A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

Abstract

This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.

Keywords

Sideslip angle estimation fusion methodology kinematic-based approach model-based approach extended Kalman filter experimental verification

Get full access to this article

View all access options for this article.

References

Gustafsson

. Automotive safety systems. IEEE Signal Proc Mag 2009; 26: 32–47.

Stéphant

Charara

Meizel

. Evaluation of a sliding mode observer for vehicle sideslip angle. Control Eng Pract 2007; 15: 803–812.

Fukada

. Slip-angle estimation for vehicle stability control. Vehicle Syst Dyn 1999; 32: 375–388.

Jia

Ran

, et al. A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road. Vehicle Syst Dyn 2014; 52: 280–308.

Zhang

J-X

. Vehicle sideslip angle estimation based on hybrid Kalman filter. Math Probl Eng 2016; 2016: 3269142.

Salvatore

Mario

. Constrained nonlinear filter for vehicle sideslip angle estimation with no a priori knowledge of tyre characteristics. Control Eng Pract 2018; 71: 10–17.

Sasaki

Takatoshi

. A side-slip angle estimation using neural network for a wheeled vehicle. SAE technical paper 2000-01-0695, 2000.

Miao

P-H

Liu

G-H

. Sideslip angle soft-sensor based on neural network left inversion for multi-wheel independently driven electric vehicles. In: International joint conference on neural networks (IJCNN), Beijing, China, 6–11 July 2014.

Dai

Y-F

Luo

Y-G

Chu

W-B

, et al. Vehicle state estimation based on the integration of low-cost GPS and INS. In: Proceedings of the international conference on advanced vehicle technologies and integration, Changchun, China, July 2012.

10.

Bevly

Ryu

Gerdes

. Integrating INS sensors with GPS measurements for continuous estimation of vehicle sideslip, roll, and tire cornering stiffness. IEEE Trans Intell Transport Syst 2006; 7: 483–493.

11.

Nguyen

Wang

Y-F

Sehoon

, et al. GPS based estimation of vehicle sideslip angle using multi-rate Kalman filter with prediction of course angle measurement residual. In: SAE-China and FISITA (eds) Proceedings of the FISITA 2012 world automotive congress, lecture notes in electrical engineering, vol. 194. Berlin: Springer, 2013, pp.597–609.

12.

Hsu

Tomizuka

. Analyses of vision-based lateral control for automated highway system. Veh Syst Dyn 1998; 30: 345–373.

13.

Wang

Y-F

Nguyen

Fujimoto

, et al. Vision based multi-rate estimation and control of body slip angle for electric vehicles. In: IECON 2012—38th Annual conference on IEEE industrial electronics society, Montreal, QC, Canada, 25–28 October 2012, pp.4278–4283. New York: IEEE.

14.

Klier

Reim

Stapel

. Robust estimation of vehicle sideslip angle—an approach w/o vehicle and tire models. SAE technical paper 2008-01-0582, 2008.

15.

Cheli

Sabbioni

Pesce

, et al. A methodology for vehicle sideslip angle identification: comparison with experimental data. Veh Syst Dyn 2007; 45: 549–563.

16.

Chen

. Reliable sideslip angle estimation of four-wheel independent drive electric vehicle by information iteration and fusion. Hindawi Math Probl Eng 2018; 2018: 9075372.

17.

Chan

C-Y

Wang

. A reliable fusion methodology for simultaneous estimation of vehicle sideslip and yaw angles. IEEE Trans Veh Technol 2016; 65: 4440–4458.

18.

Piyabongkarn

Rajamani

Grogg

, et al. Development and experimental evaluation of a slip angle estimator for vehicle stability control. IEEE T Contr Syst T 2009; 17: 78–88.

19.

Han

Y-S

Hahn

Lee

. New adaptive approaches to real-time estimation of vehicle sideslip angle. Contr Eng Pract 2009; 17: 1367–1379.

20.

Singh

. Virtual sensor for vehicle sideslip angle based on tire model adaptation. arXiv preprint arXiv:1811.09997, 2018.

21.

Selmanaj

Corno

Panzani

, et al. Vehicle sideslip estimation: a kinematic based approach. Contr Eng Pract 2017; 67: 1–12.

22.

Tommaso

Capitani

Annicchiarico

. An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements. Proc IMechE, Part D: J Automobile Engineering 2019; 233: 1864–1878.