Estimation of the power of mechanical losses in the engine, transmission and wheels of car on the stand with running drums

The efficiency of a car is considered through the amount of energy loss spent on transmission from the engine to the driving wheels of the car. Analytical and experimental methods for assessing mechanical losses are analyzed. The advantages and disadvantages of road and bench tests of a car in free run modes are indicated. A description of the diagnostic equipment - a stand with running drums, used to simulate the movement of a car in laboratory conditions is given. The components of the necessary measuring equipment for recording the speed and torque on the wheels of a car are considered. The list of primary measuring sensors and main transducers is indicated, which transmit information to the computer. The results of the car run-out on the stand are given: the change in the instantaneous speed from time to time. The primary assessment of the regression model is made and the values of the coefficients are obtained by the method of least squares of deviations of the vehicle speed. A mathematical model for the subsequent processing of experimental data has been developed. The purpose of mathematical modeling is to separate mechanical losses by power units separately for the engine, transmission and car wheels. An assessment was made of the amount of energy losses in the stand itself with running drums. The characteristic of the stand has been obtained, which must be taken into account in the measurement procedure. The results of experimental studies for the GAZ-31029 car are presented. The results of the influence of the technical condition of transmission units and vehicle wheels on the value of the power of mechanical losses are presented. Car tire pressure studies have been conducted. The graphical dependences of the power of mechanical losses depending on the speed of the car are obtained. Recommendations have been developed for diagnosing the general condition of the vehicle by the amount of mechanical losses at the stand with running drums. The ways of further improvement of the method are given. The main conclusions based on the research results are formulated.

Comparative Analysis and Statistical Optimization of Fuel Economy for Sustainable Vehicle Routings

In this 21st century, there has been an increase in the usage of renewable products for the economic drifting of vehicle transportations systems. Furthermore, due to recent trends in climate change, researchers have started focusing on statistical optimization techniques for sustainable vehicle routings. However, until now, a major gap has been noticed in the multidomain statistical analysis for optimizing the parametric levels of the vehicle fuel economy. Therefore, in this research work, two widely utilized cars (Toyota and GMC Yukon) are considered on a particular route of Jeddah for the collection of the fuel economy data under the realistic conditions of air conditioner temperature, traffic patterns, and tire pressure. The outcomes of the factorial design of the experiment highlight that the fuel economy is optimal under the low air conditioner temperature, light traffic patterns, and 34 PSI tire pressure. Three replications of the fuel economy have been considered, and the statistical significance of the correlated variables has been justified by implementing the analysis of variance (ANOVA) approach on the various levels of fuel economy. During the analysis, the statistical hypothesis for random exogenous factors has been developed by incorporating a multivariate regression model. The outcomes highlight that both air conditioner temperature and traffic patterns in Jeddah have a significant negative effect on fuel economy. Results also depict that the effect of air conditioner temperature, traffic patterns, and tire pressure is substantially higher for heavy-engine automobiles such as the GMC Yukon compared to light-engine cars (Toyota Corolla). Furthermore, a normality test has also been considered to validate the outcomes of the proposed model. Therefore, it is highly recommended to utilize the proposed methodology in optimizing the trends of fuel economy for sustainable vehicle routings. Based on the findings of multidomain statistical analysis, it is also highly recommended the utilization of the Toyota Corolla car model for investigating the correlation of external undeniable factors (braking frequency, metrological conditions, etc.) with the trends of vehicle fuel economy.

Methodology for Assessing and Managing the Environmental Performance of Skidding and Feller Buncher Tractors

Systematic assessments on the effects of skidding systems on features of forest blueberry pine soil were conducted as part of this study. Assessing the ecological efficiency of forest skidding machines showed that the most significant impact (by 2.0–2.2 times) on soil compaction was observed at loading sites rather than during transportation. Lightweight loam density and sand density increased by 25% and 2%, respectively, after more than two passages of the skidding system. Pressure in 33L-32 tires of forestry machinery in operation on a solid surface varied from 46.5 kPa to 196 kPa at maximum load. Studying the impact of tires on soil compaction showed that the environmental efficiency of forestry equipment can be enhanced if the optimal tire pressure at average loads does not exceed 70 kPa for tracked vehicles and 150 kPa for wheeled vehicles in summer seasons. When ground grips were fully immersed, the pressure of forwarders on soil was reduced. These study results can be used to establish organizational and technological measures in order to manage the negative impact of skidding systems and to increase the environmental effects of their performance.

Effect of tire slip losses on the energy demand and fuel consumption of a light-duty vehicle

The contribution of the tire-road slip of traction wheels to the total resistance opposing the motion of a light-duty commercial vehicle has been investigated through the simulation of several homologation and custom driving cycles. The calculation of the contribution of the tire slip losses was based on the estimation of the longitudinal tire slip, by means of Pacejka’s MF5.2 tire model. In this work, the computational steps required to evaluate this contribution were implemented in a previously developed fuel consumption simulation tool. Simulations were performed under several vehicle loading conditions and tire inflation pressures on traction and non-traction wheels, and considering different tire-road adherence conditions, in order to obtain a characterization of the tire slip losses over a wide range of working conditions. An analysis of the results shows that, although the contribution of tire slip losses to the total vehicle energy demand and fuel consumption may be relevant – especially under low-load, low adherence conditions – the sensitivity of the average on-cycle vehicle energy/fuel consumption to changes in the tire inflation pressure is only affected slightly by tire slip losses. Therefore, tire slip losses can be neglected in practice, when the aim of a simulation is to optimize the tire pressure to achieve average vehicle working conditions over a driving cycle.

