scholarly journals Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Liang Hao ◽  
Lixin Guo ◽  
Shuwei Liu
Keyword(s):  
Working Condition ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Lateral Acceleration ◽  
Measurement Information ◽  
Tire Model ◽  
Steering Angle ◽  
Soft Sensing ◽  
Adaptive Unscented Kalman Filter ◽  
Cost Measurement

Vehicle running state adaptive unscented Kalman filter soft-sensing algorithm is put forward in this paper based on traditional UKF which can estimate vehicle running state parameters and suboptimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time. Meanwhile 3-DOF dynamic model of vehicle and HSRI tire model are established. So vehicle running state can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and handwheel steering angle. Under the typical working condition, AUKF soft-sensing algorithm is verified with substantial vehicle tests. Comparing with UKF soft-sensing algorithm, the result indicates AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of vehicle running state more precisely than UKF soft-sensing algorithm.

An Analytical Transient Tire Model

2001 ◽  
Vol 29 (2) ◽  
pp. 108-132 ◽  
Author(s):  
A. Ghazi Zadeh ◽  
A. Fahim
Keyword(s):  
Transient Behavior ◽  
Stability Control ◽  
Vehicle Stability ◽  
Tire Model ◽  
Steering Angle ◽  
First Order ◽  
Vehicle Stability Control ◽  
Proposed Model ◽  
Depth Study ◽  
And Performance

Abstract The dynamics of a vehicle's tires is a major contributor to the vehicle stability, control, and performance. A better understanding of the handling performance and lateral stability of the vehicle can be achieved by an in-depth study of the transient behavior of the tire. In this article, the transient response of the tire to a steering angle input is examined and an analytical second order tire model is proposed. This model provides a means for a better understanding of the transient behavior of the tire. The proposed model is also applied to a vehicle model and its performance is compared with a first order tire model.

scholarly journals Evaluation and application of a low-cost measurement network to study intra-urban temperature differences during summer 2018 in Bern, Switzerland

Urban Climate ◽  
2021 ◽  
Vol 37 ◽  
pp. 100817
Author(s):  
Moritz Gubler ◽  
Andreas Christen ◽  
Jan Remund ◽  
Stefan Brönnimann
Keyword(s):  
Low Cost ◽  
Cost Measurement

scholarly journals Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 607
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang ◽  
Guimei Cui
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Support Vector ◽  
Multiple Model ◽  
Single Model ◽  
Adaptive Unscented Kalman Filter ◽  
Time Periods ◽  
Atmospheric Pm2.5 ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

The PM2.5 concentration model is the key to predict PM2.5 concentration. During the prediction of atmospheric PM2.5 concentration based on prediction model, the prediction model of PM2.5 concentration cannot be usually accurately described. For the PM2.5 concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM2.5 concentration may be different, and the single model cannot play the corresponding ability to predict PM2.5 concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM2.5 concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM2.5 concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM2.5 concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM2.5 concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM2.5 concentration prediction is improved in whole time period.

On-site determination of formaldehyde: A low cost measurement device for museum environments

2008 ◽  
Vol 623 (1) ◽  
pp. 109-116 ◽  
Author(s):  
L.T. Gibson ◽  
W.J. Kerr ◽  
A. Nordon ◽  
J. Reglinski ◽  
C. Robertson ◽  
...  
Keyword(s):  
Low Cost ◽  
Measurement Device ◽  
Cost Measurement

Identification of Tire Model Parameters Through Full Vehicle Experimental Tests

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Francesco Braghin ◽  
Federico Cheli ◽  
Edoardo Sabbioni
Keyword(s):  
Laboratory Tests ◽  
Experimental Tests ◽  
Contact Forces ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Tire Model ◽  
Identification Procedure ◽  
Sideslip Angle ◽  
Tyre Model

Individual tire model parameters are traditionally derived from expensive component indoor laboratory tests as a result of an identification procedure minimizing the error with respect to force and slip measurements. These parameters are then transferred to vehicle models used at a design stage to simulate the vehicle handling behavior. A methodology aimed at identifying the Magic Formula-Tyre (MF-Tyre) model coefficients of each individual tire for pure cornering conditions based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers) is instead presented in this paper. The resulting tire model thus includes vertical load dependency and implicitly compensates for suspension geometry and compliance (i.e., scaling factors are included into the identified MF coefficients). The global number of tests (indoor and outdoor) needed for characterizing a tire for handling simulation purposes can thus be reduced. The proposed methodology is made in three subsequent steps. During the first phase, the average MF coefficients of the tires of an axle and the relaxation lengths are identified through an extended Kalman filter. Then the vertical loads and the slip angles at each tire are estimated. The results of these two steps are used as inputs to the last phase, where, the MF-Tyre model coefficients for each individual tire are identified through a constrained minimization approach. Results of the identification procedure have been compared with experimental data collected on a sport vehicle equipped with different tires for the front and the rear axles and instrumented with dynamometric hubs for tire contact forces measurement. Thus, a direct matching between the measured and the estimated contact forces could be performed, showing a successful tire model identification. As a further verification of the obtained results, the identified tire model has also been compared with laboratory tests on the same tire. A good agreement has been observed for the rear tire where suspension compliance is negligible, while front tire data are comparable only after including a suspension compliance compensation term into the identification procedure.

Sign in / Sign up

Export Citation Format

Share Document