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Yoichi Hori - One of the best experts on this subject based on the ideXlab platform.

  • lateral stability control of electric vehicle based on disturbance accommodating kalman filter using the integration of single antenna gps receiver and yaw rate sensor
    Journal of Electrical Engineering & Technology, 2013
    Co-Authors: Binh Minh Nguyen, Hiroshi Fujimoto, Yafei Wang, Yoichi Hori
    Abstract:

    This paper presents a novel lateral stability control system for electric vehicle based on Sideslip Angle estimation through Kalman filter using the integration of a single antenna GPS receiver and yaw rate sensor. Using multi-rate measurements including yaw rate and course Angle, time-varying parameters disappear from the measurement equation of the proposed Kalman filter. Accurate Sideslip Angle estimation is achieved by treating the combination of model uncertainties and external disturbances as extended states. Active front steering and direct yaw moment are integrated to manipulate Sideslip Angle and yaw rate of the vehicle. Instead of decoupling control design method, a new control scheme, "two-input two-output controller", is proposed. The extended states are utilized for disturbance rejection that improves the robustness of lateral stability control system. The effectiveness of the proposed methods is verified by computer simulations and experiments.

  • estimation of Sideslip and roll Angles of electric vehicles using lateral tire force sensors through rls and kalman filter approaches
    IEEE Transactions on Industrial Electronics, 2013
    Co-Authors: Kang-hyun Nam, Hiroshi Fujimoto, Yoichi Hori
    Abstract:

    Robust estimation of vehicle states (e.g., vehicle Sideslip Angle and roll Angle) is essential for vehicle stability control applications such as yaw stability control and roll stability control. This paper proposes novel methods for estimating Sideslip Angle and roll Angle using real-time lateral tire force measurements, obtained from the multisensing hub units, for practical applications to vehicle control systems of in-wheel-motor-driven electric vehicles. In vehicle Sideslip estimation, a recursive least squares (RLS) algorithm with a forgetting factor is utilized based on a linear vehicle model and sensor measurements. In roll Angle estimation, the Kalman filter is designed by integrating available sensor measurements and roll dynamics. The proposed estimation methods, RLS-based Sideslip Angle estimator, and the Kalman filter are evaluated through field tests on an experimental electric vehicle. The experimental results show that the proposed estimator can accurately estimate the vehicle Sideslip Angle and roll Angle. It is experimentally confirmed that the estimation accuracy is improved by more than 50% comparing to conventional method's one (see rms error shown in Fig. 4). Moreover, the feasibility of practical applications of the lateral tire force sensors to vehicle state estimation is verified through various test results.

  • gps based estimation of vehicle Sideslip Angle using multi rate kalman filter with prediction of course Angle measurement residual
    2013
    Co-Authors: Binh Minh Nguyen, Hiroshi Fujimoto, Yafei Wang, Yoichi Hori
    Abstract:

    In this paper, a new vehicle Sideslip Angle estimation based on GPS is proposed. Course Angle obtained from GPS receiver can be utilized as one measurement for estimation design, together with the yaw rate from gyroscope. While yaw rate is sampled every 1 ms, the sampling time of course Angle is much longer (200 ms). During inter-samples (between two updates of course Angle), the conventional estimation method relies upon only yaw rate measurement. In order to enhance the estimation accuracy, multi-rate Kalman filter with the prediction of course Angle measurement residual during inter-samples is designed. Experiments are conducted to verify the effectiveness of the proposed algorithm.

  • lateral stability control of in wheel motor driven electric vehicles based on Sideslip Angle estimation using lateral tire force sensors
    IEEE Transactions on Vehicular Technology, 2012
    Co-Authors: Kang-hyun Nam, Hiroshi Fujimoto, Yoichi Hori
    Abstract:

