Tire Pressure Research Articles

Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals

Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

Оценка влияния различных газов на давление в шинах при выделении тепла

Transportation of minerals during open-pit mining works is mainly provided by heavy vehicles. One of the most dangerous accidents during the operation of vehicles in open-pit mines is the destruction of large-scale tires. The main cause of the destruction of tires is heating of rubber causing the increase of pressure of injected gas and change of properties of tire material. Apart from friction processes and rubber deformation causing heating of wheels, the temperature of tires can increase when affected by external heat sources. At coal mines, sources of auto-ignition of fossil fuels often located along the road shoulders and sometimes on the road surface can be additional sources of heat for the vehicles. According to the calculations, the increase in tire pressure does not depend on the type of gas injected. The temperature rise in tires, however, is significantly affected by the specific heat capacity and the density of the gas used. According to the conducted studies, carbon dioxide is a more effective gas compared with air or nitrogen. With a certain amount of heat emitted in the tire, air or nitrogen heats up from 0 to 100 °С, while the temperature of carbon dioxide will increase only to 72 °С. Due to heating, the air and nitrogen pressure in the tires will increase from 6 to 8.2 atm, whereas carbon dioxide pressure will increase only to 7.5 atm. Unlike air, carbon dioxide is not involved in the combustion and oxidation of heated rubber. Methane and its homologs are even more effective. For example, propane injected into tires, when emitting the same amount of heat, will heat up from 0 only to 28 °С, and its pressure will increase from 6 to 6.6 atm. The combustion of these hydrocarbons can be prevented by using inhibitors. Thus, adding 2–3 % of freons will make these gases non-flammable and explosion-proof.

Prediction of Noise Levels According to Some Exploitation Parameters of an Agricultural Tractor: A Machine Learning Approach

The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further.

Performance Comparison of Conventional and Biopolymer-modified Asphalt Mixtures for Airport Pavement

Raising the temperature of airport pavement softens its surface, leading to rutting or thermal cracking. As aircraft manufacturers lean toward heavier planes with higher tire pressures, challenges arise. To tackle these problems, incorporating polymers like high-density polyethylene into asphalt binders has emerged as a solution. This study investigates biopolymer-modified asphalt, blending conventional asphalt with highdensity polyethylene and pine resin. This study aims to compare the performance of asphalt mixtures using both conventional and biopolymer-modified asphalt binders. Various tests‒physical, Fourier transform infrared, energy dispersive X-ray, dynamic shear rheometer, volumetric properties, Marshall stability, retained stability, indirect tensile strength and Cantabro loss-were conducted. The results highlighted that integrating pine resin and high-density polyethylene increased the performance grade (PG) of the conventional asphalt from PG 64 to PG 82. Asphalt mixtures using biopolymer-modified binders exhibited superior stability, stiffness and resistance to moisture damage compared to those with conventional asphalt. These properties aligned with the specifications outlined in Item P-401 of the Federal Aviation Administration Advisory Circular 150/5370-10H. Keywords: Pine resin, High-density polyethylene, Biopolymer-modified asphalt, Performance grade 64, Performance grade 82

Comparative Measurement of Horizontal Penetrometry with a Focus on the Degree of Soil Compaction in Real Work Conditions

Potential soil production is closely related to the physical and mechanical properties. The aim of this paper was to evaluate the effect of different levels of soil compaction created by tractor chassis. The total area of the experimental plot was 13.22 ha. Up until 2019, a conventional tillage system had been used. The measurements were carried out with an innovative measuring device that allows for the continuous measurement of the horizontal penetrometry for comparative measurements while driving, which was designed at the Slovak University of Agriculture in Nitra. The measuring device measured the soil resistance in the tire track (On-track) and out of track (Off-track) as well as in three (50 s) sequences within one tractor pass. Three lines were chosen, where in each a pair of combinations was made. The results were subjected, in addition to graphical evaluation, to single factor ANOVA analysis. When comparing individual passes (PH1 to PH6), the statistical analysis showed that the results of the horizontal resistance measurements proved to be statistically significant (p < 0.05) with respect to the weight, number of passes, and tire underinflation. The highest compaction was caused by the first pass, while the higher weight of the tractor during the next pass had a smaller effect. Underinflating the tires ensured a reduction in compaction. Reducing the tractor tire pressure to 0.15 MPa resulted in a reduction in soil compaction of up to 16%.

