Browsing by Author "Khaleghian, Seyedmeysam"

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    3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles
    Maurya, Deepam; Khaleghian, Seyedmeysam; Sriramdas, Rammohan; Kumar, Prashant; Kishore, Ravi Anant; Kang, Min-Gyu; Kumar, Vireshwar; Song, Hyun-Cheol; Lee, Seul-Yi; Yan, Yongke; Park, Jung-Min (Jerry); Taheri, Saied; Priya, Shashank (2020-10-26)
    The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability. Designing efficient sensors for smart tires for autonomous vehicles remains a challenge. Here, the authors present a tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis.
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    The Application of Intelligent Tires and Model Base Estimation Algorithms in Tire-road Contact Characterization
    Khaleghian, Seyedmeysam (Virginia Tech, 2017-02-13)
    Lack of drivers knowledge about the abrupt changes in pavement friction and poor performance of the vehicle stability, traction and ABS controllers on the low friction surfaces are the most important factors affecting car crashes. Due to its direct relation to vehicle stability, accurate estimation of tire-road characteristics is of interest to all vehicle and tire companies. Many studies have been conducted in this field and researchers have used different tools and have proposed different algorithms. One such concept is the Intelligent Tire. The application of intelligent tire in tire-road characterization is investigated in this study. Three different test setups were used in this research to study the application of the intelligent tires to improve mobility; first, a wheeled ground robot was designed and built. A Fuzzy Logic algorithm was developed and validated using the robot for classifying different road surfaces such as asphalt, concrete, grass, and soil. The second test setup is a portable tire testing trailer, which is a quarter car test rig installed in a trailer and towed by a truck. The trailer was equipped with different sensors including an accelerometer attached to the center of the tire inner-liner. Using the trailer, acceleration data was collected under varying conditions and a Neural Network (NN) algorithm was developed and trained to estimate the contact patch length, effective tire rolling radius and tire normal load. The third test setup developed for this study was an instrumented Volkswagen Jetta. Different sensors were installed to measure vehicle dynamic response. Additionally, one front and one rear tire was instrumented with an accelerometer attached to their inner-liner. Two intelligent tire based algorithms, a tire pressure estimation algorithm and a road condition monitoring algorithm, were developed and trained using the experimental data from the instrumented VW Jetta. The two-step pressure monitoring algorithm uses the acceleration signal from the intelligent tire and the wheel angular velocity to monitor the tire pressure. Also, wet and dry surfaces are distinguished using the acceleration signal from the intelligent tire and the wheel angular velocity through the surface monitoring algorithm. Some of the model based tire-road friction estimation algorithms, which are widely used for tire-road friction estimation, were also introduced in this study and the performance of each algorithm was evaluated in high slip and low slip maneuvers. Finally a new friction estimation algorithm was developed, which is a combination of experiment based and vehicle dynamic based approaches and its performance was also investigated.
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    Asperity-based modification on theory of contact mechanics and rubber friction for self-affine fractal surfaces
    Emami, Anahita; Khaleghian, Seyedmeysam; Taheri, Saied (2021-05-15)
    Abstract Modeling the real contact area plays a key role in every tribological process, such as friction, adhesion, and wear. Contact between two solids does not necessarily occur everywhere within the apparent contact area. Considering the multiscale nature of roughness, Persson proposed a theory of contact mechanics for a soft and smooth solid in contact with a rigid rough surface. In this theory, he assumed that the vertical displacement on the soft surface could be approximated by the height profile of the substrate surface. Although this assumption gives an accurate pressure distribution at the interface for complete contact, when no gap exists between two surfaces, it results in an overestimation of elastic energy stored in the material for partial contact, which typically occurs in many practical applications. This issue was later addressed by Persson by including a correction factor obtained from the comparison of the theoretical results with molecular dynamics simulation. This paper proposes a different approach to correct the overestimation of vertical displacement in Persson’s contact theory for rough surfaces with self-affine fractal properties. The results are compared with the correction factor proposed by Persson. The main advantage of the proposed method is that it uses physical parameters such as the surface roughness characteristics, material properties, sliding velocity, and normal load to correct the model. This method is also implemented in the theory of rubber friction. The results of the corrected friction model are compared with experiments. The results confirm that the modified model predicts the friction coefficient as a function of sliding velocity more accurately than the original model.
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    Estimation of the Tire Contact Patch Length and Normal Load Using Intelligent Tires and Its Application in Small Ground Robot to Estimate the Tire-Road Friction
    Khaleghian, Seyedmeysam; Ghasemalizadeh, O.; Taheri, Saied (The Tire Society, 2016)
    Tire-road friction estimation is one of the most popular problems for the tire and vehicle industry. Accurate estimation of the tire-road friction leads to better performance of the traction and antilock braking system controllers, which reduces the number of accidents. Several researchers have worked in the field of friction estimation, and many tire models have been developed to predict the tire-road friction. In this article, an intelligent tire, which has an embedded accelerometer placed on the inner liner of the tire, is used to estimate the tire contact patch length parameter and normal load. To accomplish this, first, an existing tire testing trailer equipped with a force hub to measure tire forces and moments, a high-accuracy encoder to measure the angular velocity of the wheel, and VBOX, which is a global positioning system–based device, to estimate the longitudinal speed of the trailer was used. As a practical application for the normal load algorithm, a wheeled ground robot, which is equipped with several sensors, including an accelerometer and a flexible strain sensor inside the tire (used for terrain identification purposes), was designed and built. A set of algorithms was developed and used with the test data that were collected with both the trailer and the robot, and the contact patch length and the normal load were estimated. Also, the friction potential between the tire and the road was evaluated using a small ground robot.
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    Terrain classification using intelligent tire
    Khaleghian, Seyedmeysam; Taheri, Saied (Pergamon, 2017)
    A wheeled ground robot was designed and built for better understanding of the challenges involved in utilization of accelerometer-based intelligent tires for mobility improvements. Since robot traction forces depend on the surface type and the friction associated with the tire-road interaction, the measured acceleration signals were used for terrain classification and surface characterization. To accomplish this, the robot was instrumented with appropriate sensors (a tri-axial accelerometer attached to the tire innerliner, a single axis accelerometer attached to the robot chassis and wheel speed sensors) and a data acquisition system. Wheel slip was measured accurately using encoders attached to driven and non-driven wheels. A fuzzy logic algorithm was developed and used for terrain classification. This algorithm uses the power of the acceleration signal and wheel slip ratio as inputs and classifies all different surfaces into four main categories; asphalt, concrete, grass, and sand. The performance of the algorithm was evaluated using experimental data and good agreements were observed between the surface types and estimated ones.