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Junmin Wang - One of the best experts on this subject based on the ideXlab platform.
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A New Method in Estimating Vehicle Center of Gravity Position Parameters Based on Ackermann’s Steering
Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeli, 2016Co-Authors: Junmin WangAbstract:The determination of vehicle’s center of Gravity Position is an important but challenging task for control of advanced vehicles such as automated vehicles, especially under daily usage condition where the system configurations and payload condition may change. To address this problem, a new method is proposed in this paper to estimate the vehicle’s 3-dimensional center of Gravity Position parameters without relying on detailed suspension configuration parameters or lateral tire force models. In the estimation problem, the vehicle’s planar dynamic equations are synthesized together to reduce the number of unknown lateral tire forces, then the condition of Ackermann’s Steering Geometry can be found to eliminate the influence of the remaining unknown front wheel lateral tire forces. When the unknown tire forces are cancelled, the recursive least squares (RLS) regression technique is used to identify the 3-dimensional center of Gravity Position parameters. The vehicle model with the sprung mass modeled as an inverted pendulum is developed to assist the analysis and conversion of sensor measured signals. Simulations conducted in a high-fidelity CarSim® vehicle model have demonstrated the capability of this proposed method in estimating the vehicle’s center of Gravity Position parameters.
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real time estimation of center of Gravity Position for lightweight vehicles using combined akf ekf method
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Xiaoyu Huang, Junmin WangAbstract:In this paper, a real-time center of Gravity (CG) Position estimator, which is based on a combined adaptive Kalman filter-extended Kalman filter (AKF-EKF) approach, for lightweight vehicles (LWVs) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, particularly for LWVs, whose CG Positions can be substantially varied by the payloads on board. The proposed estimation method, taking advantage of the separate front/rear torque control capability available in numerous LWV prototypes, only requires that the vehicle be excited longitudinally and/or vertically, thus avoiding potentially dangerous excitation of the vehicle lateral/yaw/roll motions. Moreover, additional parameters, such as vehicle moments of inertia, suspension parameters, and the tire/road friction coefficient (TRFC), are not necessary. A three-degree-of-freedom (3-DOF) vehicle dynamics model, taking the vehicle longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed as states, is employed in the filter formulation. The designed estimator consists of two parts: an AKF for filtering noisy states and an EKF for estimating parameters. To minimize the effects of undesirable oscillation and bias in the filtered states, the optimization-based AKF judiciously tunes the suboptimal process noise covariance matrix in real time. Meanwhile, the EKF utilizes the filtered states from the AKF and takes the parameters as random walks. Simulation results exhibit the advantages of the AKF over the standard KF with fixed covariance matrices. Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG Position with acceptable accuracy. Moreover, an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.
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Real-Time Estimation of Center of Gravity Position for Lightweight Vehicles Using Combined AKF–EKF Method
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Xiaoyu Huang, Junmin WangAbstract:In this paper, a real-time center of Gravity (CG) Position estimator, which is based on a combined adaptive Kalman filter-extended Kalman filter (AKF-EKF) approach, for lightweight vehicles (LWVs) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, particularly for LWVs, whose CG Positions can be substantially varied by the payloads on board. The proposed estimation method, taking advantage of the separate front/rear torque control capability available in numerous LWV prototypes, only requires that the vehicle be excited longitudinally and/or vertically, thus avoiding potentially dangerous excitation of the vehicle lateral/yaw/roll motions. Moreover, additional parameters, such as vehicle moments of inertia, suspension parameters, and the tire/road friction coefficient (TRFC), are not necessary. A three-degree-of-freedom (3-DOF) vehicle dynamics model, taking the vehicle longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed as states, is employed in the filter formulation. The designed estimator consists of two parts: an AKF for filtering noisy states and an EKF for estimating parameters. To minimize the effects of undesirable oscillation and bias in the filtered states, the optimization-based AKF judiciously tunes the suboptimal process noise covariance matrix in real time. Meanwhile, the EKF utilizes the filtered states from the AKF and takes the parameters as random walks. Simulation results exhibit the advantages of the AKF over the standard KF with fixed covariance matrices. Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG Position with acceptable accuracy. Moreover, an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.
