Accurate State Estimation for Electro-Mechanical Brake Systems
Electro-mechanical brake (EMB) system is an electric motor based braking force generation module, and it requires various sensors such as motor position, motor current and clamping force sensor for stable vehicle deceleration control. Because fault in these sensors can lead to degradation of the system performance, system monitoring is essential. To build a model based state estimator for the braking system, there are some requirements: the mathematical model presenting the nonlinearity and disturbance of the real system, fast response time and the accurate estimation. To solve this problem, this paper proposes a new EMB model which clamping force term is divided into the linear and nonlinear compensation part, and Kalman filter algorithm is applied to design the state estimator. The proposed model is simple and linear, and Kalman filter algorithm is robust to system noise and guarantees the fast computation time. Additionally, the braking direction aware and contact point aware clamping force estimation techniques are introduced, and they help to improve the accuracy of the state estimation. Lastly, the proposed approach is verified through experiments on the EMB test bench.
KeywordsState estimation Kalman filter Mathematical modeling Electromechanical Brake Nonlinear compensation
This work was supported by the DGIST R&D Program of the Ministry of Science and ICT (19-IT-01).
- 1.Schwarz R, Isermann R, Böhm J, Nell J, Rieth P (1998) Modeling and control of an electromechanical disk brake. SAE Tech. Paper 980600. https://doi.org/10.4271/980600
- 4.Hwang W, Han K, Huh K, Jung J, Kim M (2011) Model-based sensor fault detection algorithm design for electro-mechanical brake. In: 14th International IEEE conference on intelligent transportation systems (ITSC), 2011, pp. 962–967Google Scholar
- 6.Cuibus M, Bostan V, Ambrosii S, Ilas C, Magureanu R (2000) Luenberger, Kalman and neural network observers for sensorless induction motor control. In: Proceedings in the third international power electronics and motion control conference, 2000 (IPEMC 2000), vol. 3, pp 1256–1261Google Scholar
- 7.UmaMageswari A, Joseph Ignatious J, Vinodha R (2012) A comparitive study of Kalman filter, extended kalman filter and unscented Kalman filter for harmonic analysis of the non-stationary signals. Int J Sci Eng Res 3(7):1–9Google Scholar
- 8.Zhang Y, Zhao Z, Lu T, Yuan L, Xu W, Zhu J (2009) A comparative study of Luenberger observer, sliding mode observer and extended Kalman filter for sensorless vector control of induction motor drives. In: Energy Conversion Congress and Exposition (ECCE), 2009. pp 2466–2473Google Scholar
- 16.Schwarz R, Isermann R, Böhm J, Nell J, Rieth P (1999) Clamping force estimation for a brake-by-wire actuator. (No. 1999-01-0482). SAE Technical PaperGoogle Scholar
- 21.Maxon EC-4 pole 305015 200 W. https://www.maxonmotor.com/maxon/view/content/ec-4polemotors. Retrieved 10 Aug 2018