A laboratory investigation into tyre-pavement contact behaviour of passenger car

ABSTRACT Understanding tyre-pavement contact behaviour helps to address pavement skid resistance which is highly related to traffic safety. In this study, a pressure distribution measurement system was employed to investigate the tyre-pavement contact behaviour of a passenger car in laboratory. The pavement macrotexture was characterised by a 3D scanner and the sand patch method. It was shown that the asphalt pavement gradation, the vertical force, the tyre inclination angle and the tyre pressure significantly have effects on tyre-pavement contact behaviour. The cumulative frequency curve of tyre-pavement contact stress can be well fitted by the Boltzmann model. Under the vertical force of 4 kN, the average contact stress increases significantly when the inclination angle exceeds 3.2°, while an inclination angle within 3.2° has almost no effect. The force on the rib on the side that the tyre leans to changes the most with the inclination angle. As the tyre pressure increases, the average contact stress increases linearly and the contact area decreases linearly. With the increase of tyre pressure, the contact region gradually shrinks towards the middle and the contact force slightly increases in the middle and decreases at the edges.

An algorithm for estimating the ultimate speed of vehicles on wet roads

Real-time estimation of a vehicle’s ultimate hydroplaning speed is crucial for ensuring its safety and stability, especially in rainy weather conditions. This paper proposes an algorithm for estimating the ultimate hydroplaning speed of vehicles on wet and slippery road surfaces based on finite element analysis of tire. Using the ABAQUS simulation software, a finite element model of a 205/55 R16 radial tire is established. Subsequently, a fluid-structure coupling model between the tire, water layer, and road surface is developed. The finite element method is employed to simulate and analyze the effects of tire inflation pressure, water layer thickness, vertical load, and tread wear on the vehicle’s ultimate hydroplaning speed. Simulation results indicate that an increase in tire inflation pressure and vertical load leads to an increase in the ultimate hydroplaning speed, while tread wear and an increase in water layer thickness result in a decrease. Finally, based on the simulation analysis data, a BP neural network-based estimation algorithm for the vehicle’s ultimate hydroplaning speed is proposed by combining the relationship between tire inflation pressure, tire vertical load, tread wear, water layer thickness, and ultimate hydroplaning speed. The results show that the estimated ultimate hydroplaning velocity profile has a high degree of overlap with the actual ultimate hydroplaning velocity profile, with a maximum error of no more than 5 km/h and an average percentage error of 2.31416%.

Testing & Evaluation of Road Bounce PRO-Mobile Phone App Based Technology for Road Measurement

Road roughness plays a crucial role in assessing the overall condition of a road. It impacts various factors such as vehicle operating costs, speed, comfort, safety, fuel efficiency, and wear and tear. Every road user seeks a smooth and pleasant driving experience. The measurement of road roughness is a key task that influences maintenance decisions. According to the Indian Road Congress (IRC) standards, roughness measurements should be conducted every 6 months on highways to determine the necessary functional overlay requirements based on the roughness index. Roughness measurements using profile meters namely Rod & Level, Dipstick, Merlin etc. all are time consuming. The Road Roughness Measurement System (RTRRMS) is a system installed on vehicles to assess how the vehicle reacts to the pavement profile. In India, the Central Road Research Institute (CRRI) has developed a 5th Wheel Bump Indicator that is mounted on cars to measure roughness. The RTRRMS 5th Wheel Bump Indicator utilizes binary true/false information and range-based analog data generated by digital accelerometers commonly found in smartphones. This measurement system is facilitated through an application called "ROAD BOUNCE”. This paper aims to showcase the results obtained from the testing and assessment of an App-based roughness measurement technology in terms of its precision and user-friendliness. The evaluation was conducted across various vehicles and mobile phones, considering different factors such as speed, tire pressure, gradients, and more. Keywords:- Road Bounce, RTRRMS, Roughness Measurement, Cell Phone based Roughness.

