scholarly journals Design of vehicle using Ackermann steering with IoT concept

2021 ◽  
Vol 24 (3) ◽  
pp. 1432
Author(s):  
Albert Paul Arunkumar ◽  
Palanisamy R. ◽  
Selvakumar K. ◽  
Usha S. ◽  
Thamizh Thentral T. M. ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Electric Drive ◽  
Data Transmission ◽  
Rear Axle ◽  
Front Wheel ◽  
Vehicle System ◽  
Pure Rolling ◽  
Serial Data ◽  
Minimum Angle

Electric vehicles are becoming more demanding these days. In this project the possibility of using Ackerman steering with electric drive servomotor is explained. Scalability is the advantage of using this mechanism which can be adopted for four-wheel vehicle system as well. The objective of this project is to do design a system using Ackerman steering which determines the maximum and minimum angle of the turning of the wheels. It also avoids the front tire slippage and activates pure rolling. Ackermann steering geometry is a geometric arrangement of linkages in the steering of a car or other vehicle designed to solve the problem of wheels on the inside and outside of a turn needing to trace out circles of different radii. The geometrical solution to this is for all wheels to have their axles arranged as radii of circles with a common centre point. As the rear wheels are fixed, this centre point must be on a line extended from the rear axle. Intersecting the axes of the front wheels on this line as well requires that the inside front wheel be turned, when steering, through a greater angle than the outside wheel. The microcontroller used in this project is ATMega16 andlmax232 is used for the serial data transmission.

Differential steering-based electric vehicle lateral dynamics control with rollover consideration

2019 ◽  
Vol 234 (3) ◽  
pp. 338-348
Author(s):  
Hui Jing ◽  
Rongrong Wang ◽  
Cong Li ◽  
Jinxiang Wang
Keyword(s):  
Electric Vehicles ◽  
Measurement Problem ◽  
Low Cost ◽  
Steering System ◽  
Front Wheel ◽  
Steering Control ◽  
Lateral Velocity ◽  
Lateral Dynamics ◽  
Vehicle Rollover ◽  
Differential Steering

This article investigates the differential steering-based schema to control the lateral and rollover motions of the in-wheel motor-driven electric vehicles. Generated from the different torque of the front two wheels, the differential steering control schema will be activated to function the driver’s request when the regular steering system is in failure, thus avoiding dangerous consequences for in-wheel motor electric vehicles. On the contrary, when the vehicle is approaching rollover, the torque difference between the front two wheels will be decreased rapidly, resulting in failure of differential steering. Then, the vehicle rollover characteristic is also considered in the control system to enhance the efficiency of the differential steering. In addition, to handle the low cost measurement problem of the reference of front wheel steering angle and the lateral velocity, an [Formula: see text] observer-based control schema is presented to regulate the vehicle stability and handling performance, simultaneously. Finally, the simulation is performed based on the CarSim–Simulink platform, and the results validate the effectiveness of the proposed control schema.

Vehicle coupled bifurcation analysis of steering angle and driving torque

2021 ◽  
pp. 095440702098540
Author(s):  
Xianbin Wang ◽  
Shuming Shi
Keyword(s):  
Bifurcation Analysis ◽  
Hybrid Method ◽  
Vehicle Dynamics ◽  
Front Wheel ◽  
Steering Angle ◽  
Driving Mode ◽  
Vehicle System ◽  
Autonomous Equation ◽  
Real Coded Genetic Algorithm ◽  
Driving Torque

The mechanism of vehicle dynamics steering bifurcation has almost been confirmed. But the present steering bifurcation mechanism cannot explain the bifurcation phenomena caused by the driving torque. As a result, the vehicle coupled bifurcation analysis of the steering angle and driving torque has not been studied. Based on the five degrees of freedom (5DOF) vehicle system dynamics model with driving torque involved, the vehicle dynamics equilibriums under different driving torque and driving mode were searched by a hybrid method in this paper. The hybrid method combined the real-coded Genetic Algorithm with Quasi-Newton gradient method. According to the definition of static bifurcation of nonlinear systems, the equilibrium bifurcation of 5DOF vehicle system was confirmed. Then, the 5DOF vehicle system model was transformed into autonomous equation with the front wheel steering angle as intermediate variable. From the two aspects of constant steering angle amplitude and constant driving torque, the bifurcation diagrams of different driving mode were calculated. The vehicle coupled bifurcation characteristics of steering angle and driving torque were analyzed. The results show that the values of the driving torque will directly affect the bifurcation characteristics of vehicle dynamics system. The coupled feature of the front wheel steering angle and driving torque effect on vehicle bifurcation is obvious.

Performance Comparisons Between Duals and Singles on the Rear Axle of a Front Wheel Assist Tractor

1987 ◽  
Vol 30 (3) ◽  
pp. 0641-0645 ◽  
Author(s):  
Leonard L. Bashford ◽  
Kenneth Von Bargen ◽  
Thomas R. Way ◽  
Ling Xiaoxian
Keyword(s):  
Rear Axle ◽  
Front Wheel ◽  
Performance Comparisons

scholarly journals A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

2021 ◽  
Vol 11 (21) ◽  
pp. 10187
Author(s):  
Yonghyeok Ji ◽  
Seongyong Jeong ◽  
Yeongjin Cho ◽  
Howon Seo ◽  
Jaesung Bang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Electric Vehicles ◽  
Electric Drive ◽  
Hybrid Electric Vehicles ◽  
Short Term Memory ◽  
Support Vector ◽  
Clutch Engagement ◽  
Hybrid Electric ◽  
Engine Clutch

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.

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