Research Article
PDF Zotero Mendeley EndNote BibTex Cite

Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation

Year 2021, Volume 5, Issue 1, 1 - 11, 30.06.2021
https://doi.org/10.38088/jise.755616

Abstract

In this paper, vehicle mass estimation problem was researched by the Kalman Filtering process to investigate the effectiveness of Extended Kalman Filter technique on mass estimation, which has widely been studied on the related literature with all the successful results. For the purpose, a longitudinal vehicle dynamics model was developed, and the equations were transformed into the well-known state space form. Nonlinear problem in its nature was discretised and linearized by using Euler method. In addition, road slope was calculated by a road slope inclinometer logic. IPG CarMaker® was utilized to overcome the nonlinearities, design the test environment and the conditions, as it was observed that the simulation results were not met the expectations with the logged vehicle physical test data. Several test scenarios were run to simulate level and wavy road with different vehicle speeds. The results showed that the Extended Kalman Filter could provide a reasonable convergence when the initial values were correctly selected, no nonlinear issue occurred and certain tuning parameters were defined with stable boundary conditions for mass estimation.

References

  • [1] Lingman, P., Schmidtbauer, B. (2002). Road Slope and Vehicle Mass Estimation Using Kalman Filtering. International Journal of Vehicle Mechanics and Mobility, 37 (1):12-23.
  • [2] Imsland, L., Grip, H. F.,Johansen A.T.,Fossen T. I., Kalkkuhl, C. J., Suissa, A. (2007). Nonlinear Observer For Vehicle Velocity With Friction And Road Bank Angle Adaptation With An Extended Kalman Filter. SAE International World congress Detroit, Michigan April 16-19 2007.
  • [3] Vahidi, A., Stefanopoulou, A., Peng, H., (2005). Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Vehicle System Dynamics, 43(1): 31-35.
  • [4] Wragge-Morley, R., Hermann, G., Barber, P., Burgess, S., (2015). Gradient and Mass Estimation from CAN Based Data for a light passenger Car. SAE International J. Passenger Cars-Electronic Electric Systems, 8 (1):137-145.
  • [5] Kidambi, N., Harne, R.L., Fuji, Y., Pietron, G. M., Wang, K.W., (2014). Methods in Vehicle Mass and Road Grade Estimation. SAE International J. Passenger Cars -Mechanical Systems, 7(3): 981-991.
  • [6] Huh, K., Lim, S., Jung, J., Hong, D., Han, Han, K., Jo H. Y., Yun, J. M., (2007). Vehicle Mass Estimator for Adaptive Roll Stability Control. SAE International World congress Detroit, Michigan ,16-19 April 2007.
  • [7] Wenzel, T.A., Burnham, K. J., Blundell, M. V., Williams, R.A., (2007). Dual extended Kalman filter for vehicle state and parameter estimation. Vehicle System Dynamics, 44(2): 153–171.
  • [8] Pence., B. L., Fathy K. H., Stein, J. L., (2009). Sprung Mass Estimation for Off-Road Vehicles via Base-Excitation Suspension Dynamics and Recursive Least Squares. American Control Conference, Hyatt Regency Riverfront, St. Louis, MO, USA. June 10-12, 2009.
  • [9] Bae, H. S., Gerdes J. C., (2003). Parameter Estimation And Command Modification For Longitudinal Control Of Heavy Vehicles. California PATH Research Report UCB-ITS-PRR-2003-16. ISSN: 1055-1425.
  • [10] Raffone, R., (2013). Road Slope and Vehicle Mass Estimation for Light Commercial Vehicle using linear Kalman filter and RLS with forgetting factor integrated approach. 16th International Conference on Information Fusion Istanbul, Turkey, July 9-12, 2013.
  • [11] Holm, E.J. (2011). Vehicle Road Grade And Mass Estimation Using Kalman Filter. MSc. Thesis, Linköping University, Linköping.
  • [12] URL 1: Ipg Automotive (2019). CarMaker: Virtual testing of automobiles and light-duty vehicles. https://ipg-automotive.com/products-services/simulation-software/carmaker/. [Accessed: 13 December 2019.
  • [13] Karoshi., P., Ager, M., Schabauer, M., and Lex, C., (2017). Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade. Advanced Microsystems for Automotive Applications 2017. 26 September, Berlin, Germany. pp. 87-100.
  • [14] Lundin, B., Olsson, A., (2012). Estimation of Vehicle Mass Using and Extended Kalman Filter. MSc. Thesis. Chalmers University of Technology, Gothenburg, Sweden.
  • [15] Roger R. L., (2018). “Kalman and Bayesian Filters in Python” https://www.academia.edu/37533812/Kalman_and_Bayesian_Filters_in_Python [Accessed: 14 December 2019]
  • [16] Ohnishi, H., Ishii, J., Kayano M., Katayama, H., (2000), “A Study On Road Slope Estimation For Automatic Transmission Control” ,Society of Automotive Engineers of Japan, 21(2): 235-240.

