The Experts below are selected from a list of 35394 Experts worldwide ranked by ideXlab platform
Te Chen - One of the best experts on this subject based on the ideXlab platform.
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estimation of Longitudinal Force and sideslip angle for intelligent four wheel independent drive electric vehicles by observer iteration and information fusion
Sensors, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Exact estimation of Longitudinal Force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for Longitudinal Force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for Longitudinal Force estimation, the Longitudinal Force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for Longitudinal Force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated Longitudinal Force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
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estimation of Longitudinal Force lateral vehicle speed and yaw rate for four wheel independent driven electric vehicles
Mechanical Systems and Signal Processing, 2018Co-Authors: Te Chen, Long Chen, Haobing Jiang, Yingfeng CaiAbstract:Abstract Accurate estimation of Longitudinal Force, lateral vehicle speed and yaw rate is of great significance to torque allocation and stability control for four-wheel independent driven electric vehicle (4WID-EVs). A fusion method is proposed to estimate the Longitudinal Force, lateral vehicle speed and yaw rate for 4WID-EVs. The electric driving wheel model (EDWM) is introduced into the Longitudinal Force estimation, the Longitudinal Force observer (LFO) is designed firstly based on the adaptive high-order sliding mode observer (HSMO), and the convergence of LFO is analyzed and proved. Based on the estimated Longitudinal Force, an estimation strategy is then presented in which the strong tracking filter (STF) is used to estimate lateral vehicle speed and yaw rate simultaneously. Finally, co-simulation via Carsim and Matlab/Simulink is carried out to demonstrate the effectiveness of the proposed method. The performance of LFO in practice is verified by the experiment on chassis dynamometer bench.
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Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion
Mathematical Problems in Engineering, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Accurate estimation of Longitudinal Force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the Longitudinal Force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the Longitudinal Force estimation, considering the Longitudinal Force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of Longitudinal Force, and the extended Kalman filter is utilized to estimate the unbiased Longitudinal Force. Using the estimated Longitudinal Force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.
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multi objective coordination control strategy of distributed drive electric vehicle by orientated tire Force distribution method
IEEE Access, 2018Co-Authors: Long Chen, Te Chen, Xing Xu, Haobin JiangAbstract:In order to improve the extension mileage and ensure the yaw stability of the distributed drive electric vehicle simultaneously, a multi-objective coordination control strategy is presented in this paper, in which the optimal vehicle-state estimation method and the orientated tire Force distribution method are designed. The Longitudinal Force estimation is designed using the unknown input observer Kalman filter, a novel yaw rate and a vehicle sideslip angle estimation scheme is proposed by an observer error compensation method. A multi-objective hierarchical vehicle motion control strategy is developed to monitor the yaw stability and improve the energy efficiency of vehicle in real time. In the upper layer controller design, a sliding mode controller is proposed to track the desired yaw rate. In the lower layer controller design, the orientated tire Force distribution scheme is presented to improve the motor efficiency and reduce the energy loss caused by vehicle steering resistance while ensuring the yaw stability of vehicle. Finally, the effectiveness of the proposed estimation method and the global coordination control strategy is validated by simulation in CarSim-Simulink joint platform and road test.
Long Chen - One of the best experts on this subject based on the ideXlab platform.
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estimation of Longitudinal Force and sideslip angle for intelligent four wheel independent drive electric vehicles by observer iteration and information fusion
Sensors, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Exact estimation of Longitudinal Force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for Longitudinal Force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for Longitudinal Force estimation, the Longitudinal Force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for Longitudinal Force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated Longitudinal Force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
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estimation of Longitudinal Force lateral vehicle speed and yaw rate for four wheel independent driven electric vehicles
Mechanical Systems and Signal Processing, 2018Co-Authors: Te Chen, Long Chen, Haobing Jiang, Yingfeng CaiAbstract:Abstract Accurate estimation of Longitudinal Force, lateral vehicle speed and yaw rate is of great significance to torque allocation and stability control for four-wheel independent driven electric vehicle (4WID-EVs). A fusion method is proposed to estimate the Longitudinal Force, lateral vehicle speed and yaw rate for 4WID-EVs. The electric driving wheel model (EDWM) is introduced into the Longitudinal Force estimation, the Longitudinal Force observer (LFO) is designed firstly based on the adaptive high-order sliding mode observer (HSMO), and the convergence of LFO is analyzed and proved. Based on the estimated Longitudinal Force, an estimation strategy is then presented in which the strong tracking filter (STF) is used to estimate lateral vehicle speed and yaw rate simultaneously. Finally, co-simulation via Carsim and Matlab/Simulink is carried out to demonstrate the effectiveness of the proposed method. The performance of LFO in practice is verified by the experiment on chassis dynamometer bench.
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Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion
Mathematical Problems in Engineering, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Accurate estimation of Longitudinal Force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the Longitudinal Force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the Longitudinal Force estimation, considering the Longitudinal Force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of Longitudinal Force, and the extended Kalman filter is utilized to estimate the unbiased Longitudinal Force. Using the estimated Longitudinal Force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.