    This paper presents a method for using lateral tire force sensors to estimate vehicle Sideslip Angle and to improve vehicle stability of in-wheel-motor-driven electric vehicles (IWM-EVs). Considering that the vehicle motion is governed by tire forces, lateral tire force measurements give practical benefits in estimation and motion control. To estimate the vehicle Sideslip Angle, a state observer derived from the extended-Kalman-filtering (EKF) method is proposed and evaluated through field tests on an experimental IWM-EV. Experimental results show the ability of a proposed observer to provide accurate estimation. Moreover, using the estimated Sideslip Angle and tire cornering stiffness, the vehicle stability control system, making best use of the advantages of IMW-EVs with a steer-by-wire system, is proposed. Computer simulation using Matlab/Simulink-Carsim and experiments are carried out to demonstrate the effectiveness of the proposed stability control system. Practical application of lateral tire force sensors to vehicle control systems is discussed for future personal electric vehicles.

  • Sideslip Angle estimation using gps and disturbance accommodating multi-rate Kalman filter for electric vehicle stability control
    2012 IEEE Vehicle Power and Propulsion Conference, 2012
    Co-Authors: Binh Minh Nguyen, Hiroshi Fujimoto, Yafei Wang, Yoichi Hori
    Abstract:

    This paper describes a new method of Sideslip Angle estimation for electric vehicle stability control system. Multi-rate Kalman filter is designed based on the measurements of yaw rate (updated every 1 ms) and course Angle from GPS receiver (updated every 200 ms). By disturbance accommodating method, robust estimation of Sideslip Angle is achieved. Direct yaw moment generated by in-wheel motors' torque is used to control both yaw rate and Sideslip Angle of electric vehicle. Experiments are conducted to verify the effectiveness of the proposal.

Hiroshi Fujimoto - One of the best experts on this subject based on the ideXlab platform.

  • driving force controller for electric vehicle considering Sideslip Angle based on brush model
    International Conference on Mechatronics, 2019
    Co-Authors: Hiroyuki Fuse, Hiroshi Fujimoto
    Abstract:

    Electric vehicle (EV) has great advantages in vehicle maneuverability with the use of electric motor. As one of EV $\mathbf{s}$ motion control methods, a driving force controller with slip ratio limiter has been proposed. In the conventional methods, the value of slip ratio limiter was fixed regardless of Sideslip Angle of tire. This paper proposes a variable slip ratio limiter considering Sideslip Angle based on brush model. Driving force controller can secure traction of tire even during cornering. Not only that, it is also suggested that tire workload can be limited to desired value. Its effectiveness was experimentally verified with acceleration cornering on slippery road.

  • lateral stability control of electric vehicle based on disturbance accommodating kalman filter using the integration of single antenna gps receiver and yaw rate sensor
    Journal of Electrical Engineering & Technology, 2013
    Co-Authors: Binh Minh Nguyen, Hiroshi Fujimoto, Yafei Wang, Yoichi Hori
    Abstract:

    This paper presents a novel lateral stability control system for electric vehicle based on Sideslip Angle estimation through Kalman filter using the integration of a single antenna GPS receiver and yaw rate sensor. Using multi-rate measurements including yaw rate and course Angle, time-varying parameters disappear from the measurement equation of the proposed Kalman filter. Accurate Sideslip Angle estimation is achieved by treating the combination of model uncertainties and external disturbances as extended states. Active front steering and direct yaw moment are integrated to manipulate Sideslip Angle and yaw rate of the vehicle. Instead of decoupling control design method, a new control scheme, "two-input two-output controller", is proposed. The extended states are utilized for disturbance rejection that improves the robustness of lateral stability control system. The effectiveness of the proposed methods is verified by computer simulations and experiments.

  • estimation of Sideslip and roll Angles of electric vehicles using lateral tire force sensors through rls and kalman filter approaches
    IEEE Transactions on Industrial Electronics, 2013
    Co-Authors: Kang-hyun Nam, Hiroshi Fujimoto, Yoichi Hori
    Abstract:

    Robust estimation of vehicle states (e.g., vehicle Sideslip Angle and roll Angle) is essential for vehicle stability control applications such as yaw stability control and roll stability control. This paper proposes novel methods for estimating Sideslip Angle and roll Angle using real-time lateral tire force measurements, obtained from the multisensing hub units, for practical applications to vehicle control systems of in-wheel-motor-driven electric vehicles. In vehicle Sideslip estimation, a recursive least squares (RLS) algorithm with a forgetting factor is utilized based on a linear vehicle model and sensor measurements. In roll Angle estimation, the Kalman filter is designed by integrating available sensor measurements and roll dynamics. The proposed estimation methods, RLS-based Sideslip Angle estimator, and the Kalman filter are evaluated through field tests on an experimental electric vehicle. The experimental results show that the proposed estimator can accurately estimate the vehicle Sideslip Angle and roll Angle. It is experimentally confirmed that the estimation accuracy is improved by more than 50% comparing to conventional method's one (see rms error shown in Fig. 4). Moreover, the feasibility of practical applications of the lateral tire force sensors to vehicle state estimation is verified through various test results.

  • gps based estimation of vehicle Sideslip Angle using multi rate kalman filter with prediction of course Angle measurement residual
    2013
    Co-Authors: Binh Minh Nguyen, Hiroshi Fujimoto, Yafei Wang, Yoichi Hori
    Abstract:

    In this paper, a new vehicle Sideslip Angle estimation based on GPS is proposed. Course Angle obtained from GPS receiver can be utilized as one measurement for estimation design, together with the yaw rate from gyroscope. While yaw rate is sampled every 1 ms, the sampling time of course Angle is much longer (200 ms). During inter-samples (between two updates of course Angle), the conventional estimation method relies upon only yaw rate measurement. In order to enhance the estimation accuracy, multi-rate Kalman filter with the prediction of course Angle measurement residual during inter-samples is designed. Experiments are conducted to verify the effectiveness of the proposed algorithm.

  • lateral stability control of in wheel motor driven electric vehicles based on Sideslip Angle estimation using lateral tire force sensors
    IEEE Transactions on Vehicular Technology, 2012
    Co-Authors: Kang-hyun Nam, Hiroshi Fujimoto, Yoichi Hori
    Abstract:

    This paper presents a method for using lateral tire force sensors to estimate vehicle Sideslip Angle and to improve vehicle stability of in-wheel-motor-driven electric vehicles (IWM-EVs). Considering that the vehicle motion is governed by tire forces, lateral tire force measurements give practical benefits in estimation and motion control. To estimate the vehicle Sideslip Angle, a state observer derived from the extended-Kalman-filtering (EKF) method is proposed and evaluated through field tests on an experimental IWM-EV. Experimental results show the ability of a proposed observer to provide accurate estimation. Moreover, using the estimated Sideslip Angle and tire cornering stiffness, the vehicle stability control system, making best use of the advantages of IMW-EVs with a steer-by-wire system, is proposed. Computer simulation using Matlab/Simulink-Carsim and experiments are carried out to demonstrate the effectiveness of the proposed stability control system. Practical application of lateral tire force sensors to vehicle control systems is discussed for future personal electric vehicles.

Kang-hyun Nam - One of the best experts on this subject based on the ideXlab platform.

  • Disturbance Observer-Based Sideslip Angle Control for Improving Cornering Characteristics of In-Wheel Motor Electric Vehicles
    International Journal of Automotive Technology, 2018
    Co-Authors: Hee Seong Kim, Young Jin Hyun, Kang-hyun Nam
    Abstract:

    In this paper, a robust Sideslip Angle controller based on the direct yaw moment control (DYC) is proposed for in-wheel motor electric vehicles. Many studies have demonstrated that the DYC is one of the effective methods to improve vehicle maneuverability and stability. Previous approaches to achieve the DYC used differential braking and active steering system. Not only that, the conventional control systems were commonly dependent on the feedback of the yaw rate. In contrast to the traditional control schemes, however, this paper proposes a novel approach based on Sideslip Angle feedback without controlling the yaw rate. This is mainly because if the vehicle Sideslip Angle is controlled properly, the intended Sideslip Angle helps the vehicle to pass through the corner even at high speed. On the other hand, the vehicle may become unstable because of the too large Sideslip caused by unexpected yaw disturbances and model uncertainties of time-varying parameters. From this aspect, disturbance observer (DOB) is employed to assure robust performance of the controller. The proposed controller was realized in CarSim model described actual electric vehicle and verified through computer simulations.