Year 2021, Volume 5, Issue 1, 1 - 11, 30.06.2021
https://doi.org/10.38088/jise.755616

Abstract

References

  • [1] Lingman, P., Schmidtbauer, B. (2002). Road Slope and Vehicle Mass Estimation Using Kalman Filtering. International Journal of Vehicle Mechanics and Mobility, 37 (1):12-23.
  • [2] Imsland, L., Grip, H. F.,Johansen A.T.,Fossen T. I., Kalkkuhl, C. J., Suissa, A. (2007). Nonlinear Observer For Vehicle Velocity With Friction And Road Bank Angle Adaptation With An Extended Kalman Filter. SAE International World congress Detroit, Michigan April 16-19 2007.
  • [3] Vahidi, A., Stefanopoulou, A., Peng, H., (2005). Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Vehicle System Dynamics, 43(1): 31-35.
  • [4] Wragge-Morley, R., Hermann, G., Barber, P., Burgess, S., (2015). Gradient and Mass Estimation from CAN Based Data for a light passenger Car. SAE International J. Passenger Cars-Electronic Electric Systems, 8 (1):137-145.
  • [5] Kidambi, N., Harne, R.L., Fuji, Y., Pietron, G. M., Wang, K.W., (2014). Methods in Vehicle Mass and Road Grade Estimation. SAE International J. Passenger Cars -Mechanical Systems, 7(3): 981-991.
  • [6] Huh, K., Lim, S., Jung, J., Hong, D., Han, Han, K., Jo H. Y., Yun, J. M., (2007). Vehicle Mass Estimator for Adaptive Roll Stability Control. SAE International World congress Detroit, Michigan ,16-19 April 2007.
  • [7] Wenzel, T.A., Burnham, K. J., Blundell, M. V., Williams, R.A., (2007). Dual extended Kalman filter for vehicle state and parameter estimation. Vehicle System Dynamics, 44(2): 153–171.
  • [8] Pence., B. L., Fathy K. H., Stein, J. L., (2009). Sprung Mass Estimation for Off-Road Vehicles via Base-Excitation Suspension Dynamics and Recursive Least Squares. American Control Conference, Hyatt Regency Riverfront, St. Louis, MO, USA. June 10-12, 2009.
  • [9] Bae, H. S., Gerdes J. C., (2003). Parameter Estimation And Command Modification For Longitudinal Control Of Heavy Vehicles. California PATH Research Report UCB-ITS-PRR-2003-16. ISSN: 1055-1425.
  • [10] Raffone, R., (2013). Road Slope and Vehicle Mass Estimation for Light Commercial Vehicle using linear Kalman filter and RLS with forgetting factor integrated approach. 16th International Conference on Information Fusion Istanbul, Turkey, July 9-12, 2013.
  • [11] Holm, E.J. (2011). Vehicle Road Grade And Mass Estimation Using Kalman Filter. MSc. Thesis, Linköping University, Linköping.
  • [12] URL 1: Ipg Automotive (2019). CarMaker: Virtual testing of automobiles and light-duty vehicles. https://ipg-automotive.com/products-services/simulation-software/carmaker/. [Accessed: 13 December 2019.
  • [13] Karoshi., P., Ager, M., Schabauer, M., and Lex, C., (2017). Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade. Advanced Microsystems for Automotive Applications 2017. 26 September, Berlin, Germany. pp. 87-100.
  • [14] Lundin, B., Olsson, A., (2012). Estimation of Vehicle Mass Using and Extended Kalman Filter. MSc. Thesis. Chalmers University of Technology, Gothenburg, Sweden.
  • [15] Roger R. L., (2018). “Kalman and Bayesian Filters in Python” https://www.academia.edu/37533812/Kalman_and_Bayesian_Filters_in_Python [Accessed: 14 December 2019]
  • [16] Ohnishi, H., Ishii, J., Kayano M., Katayama, H., (2000), “A Study On Road Slope Estimation For Automatic Transmission Control” ,Society of Automotive Engineers of Japan, 21(2): 235-240.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Mert BÜYÜKKÖPRÜ (Primary Author)
Groupe Renault
0000-0003-3493-8323
Türkiye


