The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform

Xingqun Cheng - One of the best experts on this subject based on the ideXlab platform.

  • a novel h and ekf joint estimation method for determining the center of gravity Position of electric vehicles
    Applied Energy, 2017
    Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun Cheng
    Abstract:

    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.

  • estimation of center of gravity Position for distributed driving electric vehicles based on combined h ekf method
    Energy Procedia, 2016
    Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle Gong
    Abstract:

    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.

  • a novel h and ekf joint estimation method for determining the center of gravity Position of electric vehicles
    Applied Energy, 2017
    Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun Cheng
    Abstract:

    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.

  • estimation of center of gravity Position for distributed driving electric vehicles based on combined h ekf method
    Energy Procedia, 2016
    Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle Gong
    Abstract:

    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.

  • a novel h and ekf joint estimation method for determining the center of gravity Position of electric vehicles
    Applied Energy, 2017
    Co-Authors: Cheng Lin, Xinle Gong, Rui Xiong, Xingqun Cheng
    Abstract:

    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.

  • estimation of center of gravity Position for distributed driving electric vehicles based on combined h ekf method
    Energy Procedia, 2016
    Co-Authors: Cheng Lin, Xingqun Cheng, Hong Zhang, Xinle Gong
    Abstract:

    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.

Junmin Wang - One of the best experts on this subject based on the ideXlab platform.

  • real time estimation of center of gravity Position for lightweight vehicles using combined akf ekf method
    IEEE Transactions on Vehicular Technology, 2014
    Co-Authors: Xiaoyu Huang, Junmin Wang
    Abstract:

    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.

Ryoichi Nagatomi - One of the best experts on this subject based on the ideXlab platform.

  • Position of compression garment around the knee affects healthy adults knee joint Position sense acuity
    Human Movement Science, 2019
    Co-Authors: Li Yin Zhang, Janos Negyesi, Tibor Hortobagyi, Takeshi Okuyama, Mami Tanaka, Ryoichi Nagatomi
    Abstract:

    Abstract Athletes use compression garments (Cgs) to improve sport performance, accelerate rehabilitation from knee injuries or to enhance joint Position sense (JPS). The Position of Cgs around the knee may affect knee JPS but the data is inconsistent. The purpose of the present study was to determine the effects of Cg Position on healthy adults’ knee joint Position sense acuity. In a counterbalanced, single-blinded study, 16 healthy young adults (8 female, age: 25.5 y) performed an active knee joint Position-matching task with and without (CON) a below-knee (BK), above-knee (AK), or whole-knee (WK) Cg in a randomized order on the dominant (CompDom) or the non-dominant leg (CompNon-Dom). We also determined the magnitude of tissue compression by measuring anatomical thigh and calf cross sectional area (CSA) in standing using magnetic resonance imaging (MRI). Subjects had less absolute rePositioning error (magnitude of error) in BK compared with CON condition. On the other hand, the analysis of the direction of error (constant error) revealed that in each condition subjects tended to underestimate the target Position (AK, BK and CON: 75%; WK: 94%). In WK condition there was a significantly larger negative error (−2.7 ± 3.4) as compared with CON (−1.6 ± 3.7) condition. There also was less variable error, in WK compared to BK and CON conditions, indicating less variability in their Position sense using a WK Cg, regardless of the underestimation. Cg reduced thigh CSA by 4.5 cm2 or 3% and calf CSA by Δ1.3 cm2 or 1%. The Position of Cg relative to the knee modifies knee JPS. The findings helps us better understand how the application of a WK Cg may support athletic activities.