Development of an Integrated Estimation Method for Vehicle States, Parameters and Tire Forces

Stability and desirable performance of vehicle control systems are directly dependent on the quality and accuracy of sensory and estimated data provided to the controllers. Tire forces and vehicle states such as lateral and longitudinal velocities are required information for most vehicle control systems. However, there are challenges associated with efficient estimation of tire forces and vehicle states. Furthermore, changes in vehicle inertial parameters, road grade, and bank angle all have major influences on both tire forces and vehicle states. Efficient identification of these parameters requires sufficient information about a set of vehicle states and tire forces. This duality relationship mandates the development of efficient methods for simultaneous estimation of tire forces, vehicle states, and vehicle and road parameters. This research proposes the design of an integrated estimation structure that can simultaneously estimate tire forces, vehicle velocity, vehicle inertial parameters, and road angles. The proposed structure is robust against variations in tire parameters because of tire brand, wear, and road friction coefficient. The methods developed in this thesis are all validated experimentally on multiple vehicle platform.4 month

Investigation on dynamics of a three-directional coupled vehicle-road system

When a vehicle is braking or turning, the longitudinal or lateral tire forces increase greatly and it is necessary to consider the effects of vertical, longitudinal and lateral tire forces on vehicle and road dynamics. This work aims to propose a three-directional coupled vehicle-road system for revealing the properties of three-directional (3D) interaction between vehicle and road. A 23-DOF full-body heavy vehicle model considering the nonlinearity of suspension damping and tire stiffness is built, and a double-layer rectangular thin plate on viscoelastic foundation with four simply supported boundaries is employed to model the road. The equations of motion of vehicle and road, and the 3D tire forces connecting the vehicle and road are formulated. The responses of 3D coupled, vertical coupled and uncoupled vehicle-road model are compared in four maneuver conditions and the effects of parameters on 3D vehicle-road interaction are discussed. It is found that both the 3D coupled model and the vertical coupled model are good enough to predict vehicle responses accurately, but the 3D coupled model is the most suitable for calculating road responses accurately. During the maneuver of sharp steering or emergency braking, or when a vehicle runs on a road with small surface roughness and big adhesion coefficient, the role of 3D vehicle-road interaction becomes too important to be neglected

Towards Intelligent Tire and Self-Powered Sensing Systems

Tires are the interface between a vehicle and the ground providing forces and isolation to the vehicle. For vehicle safety, stability, maintenance, and performance, it is vital to estimate or measure tire forces, inflation pressure, and contact friction coefficient. Estimation methods can predict tire forces to some extent however; they fail in harsh maneuvers and are dependent on road surface conditions for which there is no robust estimation method. Measurement devices for tire forces exist for vehicle testing but at the cost of tens of thousands of dollars. Tire pressure-monitoring sensors (TPMS) are the only sensors available in newer and higher end vehicles to provide tire pressure, but there are no sensors to measure road surface condition or tire forces for production vehicles. With the prospect of autonomous driving on roads in near future, it is paramount to make the vehicles safe on any driving and road condition. This is only possible by additional sensors to make up for the driver’s cognitive and sensory system. Measuring road condition and tire forces especially in autonomous vehicles are vital in their safety, reliability, and public confidence in automated driving. Real time measurement of road condition and tire forces in buses and trucks can significantly improve the safety of road transportation system, and in miming/construction and off-road vehicles can improve performance, tire life and reduce operational costs. In this thesis, five different types of sensors are designed, modelled, optimized and fabricated with the objective of developing an intelligent tire. In order to design these sensors,~both electromagnetic generator (EMG) and triboelectric nanogenerators (TENG) are used. In the first two initial designed sensors, with the combination of EMG and TENG into a single package, two hybridized sensors are fabricated with promising potential for self-powered sensing. The potential of developed sensors are investigated for tire-condition monitoring system (TCMS). Considering the impressive properties of TENG units of the developed hybridized devices, three different flexible nanogenerators, only based on this newly developed technology, are developed for TCMS. The design, modelling, working mechanism, fabrication procedure, and experimental results of these TENG sensors are fully presented for applications in TCMS. Among these three fabricated sensors, one of them shows an excellent capability for TCMS because of its high flexibility, stable and high electrical output,and an encapsulated structure. The high flexibility of developed TENG sensor is a very appealing feature for TCMS, which cannot be found in any available commercial sensor. The fabricated TENG sensors are used for developing an intelligent tire module to be eventually used for road testing. Several laboratory and road tests are performed to study the capability of this newly developed TENG-based sensor for tire-condition monitoring system. However the development of this sensor is in its early stage, it shows a promising potential for installation into the hostile environment of tires and measuring tire-road interacting forces. A comparative studies are provided with respect to Michigan Scientific transducer to investigate the potential of this flexible nanogenerator for TCMS. It is worth mentioning that this PhD thesis presents one of the earliest works on the application of TENG-based sensor for a real-life system. Also, the potential of commercially available thermally and mechanically durable Micro Fiber Composite (MFC) sensor is experimentally investigated for TCMS with fabricating another set of intelligent tire. Several testing scenarios are performed to examine the potential of these sensors for TCMS taking into account a simultaneous measurement from Michigan Scientific transducer. Although both flexibility and the cost of this sensor is not comparable with the fabricated TENG device, they have shown a considerable and reliable performance for online measuring of tire dynamical parameters in different testing scenarios, as they can be used for both energy harvesting and sensing application in TCMS. The extensive road testing results based on the MFC sensors provide a valuable set of data for future research in TCMS. It is experimentally shown that MFC sensor can generate up to 1.4 $\mu W$ electrical power at the speed of 28 $[kph]$. This electrical output shows the high capability of this sensor for self-powered sensing application in TCMS. Results of this thesis can be used as a framework by researchers towards self-powered sensing system for real-world applications such as intelligent tires

