Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle

Wang, Zhenpo; Wang, Yachao; Zhang, Lei; Liu, Mingchun

doi:10.3390/en10070947

Cited by 65 publications

(39 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e 2-DoF vehicle model acts as the desired model. Yaw rate ω rd 1 and sideslip angle β d 1 of the vehicle based on the 2-DoF model are shown as follows [38,41,42]:…”

Section: Traditional Stability Control Of Intelligent Electric Vehiclesmentioning

confidence: 99%

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

Zhao

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

“…e 2-DoF vehicle model acts as the desired model. Yaw rate ω rd 1 and sideslip angle β d 1 of the vehicle based on the 2-DoF model are shown as follows [38,41,42]:…”

Section: Traditional Stability Control Of Intelligent Electric Vehiclesmentioning

confidence: 99%

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

Zhao

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Even though variable torque distribution of the distributed‐drive EV was discussed for multiobjective coordination, multiobjective optimization for integrated vehicle dynamics management of the distributed‐drive EV with AFS is still a new research area in EV technology. As shown in Figure , an integrated vehicle dynamics management method of the distributed‐drive EV with AFS is studied in this paper to coordinate the commands for vehicle lateral dynamics, longitudinal dynamics, and energy saving control from the top to lower layer to regulate the steering system, traction system, and braking system.…”

Section: Introductionmentioning

confidence: 99%

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

Huang

Wong

2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation.

show abstract

“…By replacing the mechanical transmission system with an independent and direct drive system, IWMEVs have great potential to achieve better dynamic control, smaller space utilization, higher driving efficiency, and redundant driving system, etc. [1][2][3], so as to improve overall vehicle performance. As an ideal carrier for advanced vehicle dynamics control system, IWMEVs have played a significant role in defining the development direction for the next generation EVs.…”

Section: Introductionmentioning

confidence: 99%

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicles with In-Wheel Vibration Absorbers

Liu

Zhang

2017

Energies

Self Cite

View full text Add to dashboard Cite

This paper presents an in-wheel vibration absorber for in-wheel-motor electric vehicles (IWM EVs), and a corresponding control strategy to improve vehicle ride comfort. The proposed in-wheel vibration absorber, designed for suppressing the motor vibrations, is composed of a spring, an annular rubber bushing, and a controllable damper. The parameters of the in-wheel spring and rubber bushing are determined by an improved particle swarm optimization (IPSO) algorithm, which is executed under the typical driving conditions and can absorb vibration passively. To deal with negative interaction effects between vehicle suspension and in-wheel absorber, a linear quadratic regulator (LQR) algorithm is developed to control suspension damper, and meanwhile a fuzzy proportional-integral-derivative (PID) method is developed to control in-wheel damper as well. Through four evaluation indexes, i.e., vehicle body vertical acceleration, suspension dynamic deflection, wheel dynamic load, and motor wallop, simulation results show that, compared to the conventional electric wheel, the proposed suspension LQR control effectively improves vehicle ride comfort, and the in-wheel absorber exhibits excellent performance in terms of wheel and motor vibration suppression.

show abstract

Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle

Cited by 65 publications

References 30 publications

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

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

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

Ride Comfort Optimization of In-Wheel-Motor Electric Vehicles with In-Wheel Vibration Absorbers

Contact Info

Product

Resources

About