Nonlinear Frictions Identification in Time-Variant Automotive Systems

Davide Tebaldi; Roberto Zanasi

doi:10.1109/JAS.2025.125294

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

D. Tebaldi and R. Zanasi, “Nonlinear frictions identification in time-variant automotive systems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125294

Citation:

D. Tebaldi and R. Zanasi, “Nonlinear frictions identification in time-variant automotive systems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125294

D. Tebaldi and R. Zanasi, “Nonlinear frictions identification in time-variant automotive systems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125294

Citation:

D. Tebaldi and R. Zanasi, “Nonlinear frictions identification in time-variant automotive systems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125294

PDF( 1487 KB)

Nonlinear Frictions Identification in Time-Variant Automotive Systems

doi: 10.1109/JAS.2025.125294

Davide Tebaldi^{,
,},
Roberto Zanasi

Funds: The work was partly supported by the University of Modena and Reggio Emilia through the action FARD (Finanziamento Ateneo Ricerca Dipartimentale) 2023/2024, and funded under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 1.5 – NextGenerationEU, Call for tender n. 3277 dated 30/12/2021 Award number: 0001052 dated 23/06/2022

More Information

Author Bio:
Davide Tebaldi (Member, IEEE) received the B.Eng degree in electronics engineering in 2015 and the M.Eng degree in electronics engineering (cum laude) in 2018 from the University of Modena and Reggio Emilia, Italy, the Ph.D. degree in information and communication technologies in 2022 from the University of Modena and Reggio Emilia, Italy, discussing a thesis titled Mathematical Modeling Control and Simulation of Hybrid Electric Vehicles. From October 2021 to January 2022, he was a visiting scholar at the Center for Automotive Research, The Ohio State University, USA. From March 2022 to February 2023, he held a post-doc position at the University of Modena and Reggio Emilia, Italy. From March 2023, he has been an Assistant Professor at the University of Modena and Reggio Emilia, Italy. His research interests include the energetic modeling, control, energy efficiency analysis and simulation of mechatronic systems, with main application in the automotive, electrical machines and power electronics fields. Another branch of his research interests concerns the collaborative robotics field, with specific reference to the energy efficiency improvement and to the human-robot safe interaction

Roberto Zanasi received the degree in electrical engineering (cum laude) from the University of Bologna in 1986, the Ph.D. degree in systems engineering in 1992. From 1994 to 1998, he was a Researcher in automatic controls with the Department of Electronics, Computer and System Science, University of Bologna. From 1998 to 2004, he was an Associate Professor of automatic control with the “Enzo Ferrari” Engineering Department, University of Modena and Reggio Emilia, Italy. From 2004, he has been a Full Professor of Automatic Control with the “Enzo Ferrari” Engineering Department, University of Modena and Reggio Emilia, Italy. He held the position of Visiting Scientist with the IRIMS of Moscow, Russia in 1991, at the MIT of Boston, USA in 1992, and at the Université Catholique de Louvain, Belgiam in 1995. He has authored or coauthored many technical and scientific publications, and five books on automatic controls, simulation and digital control systems. His research interests include, mathematical modeling, power-oriented graphs, simulation, automotive, control of multi-phase electrical motors, variable-structure systems, sliding mode control with integral action, robotics, linear and nonlinear control
Corresponding author: Davide Tebaldi, e-mail: davide.tebaldi@unimore.it
Received Date: 2024-08-01
Revised Date: 2024-11-13
Accepted Date: 2025-02-03

Available Online: 2025-04-07

Abstract

Abstract

In this paper, the problem of nonlinear frictions identification in a class of nonlinear systems embedding different automotive case studies is addressed. The power-oriented modeling of the system dynamics is first addressed. Next, the identification of the nonlinear friction coefficients representing the system losses, which can have different symmetric or asymmetric characteristics, is addressed using a parabolic interpolation. To show the versatility of the procedure, two automotive physical systems composing the vehicle powertrain are considered as case studies for the identification, namely a Full Toroidal Variator and a Gearbox. The novelty of this work consists of the proposal of a general approach to model nonlinear frictions in a wide class of automotive systems, and in their identification using the proposed least-square-based algorithm. With reference to the latter, we also provide a necessary condition to avoid the rank deficiency problem and considerations about how to increase the identification accuracy.
- Automotive,
- identification,
- modeling,
- powertrain components,
- time-varying systems

