Browse Topic: Tire friction
This paper proposes a nonlinear and robust State-Dependent Riccati Equation (SDRE) combined with H∞ control architecture for brake- by-wire systems, specifically designed to handle severe tire-road friction variations and μ-split scenarios. The primary objective is to maximize deceleration capabilities while rigorously maintaining yaw stability, trajectory tracking, and passenger comfort through jerk limitation. Situated within the domain of active safety, this research addresses robustness against real-world uncertainties by utilizing a high-fidelity 14-degree-of-freedom vehicle model that accounts for longitudinal, lateral, and yaw dynamics, suspension-induced pitch and roll effects, and nonlinear tire behavior with explicit load transfer. To ensure near-optimal slip tracking under variable surface conditions, the system employs online friction estimation via Extended and Unscented Kalman Filters (EKF/UKF) fusing wheel and IMU data to adaptively adjust slip targets. The control strategy is bifurcated: the SDRE component manages dominant nonlinearities through state-dependent gains to prevent wheel lock-up, while the H∞ component provides robust disturbance rejection against parametric uncertainties such as mass variations and sensor noise. Control efforts are distributed via a Quadratic Programming (QP) torque allocator featuring anti-windup mechanisms and explicit saturation handling to compensate for lateral drift during μ-split braking. Validation is conducted through a Model- in-the-Loop (MIL) to Software-in-the-Loop (SIL) pipeline using scenarios including wet surfaces and panic braking. Simulation results demonstrate enhanced yaw stability and controlled deceleration profiles compared to conventional baselines, ensuring computational feasibility for automotive Electronic Control Units (ECUs).
Unlike internal combustion engine (IC Engine) vehicles, the rapidly growing electric vehicle (EV) market demands tyres with superior yet often conflicting performance characteristics. The increased weight of EVs, due to their heavy batteries, necessitates robust tyres with reinforcement and higher inflation pressure. Conversely, increased wear due to higher initial torque and the need for lower rolling resistance to extend range, combined with the requirement for better grip for improved handling, call for advanced compound and tread pattern designs. EV tyres need to be stiffer, lighter, and low hysteresis, making it very hard to reduce low-frequency (20-200 Hz) interior noise that was previously masked by engine noise. This study investigates the low-frequency (20-200 Hz) structural-borne interior noise performance of EV tyres using both experimental and simulation tools. By wisely tuning the tyre's stiffness, mass, and damping properties, the necessary noise targets can be achieved. These findings can help tyre development engineers devise more effective and quicker noise reduction strategies for EVs with minimal compromise on other tyre performance aspects.
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a constant water depth. Therefore, the water pressure in the tread grooves will also increase as the longitudinal speed of the vehicle approaches the critical hydroplaning speed. Unlike conventional systems, the proposed intelligent tire design utilizes the amplitude and shape of the measured pressure signals from the tread grooves for estimating the total hydroplaning risk in both shallow and deep water conditions. Experimental results indicate that peak groove water pressure increases with the risk of total hydroplaning. Furthermore, the overall shape of the pressure signal will also be influenced by the total hydroplaning risk. By addressing the limitations of current intelligent tire systems, the proposed intelligent tire design offers a robust solution for real-time total hydroplaning risk estimation across diverse driving conditions.
The increased importance of aerodynamics to help with overall vehicle efficiency necessitates a desire to improve the accuracy of the measuring methods. To help with that goal, this paper will provide a method for correcting belt-whip and wheel ventilation drag on single and 3-belt wind tunnels. This is primarily done through a method of analyzing rolling-road only speed sweeps but also physically implementing a barrier. When understanding the aerodynamic forces applied to a vehicle in a wind tunnel, the goal is to isolate only those forces that it would see in the real-world. This primarily means removing the weight of the vehicle from the vertical force and the rolling resistance of the tires and bearings from the longitudinal force. This is traditionally done by subtracting the no-wind forces from the wind at testing velocity forces. The first issue with the traditional method is that a boundary layer builds up on the belt(s), which can then influence a force onto the vehicle’s undercarriage. The wheels and tires impart energy into the air relative to the velocity they rotate, typically called pumping-losses or ventilation-drag. These pumping-losses will be measured in the no-wind condition and subsequently subtracted out in the traditional method. This paper will cover methods for eliminating or reducing these effects and covering the consequences of doing so. It will start by going into the effects of using solid tires in reduced-scale model testing. Then it will explore expanding upon that for full vehicles using traditional pneumatic tires. In doing so, we will cover breaking out belt-whip and wheel pumping-losses. This paper will also cover some example results and suggested next steps.