On the handling performance of a vehicle with different front-to-rear wheel torque distributions

The handling characteristic is a classical topic of vehicle dynamics. Usually, vehicle handling is studied through the analysis of the understeer coe�cient in quasi-steady-state maneuvers. In this paper, experimental tests are performed on an electric vehicle with four independent mo- tors, which is able to reproduce front-wheel-drive, rear-wheel-drive and all-wheel-drive (FWD, RWD and AWD, respectively) architectures. The handling characteristics of each architecture are inferred through classical and new concepts. More speci�cally, the study presents a pro- cedure to compute the longitudinal and lateral tire forces, which is based on a �rst estimate and a subsequent correction of the tire forces that guarantee the equilibrium. A yaw moment analysis is then performed to identify the contributions of the longitudinal and lateral forces. The results show a good agreement between the classical and new formulations of the un- dersteer coe�cient, and allow to infer a relationship between the understeer coe�cient and the yaw moment analysis. The handling characteristics for the considered maneuvers vary with the vehicle speed and front-to-rear wheel torque distribution. In particular, an apparently surprising result arises at low speed, where the RWD architecture is the most understeering con�guration. This outcome is discussed through the yaw moment analysis, highlighting the yaw moment caused by the longitudinal forces of the front tires, which is signi�cant for high values of lateral acceleration and steering angle

Fuzzy Logic based Modelling and Simulation Approach for the estimation of Tire Forces

AbstractTire modeling is an important aspect of vehicle dynamics as the forces and moments required to control the vehicle's motion are eventually transmitted through the tire and the tire road interface is also an important source for the dynamic excitation of the vehicle. This paper presents a fuzzy logic based approach for estimating tire forces, aligning moment of tire for the different slip ratio and slip angles. Proposed fuzzy logic approach requires slip angle and slip ratio, as the input variables, and estimates the longitudinal force, lateral force, aligning moment as the output variables. Membership functions of input, output variables and fuzzy rules are formulated based on the values obtained using the widely adopted Magic formula for tire model. Simulation values for longitudinal, lateral forces and aligning moment of the tire using the proposed fuzzy model is found to provide good correlation with the magic model. Proposed fuzzy logic frame work does not require the estimation of model parameters used in the Magic formula and it will be useful in developing vehicle control system

Intelligent Torque Vectoring Approach for Electric Vehicles with Per-Wheel Motors

Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.The research leading to these results has been supported by the ECSEL Joint Undertaking under Grant agreement no. 662192 (3Ccar).This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation program and the ECSEL member states

Series active variable geometry suspension application to comfort enhancement

This paper explores the potential of the Series Active Variable Geometry Suspension (SAVGS) for comfort and road holding enhancement. The SAVGS concept introduces significant nonlinearities associated with the rotation of the mechanical link that connects the chassis to the spring-damper unit. Although conventional linearization procedures implemented in multi-body software packages can deal with this configuration, they produce linear models of reduced applicability. To overcome this limitation, an alternative linearization approach based on energy conservation principles is proposed and successfully applied to one corner of the car, thus enabling the use of linear robust control techniques. An H∞ controller is synthesized for this simplified quarter-car linear model and tuned based on the singular value decomposition of the system's transfer matrix. The proposed control is thoroughly tested with one-corner and full-vehicle nonlinear multi-body models. In the SAVGS setup, the actuator appears in series with the passive spring-damper and therefore it would typically be categorized as a low bandwidth or slow active suspension. However, results presented in this paper for an SAVGS-retrofitted Grand Tourer show that this technology has the potential to also improve the high frequency suspension functions such as comfort and road holding

Modelling and validation of off-road vehicle ride dynamics

Increasing concerns on human driver comfort/health and emerging demands on suspension systems for off-road vehicles call for an effective and efficient off-road vehicle ride dynamics model. This study devotes both analytical and experimental efforts in developing a comprehensive off-road vehicle ride dynamics model. A three-dimensional tire model is formulated to characterize tire–terrain interactions along all the three translational axes. The random roughness properties of the two parallel tracks of terrain profiles are further synthesized considering equivalent undeformable terrain and a coherence function between the two tracks. The terrain roughness model, derived from the field-measured responses of a conventional forestry skidder, was considered for the synthesis. The simulation results of the suspended and unsuspended vehicle models are derived in terms of acceleration PSD, and weighted and unweighted rms acceleration along the different axes at the driver seat location. Comparisons of the model responses with the measured data revealed that the proposed model can yield reasonably good predictions of the ride responses along the translational as well as rotational axes for both the conventional and suspended vehicles. The developed off-road vehicle ride dynamics model could serve as an effective and efficient tool for predicting vehicle ride vibrations, to seek designs of primary and secondary suspensions, and to evaluate the roles of various operating conditions