FullText(HTML)

References(28)

References

[1]	J. Han, D. Shen, D. Karbowski, and A. Rousseau, “Leveraging multiple connected traffic light signals in an energy-efficient speed planner,” IEEE Control Syst. Lett., vol. 5, no. 6, pp. 2078–2083, Dec. 2021. doi: 10.1109/LCSYS.2020.3047605
[2]	Y. Zhao, Y. Cai, and Q. Song, “Energy control of plug-in hybrid electric vehicles using model predictive control with route preview,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1948–4854, Dec. 2021. doi: 10.1109/JAS.2017.7510889
[3]	J. Heredia, R. B. Kirschner, C. Schlette, S. Abdolshah, S. Haddadin, and M. B. Kjægaard, “ECDP: Energy consumption disaggregation pipeline for energy optimization in lightweight robots,” IEEE Robot. Automat. Lett., vol. 8, no. 10, pp. 6107–6114, Oct. 2023. doi: 10.1109/LRA.2023.3301311
[4]	T. Li, X. Sun, G. Lei, Y. Guo, Z. Yang, and J. Zhu, “Finite-control-set model predictive control of permanent magnet synchronous motor drive systems-an overview,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2087–2105, Dec. 2022. doi: 10.1109/JAS.2022.105851
[5]	L. Wu, J. Liu, S. Vazquez, and S. K. Mazumder, “Sliding mode control in power converters and drives: A review,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 392–406, Mar. 2022. doi: 10.1109/JAS.2021.1004380
[6]	A. L. de Oliveira, C. E. Capovilla, I. R. Santana Casella, J. L. Azcue-Puma, and A. J. Sguarezi Filho, “Co-simulation of an SRG wind turbine control and GPRS/EGPRS wireless standards in smart grids,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 656–663, Mar. 2021. doi: 10.1109/JAS.2021.1003883
[7]	D. Tebaldi and R. Zanasi, “Systematic modeling of complex time-variant gear systems using a power-oriented approach”, Control Engineering Practice, vol. 132, Mar. 2023. DOI: 10.1016/j.conengprac.2022.105420.
[8]	D. Tebaldi and R. Zanasi, “Instantaneous gearshift model based on gear-dependent angular momentum,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 13952–13957, 2020. doi: 10.1016/j.ifacol.2020.12.912
[9]	A. Jahangir Moshayedi, K. S. Reza, A. Sohail Khan, and A. Nawaz, “Integrating virtual reality and robotic operation system (ROS) for AGV Navigation”, EAI Endorsed Trans. AI Robot., vol. 2, Apr. 2023, DOI: 10.4108/airo.v2i1.3181.
[10]	Z. Li, S. Li, O. O. Bamasag, A. Alhothali, and X. Luo, “Diversified regularization enhanced training for effective manipulator calibration,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 11, pp. 8778–8790, Nov. 2023. doi: 10.1109/TNNLS.2022.3153039
[11]	Z. Li, S. Li, and X. Luo, “An overview of calibration technology of industrial robots,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 23–36, Jan. 2021. doi: 10.1109/JAS.2020.1003381
[12]	Z. Li, S. Li, A. Francis, and X. Luo, “A novel calibration system for robot ARM via an open dataset and a learning perspective,” IEEE Trans. Circuits Syst. II, Express Briefs, vol. 69, no. 12, pp. 5169–5173, Dec. 2022. doi: 10.1109/TCSII.2022.3199158
[13]	A. J. Moshayedi, A. S. Khan, S. Yang, and S. M. Zanjani, “Personal image classifier based handy pipe defect recognizer (HPD): Design and test”, in Proc. 7th Int. Conf. Intelligent Computing and Signal Processing, Xi’an, China, 2022, pp. 1721−1728, DOI: 10.1109/ICSP54964.2022.9778676.
[14]	E. Aghajari and A. AbdulKarim AbdulRahim, “Prediction of short circuit current of wind turbines based on artificial neural network model”, EAI Endorsed Trans. AI Robot., vol. 3, Jul. 2024, DOI: 10.4108/airo.5955
[15]	G. Xu, A. Sohail Khan, A. J. Moshayedi, X. Zhang, and Y. Shuxin, “The object detection, perspective and obstacles in robotic: A review,” EAI Endorsed Trans. AI Robotics, vol. 1, p. e13, Oct. 2022. doi: 10.4108/airo.v1i1.2709