Analysis of tire-pavement interaction modeling and rolling energy consumption based on finite element simulation

Energy consumption is caused by the rolling resistance of tires as a vehicle moves forward, which contributes to resource consumption and the greenhouse effect. The objective of this paper is to investigate the variation of tire-pavement rolling energy consumption under different operating condition factors. For this purpose, a rolling finite element dynamic model was developed based on real tire and road contact. The correlation between the effective radius and free angle is determined during the free rolling process. The variations of different working conditions on rolling energy consumption under tire-road coupling were analyzed according to both tire and pavement factors, and the influence factors were analyzed by grey correlation analysis. The results show that the factors affecting the significance of rolling energy consumption in descending order are tire load, tire pressure, coefficient of friction, pavement stiffness, and rolling speed. Specifically, energy consumption can be increased by increasing the tire load or friction coefficient, the cumulative increase of rolling energy consumption reaches 68.3% and 14.51%, respectively. Energy consumption can be decreased by increasing the tire pressure or pavement stiffness, the cumulative decrease in energy consumption is 20.45% and 5%, respectively. In addition, the rolling energy consumption increases and then decreases within an increasing speed range(10–80 km/h). The speed trend inflection point is 20 km/h. The research results can provide a technical reference for the development of a low-energy consumption design scheme for tire-pavement systems. To better serve the construction needs of green low-carbon and sustainable road transportation.

The effect of tire inflation pressure and tillage systems on soil properties, growth and yield of maize and soybean in a silty clay loam soil

AbstractSoil compaction causes adverse effects on soil structure and the performance of crops. There is significant literature supporting the hypothesis that reducing tire inflation pressure can help to minimize compaction, but there is no data on the potential benefits of high flexion tires operating at reduced tire pressures in Midwestern United States agriculture. Hence, a field‐scale study was established in Illinois to determine the potential benefits of high flexion tires at low tire pressure (LTP) in comparison with those operated at standard tire inflation pressure (STP) on soil condition, crop growth and yield of maize and soybean for three tillage systems; deep tillage (450 mm), shallow tillage (100 mm) and no‐till. Two adjacent experiments were established in typical maize/soybean and soybean/maize rotations, respectively. The experiment used a 2 × 3 factorial design with five completely randomized blocks. The results showed that the use of LTP tires resulted in lower soil penetrometer resistance for three tillage systems in 2017 and 2018 in the maize field and 2018 in the soybean field. This improved plant establishment and the number of plants per hectare of maize in both 2016 (*p ≤ .05) and 2018 (**p ≤ .01) and plant establishment (***p ≤ .001) and the number of plants per hectare (***p ≤ .001) of soybean in 2018. The penetrometer resistance was higher in the no‐till plots compared to deep and shallow tillage plots in maize and was higher in the deep tillage plots compared to the shallow tillage in the soybean field. The use of LTP tires resulted in an increased grain yield of maize by 4.31% (15.02 Mg ha−1) and 2.70% (14.76 Mg ha−1) in 2017 (**p ≤ .01) and 2018 (*p ≤ .05), respectively, and soybean by 3.70% (4.25 Mg ha−1) in 2018 (*p ≤ .05). The depth of tillage had a significant effect on soybean and maize yields in 2017 (***p ≤ .001) and 2018 (***p ≤ .001), respectively, with higher yields of both soybean and maize in the deep and shallow tillage compared to no‐till plots. The study concludes that the use of the LTP systems can be a potential means of addressing soil compaction and maintaining soil porosity while increasing crop productivity in silty clay loam soils in Central Illinois.