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EKF-Based Vehicle Center of Gravity Position Real-Time Estimation in Longitudinal Maneuvers With Road Course Elevation
Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control, 2012Co-Authors: Xiaoyu Huang, Junmin WangAbstract:In this paper, a real-time estimator, based on an extended Kalman filter (EKF), for the Position of vehicle center of Gravity (CG) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, especially for lightweight vehicles (LWVs), whose CG Positions can be substantially varied by freight goods or passengers onboard. The proposed estimation method, unlike many existing ones, extracts signals only from vehicle longitudinal maneuvers in which road course elevation may exist. A three-state vehicle dynamic model, including the longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed of the vehicle, is employed in the EKF formulation. With the help of the GPS altitude measurement, the road grade, which provides excitation for the estimation of the CG height, can also be obtained using a typical Kalman filter. Simulation studies based on a CarSim® vehicle model show that the proposed estimator is capable of accurately estimating both the CG longitudinal location and the CG height without a priori knowledge of the tire-road contact condition. Moreover, though the performance of the CG height estimation largely depends on the road grade variations, the CG longitudinal location can always be accurately estimated, even on a horizontal road.© 2012 ASME
Xingqun Cheng - One of the best experts on this subject based on the ideXlab platform.
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a novel h and ekf joint estimation method for determining the center of Gravity Position of electric vehicles
Applied Energy, 2017Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun ChengAbstract:In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of Gravity (CG) Position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG Position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.
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A novel H∞ and EKF joint estimation method for determining the center of Gravity Position of electric vehicles
Applied Energy, 2017Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun ChengAbstract:In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of Gravity (CG) Position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG Position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.
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estimation of center of Gravity Position for distributed driving electric vehicles based on combined h ekf method
Energy Procedia, 2016Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle GongAbstract:Abstract It is essential to get the accurate knowledge of the center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG Positions can be affected by the loading conditions. This paper focuses on a CG Position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.
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Estimation of Center of Gravity Position for Distributed Driving Electric Vehicles Based on Combined H∞-EKF Method☆
Energy Procedia, 2016Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle GongAbstract:Abstract It is essential to get the accurate knowledge of the center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG Positions can be affected by the loading conditions. This paper focuses on a CG Position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.
Xinle Gong - One of the best experts on this subject based on the ideXlab platform.
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a novel h and ekf joint estimation method for determining the center of Gravity Position of electric vehicles
Applied Energy, 2017Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun ChengAbstract:In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of Gravity (CG) Position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG Position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.
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A novel H∞ and EKF joint estimation method for determining the center of Gravity Position of electric vehicles
Applied Energy, 2017Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun ChengAbstract:In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of Gravity (CG) Position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG Position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.
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estimation of center of Gravity Position for distributed driving electric vehicles based on combined h ekf method
Energy Procedia, 2016Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle GongAbstract:Abstract It is essential to get the accurate knowledge of the center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG Positions can be affected by the loading conditions. This paper focuses on a CG Position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.
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Estimation of Center of Gravity Position for Distributed Driving Electric Vehicles Based on Combined H∞-EKF Method☆
Energy Procedia, 2016Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle GongAbstract:Abstract It is essential to get the accurate knowledge of the center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG Positions can be affected by the loading conditions. This paper focuses on a CG Position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.
Cheng Lin - One of the best experts on this subject based on the ideXlab platform.
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a novel h and ekf joint estimation method for determining the center of Gravity Position of electric vehicles
Applied Energy, 2017Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun ChengAbstract:In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of Gravity (CG) Position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG Position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.
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A novel H∞ and EKF joint estimation method for determining the center of Gravity Position of electric vehicles
Applied Energy, 2017Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun ChengAbstract:In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of Gravity (CG) Position estimation is of great significance. In this study, a novel approach based on combined H∞–extended Kalman filter (H∞–EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG Position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.
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estimation of center of Gravity Position for distributed driving electric vehicles based on combined h ekf method
Energy Procedia, 2016Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle GongAbstract:Abstract It is essential to get the accurate knowledge of the center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG Positions can be affected by the loading conditions. This paper focuses on a CG Position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.
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Estimation of Center of Gravity Position for Distributed Driving Electric Vehicles Based on Combined H∞-EKF Method☆
Energy Procedia, 2016Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle GongAbstract:Abstract It is essential to get the accurate knowledge of the center of Gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG Positions can be affected by the loading conditions. This paper focuses on a CG Position estimator for a DDEV based on the combined H ∞ —extended Kalman filter (H ∞ —EKF) approach. The designed estimator consists of two parts: an H ∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H ∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H ∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.
