Hitch Force Estimation for Electric Caravan with Deep Learning Method

Abstract

The determination of the hitch force at the articulation point in the vehicle-caravan system is an important parameter that affects the stability of these systems. Especially in the case of electric propulsion generation in the caravan system, determining the effect of this electric propulsion on the vehicle emerges as a data that must be obtained directly or indirectly. In this paper, a deep neural network (DNN) is designed for hitch force estimation. It is modelled to better understand the forces acting on the vehicle-caravan system. The inputs to be applied to the DNN have been selected to consist of parameters affecting the hitch force. While estimating the hitch force at the articulation point, only the sensors in the caravan are used. According to the field test results consisting of 6.09 km, it has been shown that with a DNN, the hitch force can be predicted with an error of 12.26% using only the sensors in the caravan. Compared to existing model-based techniques that achieve an error of 9.5% using inertial measurement unit (IMU) and global positioning system (GPS) sensors in the towing vehicle, the proposed method is considered a practical and sensor-efficient option. The obtained results confirm that DNN-based prediction methods can be an alternative technique for vehicle-caravan systems and show the potential for further accuracy improvements through additional training data and different test scenarios.

Keywords

• deep nueral network
• estimation
• vehicle-caravan system

References

1. [1] R. Freimann, S. Maier, and A. Sannia, “Self-Propelled Trailers–An Approach to Type Approval,” in 21. Internationales Stuttgarter Symposium, 2021, pp. 16–28.
2. [2] Y. He, H. Elmaraghy, and W. Elmaraghy, “A design analysis approach for improving the stability of dynamic systems with application to the design of car-trailer systems,” Journal of Vibration and control, vol. 11, no. 12, pp. 1487–1509, 2005.
3. [3] X. Kang and W. Deng, “Vehicle-trailer handling dynamics and stability control an engineering review,” 2007.
4. [4] J. Darling, D. Tilley, and B. Gao, “An experimental investigation of car—trailer high-speed stability,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 223, no. 4, pp. 471–484, 2009.
5. [5] Y. Zhao, S. Chen, and T. Shim, “Investigation of trailer yaw motion control using active front steer and differential brake,” SAE International Journal of Materials and Manufacturing, vol. 4, no. 1, pp. 1057–1067, 2011. [6] A. Hac, D. Fulk, and H. Chen, “Stability and control considerations of vehicle-trailer combination,” SAE International Journal of Passenger Cars-Mechanical Systems, vol. 1, no. 2008-01–1228, pp. 925–937, 2008.
6. [7] R. Shamim, M. M. Islam, and Y. He, “A comparative study of active control strategies for improving lateral stability of car-trailer systems,” SAE Technical Paper, 2011-01-0959, 2011.
7. [8] M. Abroshan, R. Hajiloo, E. Hashemi, and A. Khajepour, “Model predictive-based tractor-trailer stabilisation using differential braking with experimental verification,” Vehicle system dynamics, vol. 59, no. 8, pp. 1190–1213, 2021.
8. [9] D. Kasinathan, A. Kasaiezadeh, A. Wong, A. Khajepour, S.-K. Chen, and B. Litkouhi, “An optimal torque vectoring control for vehicle applications via real-time constraints,” IEEE Trans Veh Technol, vol. 65, no. 6, pp. 4368–4378, 2015.