ACCIDENT AVOIDANCE DUE TO TIRE INFLATION

Maintaining optimal tire pressure is a crucial aspectthat allows for greater vehicle control and, as a result, increases vehicle and human safety. This study seeks to identify remedies in the event of anomalous tire pressure, thereby ensuring the vehicle's safety. According to the National Highway Traffic Safety Administration (NHTSA), abnormal tire pressure- related collisions result in 660 fatalities and 33,000 injuries each year. The pressure of the tire is checked and monitored to assure its safety, and thus the vehicle's safety. In this project, the pressure and wear and tear on the vehicle's tire are both monitored and quantified. Key Words: Tire and Wheel, air compressor, motor, pressuresensor, microcontroller, alerting

Estimation of Tire Side-Slip Angles Based on the Frequency Domain Lateral Acceleration Characteristics Inside Tires

The identification and control of tire side slip angle is the key to vehicle stability control. Intelligent tire technology based on the sensing of side-slip acceleration inside the tire provides a novel method for estimating the tire side-slip angle. This study proposed a method to estimate the tire side-slip angle by using the frequency domain lateral acceleration of the tire. First, an intelligent tire testing system was constructed by independently developing a special rim assembly and data collector. A three-axis accelerometer was placed on the right side of the tire, and the acceleration value was acquired by using a wired method with a sampling frequency of 50 kHz. Second, based on the constructed test system, a tire side deflection test was carried out on the Flat Trac bench. Through data analysis, it was found that the lateral acceleration was in the frequency domain of 400 Hz. As the side-slip angle increased from −4° to 4°, the vibration amplitude gradually decreased. Moreover, the vibration amplitude within 0.5~2 kHz was highly correlated with the side-slip angle. Subsequently, the vibration amplitude of the lateral acceleration within 2 kHz was extracted at an interval of 20 Hz as the feature point, and a frequency domain data set FDAy3 was established together with the vertical load and tire pressure. Finally, the support vector machine (SVM) algorithm was employed to make predictions on the data set. The grid search method was utilized to find the optimal parameter values of the model penalty factor c and radial basis kernel function coefficient g, which were 1.4142 and 0.0884, respectively. The results suggested that the root mean square error of the model prediction was 0.0806°, and the maximum estimated angle deviation of the prediction was 0.4587°. Meanwhile, the optimal prediction accuracy and real-time performance were achieved when the number of feature points and the feature frequency band were 25 and within 500 Hz, respectively. The findings of this study confirm that it is feasible to estimate the tire side-slip angle based on the frequency domain lateral acceleration of the tire, which provides a new method for tire side-slip angle estimation.

Chain Transmission System in ATV

The efficient transmission system for the mega ATV is the primary emphasis of this project, both in terms of design and manufacture. an all-terrain vehicle (ATV), sometimes referred to as a quad bike, that is powered by low tire pressure and has a handlebar for steering control and a seat that the user sits on. ATVS are designed to be easily operated by a single person. The rider sits on the vehicle and drives it like a motorbike on non-motorized roads. These projects' main goal is to choose the best transmission system for the ATV in order to make the most of the engine's maximal power output. Currently, the ATV is driven by a belt, which has numerous advantages but also presents a number of possible issues that should be resolved. The main issue with belt drives is power loss, which can range from 9% to 15% and can get worse if the belt slips. high creep and slippage cannot be utilized to get the driven pulley's velocity ratio to remain constant. Currently, the ATV is belt- driven, which has many advantages but also presents a number of possible issues that should be taken care of. The main issue with belt drives is power loss, which can range from 9% to 15% and can get worse if the belt slips. high creep and slippage cannot be utilized to get the driven pulley's velocity ratio to remain constant. The goal of the current effort is to reduce torque and power losses in ATVs by introducing chain transmission.

