A Robust Control Method for Lateral Stability Control of In‐Wheel Motored Electric Vehicle Based on Sideslip Angle Observer

Wang, Yaxiong; Kang, Feng; Wang, Taipeng; Ren, Hongbin

doi:10.1155/2018/8197941

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…In contrast to the EKF method, UKF utilizes a set of sigma points to realize nonlinear transformation, which acts directly on the nonlinear vehicle dynamics systems to approximate the states. In [55], an unscented Kalman filter (UKF) method making full use of driving torques from a four-wheel-drive hybrid vehicle was employed to estimate vehicle velocities on the basis of the UniTire model in different driving modes, and the UKF-based vehicle sideslip angle was obtained as valued information of lateral stability control for in-wheel motored electric vehicles [56]. Considering the effect of model non-linearity, uncertainty, and road friction conditions, an adaptive variable structural UKF (AUKF) was studied in [57] to compensate the model uncertainty for vehicle sideslip angle estimation.…”

Section: Model-based Vehicle State Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

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Section: Model-based Vehicle State Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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“…Model predictive control (MPC) has also been widely used in vehicle control systems in recent years, and better dynamic control performance can be obtained by using rolling optimization strategies. Therefore, in various studies, output torque difference between contralateral drive motors and active steering control have been applied to correct vehicle trajectory [1][2][3][4][5][6][7][8][9][10]. However, irregular roads and different adhesion limits of the four tires were not taken into account in the aforementioned studies.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Optimization of Trafficability and Roll Stability for Off-Road Vehicles Based on Wheel-Hub Drive Motors and Semi-Active Suspension

Fu,

Wan,

Liu

et al. 2024

Mathematics

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Considering the requirements pertaining to the trafficability of off-road vehicles on rough roads, and since their roll stability deteriorates rapidly when turning violently or passing slant roads due to a high center of gravity (CG), an efficient anti-slip control (ASC) method with superior instantaneity and robustness, in conjunction with a rollover prevention algorithm, was proposed in this study. A nonlinear 14 DOF vehicle model was initially constructed in order to explain the dynamic coupling mechanism among the lateral motion, yaw motion and roll motion of vehicles. To acquire physical state changes and friction forces of the tires in real time, corrected LuGre tire models were utilized with the aid of resolvers and inertial sensors, and an adaptive sliding mode controller (ASMC) was designed to suppress each wheel’s slip ratio. In addition, a model predictive controller (MPC) was established to forecast rollover risk and roll moment in reaction to the change in the lateral forces as well as the different ground heights of the opposite wheels. During experimentation, the mutations of tire adhesion capacity were quickly discerned and the wheel-hub drive motors (WHDM) and ASC maintained the drive efficiency under different adhesion conditions. Finally, a hardware-in-the-loop (HIL) platform made up of the vehicle dynamic model in the dSPACE software, semi-active suspension (SAS), a vehicle control unit (VCU) and driver simulator was constructed, where the prediction and moving optimization of MPC was found to enhance roll stability effectively by reducing the length of roll arm when necessary.

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“…The accurate derivation of torques and rotating angular speeds of each IWMEV wheel can be easily achieved without increasing any sensor, and the obtained information perception range from IWMEVs is larger than that from traditional vehicles; these two advantages provide the basis for accurate vehicle state estimation. With regard to the vehicle state estimation of IWMEVs, Wang et al used UKF to estimate the sideslip angle when tire forces are obtained with virtual lateral tire force sensors, assuming that a linear relationship exists between lateral tire force and tire slip angle when vehicle lateral acceleration is less than 0.3 g. Lian et al not only estimated the lateral tire forces with the recursive least squares algorithm, but they also estimated the sideslip angle with EKF, in which tire cornering stiffness was considered in the design of the nonlinear observer of the sideslip angle. Jin and Yin proposed a novel method to estimate lateral tire–road forces and the vehicle sideslip angle by utilizing real‐time measurements.…”

Section: Introductionmentioning

confidence: 99%

An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

Huang

Lei

Han

et al. 2019

Adaptive Control & Signal

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Vehicle state is essential for active safety stability control. However, the accurate measurement of some vehicle states is difficult to achieve without the use of expensive equipment. To improve estimation accuracy in real time, this paper proposes an estimator of vehicle velocity based on the adaptive unscented Kalman filter (AUKF) for an in-wheel-motored electric vehicle (IWMEV). Given the merits of an independent drive structure, the tire forces of the IWMEV can be directly calculated through a vehicle dynamic model. Additionally, by means of the normalized innovation square, the validity of vehicle velocity estimation can be detected, and the sliding window length can be adjusted adaptively; thus, the steady-state error and the dynamic performance of the IWMEV are demonstrated to be simultaneously improved over an alternative approach in comparisons. Then, an adaptive adjustment strategy for the noise covariance matrices is introduced to overcome the impact of parameter uncertainties. The numerically simulated and experimental results prove that the proposed vehicle velocity estimator based on AUKF not only improves estimation accuracy but also possesses strong robustness against parameter uncertainties. The deployment of the estimation algorithm by using a single-chip microcomputer verifies the strong real-time performance and easy-to-implement characteristics of the proposed algorithm. KEYWORDSadaptive adjustment, electric vehicle, in-wheeled motored, state estimation, unscented Kalman filter 1676

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A Robust Control Method for Lateral Stability Control of In‐Wheel Motored Electric Vehicle Based on Sideslip Angle Observer

Cited by 6 publications

References 24 publications

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Synthetic Optimization of Trafficability and Roll Stability for Off-Road Vehicles Based on Wheel-Hub Drive Motors and Semi-Active Suspension

An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

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