From humble Chevrolet Bolts to six-figure Lucid Airs, every EV can reverse its electric motors to slow the vehicle while harvesting energy for the battery, the efficient tag-team process known as regenerative braking. Today's EVs do this so well that traditional friction brakes, which clamp onto a spinning wheel rotor or drum, can seem an afterthought. Witness Volkswagen's decision to equip its ID.4 with old-fashioned rear drum brakes, with VW claiming drums reduce EV rolling resistance and offer superior performance after long periods of disuse.
For all the engineering that takes place at the Treadwell Research Park (TRP), Discount Tire's chief product and technical officer John Baldwin told SAE Media that there's actually something akin to magic in the way giga-reams of test data are converted into information non-engineers can usefully understand. TRP is where Discount Tire generates data used by the algorithms behind its Treadwell tire shopping guide. The consumer-facing Treadwell tool, available in an app, a website and in stores, provides tire shoppers with personalized, simple-to-understand recommendations that are mostly based on a five-star scale. Discount Tire and its partners have tested over 20,000 SKUs, representing 500 to 1000 different types of tires over the years, Baldwin said, including variants and updates. Testing a tire to discover it has an 8.2 rolling resistance coefficient is one thing. The trick is finding a way to explain it to someone standing in a tire shop.
Over the past twenty years, the automotive sector has increasingly prioritized lightweight and eco-friendly products. Specifically, in the realm of tyres, achieving reduced weight and lower rolling resistance is crucial for improving fuel efficiency. However, these goals introduce significant challenges in managing Noise, Vibration, and Harshness (NVH), particularly regarding mid-frequency noise inside the vehicle. This study focuses on analyzing the interior noise of a passenger car within the 250 to 500 Hz frequency range. It examines how tyre tread stiffness and carcass stiffness affect this noise through structural borne noise test on a rough road drum and modal analysis, employing both experimental and computational approaches. Findings reveal that mid-frequency interior noise is significantly affected by factors such as the tension in the cap ply, the stiffness of the belt, and the properties of the tyre sidewall.
Planning for charging in transport missions is vital when commercial long-haul vehicles are to be electrified. In this planning, accurate range prediction is essential so the trucks reach their destinations as planned. The rolling resistance significantly influences truck energy consumption, often considered a simple constant or a function of vehicle speed only. This is, however, a gross simplification, especially as the tire temperature has a significant impact. At 80 km/h, a cold tire can have three times higher rolling resistance than a warm tire. A temperature-dependent rolling resistance model is proposed. The model is based on thermal networks for the temperature at four places around the tire. The model is tuned and validated using previously published data measured by Scania on rolling resistance, tire shoulder, and tire apex temperature measurements with a truck in a climate wind tunnel with ambient temperatures ranging from -30 to 25 °C at an 80 km/h constant speed. Dynamic tire simulations were conducted using a heat transfer model, considering road, ambient, shoulder, and apex temperatures. The simulation results were compared with measured data for ambient, shoulder, and apex temperatures, and the model captures both time constants and stationary levels. The resulting model can predict the dynamics of the rolling resistance and will, therefore, give a more accurate prediction when tires are cold and warming up. Driving range simulations of a long haulage battery-electric truck have also been conducted demonstrating how the range changes with varying ambient temperatures as well as the influence a snapshot consumption has on range estimation.
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