Numerical and experimental studies on the carcass characteristics of a radial tire

In this research, the carcass characteristics of a radial tire were analyzed by performing numerical simulations on ABAQUS software based on the single factor control variable method, and the feasibility of the proposed numerical model was verified by executing experiments on a tire uniformity testing machine. It was found that when the tire crossed the bump, the preload had positive effects on the internal energies and friction–dissipation fluctuation of the tire, but had little influence on its kinetic energy fluctuation. Tire speed was positively correlated with the fluctuation of kinetic energy, but negatively correlated with the friction–dissipation fluctuation of the tire. In addition, there was a slight correlation between the speed and the internal energy fluctuation of the tire. Moreover, the change in the tire pressure had little influence on the kinetic energy, internal energy, and friction dissipation of the tire.

Brake Disc Deformation Detection Using Intuitive Feature Extraction and Machine Learning

In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the vehicle. Our core hypothesis is that these signals contain vibrations caused by brake disc deformation. Since faults of this kind are typically monitored by the driver of the vehicle, the development of automatic fault-detection systems becomes more important with the rise of autonomous driving. In addition, the new brake boosters separate the brake pedal from the hydraulic system which results in less significant effects on the brake pedal force. Our paper offers two important contributions. Firstly, we provide a detailed description of our novel measurement scheme, the type and placement of the used sensors, signal acquisition and data characteristics. Then, in the second part of our paper we detail mathematically justified signal representations and different algorithms to distinguish between deformed and normal brake discs. For the proper understanding of the phenomenon, different brake discs were used with measured runout values. Since, in addition to brake disc deformation, the vibrations recorded by our accelerometers are nonlinearly dependent on a number of factors (such as the velocity, suspension, tire pressure, etc.), data-driven models are considered. Through experiments, we show that the proposed methods can be used to recognize faults in the braking system caused by brake disc deformation.

A Fuzzy Logic-Based Automobile Fault Detection System Using Mamdani Algorithm

Due to advancement and complexity of modern automobiles, fault detection has gone beyond manual or trial by error methods. The fault detection technologies in automotive industry is used to identify any potential or already existing fault in automobiles. Faults in automobiles are usually mechanical or electrical faults that may include airbag control unit, radiator, gearbox, transmission control unit, tyre pressure, brakes, air conditioner, cylinder casket, alternator, hubs malfunctions etc. Each fault has a specific or related sign and symptoms. There are several methods of fault detections in automobiles like the binary logic technique, the fuzzy logic method technique and artificial intelligence technique with different algorithms. In this research work, we employed a fuzzy logic based technique that uses a Mamdani Algorithm which presented a better fault detection mechanism. Mamdani’s algorithm was proposed by Ebrahim Mamdani as a fuzzy inference method which has a rule-bases that are more intuitive and easier to analyse and implement. Mamdani’s algorithm produces fuzzy sets that originate from fuzzy inference system’s output membership function for decision making. This research work is a web-based technology that was implemented using JavaScript, JQuery and SQL server, ASP.Net, Bootstrap 3.5 and CSS. The output of the system showed a greater improvement from other existing methods of fault detections in automobiles.

In situ mechanical response characteristics of Autonomous Rail Rapid Transit (ART) applied to semi-flexible asphalt pavements

Autonomous Rail Rapid Transit (ART), featuring multi-axles, high tire pressure, and complete channelization, leads to severe rutting on conventional asphalt pavements. Semi-flexible pavement materials (SFP) were applied to ART corridors to mitigate rutting. Mechanical response characteristics were obtained by embedding sensors to understand the interaction between ART and SFP. Results found that the damage of corridor pavement shifted from rutting to top-down transverse cracking after adopting SFP. Nonresidual longitudinal tensile strains primarily caused top-down transverse cracking. The compressive stress at the bottom of corridor pavement caused by ART is 41%, 36%, and 58% higher than that of the BUS, respectively, under the condition of constant speed, acceleration, and deceleration. The maximum strain response caused by ART under a high-temperature environment was ten times higher than that of buses. These findings can provide data support for numerical simulations, material failures, and structural damage of ART corridors. Highlights Mechanical responses of semi-flexible pavement materials influenced by Autonomous Rail Rapid Transit (ART) were first captured using sensors. ART with high tire pressure exhibits a multi-axial cumulative effect in high-temperature conditions, resulting in pavement strains that can be up to ten times those of buses. After adopting semi-flexible pavement, the damage in ART corridors shifted from rutting to top-down transverse cracking. Nonresidual longitudinal tensile strains are the underlying cause of top-down transverse cracking.