  • estimation of Sideslip and roll Angles of electric vehicles using lateral tire force sensors through rls and kalman filter approaches
    IEEE Transactions on Industrial Electronics, 2013
    Co-Authors: Kang-hyun Nam, Hiroshi Fujimoto, Yoichi Hori
    Abstract:

    Robust estimation of vehicle states (e.g., vehicle Sideslip Angle and roll Angle) is essential for vehicle stability control applications such as yaw stability control and roll stability control. This paper proposes novel methods for estimating Sideslip Angle and roll Angle using real-time lateral tire force measurements, obtained from the multisensing hub units, for practical applications to vehicle control systems of in-wheel-motor-driven electric vehicles. In vehicle Sideslip estimation, a recursive least squares (RLS) algorithm with a forgetting factor is utilized based on a linear vehicle model and sensor measurements. In roll Angle estimation, the Kalman filter is designed by integrating available sensor measurements and roll dynamics. The proposed estimation methods, RLS-based Sideslip Angle estimator, and the Kalman filter are evaluated through field tests on an experimental electric vehicle. The experimental results show that the proposed estimator can accurately estimate the vehicle Sideslip Angle and roll Angle. It is experimentally confirmed that the estimation accuracy is improved by more than 50% comparing to conventional method's one (see rms error shown in Fig. 4). Moreover, the feasibility of practical applications of the lateral tire force sensors to vehicle state estimation is verified through various test results.

  • lateral stability control of in wheel motor driven electric vehicles based on Sideslip Angle estimation using lateral tire force sensors
    IEEE Transactions on Vehicular Technology, 2012
    Co-Authors: Kang-hyun Nam, Hiroshi Fujimoto, Yoichi Hori
    Abstract:

    This paper presents a method for using lateral tire force sensors to estimate vehicle Sideslip Angle and to improve vehicle stability of in-wheel-motor-driven electric vehicles (IWM-EVs). Considering that the vehicle motion is governed by tire forces, lateral tire force measurements give practical benefits in estimation and motion control. To estimate the vehicle Sideslip Angle, a state observer derived from the extended-Kalman-filtering (EKF) method is proposed and evaluated through field tests on an experimental IWM-EV. Experimental results show the ability of a proposed observer to provide accurate estimation. Moreover, using the estimated Sideslip Angle and tire cornering stiffness, the vehicle stability control system, making best use of the advantages of IMW-EVs with a steer-by-wire system, is proposed. Computer simulation using Matlab/Simulink-Carsim and experiments are carried out to demonstrate the effectiveness of the proposed stability control system. Practical application of lateral tire force sensors to vehicle control systems is discussed for future personal electric vehicles.

Francesco Timpone - One of the best experts on this subject based on the ideXlab platform.

  • Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model
    IEEE Access, 2020
    Co-Authors: Basilio Lenzo, Feliciano Di Biase, Francesco Timpone
    Abstract:

    Vehicle Sideslip Angle is a key state for lateral vehicle dynamics, but measuring it is expensive and unpractical. Still, knowledge of this state would be really valuable for vehicle control systems aimed at enhancing vehicle safety, to help to reduce worldwide fatal car accidents. This has motivated the research community to investigate techniques to estimate vehicle Sideslip Angle, which is still a challenging problem. One of the major issues is the need for accurate tyre model parameters, which are difficult to characterise and subject to change during vehicle operation. This paper proposes a new method for estimating vehicle Sideslip Angle using an Extended Kalman Filter. The main novelties are: i) the tyre behaviour is described using a Rational tyre model whose parameters are estimated and updated online to account for their variation due to e.g. tyre wear and environmental conditions affecting the tyre behaviour; ii) the proposed technique is compared with two other methods available in the literature by means of experimental tests on a heavy-duty vehicle. Results show that: i) the proposed method effectively estimates vehicle Sideslip Angle with an error limited to 0.5 deg in standard driving conditions, and less than 1 deg for a high-speed run; ii) the tyre parameters are successfully updated online, contributing to outclassing estimation methods based on tyre models that are either excessively simple or with non-varying parameters.

