scholarly journals Design of a Novel Nonlinear Observer to Estimate Sideslip Angle and Tire Forces for Distributed Electric Vehicle

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Chuanxue Song ◽  
Feng Xiao ◽  
Shixin Song ◽  
Shaokun Li ◽  
Jianhua Li
Keyword(s):  
Electric Vehicle ◽  
Vehicle Dynamics ◽  
Unscented Kalman Filter ◽  
Lateral Force ◽  
Nonlinear Observer ◽  
Accurate Estimation ◽  
State Variables ◽  
Force Estimation ◽  
Sideslip Angle ◽  
Vehicle Dynamics Control

For four-wheel independently driven (4WD) distributed electric vehicle (DEV), vehicle dynamics control systems such as direct yaw moment control (DYC) can be easily achieved. Accurate estimation of vehicle state variables and uncertain parameters can improve the robustness of vehicle dynamics control system. Various sensors are generally equipped to the acquisition of the vehicle dynamics. For both technical and economic reasons, some fundamental vehicle parameters, such as the sideslip angle and tire-road forces, can hardly be obtained through sensors directly. Therefore, this paper presented a state observer to estimate these variables based on Unscented Kalman Filter (UKF). To improve the accuracy of UKF, measurement noise covariance is also self-adaptive regulated. In addition, a nonlinear dynamics tire model is utilized to improve the accuracy of tire lateral force estimation. The simulation and experiment results show that the proposed observer can provide the precision values of the vehicle state.

Mechatronic Design of an Integrated Vehicle Dynamics Control for an Energetic Optimized Battery Electric Vehicle

2014 ◽  
Vol 214 ◽  
pp. 94-105
Author(s):  
Robert Buchta ◽  
Xiao Bo Liu-Henke
Keyword(s):  
Electric Vehicle ◽  
Vehicle Dynamics ◽  
Design Approach ◽  
Battery Electric Vehicle ◽  
Hardware In The Loop ◽  
Functional Design ◽  
Holistic Model ◽  
Mechatronic Design ◽  
The University ◽  
Vehicle Dynamics Control

Focus of this contribution is the constructive and functional design of an entire energetic optimized battery electric vehicle. This vehicle called M(echatronic)-Mobile was designed at the university Ostfalia using a holistic model based design approach in a continuous verification-orientated process from Model-in-the-Loop (MiL) over Software-in-the-Loop (SiL) to Hardware-in-the-Loop (HiL).

Performance comparison of electric-vehicle drivetrain architectures from a vehicle dynamics perspective

2019 ◽  
Vol 234 (4) ◽  
pp. 915-935
Author(s):  
Kerem Bayar
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Vehicle Dynamics ◽  
Tracking Control ◽  
Tracking Error ◽  
Vehicle Stability ◽  
Electric Motors ◽  
Sideslip Angle ◽  
Stopping Distance ◽  
Braking System

Recent electric vehicle studies in literature utilize electric motors within an anti-lock braking system, traction-control system, and/or vehicle-stability controller scheme. Electric motors are used as hub motors, on-board motors, or axle motors prior to the differential. This has led to the need for comparing these different drivetrain architectures with each other from a vehicle dynamics standpoint. With this background in place, using MATLAB simulations, these three drivetrain architectures are compared with each other in this study. In anti-lock braking system and vehicle-stability controller simulations, different control approaches are utilized to blend the electric motor torque with hydraulic brake torque; motor ABS, torque decomposition, and optimal slip-tracking control strategies. The results for the anti-lock braking system simulations can be summarized as follows: (1) Motor ABS strategy improves the stopping distance compared to the standard anti-lock braking system. (2) In case the motors are not solely capable of providing the required braking torque, torque decomposition strategy becomes a good solution. (3) Optimal slip-tracking control strategy improves the stopping distance remarkably compared to the standard anti-lock braking system, motor anti-lock braking system, and torque decomposition strategies for all architectures. The vehicle-stability controller simulation results can be summarized as follows: (1) higher affective wheel inertia of the on-board and hub motor architecture dictates a higher need of wheel torque in order to generate the tire force required for the desired yaw rate tracking. A higher level of torque causes a higher level of tire slip. (2) Optimal slip-tracking control strategy reduces the tire slip trends drastically and distributes the traction/braking action to each tire with the control-allocation algorithm specifying the reference slip values. This reduces reference tire slip-tracking error and reduces vehicle sideslip angle. (3) Tire slip trends are lower with the hub motor architecture, compared to the other architectures, due to more precise slip control.

Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control

2004 ◽  
Vol 126 (2) ◽  
pp. 243-254 ◽  
Author(s):  
Jihan Ryu ◽  
J. Christian Gerdes
Keyword(s):  
Global Positioning System ◽  
Vehicle Dynamics ◽  
Inertial Sensors ◽  
Longitudinal Velocity ◽  
Positioning System ◽  
Sideslip Angle ◽  
Global Positioning ◽  
Reduce Measurement Error ◽  
Gps System ◽  
Vehicle Dynamics Control

This paper demonstrates a method of estimating several key vehicle states—sideslip angle, longitudinal velocity, roll and grade—by combining automotive grade inertial sensors with a Global Positioning System (GPS) receiver. Kinematic Kalman filters that are independent of uncertain vehicle parameters integrate the inertial sensors with GPS to provide high update estimates of the vehicle states and the sensor biases. Using a two-antenna GPS system, the effects of pitch and roll on the measurements can be quantified and are demonstrated to be quite significant in sideslip angle estimation. Employing the same GPS system as an input to the estimator, this paper develops a method that compensates for roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. In addition, calibration procedures for the sensitivity and cross-coupling of inertial sensors are provided to further reduce measurement error. The resulting state estimates compare well to the results from calibrated models and Kalman filter predictions and are clean enough to use in vehicle dynamics control systems without additional filtering.

Active Kinematics Suspension Integrating Milliken Moment Method in the Control Logic

2016 ◽  
Author(s):  
Isabel Ramirez Ruiz ◽  
Edoardo Sabbioni ◽  
Federico Cheli
Keyword(s):  
Vehicle Dynamics ◽  
Moment Method ◽  
Full Range ◽  
Operating Conditions ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Slip Angle ◽  
Control Logic ◽  
Sideslip Angle ◽  
Vehicle Dynamics Control

The idea behind the active kinematics suspension is to enhance its performance of vehicle dynamics. This includes improve steady and dynamic limit stability and faster transient reaction through optimized lateral and longitudinal dynamics. The driver’s benefits are: improved safety and higher driving pleasure. To achieve more control over the position of the rear wheels and thus the tire contact patch on the ground, the active suspension introduces one independent linear actuator at each rear wheel that controls the wheels’ camber freely. This paper will present the vehicle dynamics control logic methodology of a rear active vehicle suspension implementing the Milliken Moment Method (MMM) diagram to improve the vehicle stability and controllability, achieving gradually the front and rear axle limits. A Multibody vehicle model has been used to achieve a high fidelity simulation to generate the Milliken Moment Diagram (MMD) also known as the CN-AY diagram, where the vehicle’s yaw moment coefficient (CN) about the CG versus its lateral acceleration (AY) is mapped for different vehicle sideslip angle and steering wheel angles. With the Moment Method computer program it is possible to create the limit of the diagram over the full range of steering wheel angle and side slip angle for numerous changes in vehicle configuration of rear camber wheels and operating conditions. The vehicle dynamics control logic uses the maps like a vehicle maneuvering area under different vehicle active configurations where vehicle’s control is most fundamentally expressed as a yawing moment to quantify the directional stability.

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