IMECE2002-32157 GPS-BASED REAL-TIME IDENTIFICATION OF TIRE-ROAD FRICTION COEFFICIENT

ABSTRACT Vehicle control systems such as collision avoidance, adaptive cruise control and automated lane-keeping systems as well as ABS and stability control systems can benefit significantly from being made "road-adaptive". The estimation of tire-road friction coefficient at the wheels allows the control algorithm in such systems to adapt to external driving conditions. This paper develops a new tire-road friction coefficient estimation algorithm based on measurements related to the lateral dynamics of the vehicle. A lateral tire force model parameterized as a function of slip angle, friction coefficient, normal force and cornering stiffness is used. A real-time parameter identification algorithm that utilizes measurements from a differential GPS system and a gyroscope is used to identify the tire-road friction coefficient and cornering stiffness parameters of the tire. The advantage of the developed algorithm is that it does not require large longitudinal slip in order to provide reliable friction estimates. Simulation studies indicate that a parameter convergence rate of one second can be obtained. Experiments conducted on both dry and slippery road indicate that the algorithm can work very effectively in identifying a slippery road. Two other new approaches to realtime tire road friction identification system are also discussed in the paper

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

On line estimation of rolling resistance for intelligent tires

The analysis of a rolling tire is a complex problem of nonlinear elasticity. Although in the technical literature some tire models have been presented, the phenomena involved in the tire rolling are far to be completely understood. In particular, small knowledge comes even from experimental direct observation of the rolling tire, in terms of dynamic contact patch, instantaneous dissipation due to rubber-road friction and hysteretic behavior of the tire structure, and instantaneous grip. This paper illustrates in details a new powerful technology that the research group has developed in the context of the project OPTYRE. A new wireless optical system based on Fiber Bragg Grating strain sensors permits a direct observation of the inner tire stress when rolling in real conditions on the road. From this information, following a new suitably developed tire model, it is possible to identify the instant area of the contact patch, the grip conditions as well the instant dissipation, which is the object of the present work

Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental settin

A multisensing setup for the intelligent tire monitoring

The present paper offers the chance to experimentally measure, for the first time, the internal tire strain by optical fiber sensors during the tire rolling in real operating conditions. The phenomena that take place during the tire rolling are in fact far from being completely understood. Despite several models available in the technical literature, there is not a correspondently large set of experimental observations. The paper includes the detailed description of the new multi-sensing technology for an ongoing vehicle measurement, which the research group has developed in the context of the project OPTYRE. The experimental apparatus is mainly based on the use of optical fibers with embedded Fiber Bragg Gratings sensors for the acquisition of the circumferential tire strain. Other sensors are also installed on the tire, such as a phonic wheel, a uniaxial accelerometer, and a dynamic temperature sensor. The acquired information is used as input variables in dedicated algorithms that allow the identification of key parameters, such as the dynamic contact patch, instantaneous dissipation and instantaneous grip. The OPTYRE project brings a contribution into the field of experimental grip monitoring of wheeled vehicles, with implications both on passive and active safety characteristics of cars and motorbikes

