Stability control of in-wheel motor electric vehicles under extreme conditions

To investigate the stability of in-wheel motor electric vehicles (IWMEVs) under extreme conditions, a novel control strategy including active rear steering (ARS) mode and direct yaw moment control (DYC) mode is proposed in this paper, utilizing the adaptive dynamic neural network (ADNN) algorithm to make the most of the two control modes. Firstly, a three-degree of freedom nonlinear vehicle model as well as some subsystems is established. Then, a two-layer stability control strategy is put forward, where the upper-layer calculates the desired rear steering angle as well as the differential torque of the rear wheels and the lower-layer executes commands and returns relevant signals. Besides, a stability controller based on ADNN algorithm is designed in the upper-layer so as to take advantage of the two modes under extreme conditions. Next, the impacts of initial values of the connection weights on the ability of ADNN algorithm to train and learn are revealed. Consequently, the optimal initial values can be ascertained before the following simulations. Finally, the closed loop simulations of ARS and DYC are carried out under some extreme conditions such as high velocity and low adhesion coefficient roads, and the results indicate that compared with DYC’s difficulty in playing its role, ARS mode can significantly improve the stability of IWMEVs even under extreme conditions.

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Stability Control for Electric Vehicles with Four In-Wheel-Motors Based on Sideslip Angle

World Electric Vehicle Journal ◽

10.3390/wevj12010042 ◽

2021 ◽

Vol 12 (1) ◽

pp. 42

Author(s):

Kun Yang ◽

Danxiu Dong ◽

Chao Ma ◽

Zhaoxian Tian ◽

Yile Chang ◽

...

Keyword(s):

Electric Vehicles ◽

Control Method ◽

Sliding Mode ◽

Stability Control ◽

Torque Control ◽

Wheel Load ◽

Sideslip Angle ◽

Distribution Method ◽

Load Rate ◽

The Stability

Tire longitudinal forces of electrics vehicle with four in-wheel-motors can be adjusted independently. This provides advantages for its stability control. In this paper, an electric vehicle with four in-wheel-motors is taken as the research object. Considering key factors such as vehicle velocity and road adhesion coefficient, the criterion of vehicle stability is studied, based on phase plane of sideslip angle and sideslip-angle rate. To solve the problem that the sideslip angle of vehicles is difficult to measure, an algorithm for estimating the sideslip angle based on extended Kalman filter is designed. The control method for vehicle yaw moment based on sliding-mode control and the distribution method for wheel driving/braking torque are proposed. The distribution method takes the minimum sum of the square for wheel load rate as the optimization objective. Based on Matlab/Simulink and Carsim, a cosimulation model for the stability control of electric vehicles with four in-wheel-motors is built. The accuracy of the proposed stability criterion, the algorithm for estimating the sideslip angle and the wheel torque control method are verified. The relevant research can provide some reference for the development of the stability control for electric vehicles with four in-wheel-motors.

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Vehicle Stability Control on Tire Burst Steering and Braking Condition With Active Steering System

Volume 3: 20th International Conference on Advanced Vehicle Technologies; 15th International Conference on Design Education ◽

10.1115/detc2018-85283 ◽

2018 ◽

Author(s):

Yaqi Dai ◽

Jian Song ◽

Liangyao Yu

Keyword(s):

Control Strategy ◽

Steering System ◽

Stability Control ◽

Steering Angle ◽

Tire Force ◽

Vehicle Stability Control ◽

Yaw Moment Control ◽

Moment Control ◽

Control Layer ◽

Yaw Moment

By analyzing the key safety problems under the front-outside-tire burst steering condition, a vehicle stability control strategy is proposed in this paper, which is based on active front steering and differential braking systems. Taken both the handling stability and safety into account, we divided the whole control strategy into two layers, which are yaw moment control layer and the additional steering angle & tire force distribution layer. To solve the similar linear problem concisely, the LQR control is adopted in the yaw moment control layer. To achieve the goal of providing enough additional lateral force and yaw moment while keeping the burst tire in appropriate condition, the additional steering angle provided by active front steering system and the tire force distribution was adjusted step by step. To test the proposed control strategy performance, we modelling a basic front-outside-tire burst steering condition, in which the tire blows out once the vertical pressure reach the predefined critical value. Through simulation on different adhesion coefficient road, the control strategy proposed in this paper performance quite better compare with the uncontrolled one in aspect of movement, burst tire protection, handling stability.

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Coordinated Stability Control Strategy for Intelligent Electric Vehicles Using Vague Set Theory

Mathematical Problems in Engineering ◽

10.1155/2020/6793821 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Yilin He ◽

Jian Ma ◽

Xuan Zhao ◽

Ruoyang Song ◽

Xiaodong Liu ◽

...

