Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm

Quan, Lingxiao; Chang, Ronglei; Guo, Changhong

doi:10.3390/app131910734

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…where ( ) wt and ( ) vt are the process and measurement noise, 17)-( 20) to form the measurement equation. In a short period, the road adhesion coefficient is regarded as unchanged [33], that is:…”

Section: B Road Adhesion Coefficient Estimation Algorithmmentioning

confidence: 99%

Platoon Joint EKF for Improved Road Friction Estimation in Autonomous Platoons

Su,

Chen,

Zhang

et al. 2024

IEEE Access

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The advantages inherent to autonomous vehicle platoons render them a pivotal component in the evolution of intelligent transportation systems. Given the minimal spacing between vehicles in such a platoon, ensuring collision-free travel necessitates a more rapid estimation of the road friction coefficient. Capitalizing on the vehicle-to-vehicle (V2V) communication prevalent amongst platoon members, this paper introduces a platoon joint extended Kalman filter (EKF) estimator to expedite the trailing vehicles' estimation of the road friction coefficient. A decision logic module is also designed within the EKF estimator of the following vehicles to process signals transmitted from the leading vehicle, achieving the objectives of signal assessment and reception. In the simulation scenarios presented in this study, the results of the platoon joint EKF estimation for the road friction coefficient, when compared to the results of an individual vehicle EKF estimation, show an improvement in estimation speed by 50% for butt roads and between 25%-80% for split roads. This attests to the efficacy of the proposed method, offering a reference for autonomous vehicle platoons to swiftly estimate road conditions and adjust inter-vehicle distances, thereby enhancing the safety of the platoon when road information changes.INDEX TERMS autonomous vehicle platoon, extended Kalman filter, road adhesion coefficient, state estimation.

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Section: B Road Adhesion Coefficient Estimation Algorithmmentioning

confidence: 99%

Platoon Joint EKF for Improved Road Friction Estimation in Autonomous Platoons

Su,

Chen,

Zhang

et al. 2024

IEEE Access

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“…Enisz et al [12] proposed a discrete-time extended Kalman filter based on the Pacejka tire model to estimate the instantaneous value and maximum value of the TRFC according to the quality of the road surface, which has a high accuracy in processing low-dimensional information, but the accuracy decreases in processing high-dimensional information. Quan et al [13] proposed an adaptive generalized high-order cubature Kalman filtering method to update the covariance of measurement noise and achieve an accurate estimation of the TRFC at high and low speeds. This estimation method can effectively deal with the estimation accuracy of high and low dimensions, but has weak resistance to non-Gaussian noise.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Mathematical Models for Precise Estimation of Tire–Road Friction Coefficient of Distributed Drive Electric Vehicles Based on Sensorless Control of the Permanent Magnet Synchronous Motor

Yu,

Hu,

Zeng

2024

Symmetry

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In order to reduce the use of wheel angular velocity sensors and improve the estimation accuracy and robustness of the tire–road friction coefficient (TRFC) in non-Gaussian noise environments, this paper proposes a sensorless control-based distributed drive electric vehicle TRFC estimation algorithm using a permanent magnet synchronous motor (PMSM). The algorithm replaces the wheel angular velocity signal with the rotor speed signal obtained from the sensorless control of the PMSM. Firstly, a seven-degree-of-freedom vehicle dynamics model and a mathematical model of the PMSM are established, and the maximum correntropy singular value decomposition generalized high-degree cubature Kalman filter algorithm (MCSVDGHCKF) is derived. Secondly, a sensorless control system of a PMSM based on the MCSVDGHCKF algorithm is established to estimate the rotor speed and position of the PMSM, and its effectiveness is verified. Finally, the feasibility of the algorithm for TRFC estimation in non-Gaussian noise is demonstrated through simulation experiments, the Root Mean Square Error (RMSE) of TRFC estimates for the right front wheel and the left rear wheel were reduced by at least 41.36% and 40.63%, respectively. The results show that the MCSVDGHCKF has a higher accuracy and stronger robustness compared to the maximum correntropy high-degree cubature Kalman filter (MCHCKF), singular value decomposition generalized high-degree cubature Kalman filter (SVDGHCKF), and high-degree cubature Kalman filter (HCKF).

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“…This article adopts the GHCKF algorithm [33]: this is concise in form, computationally efficient, and has better scalability. It considers discrete nonlinear systems such as in Equation ( 9), which limit the nonlinear filtering integral equation to the real number field R n :…”

Section: The Vehicle State Estimation Modelmentioning

confidence: 99%

Vehicle State Joint Estimation Based on Lateral Stiffness

Quan,

Chang,

Guo

et al. 2023

Sensors

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In this study, a vehicle state joint estimation method based on lateral stiffness was applied to estimate the running states of electric vehicles driven by rear-drive, in-wheel motors. Different from the estimation methods used in other research, the joint estimator designed in this study uses the least-squares (LS) algorithm to estimate the lateral stiffness of the front and rear axles of the vehicle, deploying the high-degree cubature Kalman filter algorithm to estimate the vehicle state. We establish a three-degree-of-freedom nonlinear vehicle model with longitudinal velocity, lateral velocity, and yaw rate, and the lateral stiffness of the front and rear axles as the principal parameters. For the low-speed running state of the vehicle, a linearized magic tire model with high fitting accuracy was used to calculate the lateral force of the entire vehicle. The LS algorithm with a forgetting factor was used to design a lateral stiffness estimator to assess the front-axle and rear-axle lateral stiffness of the entire vehicle. The generalized high-degree cubature Kalman filter (GHCKF) algorithm was used to design the vehicle state estimator and further improve the GHCKF algorithm. A vehicle state estimator, using the square root generalized high-degree cubature Kalman filter (SRGHCKF), was designed. Therefore, the joint estimator, comprising a lateral stiffness estimator and a vehicle state estimator, adopts the LS-GHCKF/SRGHCKF algorithm and enables the estimation of the lateral stiffness, the longitudinal velocity, the lateral velocity, and the yaw rate of the entire vehicle during the driving process. A double lane change and slalom simulation were performed to analyze the feasibility and accuracy of the joint estimation algorithm and verify the results of the LS-GHCKF algorithm and the LS-SRGHCKF algorithm. Further, a low-speed driving experiment was carried out for electric vehicles driven by rear in-wheel motors. The inertial navigation system (INS), the global positioning system (GPS), the real-time kinematic (RTK), and an angle sensor were used to collect real-time vehicle data. The results were compared to verify the feasibility of the joint estimator and the progressiveness of the algorithm. The experimental verification and simulation both show that the vehicle state joint estimator, designed based on the LS-GHCKF/SRGHCKF algorithm, can accurately estimate the real-time state of the vehicle. Additionally, the LS-SRGHCKF algorithm shows better effectiveness and robustness than the LS-GHCKF algorithm.

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Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm

Cited by 6 publications

References 19 publications

Platoon Joint EKF for Improved Road Friction Estimation in Autonomous Platoons

Platoon Joint EKF for Improved Road Friction Estimation in Autonomous Platoons

Study on Mathematical Models for Precise Estimation of Tire–Road Friction Coefficient of Distributed Drive Electric Vehicles Based on Sensorless Control of the Permanent Magnet Synchronous Motor

Vehicle State Joint Estimation Based on Lateral Stiffness

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