Traction control of an electric vehicle based on nonlinear observers

[ES] En este trabajo se propone una estrategia de control de tracción para un vehículo eléctrico de cuatro ruedas, basada en observadores no lineales que permiten estimar la fuerza máxima que se puede transferir al suelo. El conocimiento de la fuerza máxima permite realizar un control del deslizamiento de los neumáticos de tracción, evitando que las ruedas patinen aún en superficies de baja adherencia. La estrategia propuesta permite además evitar que se produzca un momento de guiño no deseado en el vehículo cuando las condiciones de suelo a cada lado del mismo son diferentes. Con ello se logra mejorar la eficiencia y el control del vehículo, evitando posibles pérdidas de estabilidad que pueden resultar en riesgos para sus ocupantes. Tanto el observador como el control propuestos son diseñados en base a un modelo dinámico rotacional de la rueda y un modelo de fuerzas de brush. Se presentan resultados de simulación obtenidos empleando un modelo completo de vehículo sobre la plataforma Simulink/CarSim.[EN] A traction control strategy for a four-wheel electric vehicle is proposed in this paper. The strategy is based on nonlinear observers which allows estimating the maximum force that can be transmitted to the road. Knowledge of the maximum force allows controlling the slip of the driving wheels, preventing the wheel’s slippage in low-grip surfaces. The proposed strategy also allows to avoid the undesired yaw moment in the vehicle which occurs when road conditions on either side of it are dierent. This improves the eciency and the control of the vehicle, avoiding possible losses of stability that can result in risks for its occupants. Both the proposed observer and the control strategy are designed based on a dynamic rotational model of the wheel and a brush force model. Simulation results are obtained based on a complete vehicle model on the Simulink/CarSim platform.Este trabajo fue financiado por la Universidad Nacional de Rıo Cuarto, FONCyT-ANPCyT (Subsidio PICT-2014-2760) y CONICET (Subsidio PIP 2014-2016 GI 11220130100517CO).Aligia, DA.; Magallán, GA.; De Angelo, CH. (2017). Control de Tracción para un Vehículo Eléctrico basado en Observadores no Lineales. Revista Iberoamericana de Automática e Informática industrial. 15(1):112-123. https://doi.org/10.4995/riai.2017.8736OJS112123151Baet, G., Charara, A., Dherbomez, G., 2007. An observer of tire-road forcesand friction for active security vehicle systems. IEEE/ASME Transactionson Mechatronics 12 (6), 651-661.Biagiola, S., Solsona, J., 2006. State estimation in batch processes using a non-linear observer. Mathematical and Computer Modelling 44 (11-12), 1009-1024. https://doi.org/10.1016/j.mcm.2006.03.005Changsun, A., Huei, P., Tseng, H. E., 2013. Robust estimation of road frictional coefficient. 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A survey of traction control and antilock braking systems of full electric vehicles with individually con-trolled electric motors. IEEE Transactions on Vehicular Technology 64 (9), 3878-3896. https://doi.org/10.1109/TVT.2014.2361860Kuntanapreeda, S., 2014. Traction control of electric vehicles using sliding-mode controller with tractive force observer. International Journal of Vehicular Technology 2014. https://doi.org/10.1155/2014/829097Li, L., Yang, K., Jia, G., Ran, X., Song, J., Han, Z.-Q., 2015. Comprehensive tire-road friction coefficient estimation based on signal fusion method under complex maneuvering operations. Mechanical Systems and Signal Processing 56, 259-276. https://doi.org/10.1016/j.ymssp.2014.10.006Loeb, J. S., Guenther, D. A., Chen, H.-H. F., Ellis, J. R., 1990. Lateral stiness, cornering stiness and relaxation length of the pneumatic tire. Tech. rep., SAE Technical Paper.Magallan, G. A., De Angelo, C. H., Garcia, G. O., 2009. A neighbourhood-electric vehicle development with individual traction on rear wheels. Inter-national Journal of Electric and Hybrid Vehicles 2 (2), 115-136. https://doi.org/10.1504/IJEHV.2009.029037Magallan, G. A., De Angelo, C. H., Garcia, G. O., 2011. Maximization of the traction forces in a 2wd electric vehicle. IEEE Transactions on Vehicular Technology 60 (2), 369-380. https://doi.org/10.1109/TVT.2010.2091659Mooryong, C., Oh, J. J., Choi, S. B., 2013. Linearized recursive least squares methods for real-time identification of tire-road friction coefficient. IEEE Transactions on Vehicular Technology 62 (7), 2906-2918. https://doi.org/10.1109/TVT.2013.2260190Pacejka, H. B., 2005. Tyre and vehicle dynamics, 2nd Edition. Elsevier.Pacejka, H. B., Sharp, R. S., 1991. Shear force development by pneumatic ty-res in steady state conditions: a review of modelling aspects. Vehicle system dynamics 20 (3-4), 121-175. https://doi.org/10.1080/00423119108968983Rajamani, R., 2011. Vehicle dynamics and control. Springer.Rajamani, R., Phanomchoeng, G., Piyabongkarn, D., Lew, J. Y., 2012. Algorithms for real-time estimation of individual wheel tire-road friction coefficients. IEEE/ASME Transactions on Mechatronics 17 (6), 1183-1195. https://doi.org/10.1109/TMECH.2011.2159240Sanghyun, H., Hedrick, J. K., 2013. Tire-road friction coefficient estimation with vehicle steering. In: 2013 IEEE Intelligent Vehicles Symposium. Pp.1227-1232.Serrano-Iribarnegaray, L., Martinez-Roman, J., Aug 2007. A unified approach to the very fast torque control methods for DC and AC machines. IEEE Transactions on Industrial Electronics 54 (4), 2047-2056. https://doi.org/10.1109/TIE.2007.895148Singh, K. B., Taheri, S., 2015. Estimation of tire-road friction coefficient and its application in chassis control systems. Systems Science & Control Engineering 3 (1), 39-61. https://doi.org/10.1080/21642583.2014.985804Sui, D., Johansen, T. A., 2010. Moving horizon estimation for tire-road friction during braking. In: 2010 IEEE International Conference on Control Applications (CCA). pp. 1379-1384. https://doi.org/10.1109/CCA.2010.5611307Tesheng, H., 2013a. Direct longitudinal tire force control under simultaneous acceleration/deceleration and turning. In: American Control Conference (ACC), 2013. pp. 2147-2152. https://doi.org/10.1109/ACC.2013.6580153Tesheng, H., 2013b. Robust estimation and control of tire traction forces. IEEE Transactions on Vehicular Technology 62 (3), 1378-1383. https://doi.org/10.1109/TVT.2012.2230656Wanki, C., Jangyeol, Y., Seongjin, Y., Bongyeong, K., Kyongsu, Y., 2010.Estimation of tire forces for application to vehicle stability control. IEEE Transactions on Vehicular Technology 59 (2), 638-649 https://doi.org/10.1109/TVT.2009.203426

