Research Article
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Year 2021, Volume: 5 Issue: 1, 1 - 11, 30.06.2021

Mert Büyükköprü , Erdem Uzunsoy

https://doi.org/10.38088/jise.755616
Cited By: 2

Abstract

Project Number

3170303

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.

Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation

Year 2021, Volume: 5 Issue: 1, 1 - 11, 30.06.2021

Mert Büyükköprü , Erdem Uzunsoy

https://doi.org/10.38088/jise.755616
Cited By: 2

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.

Keywords

Extended Kalman Filter Mass Estimation Vehicle Longitudinal Dynamics

Supporting Institution

Groupe Renault

Project Number

3170303

Thanks

TUBITAK-TEYDEB

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.
There are 16 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Mert Büyükköprü 0000-0003-3493-8323

Erdem Uzunsoy 0000-0002-6449-552X

Project Number 3170303
Publication Date June 30, 2021
Published in Issue Year 2021Volume: 5 Issue: 1

Cite

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. https://doi.org/10.38088/jise.755616
AMA Büyükköprü M, Uzunsoy E. Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation. JISE. June 2021;5(1):1-11. doi:10.38088/jise.755616
Chicago Büyükköprü, Mert, and Erdem Uzunsoy. “Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation”. Journal of Innovative Science and Engineering 5, no. 1 (June 2021): 1-11. https://doi.org/10.38088/jise.755616.
EndNote Büyükköprü M, Uzunsoy E (June 1, 2021) Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation. Journal of Innovative Science and Engineering 5 1 1–11.
IEEE M. Büyükköprü and E. Uzunsoy, “Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation”, JISE, vol. 5, no. 1, pp. 1–11, 2021, doi: 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.
JAMA Büyükköprü M, Uzunsoy E. Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation. JISE. 2021;5:1–11.
MLA Büyükköprü, Mert and Erdem Uzunsoy. “Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation”. Journal of Innovative Science and Engineering, vol. 5, no. 1, 2021, pp. 1-11, doi:10.38088/jise.755616.
Vancouver Büyükköprü M, Uzunsoy E. Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation. JISE. 2021;5(1):1-11.

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