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multi objective coordination control strategy of distributed drive electric vehicle by orientated tire Force distribution method
IEEE Access, 2018Co-Authors: Long Chen, Te Chen, Xing Xu, Haobin JiangAbstract:In order to improve the extension mileage and ensure the yaw stability of the distributed drive electric vehicle simultaneously, a multi-objective coordination control strategy is presented in this paper, in which the optimal vehicle-state estimation method and the orientated tire Force distribution method are designed. The Longitudinal Force estimation is designed using the unknown input observer Kalman filter, a novel yaw rate and a vehicle sideslip angle estimation scheme is proposed by an observer error compensation method. A multi-objective hierarchical vehicle motion control strategy is developed to monitor the yaw stability and improve the energy efficiency of vehicle in real time. In the upper layer controller design, a sliding mode controller is proposed to track the desired yaw rate. In the lower layer controller design, the orientated tire Force distribution scheme is presented to improve the motor efficiency and reduce the energy loss caused by vehicle steering resistance while ensuring the yaw stability of vehicle. Finally, the effectiveness of the proposed estimation method and the global coordination control strategy is validated by simulation in CarSim-Simulink joint platform and road test.
Haobin Jiang - One of the best experts on this subject based on the ideXlab platform.
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estimation of Longitudinal Force and sideslip angle for intelligent four wheel independent drive electric vehicles by observer iteration and information fusion
Sensors, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Exact estimation of Longitudinal Force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for Longitudinal Force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for Longitudinal Force estimation, the Longitudinal Force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for Longitudinal Force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated Longitudinal Force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
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Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion
Mathematical Problems in Engineering, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Accurate estimation of Longitudinal Force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the Longitudinal Force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the Longitudinal Force estimation, considering the Longitudinal Force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of Longitudinal Force, and the extended Kalman filter is utilized to estimate the unbiased Longitudinal Force. Using the estimated Longitudinal Force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.
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multi objective coordination control strategy of distributed drive electric vehicle by orientated tire Force distribution method
IEEE Access, 2018Co-Authors: Long Chen, Te Chen, Xing Xu, Haobin JiangAbstract:In order to improve the extension mileage and ensure the yaw stability of the distributed drive electric vehicle simultaneously, a multi-objective coordination control strategy is presented in this paper, in which the optimal vehicle-state estimation method and the orientated tire Force distribution method are designed. The Longitudinal Force estimation is designed using the unknown input observer Kalman filter, a novel yaw rate and a vehicle sideslip angle estimation scheme is proposed by an observer error compensation method. A multi-objective hierarchical vehicle motion control strategy is developed to monitor the yaw stability and improve the energy efficiency of vehicle in real time. In the upper layer controller design, a sliding mode controller is proposed to track the desired yaw rate. In the lower layer controller design, the orientated tire Force distribution scheme is presented to improve the motor efficiency and reduce the energy loss caused by vehicle steering resistance while ensuring the yaw stability of vehicle. Finally, the effectiveness of the proposed estimation method and the global coordination control strategy is validated by simulation in CarSim-Simulink joint platform and road test.
Yingfeng Cai - One of the best experts on this subject based on the ideXlab platform.
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estimation of Longitudinal Force and sideslip angle for intelligent four wheel independent drive electric vehicles by observer iteration and information fusion
Sensors, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Exact estimation of Longitudinal Force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for Longitudinal Force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for Longitudinal Force estimation, the Longitudinal Force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for Longitudinal Force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated Longitudinal Force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
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estimation of Longitudinal Force lateral vehicle speed and yaw rate for four wheel independent driven electric vehicles
Mechanical Systems and Signal Processing, 2018Co-Authors: Te Chen, Long Chen, Haobing Jiang, Yingfeng CaiAbstract:Abstract Accurate estimation of Longitudinal Force, lateral vehicle speed and yaw rate is of great significance to torque allocation and stability control for four-wheel independent driven electric vehicle (4WID-EVs). A fusion method is proposed to estimate the Longitudinal Force, lateral vehicle speed and yaw rate for 4WID-EVs. The electric driving wheel model (EDWM) is introduced into the Longitudinal Force estimation, the Longitudinal Force observer (LFO) is designed firstly based on the adaptive high-order sliding mode observer (HSMO), and the convergence of LFO is analyzed and proved. Based on the estimated Longitudinal Force, an estimation strategy is then presented in which the strong tracking filter (STF) is used to estimate lateral vehicle speed and yaw rate simultaneously. Finally, co-simulation via Carsim and Matlab/Simulink is carried out to demonstrate the effectiveness of the proposed method. The performance of LFO in practice is verified by the experiment on chassis dynamometer bench.
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Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion
Mathematical Problems in Engineering, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Accurate estimation of Longitudinal Force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the Longitudinal Force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the Longitudinal Force estimation, considering the Longitudinal Force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of Longitudinal Force, and the extended Kalman filter is utilized to estimate the unbiased Longitudinal Force. Using the estimated Longitudinal Force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.
Xiaoqiang Sun - One of the best experts on this subject based on the ideXlab platform.
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estimation of Longitudinal Force and sideslip angle for intelligent four wheel independent drive electric vehicles by observer iteration and information fusion
Sensors, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Exact estimation of Longitudinal Force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for Longitudinal Force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for Longitudinal Force estimation, the Longitudinal Force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for Longitudinal Force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated Longitudinal Force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
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Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion
Mathematical Problems in Engineering, 2018Co-Authors: Te Chen, Haobin Jiang, Long Chen, Yingfeng Cai, Xiaoqiang SunAbstract:Accurate estimation of Longitudinal Force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the Longitudinal Force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the Longitudinal Force estimation, considering the Longitudinal Force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of Longitudinal Force, and the extended Kalman filter is utilized to estimate the unbiased Longitudinal Force. Using the estimated Longitudinal Force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.