Keyword(s):

Electric Vehicles ◽

Control Strategy ◽

Coordinated Control ◽

Stability Control ◽

Lateral Acceleration ◽

Vague Set ◽

Yaw Rate ◽

Stability Performance ◽

Control Target ◽

Simulation Results

Aiming at improving the tracking stability performance for intelligent electric vehicles, a novel stability coordinated control strategy based on preview characteristics is proposed in this paper. Firstly, the traditional stability control target is introduced with the two degrees of freedom model, which is realized by the sliding mode control strategy. Secondly, an auxiliary control target further amending the former one with the innovation formulation of the preview characteristics is established. At last, a multiple purpose Vague set leverages the contribution of the traditional target and the auxiliary preview target in various vehicle states. The proposed coordinated control strategy is analyzed on the MATLAB/CarSim simulation platform and verified on an intelligent electric vehicle established with A&D5435 rapid prototyping experiment platform. Simulation and experimental results indicate that the proposed control strategy based on preview characteristics can effectively improve the tracking stability performance of intelligent electric vehicles. In the double lane change simulation, the peak value of sideslip angle, yaw rate, and lateral acceleration of the vehicle is reduced by 13.2%, 11.4%, and 8.9% compared with traditional control strategy. The average deviations between the experimental and simulation results of yaw rate, lateral acceleration, and steering wheel angle are less than 10% at different speeds, which demonstrates the consistency between the experimental and the simulation results.

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Path Tracking and Handling Stability Control Strategy with Collision Avoidance for the Autonomous Vehicle under Extreme Conditions

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2020.3031661 ◽

2020 ◽

pp. 1-1

Author(s):

Yong Chen ◽

Sizhong Chen ◽

Hongbin Ren ◽

Zepeng Gao ◽

Zheng Liu

Keyword(s):

Collision Avoidance ◽

Control Strategy ◽

Autonomous Vehicle ◽

Stability Control ◽

Path Tracking ◽

Extreme Conditions

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Study on the stability control strategy of Triphala solution based on the balance of physical stability and chemical stabilities

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/j.jpba.2018.06.008 ◽

2018 ◽

Vol 158 ◽

pp. 247-256 ◽

Cited By ~ 6

Author(s):

Hao-zhou Huang ◽

Sheng-yu Zhao ◽

Xiu-mei Ke ◽

Jun-zhi Lin ◽

Shu-sen Huang ◽

...

Keyword(s):

Control Strategy ◽

Physical Stability ◽

Stability Control ◽

The Stability

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Vehicle Stability Control Strategy Based on Recognition of Driver Turning Intention for Dual-Motor Drive Electric Vehicle

Mathematical Problems in Engineering ◽

10.1155/2020/3143620 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Shu Wang ◽

Xuan Zhao ◽

Qiang Yu

Keyword(s):

Control System ◽

Electric Vehicle ◽

Control Strategy ◽

Stability Control ◽

Motor Drive ◽

Vehicle Stability ◽

Lane Change ◽

Vehicle Stability Control ◽

Stability Control System ◽

The Stability

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.

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Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020914666 ◽

2020 ◽

Vol 234 (9) ◽

pp. 2330-2341

Author(s):

Jiaxing Yu ◽

Xiaofei Pei ◽

Xuexun Guo ◽

JianGuo Lin ◽

Maolin Zhu

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Stability Control ◽

Robust Model Predictive Control ◽

Direct Yaw Moment Control ◽

Robust Model ◽

Yaw Moment Control ◽

Moment Control ◽

The Stability ◽

Yaw Moment

This paper proposes a framework for path tracking under additive disturbance when a vehicle travels at high speed or on low-friction road. A decoupling control strategy is adopted, which is made up of robust model predictive control and the stability control combining preview G-vectoring control and direct yaw moment control. A vehicle-road model is adopted for robust model predictive control, and a robust positively invariant set calculated online ensures state constraints in the presence of disturbances. Preview G-vectoring control in stability control generates deceleration and acceleration based on lateral jerk, later acceleration, and curvature at preview point when a vehicle travels through a cornering. Direct yaw moment control with additional activating conditions provides an external yaw moment to stabilize lateral motion and enhances tracking performance. A comparative analysis of stability performance of stability control is presented in simulations, and furthermore, many disturbances are considered, such as varying wind, road friction, and bounded state disturbances from motion planning and decision making. Simulation results show that the stability control combining preview G-vectoring control and direct yaw moment control with additional activating conditions not only guarantees lateral stability but also improves tracking performance, and robust model predictive control endows the overall control system with robustness.

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