are developed and validated, the high-fidelity electric vehicle system model is created by assembling the MapleSim vehicle dynamics model and the electrical powertrain subsystem. The simulation results, such as the vehicle’s longitudinal speed and developed motor torque and currents, are presented and studied to verify that the electric vehicle system can operate under different driving scenarios. The high-fidelity electric vehicle model will be used in future work to test and validate new power management controllers.

Construction of a Rational Tire Model for High Fidelity Vehicle Dynamics Simulation Under Extreme Driving and Environmental Conditions

2010 ◽  
Author(s):  
S. C¸ag˘lar Bas¸lamıs¸lı ◽  
Selim Solmaz
Keyword(s):  
Vehicle Dynamics ◽  
Dynamics Simulation ◽  
High Fidelity ◽  
Tire Model ◽  
Dynamics Model ◽  
Vehicle Model ◽  
Prospective Design ◽  
Magic Formula ◽  
Rational Models ◽  
Simulation Results

In this paper, a control oriented rational tire model is developed and incorporated in a two-track vehicle dynamics model for the prospective design of vehicle dynamics controllers. The tire model proposed in this paper is an enhancement over previous rational models which have taken into account only the peaking and saturation behavior disregarding all other force generation characteristics. Simulation results have been conducted to compare the dynamics of a vehicle model equipped with a Magic Formula tire model, a rational tire model available in the literature and the present rational tire model. It has been observed that the proposed tire model results in vehicle responses that closely follow those obtained with the Magic Formula even for extreme driving scenarios conducted on roads with low adhesion coefficient.

Modeling and Analysis for Powertrain Dynamics of Electric Vehicle Systems

2011 ◽  
Vol 110-116 ◽  
pp. 2426-2431 ◽  
Author(s):  
Gwangmin Park ◽  
Seonghun Lee ◽  
Sung Ho Jin ◽  
Sangshin Kwak
Keyword(s):  
Mathematical Modeling ◽  
Electric Vehicle ◽  
Vehicle Dynamics ◽  
Simulation Platform ◽  
Modeling And Analysis ◽  
Vehicle Data ◽  
Vehicle Systems ◽  
Simulation Results ◽  
Model Equations ◽  
Real Vehicle

This paper provides presents the dynamic analysis and computer simulation results of electric vehicle (EV) powertrain performance systems. The generic simulation platform of an electric vehicle is developed using based on the SimPowerSystems/SimDriveline of MATLAB. Individual components of the model are constructed based on real vehicle data and mathematical dynamic model equations. The analytic results obtained from the mathematical modeling are verified with electric vehicle dynamics using generic simulation platform.

Parameter Identification for a Longitudinal Dynamics Model Based on Road Tests of an Electric Vehicle

2016 ◽  
Author(s):  
Mohit Batra ◽  
John McPhee ◽  
Nasser L. Azad
Keyword(s):  
Electric Vehicle ◽  
Degrees Of Freedom ◽  
Estimation Algorithm ◽  
Least Square ◽  
Area Network ◽  
Dynamics Model ◽  
Longitudinal Dynamics ◽  
Vehicle Data ◽  
Parameter Estimation Algorithm ◽  
Road Testing

This paper presents a method to estimate the parameters of a longitudinal dynamic model using on-road testing of an electric vehicle. Data acquisition was undertaken on our test vehicle, a Toyota Rav4EV 2012, by collating signals from three different sources: Vehicle Measurement System (VMS) (consisting of wheel force, torque, wheel spin, wheel speed and position sensors), Global Positioning System (GPS) and the Controller Area Network (CAN) of the vehicle. A MATLAB/Simulink based non-linear least square parameter estimation algorithm was used to identify the vehicle parameters including the mass, location of center of gravity, frontal area, coefficient of drag, wheel inertia and road load parameters of the vehicle. A 14 degrees of freedom (DOF), longitudinal dynamics model of the Rav4EV was developed in the MapleSim software using the estimated parameters. The accuracy of the identified parameters and the model was validated by comparing the model output against the experimental data.

scholarly journals Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1537 ◽  
Author(s):  
Xiaohua Zeng ◽  
Haoyong Cui ◽  
Dafeng Song ◽  
Nannan Yang ◽  
Tong Liu ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Vehicle Dynamics ◽  
Hybrid Electric Vehicle ◽  
Data Driven ◽  
Dynamics Model ◽  
Power Split ◽  
Hybrid Electric

Electric Vehicle Battery SOC Estimation Based on EKF

2014 ◽  
Vol 926-930 ◽  
pp. 927-931 ◽  
Author(s):  
Hui Bao ◽  
Wei Jiang ◽  
Dan Wei
Keyword(s):  
Electric Vehicle ◽  
Good Accuracy ◽  
State Of Charge ◽  
Model Parameters ◽  
Static Characteristics ◽  
Soc Estimation ◽  
Mathematical Relation ◽  
Battery State Of Charge ◽  
Electric Vehicle Battery ◽  
Simulation Results

In order to estimate the battery state of charge (SOC) accurately, an improved Thevenin model of a battery is established, its mathematical relation is very simple, and also it is easy to realize. In addition, we identify the model parameters, and then use extended Calman filter algorithm to estimate the battery state of charge. The simulation results show that, this model can well reflect the dynamic and static characteristics of a battery, and the Calman algorithm can keep good accuracy in the estimation process.

Vehicle Steering Stability Simulation Based on G-Vectoring Control

2014 ◽  
Vol 898 ◽  
pp. 914-918
Author(s):  
Yun Yin Zhang ◽  
Chun Guang Liu ◽  
Zi Li Liao
Keyword(s):  
Vehicle Dynamics ◽  
Driving Force ◽  
Control Method ◽  
Stability Control ◽  
Lateral Acceleration ◽  
System Control ◽  
Dynamics Model ◽  
Sideslip Angle ◽  
Vehicle System ◽  
Simulation Based

A new kind of control method named "G-Vectoring control" is used in vehicle steering stability control, which uses the lateral acceleration to control the longitudinal acceleration, and improves the steering stability by redistributing the driving force. The motor and its control system as well as the vehicle system control are modeled by Matlab, the vehicle dynamics model is designed by adams. After the co-simulation of snakelike tests, the results shows that the sideslip angle is well controlled by G-Vectoring control.

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