scholarly journals Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Te Chen ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Estimation Method ◽  
Longitudinal Force ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Dynamics Model ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Kinematics Model ◽  
Model Based

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.

Model-Based Estimation for Vehicle Dynamics States at the Limit Handling

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Gang Jia ◽  
Liang Li ◽  
Dongpu Cao
Keyword(s):  
Performance Enhancement ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Dynamics Model ◽  
Sideslip Angle ◽  
Model Based ◽  
Speed Sensor ◽  
Proposed Model ◽  
Roll Rate ◽  
Lift Off

This technical brief proposes a new model-based estimation method for the vehicle sideslip angle, yaw rate, roll angle, and roll rate using unscented Kalman filter (UKF). Since a vehicle wheel could potentially lift off the ground during the limit handling, a switched vehicle roll dynamics model (wheel lift and no wheel lift) is developed and integrated within the proposed model-based estimation approach considering the availability of wheel speed sensor. The simulation results and analyses demonstrate the performance enhancement of the proposed estimation method over the method not considering wheel lift during the limit handling.

scholarly journals Running States Estimation of Autonomous Four-Wheel Independent Drive Electric Vehicle by Virtual Longitudinal Force Sensors

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Qiu Xia ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Sliding Mode ◽  
Estimation Method ◽  
Longitudinal Force ◽  
Observer Design ◽  
Unknown Input ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Electric Driving ◽  
Wheel Model ◽  
Driving Wheel

Exact sideslip angle estimation is significant to the dynamics control of four-wheel independent drive electric vehicles. It is costly and difficult-to-popularize to equip vehicular sensors for real-time sideslip angle measurement; therefore, the reliable sideslip angle estimation method is investigated in this paper. The electric driving wheel model is proposed and applied to the longitudinal force estimation. Considering that electric driving wheel model is a nonlinear model with unknown input, an unknown input estimation method is proposed to facilitate the longitudinal force observer design, in which the adaptive high-order sliding mode observer is designed to achieve the state estimation, the analytic formula of longitudinal force is obtained by decoupling electric driving wheel model, and the longitudinal force estimator is designed by recurrence estimation method. With the designed virtual longitudinal force sensor, an adaptive attenuated Kalman filtering is proposed to estimate the vehicle running state, in which the time-varying attenuation factor is applied to weaken the past data to the current filter and the covariance of process noise and measurement noise can be adjusted adaptively. Finally, simulations and experiments are conducted and the effectiveness of proposed estimation method is validated.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals Robust Directional Angle Estimation of Underwater Acoustic Sources Using a Marine Vehicle

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3062 ◽  
Author(s):  
Jinwoo Choi ◽  
Jeonghong Park ◽  
Yoongeon Lee ◽  
Jongdae Jung ◽  
Hyun-Taek Choi
Keyword(s):  
Source Localization ◽  
Angular Displacement ◽  
Estimation Method ◽  
Time Delay Estimation ◽  
Accurate Estimation ◽  
Acoustic Source ◽  
Angle Estimation ◽  
Underwater Acoustic ◽  
Acoustic Sources ◽  
Marine Vehicle

Acoustic source localization is used in many underwater applications. Acquiring an accurate directional angle for an acoustic source is crucial for source localization. To achieve this purpose, this paper presents a method for directional angle estimation of underwater acoustic sources using a marine vehicle. It is assumed that the vehicle is equipped with two hydrophones and that the acoustic source transmits a specific signal repeatedly. The proposed method provides a probabilistic model for time delay estimation. The probability is recursively updated by prediction and update steps. The prediction step performs a probability transition using the angular displacement of the marine vehicle. The predicted probability is updated using a generalized cross correlation function with a verification process using entropy measurement. The proposed method can provide a reliable and accurate estimation of the directional angles of underwater acoustic sources. Experimental results demonstrate good performance of the proposed probabilistic directional angle estimation method in both an inland water environment and a harbor environment.