Development of MEMS Composite Sensor with Temperature Compensation for Tire Pressure Monitoring System

The pressure and temperature inside the tire is mainly monitored by the tire pressure monitoring system (TPMS). In order to improve the integration of the TPMS system, moreover enhance the sensitivity and temperature-insensitivity of pressure measurement, this paper proposes a microelectromechanical (MEMS) chip-level sensor based on stress-sensitive aluminum-silicon hybrid structures with amplified piezoresistive effect and temperature-dependent aluminum-silicon hybrid structures for hardware and software temperature compensations. Two types of aluminum-silicon hybrid structures are located inside and outside the strained menbrane to simultaneously realize the measurement of pressure and temperature. The model of this composite sensor chip is firstly designed and verified for its effectiveness by using finite element numerical simulation, and then it is fabricated based on the standard MEMS process. The experiments indicate that the pressure sensitivity of the sensor is between 0.126 mV/(V·kPa) and 0.151 mV/(V·kPa) during the ambient temperature ranges from -20 ℃ to 100 ℃, while the measurement error, sensitivity and temperature coefficient of temperature-dependent hybrid structures are individually ± 0.91℃, -1.225 mV/(V·℃) and -0.150%/℃. The thermal coefficient of offset (TCO) of pressure measurement can be reduced from -3.553%FS/℃ to -0.375%FS/℃ based on the differential output of the proposed sensor. In order to obtain the better performance of temperature compensation, Elman neural network based on ant colony algorithm is applied in the data fusion of differential output to further eliminate the temperature drift error. Based on which, the overall measured error is within 3.45 kPa, which is less than ±1.15%FS. The thermal coefficient of offset (TCO) is -0.017%FS/℃, and the thermal coefficient of span (TCS) is -0.020%/FS℃. The research results may provide a useful reference for the development of the high-performance MEMS composite sensor for the TPMS system.

Estimation of Tire Mileage and Wear Using Measurement Data

Tire mileage and wear provide important information for vehicle applications. There are more and more studies discussing intelligent tires, but few focus on the role of tire mileage and wear. The conventional tire pressure monitoring system (TPMS) is one of the intelligent tire applications, but there has been no significant advancement in recent years in this regard. In order to increase the additional functions of intelligent tire applications, we propose a method that estimates the mileage and wear information of tires. The proposed method uses a three-axis sensor and a Hall sensor to implement the function. The proposed method also has a low power design to reduce the power consumption of the Hall sensor. The experimental results show the trend of tire wear status, rendering this method effective. This method also requires more accurate mileage information to support tire wear estimation. This experiment found that the correct rate of the proposed mileage estimation method is 99.4% and provides sufficient and correct mileage information for tire wear methods. If this method is used in autonomous vehicle applications, the autonomous control strategy algorithm has more conditions to plan the control strategy. The strategy system processes more meticulous control that increases the safety of autonomous vehicles.

In-Tire Distributed Optical Fiber (DOF) Sensor for the Load Assessment of Light Vehicles in Static Conditions

Modern vehicles are using control and safety driving algorithms fed by various evaluations such as wheel speeds or road environmental conditions. Wheel load evaluation could be useful for such algorithms, particularly for extreme vehicle loading or uneven loads. For now, smart tires are only equipped by tire pressure monitoring systems (TPMS) and temperature sensors. Manufacturers are still working on in-tire sensors, such as load sensors, to create the next generation of smart tires. The present work aims at demonstrating that a static tire instrumented with an internal optical fiber allows the wheel load estimation for every wheel angular position. Experiments have been carried out with a static tire loaded with a hydraulic press and instrumented with both an internal optical fiber and an embedded laser. Load estimation is performed both from tire deflection and contact patch length evaluations. For several applied loads from 2800 to 4800 N, optical fiber load estimation is realized with a relative error of 1% to 3%, almost as precisely as that with the embedded laser, but with the advantage of the load estimation regardless of the wheel angular position. In perspective, the developed methodology based on an in-tire optical fiber could be used for continuous wheel load estimation for moving vehicles, benefiting control and on-board safety systems.

Analysis of results of investigation tests of the developed information and measuring active safety system of the vehicle

The research objective is the analysis of results of investigation tests of the information and measuring active safety system developed by FSUE “NAMI”. The practice of operation and performed studies show that modern foreign advanced driver assistance systems do not provide appropriate functioning under severe road and climate conditions of the Russian Federation creating unjustified risks and precluding from preventing a significant number of road accidents. This statement is applicable to driving on slippery surfaces, ice and snow, to the driving mode in traffic jams, in the absence of recognizable road marking, blinding of video cameras, insufficient visibility range and road lighting, etc. In order to ensure competitive ability and commercial attractiveness of the developed domestic information and measuring active safety system, within the scope of this work additional studies were performed regarding improvement of driver status monitoring functions with an expanded vector of the state of the controls and speed range, tire pressure monitoring with an expanded speed range and start-stop driving mode, dangerous obstacle approach warning and detection of deviation from the center of the lane with unrecognizable marking. In this paper, the results of investigation tests of improved functions are considered.