A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Qi, Dengliang; Feng, Jianxiang; Li, Yongbin; Wang, Lei; Song, Bao

doi:10.3390/e25030453

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Replace Equations ( 22) and (23) with Equations ( 32)- (34), Equations ( 27) and ( 28) with Equations ( 35)- (39), Equation (29) with Equation (40), and Equation (31) with Equation (41), and obtain the SRGHCKF algorithm process, as shown in Figure 4.…”

Section: Design Square Root Generalized High-degree Cubature Kalman E...mentioning

confidence: 99%

“…A traditional Kalman filter algorithm has poor accuracy and robustness in solving the problem of non-Gaussian noise. Reference [23] presented a robust hierarchical estimation scheme for the vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF) and achieved good results [23]. The adaptive volume particle filter (ACPF) proposed in reference [24] estimates key state variables such as vehicle roll angle and center-of-mass roll angle [24].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Design Square Root Generalized High-degree Cubature Kalman E...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle State Joint Estimation Based on Lateral Stiffness

Quan,

Chang,

Guo

et al. 2023

Sensors

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“…The cubature Kalman filter (CKF) is a new nonlinear Gaussian filtering method proposed by Canadian scholars in 2009 [11,12], which has been widely used in the field of vehicle control [13][14][15][16]. Qi presented a robust hierarchical estimation scheme for the vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF), and achieved good results [17]. A longitudinal-lateral cooperative estimation algorithm based on an adaptive square-root cubature Kalman-filter (ASRCKF) was proposed by Chen, this algorithm can accurately estimate the vehicle status under different driving conditions [18].…”

Section: Introductionmentioning

confidence: 99%

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

Quan,

Chang,

Guo

2023

Applied Sciences

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With regard to the rear-drive in-wheel motor vehicle, this paper studies the joint estimation method for the vehicle state and road adhesion coefficient. A nonlinear seven degrees of freedom vehicle estimation model and a tire estimation model are established. A vehicle driving state estimator and a road adhesion coefficient estimator based on the generalized high-order cubature Kalman filter (GHCKF) algorithm are designed. The vehicle state estimator combines the vehicle model and the tire model to calculate the vehicle state parameters, provides the state parameters for the road adhesion coefficient estimator, and realizes the real-time estimation of the road adhesion coefficient. The exponential fading memory adaptive algorithm is used to update the measurement noise variance, and we upgrade the GHCKF to the adaptive generalized high-order cubature Kalman filter (AGHCKF), which estimates the vehicle state and road adhesion coefficient. The typical working conditions using the double GHCKF/AGHCKF estimation algorithm were simulated and analyzed. Then, high-and low-speed driving experiments based on typical working conditions were carried out. An integrated navigation system (INS), global positioning system (GPS), and real-time kinematic positioning (RTK) were used to collect the real-time data of the vehicle, and compare them with the estimated values of the joint estimator, to verify the feasibility of the vehicle-state–road-adhesion-coefficient joint estimator. We compared a high-order GHCKF algorithm, high-order improved AGHCKF algorithm, and a cubature Kalman filter (CKF) algorithm, and the simulation and experimental results show that the joint estimator using the CKF, GHCKF, and AGHCKF algorithms can realize the real-time estimation of the vehicle state and the road adhesion coefficient. The AGHCKF algorithm shows the best effectiveness and robustness of the three algorithms.

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Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

Chen,

Yu,

Pang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part E:

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The accurate acquisition of information regarding the state of a vehicle's driving is essential for the implementation of active safety control measures in vehicles. To tackle the challenge of accurately measuring the sideslip angle in distributed electric vehicles, this study proposes an optimized maximum correntropy square-root cubature Kalman filter based on African vulture optimization algorithm (AVOA-MCSCKF). This method aims to provide accurate estimation of the sideslip angle. The real-time estimation of the total vehicle mass is conducted through the application of forgetting factor recursive least squares method. Additionally, the African vulture algorithm is utilized to adaptively adjust MCSCKF. This adjustment aims to mitigate estimation inaccuracies stemming from the uncertain nature of the noise covariance matrix, ultimately leading to a more accurate estimation of the sideslip angle. In the collaborative simulation environment of Carsim/Simulink, the algorithm's accuracy and robustness are validated across various operational scenarios. The research findings indicate that AVOA-MCSCKF algorithm enhances the accuracy of sideslip angle estimation by a minimum of 51.8% when compared to both the standard covariance Kalman filter and square-root cubature Kalman filter filter. This approach effectively addresses the challenging estimation issue of the sideslip angle in distributed drive electric vehicles operating under complex conditions, thereby improving the vehicle's active safety.

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A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Cited by 4 publications

References 32 publications

Vehicle State Joint Estimation Based on Lateral Stiffness

Vehicle State Joint Estimation Based on Lateral Stiffness

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

Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

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