Xiaoyu Huang - One of the best experts on this subject based on the ideXlab platform.
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real time estimation of center of Gravity Position for lightweight vehicles using combined akf ekf method
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Xiaoyu Huang, Junmin WangAbstract:In this paper, a real-time center of Gravity (CG) Position estimator, which is based on a combined adaptive Kalman filter-extended Kalman filter (AKF-EKF) approach, for lightweight vehicles (LWVs) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, particularly for LWVs, whose CG Positions can be substantially varied by the payloads on board. The proposed estimation method, taking advantage of the separate front/rear torque control capability available in numerous LWV prototypes, only requires that the vehicle be excited longitudinally and/or vertically, thus avoiding potentially dangerous excitation of the vehicle lateral/yaw/roll motions. Moreover, additional parameters, such as vehicle moments of inertia, suspension parameters, and the tire/road friction coefficient (TRFC), are not necessary. A three-degree-of-freedom (3-DOF) vehicle dynamics model, taking the vehicle longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed as states, is employed in the filter formulation. The designed estimator consists of two parts: an AKF for filtering noisy states and an EKF for estimating parameters. To minimize the effects of undesirable oscillation and bias in the filtered states, the optimization-based AKF judiciously tunes the suboptimal process noise covariance matrix in real time. Meanwhile, the EKF utilizes the filtered states from the AKF and takes the parameters as random walks. Simulation results exhibit the advantages of the AKF over the standard KF with fixed covariance matrices. Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG Position with acceptable accuracy. Moreover, an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.
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Real-Time Estimation of Center of Gravity Position for Lightweight Vehicles Using Combined AKF–EKF Method
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Xiaoyu Huang, Junmin WangAbstract:In this paper, a real-time center of Gravity (CG) Position estimator, which is based on a combined adaptive Kalman filter-extended Kalman filter (AKF-EKF) approach, for lightweight vehicles (LWVs) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, particularly for LWVs, whose CG Positions can be substantially varied by the payloads on board. The proposed estimation method, taking advantage of the separate front/rear torque control capability available in numerous LWV prototypes, only requires that the vehicle be excited longitudinally and/or vertically, thus avoiding potentially dangerous excitation of the vehicle lateral/yaw/roll motions. Moreover, additional parameters, such as vehicle moments of inertia, suspension parameters, and the tire/road friction coefficient (TRFC), are not necessary. A three-degree-of-freedom (3-DOF) vehicle dynamics model, taking the vehicle longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed as states, is employed in the filter formulation. The designed estimator consists of two parts: an AKF for filtering noisy states and an EKF for estimating parameters. To minimize the effects of undesirable oscillation and bias in the filtered states, the optimization-based AKF judiciously tunes the suboptimal process noise covariance matrix in real time. Meanwhile, the EKF utilizes the filtered states from the AKF and takes the parameters as random walks. Simulation results exhibit the advantages of the AKF over the standard KF with fixed covariance matrices. Experimental results obtained from vehicle road tests show that the proposed estimator is capable of estimating the CG Position with acceptable accuracy. Moreover, an investigation of the two-layer persistent excitation (PE) condition reveals that, although the CG height estimation largely depends on the excitation level in the maneuver, the CG longitudinal location can be always estimated via the input torque injections.
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EKF-Based Vehicle Center of Gravity Position Real-Time Estimation in Longitudinal Maneuvers With Road Course Elevation
Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control, 2012Co-Authors: Xiaoyu Huang, Junmin WangAbstract:In this paper, a real-time estimator, based on an extended Kalman filter (EKF), for the Position of vehicle center of Gravity (CG) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, especially for lightweight vehicles (LWVs), whose CG Positions can be substantially varied by freight goods or passengers onboard. The proposed estimation method, unlike many existing ones, extracts signals only from vehicle longitudinal maneuvers in which road course elevation may exist. A three-state vehicle dynamic model, including the longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed of the vehicle, is employed in the EKF formulation. With the help of the GPS altitude measurement, the road grade, which provides excitation for the estimation of the CG height, can also be obtained using a typical Kalman filter. Simulation studies based on a CarSim® vehicle model show that the proposed estimator is capable of accurately estimating both the CG longitudinal location and the CG height without a priori knowledge of the tire-road contact condition. Moreover, though the performance of the CG height estimation largely depends on the road grade variations, the CG longitudinal location can always be accurately estimated, even on a horizontal road.© 2012 ASME