  • Real-time estimation of the vehicle Sideslip Angle through regression based on principal component analysis and neural networks
    2017 IEEE International Systems Engineering Symposium (ISSE), 2017
    Co-Authors: Francesco Timpone, Massimiliano De Martino, Flavio Farroni, Nicola Pasquino, Aleksandr Sakhnevych
    Abstract:

    Accurate estimation of the vehicle Sideslip Angle is fundamental in vehicle dynamics control and stability. In this paper two different methods for vehicle Sideslip estimation, based on Principal Component Analysis (PCA) and Neural Networks (NN), are presented comparing the procedure responses with full-scale vehicle acquired test data. The estimation algorithms use driver's steering Angle, lateral and longitudinal accelerations, wheel angular velocities and yaw rate measured from sensors integrated in a test vehicle, and are validated by comparison with the measurements of the Sideslip Angle provided by an optical Correvit sensor suitably mounted on board, serving as the reference system in terms of accuracy of slip-free measurement of longitudinal and transverse vehicle dynamics. The procedure results, based on both the original (RAW) and the reduced (PCA) data sets, are compared to the acquired Sideslip Angle, using the estimated channel as an input for the TRICK tool to evaluate the accuracy of the results and the potential of the estimation process in terms of tire interaction curves.

Te Chen - One of the best experts on this subject based on the ideXlab platform.

  • Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information
    World Electric Vehicle Journal, 2019
    Co-Authors: Te Chen, Xing Xu, Long Chen, Haobin Jiang
    Abstract:

    Accurate and reliable estimation information of Sideslip Angle is very important for intelligent motion control and active safety control of an autonomous vehicle. To solve the problem of Sideslip Angle estimation of an autonomous vehicle, a Sideslip Angle fusion estimation method based on robust cubature Kalman filter and wheel-speed coupling relationship is proposed in this paper. The vehicle dynamics model, tire model, and wheel speed coupling model are established and discretized, and a robust cubature Kalman filter is designed for vehicle running state estimation according to the discrete vehicle model. An adaptive measurement-update solution of the robust cubature Kalman filter is presented to improve the robustness of estimation, and then, the wheel-speed coupling relationship is introduced to the measurement update equation of the robust cubature Kalman filter and an adaptive Sideslip Angle fusion estimation method is designed. The simulations in the CarSim-Simulink co-simulation platform and the actual vehicle road test are carried out, and the effectiveness of the proposed estimation method is validated by corresponding comparative analysis results.

  • estimation of longitudinal force and Sideslip Angle for intelligent four wheel independent drive electric vehicles by observer iteration and information fusion
    Sensors, 2018
    Co-Authors: Te Chen, Long Chen, Haobin Jiang, Yingfeng Cai, Xiaoqiang Sun
    Abstract:

    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.

  • Robust Sideslip Angle observer with regional stability constraint for an uncertain singular intelligent vehicle system
    IET Control Theory & Applications, 2018
    Co-Authors: Te Chen, Long Chen, Xing Xu
    Abstract:

    The Sideslip Angle is an important state parameter for vehicle stability control; in this study, a robust Sideslip Angle observer with regional stability constraint is presented. In order to achieve more accurate Sideslip Angle estimation, the vehicle heading Angle and yaw Angle are distinguished and the relational expression between vehicle heading Angle, yaw Angle and Sideslip Angle was applied to the singular vehicle modelling process. Considering the time-varying tire cornering stiffness, the uncertain singular vehicle model is established by integrating the singular vehicle dynamic equation and model uncertainty. Then, a robust Sideslip Angle observer whose pole assignment has the regional stability constraint is designed. Simulation studies and experimental results illustrate the effectiveness of the proposed Sideslip Angle estimation method.

  • Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion
    Mathematical Problems in Engineering, 2018
    Co-Authors: Te Chen, Long Chen, Haobin Jiang, Yingfeng Cai, Xiaoqiang Sun
    Abstract:

    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.