MPC-based yaw stability control in in-wheel-motored EV via active front steering and motor torque distribution

Ren, Bingtao; Chen, Hong; Zhao, Haiyan; Yuan, Ling

doi:10.1016/j.mechatronics.2015.10.002

Cited by 154 publications

(76 citation statements)

References 29 publications

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“…In addition to yaw rate-based control, other control schemes may be appropriate for maximizing the lateral acceleration, e.g., "sideslip-based control". Among these technologies, active front steering (AFS) directly modifies road wheel angles [10], [11]. Unlike in the yaw rate-based method, lateral tire force can be directly controlled to track the reference sideslip without intervention of longitudinal force.…”

Section: A Literature Reviewmentioning

confidence: 99%

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Han

Park

Sankar³

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control methods. For safety's sake, most production vehicles are built to exhibit an understeer handling characteristics to some extent. By monitoring how much the vehicle is biased into the understeer state, the controller attempts to adjust this amount in a way that improves the vehicle cornering performance. With this particular strategy, an innovative controller can be designed without road friction information, which complicates the conventional control methods. In addition, unlike the conventional controllers, the reference yaw rate that is highly dependent on road friction need not be defined due to the proposed control structure. The optimal control problem is formulated in a model predictive control framework to handle the constraints efficiently, and simulations in various test scenarios illustrate the effectiveness of the proposed approach. Index Terms-Model Predictive Control, Constrained Control, Vehicle Handing Characteristics, and Cornering Performance. Seibum B. Choi(M'09) received the B.S. degree in mechanical engineering from Seoul National University, Seoul, Korea, in 1985, the M.S.

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Section: A Literature Reviewmentioning

confidence: 99%

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Han

Park

Sankar³

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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“…At sample time , ( ) works as predictive origin which is equal to ( | ), and the predictive state and predictive output are calculated as follows [20,21]: Optimization variables are the torque command of the four wheels. So the optimization object function in this paper at step of the optimal control problem can then be written as…”

Section: Slip Controller Designmentioning

confidence: 99%

Slip Control of Electric Vehicle Based on Tire‐Road Friction Coefficient Estimation

Cui

Dou

et al. 2017

Mathematical Problems in Engineering

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The real-time change of tire-road friction coefficient is one of the important factors that influence vehicle safety performance. Besides, the vehicle wheels' locking up has become an important issue. In order to solve these problems, this paper comes up with a novel slip control of electric vehicle (EV) based on tire-road friction coefficient estimation. First and foremost, a novel method is proposed to estimate the tire-road friction coefficient, and then the reference slip ratio is determined based on the estimation results. Finally, with the reference slip ratio, a slip control based on model predictive control (MPC) is designed to prevent the vehicle wheels from locking up. In this regard, the proposed controller guarantees the optimal braking torque on each wheel by individually controlling the slip ratio of each tire within the stable zone. Theoretical analyses and simulation show that the proposed controller is effective for better braking performance.

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“…Based on the slip control model (Equation (1)- (5)) of EV, a state-space model for MPC can be obtained, then a finite dimensional optimal control problem can be defined [8] [9]. Let the state-space model derived as Equation (10) ( 1 ) ( The optimization object function of the optimal control problem at step k can then be written as Equation (11).…”

Section: B Mpc Slip Controller Designmentioning

confidence: 99%

Research on the Composite Braking Control of Electric Vehicle

Dou¹,

Cui²,

Li³

et al. 2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)

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Abstract-A new composite braking control strategy is proposed for a rear-wheel-drive electric vehicle (EV) with both a regenerative braking system and a hydraulic braking system to achieve improved braking performance and optimal energy regeneration. Based on the model predictive control (MPC), the proposed braking control strategy maintains the slip ratio within an optimal range by adjusting the braking torque continuously, besides, to recover optimal braking energy, a braking torque allocation strategy of maximum regeneration efficiency is designed. With the control strategy, the regenerative braking system and the hydraulic braking system work synchronously to assure high regenerative efficiency and good braking performance. The proposed braking control strategy is steady and effective, as demonstrated by the simulation.Keywords-composite braking; slip ratio; electric vehicle (EV); model predictive control (MPC); regenerative braking.

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MPC-based yaw stability control in in-wheel-motored EV via active front steering and motor torque distribution

Cited by 154 publications

References 29 publications

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Slip Control of Electric Vehicle Based on Tire‐Road Friction Coefficient Estimation

Research on the Composite Braking Control of Electric Vehicle

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