Grand theft API: A forensic analysis of vehicle cloud data

Modern vehicles such as cars, trucks and motorcycles contain an increasing number of embedded computers that continuously exchange telemetry data like current mileage, tire pressure, expected range and geolocation to the manufacturer's cloud. Vehicle owners can access this data via Vehicle Assistant Apps (VAA). Naturally, this data is of increasing interest to law enforcement in criminal investigations. While manufacturers must comply with local laws requiring them to hand over the data of suspects upon the issuance of a warrant, this process can be time-consuming and cause an additional delay in a case. Making use of novel API-based access methods in cloud forensic investigations, we present a method to get permanent access to a vehicle's cloud data by directly accessing cloud servers given suspects' credentials. We analysed a set of 23 different VAAs and pointed out the potentially accessible data categories. With our proof of concept tool gta.py in combination with six provided vehicles from BMW, Dacia, Ford, Hyundai, Mercedes and Tesla, we verified the accessibility of the data categories. Our findings demonstrate that the API-based forensic acquisition and analysis of vehicle cloud data provides important insights to be considered in future digital forensic investigations of vehicles.

Analysis of tire frictional energy loss based on longitudinal slip test

Longitudinal slip tests of the 215/55 R17 tire were carried out using a six-component tire bench test instrument. By comparing with Abaqus simulation data, the longitudinal forces and slip ratios of the tire with a constant tire pressure under different speeds and vertical loads were analyzed. A tire finite element model was established, and the energy loss of the tire under the action of ground friction was examined using the model. It was predicted that the heat loss of the tire was mainly caused partially by the sliding of the tire against the ground and partially by rolling friction. The friction force of the tire is concentrated at the shoulder position. The results showed that the longitudinal force increased with the increase in the vertical load at the same slip ratio, but the velocity had little effect on the longitudinal force. In the braking slip stage, the heat loss due to friction was greater than that in the driving slip stage. The energy loss due to friction increased with the increase in the velocity and vertical load. In the whole sliding process, the energy release due to frictional work was the largest at 80 km/h and 6396 N.

Modelling of Truck Tire–Rim Slip on Sandy Loam Using Advanced Computational Techniques

Vehicles often experience low tire pressures and high torques in off-road operations, making tire–rim slip likely. Tire–rim slip is undesirable relative rotation between the tire and rim, which, in this study, is measured by the relative tire–rim slip rate. There is little research on the effect of different terrains on tire–rim slip despite its significance for off-road driving; therefore, this topic was explored through Finite Element Analysis (FEA) simulations. An upland sandy loam soil was modelled and calibrated using Smoothed-Particle Hydrodynamics (SPH), and then a Regional Haul Drive (RHD) truck tire was simulated driving over this terrain, with a drawbar load added to increase drive torque. To examine their effects, five parameters were changed: tire–rim friction coefficient, longitudinal wheel speed, drawbar load, vertical load, and inflation pressure. The simulations showed that increasing the tire–rim friction coefficient and the inflation pressure decreased the tire–rim slip while increasing the vertical and drawbar loads increased the tire–rim slip. Varying the longitudinal wheel speed had no significant effect. Tire–rim slip was more likely to occur on the soil because it happened at lower drawbar loads on the soil than on the hard surface. These research results increased knowledge of tire–rim slip mechanics and provided a foundation for exploring tire–rim slip on other terrains, such as clays or sands.