Browse Topic: Vehicle performance
The electric vehicle market, vehicle ECU computing power, and connected electronic vehicle control systems continue to grow in the automotive industry. The results of these advanced and expanded vehicle technologies will provide customers with increased cost savings, safety, and ride quality benefits. One of these beneficial technologies is the tire wearing prediction. The improved prediction of tire wear will advise a customer the best time to change tires. It is expected that this prediction algorithms will be essential part for both the optimization of the chassis control systems and ADAS systems to respond to changed tire performance that varies with a tire’s wear condition. This trend is growing, with many automakers interested in developing advanced technologies to improve product quality and safety. This study is aimed at analyzing the handling and ride comfort characteristics of the tire according to the depth of tire pattern wear change. The handing and ride comfort
There is a lack of data to support the efficacy of traditional mileage and time-based criteria for oil changes in vehicles. In this study, used-oil samples from 63 vehicles were collected and analyzed. Besides dynamic viscosity, viscosity index and activation energy were evaluated as measures of thermal stability of viscosity. The results revealed that mileage and time of use are not significantly correlated with (p > 0.05) and are thus poor indicators of oil viscosity and viscosity thermal stability measures. These findings highlight the limitations of current criteria and underscore the need for new sensing and evaluation methods to reduce costs, waste, and environmental impact while ensuring vehicle performance.
Due to the vibration of the vehicle, the performance of the vehicle carbon canisters will be changed, which will affect its control effect on the fuel evaporation emission. In this study, a vibration test platform capable of simulating vehicle vibration characteristics was used to simulate the possible vibration effects of the vehicle carbon canisters, and to analyze the absorption and desorption performance of the carbon canisters before and after long-term operation and its influence on vehicle evaporation emissions. The results show that the carbon canisters will precipitate the carbon powder after the continuous action of the forward and backward vibration of the vehicle. As a result, the ultimate adsorption and desorption amount of fuel vapor decreased, and the adsorption amount decreased more obviously. In the 48-hour Diurnal Breathing Loss (DBL) test, fuel vapor diffusion is more difficult due to the increased flow resistance of the carbon canisters after vibration, and fuel
SBW(Steer-by-wire) is a steering system that transmits the driver’s request and gives feedback to the driver through electrical signals. This system eliminates the mechanical connection of the traditional steering system, and can realize the decoupling of the steering wheel and the road wheel. In addition, this system has a perfect torque feedback system, which can accurately and delicately feedback the road surface information to the driver. However, vehicle driving deviation is one of the most common failure modes affecting vehicle performance in the automotive aftermarket, this failure mode can exacerbates tire wear, reducing their life cycle, at the same time, the driver must apply a counter torque to the steering wheel for a long time to maintain straight-line travel during driving. This increases the driver’s operational burden and poses safety hazards to the vehicle’s operation. Based on the steer-by-wire system and vehicle driving deviation characteristics, this paper proposes
Spot welds are integral to automotive body construction, influencing vehicle performance and durability. Spot welding ensures structural integrity by creating strong bonds between metal sheets, crucial for maintaining vehicle safety and performance. It is highly compatible with automation, allowing for streamlined production processes and increased efficiency in automotive assembly lines. The number and distribution of spot welds directly impact the vehicle's ability to withstand various loads and stresses, including impacts, vibrations, and torsion. Manufacturers adhere to strict quality control standards to ensure the integrity of spot welds in automotive production. Monitoring spot weld count and weld quality during manufacturing processes through advanced inspection techniques such as Image processing by YOLOv8 helps identify the number of spots and quality that could compromise safety. Automating quality control processes is paramount, and machine vision offers a promising
This SAE Standard provides minimum requirements and performance criteria for devices to prevent runaway snowmobiles due to malfunction of the speed control system.
Clutch wear is a significant factor affecting vehicle performance and maintenance costs, and understanding its dynamics is crucial for original equipment manufacturers (OEMs) to enhance product reliability and customer satisfaction. It is important to predict clutch wear to enable customers to understand the condition of their clutch and the remaining clutch life, to avoid sudden vehicle breakdowns. This paper explains the approach of measuring the clutch wear profile on an actual vehicle and simulating the same conditions on a powertrain test bench, with the establishment of a correlation in clutch wear profiles.
Dynamic Vehicle mass is one of the most critical parameters in automotive controls such as battery management, transmission shift scheduling, distance-to-empty predictions and most importantly, various active and passive safety systems. This work aims to find out dynamic Vehicle mass for Electric Vehicles in real time transient driving conditions. The work proposes a real-time approach in finding Dynamic vehicle mass where accumulated Energy based vehicle performance, an improvement to the vehicle dynamics equation, has been employed for consistent and accurate results. Factors affecting vehicle mass such as road grade, dynamic friction coefficient, driving pattern, wheel slip etc. have been considered for model optimization. Here recursive Bayesian state estimator has been used for finding vehicle mass as a constant state variable while time varying forgetting factors are used to nullify the impact of major losses. Algorithm is auto tuned using Machine Learning techniques to first
Items per page:
50
1 – 50 of 1362