4WID In-Wheel Motor EV Driving State Estimation Based on UKF

2012 ◽  
Vol 246-247 ◽  
pp. 712-717
Author(s):  
Gang Li ◽  
Chang Fu Zong ◽  
Qiang Zhang ◽  
Wei Hong
Keyword(s):  
State Estimation ◽  
Electric Vehicles ◽  
Simulation Experiment ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Estimation Algorithm ◽  
Slip Angle ◽  
Tire Model ◽  
Estimation Model ◽  
Lateral Velocity

According to the characteristics of the four independent drive (4WID) electric vehicles, the vehicle driving state estimation algorithm was designed based on the Unscented Kalman Filter (UKF). The algorithm used 3-DOF vehicle estimation model with the HSRI tire model. The 4WID EV longitudinal velocity, lateral velocity and side slip angle were estimated. The algorithm was verified through simulation experiment. The results showed that the algorithm could estimate the vehicle driving state more accurately.

scholarly journals An improved estimation method for vehicle velocity of distributed drive electric vehicle

2021 ◽  
Author(s):  
Qingqing Xiang ◽  
Zhiqiang Liu ◽  
Guang Liu
Keyword(s):  
Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Velocity Estimation ◽  
Estimation Model ◽  
Time Performance ◽  
Vehicle Velocity ◽  
Distributed Drive Electric Vehicle ◽  
Tracking Filter

Abstract In this paper, Simulink and Carsim are combined to study the velocity estimation of distributed drive electric vehicles. Firstly, the minimum co-simulation system is established to complete the design and debugging of the algorithm. Then, a new algorithm combining unscented Kalman filter and strong tracking filter is proposed based on the vehicle estimation model. The accuracy and real-time performance of the velocity estimation algorithm are validated by simulation under snake-shaped driving conditions with different road adhesion coefficients. Finally, an experimental test is carried out to verify the effectiveness of the proposed algorithm in estimating vehicle velocity.

Virtual sensor for real-time estimation of the vehicle sideslip angle

Sensor Review ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 255-272
Author(s):  
Kanwar Bharat Singh
Keyword(s):  
Steady State ◽  
Unscented Kalman Filter ◽  
Time Estimation ◽  
Experimental Tests ◽  
Slip Angle ◽  
Successful Implementation ◽  
Tire Model ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Content Type

Purpose The vehicle sideslip angle is an important state of vehicle lateral dynamics and its knowledge is crucial for the successful implementation of advanced driver-assistance systems. Measuring the vehicle sideslip angle on a production vehicle is challenging because of the exorbitant price of a physical sensor. This paper aims to present a novel framework for virtually sensing/estimating the vehicle sideslip angle. The desired level of accuracy for the estimator is to be within +/− 0.2 degree of the actual sideslip angle of the vehicle. This will make the precision of the proposed estimator at par with expensive commercially available sensors used for physically measuring the vehicle sideslip angle. Design/methodology/approach The proposed estimator uses an adaptive tire model in conjunction with a model-based observer. The performance of the estimator is evaluated through experimental tests on a rear-wheel drive vehicle. Findings Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers, within the desired accuracy levels. Originality/value This paper presents a novel framework for vehicle sideslip angle estimation. The presented framework combines an adaptive tire model, an unscented Kalman filter-based axle force observer and data from tire mounted sensors. Tire model adaptation is achieved by making extensions to the magic formula, by accounting for variations in the tire inflation pressure, load, tread-depth and temperature. Predictions with the adapted tire model were validated by running experiments on the Flat-Trac® machine. The benefits of using an adaptive tire model for sideslip angle estimation are demonstrated through experimental tests. The performance of the observer is satisfactory, in both transient and steady state maneuvers. Future work will focus on measuring tire slip angle and road friction information using tire mounted sensors and using that information to further enhance the robustness of the vehicle sideslip angle observer.

A novel battery state estimation model based on unscented Kalman filter

Ionics ◽  
2021 ◽  
Author(s):  
Jiabo Li ◽  
Min Ye ◽  
Kangping Gao ◽  
Shengjie Jiao ◽  
Xinxin Xu
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Model ◽  
Model Based

scholarly journals IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1930 ◽  
Author(s):  
Lu Xiong ◽  
Xin Xia ◽  
Yishi Lu ◽  
Wei Liu ◽  
Letian Gao ◽  
...  
Keyword(s):  
Vehicle Dynamics ◽  
Longitudinal Velocity ◽  
Estimation Method ◽  
Experimental Tests ◽  
Attitude Estimation ◽  
Measurement Unit ◽  
Slip Angle ◽  
Automated Driving ◽  
Lateral Velocity ◽  
Vehicle Dynamic Model