[16]	M. Davari, A. Harooni, A. Nasr, K. Savoji, and M. Soleimani, “Improving recognition accuracy for facial expressions using scattering wavelet”, EAI Endorsed Trans. AI Robot., vol. 3, Mar. 2024, DOI: 10.4108/airo.5145
[17]	Q. Wang, X. Zhao, P. Yang, W. Hua, and G. Buja, “Effects of triangular wave injection and current differential terms on multiparameter identification for PMSM,” IEEE Trans. Power Electron., vol. 39, no. 3, pp. 2943–2947, Mar. 2024. doi: 10.1109/TPEL.2023.3342293
[18]	N. T. Nguyen, T. N. T. Nguyen, H. N. Tong, H. V. A. Truong, and D. T. Tran, “Dynamic parameter identification based on the least squares method for a 6-DOF manipulator”, in Proc. Int. Conf. System Science and Engineering, Ho Chi Minh, Vietnam, Jul. 2023. DOI: 10.1109/ICSSE58758.2023.10227164.
[19]	K. G. Duleep, “Internal combustion engine vehicles,” Encyclopedia of Energy, pp. 497–513, 2004. doi: 10.1016/B0-12-176480-X/00178-9
[20]	J. Druant, H. Vansompel, F. De Belie, and P. Sergeant, “Loss identification in a double rotor electrical variable transmission,” IEEE Trans. Ind. Electron., vol. 64, no. 10, pp. 7731–7740, Apr. 2017. doi: 10.1109/TIE.2017.2698362
[21]	A. Wahrburg, S. Klose, D. Clever, T. Groth, S. Moberg, J. Styrud, and H. Ding, “Modeling speed-, load-, and position-dependent friction effects in strain wave gears”, in Proc. IEEE Int. Conf. Robotics and Automation, Brisbane, Australia, 2018. DOI: 10.1109/ICRA.2018.8461043.
[22]	E. Brassitos and N. Jalili, “Dynamic model development and characterization of gear bearing transmission systems: Theory and experiments,” IEEE/ASME Trans. Mechatronics, vol. 24, no. 4, pp. 1651–1661, Aug. 2019. doi: 10.1109/TMECH.2019.2916333
[23]	S. S. Groothuis, G. Rusticelli, A. Zucchelli, S. Stramigioli, and R. Carloni, “The variable stiffness actuator vsaUT-II: mechanical design, modeling, and identification,” IEEE/ASME Trans. Mechatronics, vol. 19, no. 2, pp. 589–597, Mar. 2013. doi: 10.1109/TMECH.2013.2251894
[24]	A. Gupta, “Improved hybrid preprocessing technique for effective segmentation of wheat canopies in chlorophyll fluorescence images”, EAI Endorsed Trans. AI Robot., vol. 3, Jan. 2024, DOI: 10.4108/airo.4621.
[25]	D. Tebaldi, R. Morselli, and R. Zanasi, “Estimation of physical parameters using a new discrete-time derivative algorithm,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 2367–2372, 2020. doi: 10.1016/j.ifacol.2020.12.033
[26]	S. Gao, J. Wang, C. Guan, Z. Zhou, and Z. Liu, “Wheel torque distribution control strategy for electric vehicles dynamic performance with an electric torque vectoring drive axle,” IEEE Trans. Transport. Electrif., vol. 10, no. 1, pp. 1692–1705, Mar. 2024. doi: 10.1109/TTE.2023.3267817
[27]	D. Ramsey, A. Bouscayrol, L. Boulon, and A. Desreveaux, A. Vaudrey, “Flexible simulation of an electric vehicle to estimate the impact of thermal comfort on the energy consumption,” IEEE Trans. Transport. Electrif., vol. 8, no. 2, pp. 2288–2298, Jun. 2022. doi: 10.1109/TTE.2022.3144526
[28]	D. Tebaldi, and R. Zanasi, “The power-oriented graphs modeling technique: From the fundamental principles to the systematic, step-by-step modeling of complex physical systems”, IEEE Access, doi: 10.1109/ACCESS.2025.3537862.

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(10) / Tables(5)

Get Citation

PDF

XML

Article Metrics

Article views (4) PDF downloads(2)

Nonlinear Frictions Identification in Time-Variant Automotive Systems

doi: 10.1109/JAS.2025.125294

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content