9. [10] M. Mirzaei and H. Mirzaeinejad, “Fuzzy scheduled optimal control of integrated vehicle braking and steering systems,” IEEE/ASME Transactions on Mechatronics, vol. 22, no. 5, pp. 2369–2379, 2017.
10. [11] S. Vempaty and Y. He, “A review of car-trailer lateral stability control approaches,”, SAE Technical Paper 2017-01-1580, 2017.
11. [12] A. H. Korayem, A. Khajepour, and B. Fidan, “Trailer mass estimation using system model-based and machine learning approaches,” IEEE Trans Veh Technol, vol. 69, no. 11, pp. 12536–12546, 2020.
12. [13] M. M. Islam, Y. He, S. Zhu, and Q. Wang, “A comparative study of multi-trailer articulated heavy-vehicle models,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 229, no. 9, pp. 1200–1228, 2015.
13. [14] W. Deng, Y. H. Lee, and M. Tian, “An integrated chassis control for vehicle-trailer stability and handling performance,” SAE transactions, pp. 1041–1046, 2004.
14. [15] Z. Brock, J. Nelson, and R. L. Hatton, “A comparison of lateral dynamic models for tractor-trailer systems,” in 2019 IEEE Intelligent Vehicles Symposium (IV), 2019, pp. 2052–2059.
15. [16] N. M’Sirdi, A. Boubezoul, A. Rabhi, and L. Fridman, “Sliding modes observers for estimation of performance of heavy vehicles,” in 3rd Int. Conf. on Advances in Vehicle Control and Safety, 2007, pp. 313–318.
16. [17] E. Gartley and D. M. Bevly, “Online estimation of implement dynamics for adaptive steering control of farm tractors,” IEEE/ASME Transactions on Mechatronics, vol. 13, no. 4, pp. 429–440, 2008.
17. [18] O. Khemoudj, H. Imine, and M. Djemai, “Heavy duty vehicle tyre forces estimation using variable gain sliding mode observer,” International journal of vehicle design, vol. 62, no. 2–4, pp. 274–288, 2013.
18. [19] Z. Ziaukas, M. Wielitzka, T. Ortmaier, and J.-P. Kobler, “Simultaneous estimation of steering and articulation angle in a truck-semitrailer combination solely based on trailer signals,” in 2019 American Control Conference (ACC), 2019, pp. 2509–2514.
19. [20] Z. Hu and E. M. Lavoie, “Multi-stage solution for trailer hitch angle initialization.” Google Patents, Nov. 2017.
20. [21] K. Olutomilayo and D. R. Fuhrmann, “Estimation of trailer-vehicle articulation angle using 2d point-cloud data,” in 2019 IEEE Radar Conference (RadarConf), 2019, pp. 1–6.
21. [22] S. Kim, J. Yang, and K. Huh, “Articulation angle estimation and control for reversing articulated vehicles,” in Advanced Vehicle Control AVEC’16, CRC Press, 2016, pp. 533–538.
22. [23] J.-H. Yoon and H. Peng, “Robust vehicle sideslip angle estimation through a disturbance rejection filter that integrates a magnetometer with GPS,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 191–204, 2013.
23. [24] G. Park, S. B. Choi, D. Hyun, and J. Lee, “Integrated observer approach using in-vehicle sensors and GPS for vehicle state estimation,” Mechatronics, vol. 50, pp. 134–147, 2018.
24. [25] L. Imsland, T. A. Johansen, T. I. Fossen, H. F. Grip, J. C. Kalkkuhl, and A. Suissa, “Vehicle velocity estimation using nonlinear observers,” Automatica, vol. 42, no. 12, pp. 2091–2103, 2006.
25. [26] J. J. Oh and S. B. Choi, “Vehicle velocity observer design using 6-D IMU and multiple-observer approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1865–1879, 2012.
26. [27] Y. Qin, R. Langari, Z. Wang, C. Xiang, and M. Dong, “Road excitation classification for semi-active suspension system with deep neural networks,” Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1907–1918, 2017.
27. [28] S. Blume, P. M. Sieberg, N. Maas, and D. Schramm, “Neural roll angle estimation in a model predictive control system,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 1625–1630.
28. [29] J. Garcia Guzman, L. Prieto Gonzalez, J. Pajares Redondo, M. M. Montalvo Martinez, and M. J. L. Boada, “Real-time vehicle roll angle estimation based on neural networks in IoT low-cost devices,” Sensors, vol. 18, no. 7, p. 2188, 2018.
29. [30] T. Gräber, S. Lupberger, M. Unterreiner, and D. Schramm, “A hybrid approach to side-slip angle estimation with recurrent neural networks and kinematic vehicle models,” IEEE Transactions on Intelligent Vehicles, vol. 4, no. 1, pp. 39–47, 2018.
30. [31] S. Fang and K. Yu, “Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation,” IFAC-PapersOnLine, vol. 58, no. 28, pp. 810–815, Jan. 2024, doi: 10.1016/J.IFACOL.2025.01.075.
31. [32] S. Öngir, E. C. Kaleli, M. Z. Konyar, and H. M. Ertunç, “Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method,” Applied Sciences 2025, Vol. 15, Page 123, vol. 15, no. 1, p. 123, Dec. 2024, doi: 10.3390/APP15010123.
32. [33] S. Torabi, M. Wahde, and P. Hartono, “Road grade and vehicle mass estimation for heavy-duty vehicles using feedforward neural networks,” in 2019 4th international conference on intelligent transportation engineering (ICITE), 2019, pp. 316–321.
33. [34] A. Vahidi, A. Stefanopoulou, and H. Peng, “Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments,” Vehicle System Dynamics, vol. 43, no. 1, pp. 31–55, 2005.
34. [35] A. Habibnejad Korayem, “State and Parameter Estimation of Vehicle-Trailer Systems,” University of Waterloo, 2021.
35. [36] A. H. Korayem, A. Khajepour, and B. Fidan, “A review on vehicle-trailer state and parameter estimation,” IEEE Transactions on intelligent transportation systems, 2021.