Robust Sideslip Angle Estimation for Over-Actuated Electric Vehicles: A Linear Parameter Varying System Approach

2013 ◽  
Author(s):  
Yan Chen ◽  
Junmin Wang
Keyword(s):  
Friction Force ◽  
System Approach ◽  
Estimation Method ◽  
Observer Design ◽  
Linear Parameter ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Friction Forces ◽  
Linear Parameter Varying ◽  
Parameter Varying

A new estimation method for estimating the vehicle sideslip angle, mainly based on a linear parameter varying (LPV) model with independently estimated tire friction forces, is proposed for electric ground vehicles (EGVs) with four independent in-wheel motors. By utilizing the individual wheel dynamics, the longitudinal ground friction force is estimated from a PID observer based on a descriptor linear system approach. Moreover, the lateral ground friction force for each wheel is estimated through the friction ellipse relationship given the estimated longitudinal friction force, without relying on explicit tire models. Since the estimation errors of friction forces may bring parameter uncertainty for the LPV system, robust analysis with desired H-infinity performance is given for the observer design of the LPV modeling. This method is specially proposed for large tire slip angles and lateral friction forces. Simulation results for different maneuvers validate this novel sideslip angle estimation method.

scholarly journals Design of a Novel Nonlinear Observer to Estimate Sideslip Angle and Tire Forces for Distributed Electric Vehicle

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Chuanxue Song ◽  
Feng Xiao ◽  
Shixin Song ◽  
Shaokun Li ◽  
Jianhua Li
Keyword(s):  
Electric Vehicle ◽  
Vehicle Dynamics ◽  
Unscented Kalman Filter ◽  
Lateral Force ◽  
Nonlinear Observer ◽  
Accurate Estimation ◽  
State Variables ◽  
Force Estimation ◽  
Sideslip Angle ◽  
Vehicle Dynamics Control

For four-wheel independently driven (4WD) distributed electric vehicle (DEV), vehicle dynamics control systems such as direct yaw moment control (DYC) can be easily achieved. Accurate estimation of vehicle state variables and uncertain parameters can improve the robustness of vehicle dynamics control system. Various sensors are generally equipped to the acquisition of the vehicle dynamics. For both technical and economic reasons, some fundamental vehicle parameters, such as the sideslip angle and tire-road forces, can hardly be obtained through sensors directly. Therefore, this paper presented a state observer to estimate these variables based on Unscented Kalman Filter (UKF). To improve the accuracy of UKF, measurement noise covariance is also self-adaptive regulated. In addition, a nonlinear dynamics tire model is utilized to improve the accuracy of tire lateral force estimation. The simulation and experiment results show that the proposed observer can provide the precision values of the vehicle state.

A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

2019 ◽  
Vol 234 (7) ◽  
pp. 1930-1943
Author(s):  
Xiaoyu Li ◽  
Nan Xu ◽  
Qin Li ◽  
Konghui Guo ◽  
Jianfeng Zhou
Keyword(s):  
Magnitude Estimation ◽  
Front Wheel ◽  
Lateral Acceleration ◽  
Area Network ◽  
Sideslip Angle ◽  
Small Magnitude ◽  
Angle Estimation ◽  
Model Based ◽  
Road Friction ◽  
Basic Approaches

This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.

scholarly journals An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4180
Author(s):  
Chenxi Guo ◽  
Xinhong Hao ◽  
Ping Li
Keyword(s):  
Estimation Method ◽  
Sparse Recovery ◽  
Estimation Methods ◽  
Reconstruction Method ◽  
Angle Estimation ◽  
Trilinear Decomposition ◽  
Model Based ◽  
Planar Arrays ◽  
Trilinear Model ◽  
Virtual Array

Angle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In this paper, we propose an improved trilinear model-based method for angle estimation for co-prime planar arrays in the view of trilinear decomposition, namely parallel factor analysis. Due to the principle of trilinear decomposition, our method does not require peak searching and can conduct auto-pairing easily, which can reduce the computational loads and avoid parameter selection problems. Furthermore, we exploit the virtual array concept of the whole co-prime planar array through the cross-correlation matrix obtained from the received signal data and present a matrix reconstruction method using the Khatri–Rao product to tackle the matrix rank deficiency problem in the virtual array condition. The simulation results show that our proposed method can not only achieve high estimation accuracy with low complexity compared to other similar approaches, but also utilize limited sensor number to implement the angle estimation tasks.

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