The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.

scholarly journals Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1396
Author(s):  
Hannes Heidfeld ◽  
Martin Schünemann
Keyword(s):  
Parameter Estimation ◽  
High Precision ◽  
Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Measurement Unit ◽  
Tire Model ◽  
State Estimator ◽  
Vehicle Data ◽  
Wheel Drive ◽  
Real Vehicle

Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown disturbances, such as changes in road conditions, is crucial for realization of such control systems. This article shows the design, tuning, implementation, and test of a state estimator with individual tire model adaption for direct drive electric vehicles. The vehicle dynamics are modeled using a double-track model with an adaptive tire model. State-of-the-art sensors, an inertial measurement unit, steering angle, wheel speed, and motor current sensors are used as measurements. Due to the nonlinearity of the vehicle model, an Unscented Kalman Filter (UKF) is used for simultaneous state and parameter estimation. To simplify the difficult task of UKF tuning, an optimization-based method using real-vehicle data is utilized. The UKF is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation. High precision even in severe driving maneuvers under various road conditions is achieved. Nonlinear state and parameter estimation for all wheel drive electric vehicles using UKF and optimization-based tuning is shown to provide high precision with minimal manual tuning effort.

scholarly journals Ship tracking based on the fusion of Kalman filter and particle filter

2021 ◽  
Vol 2113 (1) ◽  
pp. 012017
Author(s):  
Wanjin Xu ◽  
Jiying Li ◽  
Junjie Bai ◽  
Yingying Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Particle Filtering ◽  
Unscented Kalman Filter ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Tracking Accuracy ◽  
Gps Navigation ◽  
Degradation Degree

Abstract Aiming at the problem of low filtering accuracy and even divergence caused by model mismatch when using extended Kalman filter in ship GPS navigation and positioning state estimation, a positioning ship state estimation algorithm based on the fusion of improved unscented Kalman filter and particle filter is proposed. Compared with the traditional particle filtering algorithm, the algorithm has two improvements: first, the algorithm uses untraced Kalman as the main framework, and uses the optimal estimation of particle updating state by particle algorithm; Secondly, in the resampling process, a resampling algorithm based on weight optimization is proposed to increase the diversity of particles. The simulation results show that not only the particle degradation degree of the particle filter is reduced, but also the particle tracking accuracy is improved.

scholarly journals A Novel State Estimation Approach Based on Adaptive Unscented Kalman Filter for Electric Vehicles

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185629-185637 ◽  
Author(s):  
Jiabo Li ◽  
Min Ye ◽  
Shengjie Jiao ◽  
Wei Meng ◽  
Xinxin Xu
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Adaptive Unscented Kalman Filter

scholarly journals Cross-combined UKF for vehicle sideslip angle estimation with a modified Dugoff tire model: design and experimental results

Meccanica ◽  
2021 ◽  
Author(s):  
Elvis Villano ◽  
Basilio Lenzo ◽  
Aleksandr Sakhnevych
Keyword(s):  
Unscented Kalman Filter ◽  
Traditional Approach ◽  
Longitudinal Velocity ◽  
Average Error ◽  
Tire Model ◽  
Passenger Cars ◽  
Cross Combination ◽  
Sideslip Angle ◽  
Safety Control ◽  
Double Track

AbstractThe knowledge of key vehicle states is crucial to guarantee adequate safety levels for modern passenger cars, for which active safety control systems are lifesavers. In this regard, vehicle sideslip angle is a pivotal state for the characterization of lateral vehicle behavior. However, measuring sideslip angle is expensive and unpractical, which has led to many years of research on techniques to estimate it instead. This paper presents a novel method to estimate vehicle sideslip angle, with an innovative combination of a kinematic-based approach and a dynamic-based approach: part of the output of the kinematic-based approach is fed as input to the dynamic-based approach, and vice-versa. The dynamic-based approach exploits an Unscented Kalman Filter (UKF) with a double-track vehicle model and a modified Dugoff tire model, that is simple yet ensures accuracy similar to the well-known Magic Formula. The proposed method is successfully assessed on a large amount of experimental data obtained on different race tracks, and compared with a traditional approach presented in the literature. Results show that the sideslip angle is estimated with an average error of 0.5 deg, and that the implemented cross-combination allows to further improve the estimation of the vehicle longitudinal velocity compared to current state-of-the-art techniques, with interesting perspectives for future onboard implementation.

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