The paper introduces a novel modular estimation approach for lateral vehicle and tire dynamics using a simplified vehicle model and a non-linear estimation algorithm. A dynamics-oriented representation of lateral tire forces with a single track lateral vehicle model (STVM) has been introduced. Subsequently, extended Kalman filter (EKF) based distributed observer modules for each dynamical parameter has been designed and combined into a Unified Estimation Scheme (UES). Finally, a linear quadratic regulator (LQR) based Active Front Steering (AFS) control system has been designed using the estimated parameters. The accuracy and computational efficiency of the designed scheme has been analyzed and compared to non-modular UKF, EKF, and Particle Filter (PF) algorithms, through Monte-Carlo Simulations using the CarSim dataset for both high and low surfaces, followed by further validation using real-time dataset. The results show that the proposed system significantly improve the accuracy and speed of estimation, as well as stable performance in closed loop control.
BreuerBEichhornURothJ. Measurement of tyre/road friction ahead of the car and inside the tyre. In: Proceedings of AVEC, Yokohama, Japan, 14–17 September 1992, pp.347–353.
2.
NamKOhSFujimotoH, et al. Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors. In: Proceedings of the 2011 American control conference, Seattle, Washington, DC, 29 June–1 July 2011, pp. 4853–4858.
3.
TuononenAJ. Optical position detection to measure tyre carcass deflections. Veh Syst Dyn2008; 46(6): 471–481.
4.
AlonsoJLopezJPavónI, et al. On-board wet road surface identification using tyre/road noise and support vector machines. Appl Acoust2014; 76: 407–415.
5.
ErdoganGAlexanderLRajamaniR. Estimation of tire-road friction coefficient using a novel wireless piezoelectric tire sensor. IEEE Sens J2011; 11(2): 267–279.
6.
KhaleghianSEmamiATaheriS. A technical survey on tire-road friction estimation. Friction2017; 5(2): 123–146.
7.
SenSChakrabortySSutradharA. Estimation of tire slip-angles for vehicle stability control using kalman filtering approach. In: 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), Shillong, India, 12–13 June 2015, pp.1–6.
8.
DoumiatiMVictorinoAChararaA, et al. A method to estimate the lateral tire force and the sideslip angle of a vehicle: experimental validation. In: Proceedings of the 2010 American control conference, Baltimore, MD, 30 June – 2 July 2010, pp.6936–6942.
9.
BechtoffJKoenigLIsermannR. Cornering stiffness and sideslip angle estimation for integrated vehicle dynamics control. IFAC-PapersOnLine2016; 49(11): 297–304.
10.
HsuLYChenTL. Vehicle full-state estimation and prediction system using state observers. IEEE Trans Veh Technol2009; 58(6): 2651–2662.
11.
SenSChakrabortySSutradharA. Estimation of vehicle yaw rate and lateral motion for dynamic stability control using unscented kalman filtering (UKF) approach. In: Michael Faraday IET international summit, Kolkata, India, 12–13 September 2015, pp.24–29.
12.
DoumiatiMVictorinoAChararaA, et al. Unscented kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation. In: 2009 IEEE intelligent vehicles symposium, Xi’an, China, 3–5 June 2009, pp.901–906.
13.
JiaGLiLCaoD. Model-based estimation for vehicle dynamics states at the limit handling. J Dyn Syst Meas Contr2015; 137(10): 104501.
14.
ChakrabortySSenSSutradharA, et al. Estimation of tire-road friction coefficient and frictional force for active vehicle safety system. In: 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, India, 28 May 20 15–30 May 2015, pp. 674–679.
15.
RaitoharjuMPichéR. On computational complexity reduction methods for kalman filter extensions. IEEE Aerosp Electron Syst Mag2019; 34(10): 2–19.
16.
HuangGPMourikisAIRoumeliotisSI. On the complexity and consistency of ukf-based slam. In: 2009 IEEE International conference on robotics and automation, Kobe, Japan, 12–17 May 2009, pp.4401–4408. New York, NY: IEEE.
17.
GripHFImslandLJohansenTA, et al. Vehicle sideslip estimation. IEEE Control Syst2009; 29(5): 36–52.
18.
HaudumMEdelmannJPlöchlM, et al. Vehicle side-slip angle estimation on a banked and low-friction road. Proc IMechE, Part D: J Automobile Engineering2018; 232(12): 1584–1596.
19.
DortaTJiménezJMartínJL, et al. Overview of fpga-based multiprocessor systems. In: International conference on reconfigurable computing and FPGAs, 2009. ReConFig’09, Cancun, Mexico, 9–11 December 2009, pp.273–278. New York, NY: IEEE.
20.
RajamaniR.Vehicle dynamics and control. New York: Springer Science Business Media, 2011.
21.
CheeW.Yaw rate estimation using two 1-axis accelerometers. In: Proceedings of the 2005, American control conference, Portland, OR, 8–10 June 2005, pp. 423–428. New York, NY: IEEE.
22.
PacejkaHBBakkerE. The magic formula tyre model. Veh Syst Dyn1992; 21(Suppl. 1): 1–18.
23.
PacejkaHBesselinkI. Magic formula tyre model with transient properties. Veh Syst Dyn1997; 27(S1): 234–249.
24.
BesselinkISchmeitzAPacejkaH. An improved magic formula/swift tyre model that can handle inflation pressure changes. Veh Syst Dyn2010; 48(S1): 337–352.
25.
Canudas-de WitCTsiotrasPVelenisE, et al. Dynamic friction models for road/tire longitudinal interaction. Veh Syst Dyn2003; 39(3): 189–226.
26.
KienckeUNielsenL. Automotive control systems: for engine, driveline, and vehicle. Berlin: Springer, 2000.
27.
ArulampalamMSMaskellSGordonN, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process2002; 50: 174–188.
28.
ReinaGPaianoMBlanco-ClaracoJL. Vehicle parameter estimation using a model-based estimator. Mech Syst Signal Process2017; 87: 227–241.
29.
NamKFujimotoHHoriY. Lateral stability control of in-wheel-motor-driven electric vehicles based on sideslip angle estimation using lateral tire force sensors. IEEE Trans Veh Technol2012; 61(5): 1972–1985.
30.
MiyaharaKFujimotoHHoriY, et al. Performance benchmark of yaw rate controllers by active front steering: comparative analysis of model predictive control, linear quadratic integral control and yaw moment observer. In: IECON 2019 - 45th annual conference of the IEEE industrial electronics society, Lisbon, Portugal, 14–17 September 2009, vol. 1, pp.473–478.
31.
HavlíkJStrakaO. Performance evaluation of iterated extended Kalman filter with variable step-length. J Phys Conf Ser2015; 659: 12–22.
32.
KegelmanJCHarbottLKGerdesJC. Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers. Veh Syst Dyn2017; 55(2): 191–207.