Erdem UZUNSOY
BURSA TEKNİK ÜNİVERSİTESİ
0000-0002-6449-552X
Türkiye

Supporting Institution Groupe Renault
Project Number 3170303
Thanks TUBITAK-TEYDEB
Publication Date June 30, 2021
Published in Issue Year 2021, Volume 5, Issue 1

Cite

Bibtex @research article { jise755616, journal = {Journal of Innovative Science and Engineering}, issn = {}, eissn = {2602-4217}, address = {ursa Technical University, Mimar Sinan Campus, Mimar Sinan Mah. Mimar Sinan Blv. Eflak Cad. No:177 16310 Yıldırım, Bursa / Turkey}, publisher = {Bursa Technical University}, year = {2021}, volume = {5}, pages = {1 - 11}, doi = {10.38088/jise.755616}, title = {Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation}, key = {cite}, author = {Büyükköprü, Mert and Uzunsoy, Erdem} }
APA Büyükköprü, M. & Uzunsoy, E. (2021). Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation . Journal of Innovative Science and Engineering , 5 (1) , 1-11 . DOI: 10.38088/jise.755616
MLA Büyükköprü, M. , Uzunsoy, E. "Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation" . Journal of Innovative Science and Engineering 5 (2021 ): 1-11 <http://jise.btu.edu.tr/en/pub/issue/59439/755616>
Chicago Büyükköprü, M. , Uzunsoy, E. "Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation". Journal of Innovative Science and Engineering 5 (2021 ): 1-11
RIS TY - JOUR T1 - Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation AU - Mert Büyükköprü , Erdem Uzunsoy Y1 - 2021 PY - 2021 N1 - doi: 10.38088/jise.755616 DO - 10.38088/jise.755616 T2 - Journal of Innovative Science and Engineering JF - Journal JO - JOR SP - 1 EP - 11 VL - 5 IS - 1 SN - -2602-4217 M3 - doi: 10.38088/jise.755616 UR - https://doi.org/10.38088/jise.755616 Y2 - 2020 ER -
EndNote %0 Journal of Innovative Science and Engineering Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation %A Mert Büyükköprü , Erdem Uzunsoy %T Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation %D 2021 %J Journal of Innovative Science and Engineering %P -2602-4217 %V 5 %N 1 %R doi: 10.38088/jise.755616 %U 10.38088/jise.755616
ISNAD Büyükköprü, Mert , Uzunsoy, Erdem . "Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation". Journal of Innovative Science and Engineering 5 / 1 (June 2021): 1-11 . https://doi.org/10.38088/jise.755616
AMA Büyükköprü M. , Uzunsoy E. Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation. JISE. 2021; 5(1): 1-11.
Vancouver Büyükköprü M. , Uzunsoy E. Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation. Journal of Innovative Science and Engineering. 2021; 5(1): 1-11.
IEEE M. Büyükköprü and E. Uzunsoy , "Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation", Journal of Innovative Science and Engineering, vol. 5, no. 1, pp. 1-11, Jun. 2021, doi:10.38088/jise.755616


Creative Commons License

The works published in Journal of Innovative Science and Engineering (JISE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.