Dynamic analysis of a two independent wheel drives electric vehicle, with a traction and a stability control system

Author(s):  
D. Foito ◽  
J. Esteves ◽  
J. Maia
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shu Wang ◽  
Xuan Zhao ◽  
Qiang Yu

Vehicle stability control should accurately interpret the driving intention and ensure that the actual state of the vehicle is as consistent as possible with the desired state. This paper proposes a vehicle stability control strategy, which is based on recognition of the driver’s turning intention, for a dual-motor drive electric vehicle. A hybrid model consisting of Gaussian mixture hidden Markov (GHMM) and Generalized Growing and Pruning RBF (GGAP-RBF) neural network is constructed to recognize the driver turning intention in real time. The turning urgency coefficient, which is computed on the basis of the recognition results, is used to establish a modified reference model for vehicle stability control. Then, the upper controller of the vehicle stability control system is constructed using the linear model predictive control theory. The minimum of the quadratic sum of the working load rate of the vehicle tire is taken as the optimization objective. The tire-road adhesion condition, performance of the motor and braking system, and state of the motor are taken as constraints. In addition, a lower controller is established for the vehicle stability control system, with the task of optimizing the allocation of additional yaw moment. Finally, vehicle tests were carried out by conducting double-lane change and single-lane change experiments on a platform for dual-motor drive electric vehicles by using the virtual controller of the A&D5435 hardware. The results show that the stability control system functions appropriately using this control strategy and effectively improves the stability of the vehicle.


2019 ◽  
Author(s):  
Shu Wang ◽  
Xuan Zhao ◽  
Qiang Yu ◽  
Peilong Shi ◽  
Man Yu ◽  
...  

Author(s):  
Juan S. Núñez ◽  
Luis E. Muñoz

With the aim of prevent situations of vehicle instability against different driving maneuvers, the vehicle yaw stability becomes crucial for safe operation. This paper presents the design and simulation of a traction and a stability control system algorithms for independent four-wheel-driven electric vehicle. The stability control system consists of a multilevel algorithm divided into a high level controller and a low level controller. First, an analysis of the stability of the vehicle is performed using phase portraits analysis, both in open loop and closed loop. The stability control system is designed to generate a desired yaw moment according to the steady state cornering relationship with the steering angle input. As the test vehicle, a 14 DoF vehicle model is proposed including nonlinear tire models that allow the generation of combined forces. The vehicle model includes the powertrain dynamics. The yaw moment generation is performed using the traction and braking forces between the tires of each side of both front and rear axle. In order to generate the maximum traction forces in each of the wheels, a traction and a braking control is developed via a sliding mode controller scheme. Finally a performance comparison between a controlled and an uncontrolled vehicle is presented. The behavior of both vehicles is simulated using a classical double lane change driving maneuver.


Author(s):  
Dongliang Wang

In extreme weather condition, the electric vehicle yaw stability control accuracy is low. A new yaw stability control system for electric vehicle driven by hub motor is designed to simplify the hardware system design and improve the system response speed. The driving control module is used to analyse the driving state parameters of the vehicle and calculate the four-wheel moment to control the yaw stability of the vehicle, which is transmitted to the battery control module. The UDU in the control module adjusts the motor speed and power output in real time according to the vehicle power demand after analyzing the vehicle driving state data. In the software part of the system, the vehicle dynamic model is built and yaw stability control strategy is used to complete the vehicle yaw stability control. The experimental results show three important parameters of the designed system for evaluating the manoeuvrability tend to ideal values under the control of the system, in which yaw angular velocity is controlled from 0.277 rads to 0.286 rads and the difference between them is 0.002 and 0.011. The yaw stability control accuracy is also high.


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