Investigating the Feasibility of Integrating Pavement Friction and Texture Depth Data in Modeling for INDOT PMS

Under INDOT’s current friction testing program, the friction is measured annually on interstates but only once every three years on non-interstate roadways. The state’s Pavement Management System, however, would require current data if friction were to be included in the PMS. During routine pavement condition monitoring for the PMS, texture data is collected annually. This study explored the feasibility of using this pavement texture data to estimate the friction during those years when friction is not measured directly. After multi0ple approaches and a wide variety of ways of examining the currently available data and texture measuring technologies, it was determined that it is not currently feasible to use the texture data as a surrogate for friction testing. This is likely because the lasers used at this time are not capable of capturing the small-scale pavement microtexture. This situation may change, however, with advances in laser or photo interpretation technologies and improved access to materials data throughout the INDOT pavement network

Some remarks on wheeled autonomous vehicles and the evolution of their control design

Recent investigations on the longitudinal and lateral control of wheeled autonomous vehicles are reported. Flatness-based techniques are first introduced via a simplified model. It depends on some physical parameters, like cornering stiffness coefficients of the tires, friction coefficient of the road, ..., which are notoriously difficult to identify. Then a model-free control strategy, which exploits the flat outputs, is proposed. Those outputs also depend on physical parameters which are poorly known, i.e., the vehicle mass and inertia and the position of the center of gravity. A totally model-free control law is therefore adopted. It employs natural output variables, namely the longitudinal velocity and the lateral deviation of the vehicle. This last method, which is easily understandable and implementable, ensures a robust trajectory tracking problem in both longitudinal and lateral dynamics. Several convincing computer simulations are displayed.Comment: 9th IFAC Symposium on Intelligent Autonomous Vehicles (Leipzig, Germany, 29.06.2016 - 01.07.2016