Vehicle Performance Parameters Research Articles

Construction of systemic interaction between tools of remote monitoring of the technical condition and operation modes of a truck vehicle

Parameters of the technical condition of transportation means in modern transport-logistics and infrastructure systems are an integral element of their communication support. This is enabled by the use of remote information monitoring technologies in control processes. The object of this study is the processes of vehicle remote monitoring in terms of determining the technical condition. The work addressed the task of improving the process of vehicle technical operation through the construction of a model of the remote monitoring system of its technical condition. A remote version of the information-analytical monitoring system was implemented. The work considers the system interaction of the means of remote monitoring of the state of a vehicle to ensure control under the operating conditions of the driver's work and rest modes. Road, transport, climatic conditions, etc. were taken into account. Considering these features, an information and analytical model of the system for remote monitoring of vehicle condition was built. Features of the subject area of the system are described using a DFD diagram. A structured information model of the information-communication system has been constructed, which has the ability to actually provide vehicle remote monitoring, the driver's work and rest modes, and his/her physical condition. The results, subject to the use of the V2I information model in the field of transport, allow remote monitoring of the vehicle technical condition. Specifically, to analyze the influence of changes in the physical condition and modes of work and rest of drivers on changes in the vehicle operating parameters

Developing Fuel Efficiency and CO2 Emission Maps of a Vehicle Engine Based on the On-Board Diagnostic (OBD) Approach

A vehicle interacts with the road, other vehicles, and traffic control devices in real traffic conditions. The level of traffic influences driving patterns and, consequently, this can affect the vehicle´s fuel efficiency and emissions. This study aims to develop engine maps of fuel consumption and CO2 emissions for a light vehicle operating under real traffic conditions. A representative passenger vehicle of the Ecuadorian vehicle fleet, powered by gasoline, was selected for the experimental campaign that was developed on a test route designed according to real driving emission (RDE) regulation. An on-board diagnostic (OBD) device was used for recording in real-time engine and vehicle operating parameters. Moreover, CO2 emissions were estimated using the fuel rate registered from the OBD system of the vehicle This study proposed a novel methodology for developing two-dimensional contour engine maps based on OBD data. The result showed that the vehicle engine operated in real traffic conditions with a brake thermal efficiency (BTE) of 27%, a brake-specific fuel consumption (BSFC) of 275 g/kWh, and a carbon dioxide (CO2) energy-emission factor of 716 g/kWh. In terms of distance, the CO2 emission factor for the tested vehicle was approximately 190 g/km. Overall, this study demonstrates that the OBD approach is a potential method to be used to assess the fuel consumption and emissions of a vehicle operating under real-world traffic conditions, especially in Latin American countries, where portable emission measurement systems (PEMS) are not readily available.

Enhancing electric vehicle efficiency through model predictive control of power electronics

This study examines the improvement of electric vehicle (EV) economy by using Model Predictive Control (MPC) in power electronics, with the goal of optimizing system performance. Experimental assessments done on different battery parameters have identified a spectrum of capacities, ranging from 55 kWh to 75 kWh, and voltages, ranging from 380V to 450V, that impact the total energy storage and power production capabilities. The efficiency percentages recorded in the battery systems ranged from 90% to 95%, suggesting differences in energy losses throughout the operations of charging and discharging. Furthermore, examinations of power electronics control configurations highlighted the significance of PWM frequencies (varying from 8 kHz to 12 kHz) and modulation indices (0.75 to 0.85) on the efficiency of power conversion. The results indicated efficiency rates ranging from 94% to 97%, emphasizing the efficacy of MPC-based techniques in improving power flow. The assessment of electric vehicle (EV) performance parameters demonstrated driving ranges ranging from 140 km to 180 km, with energy consumption rates ranging from 50 kWh to 60 kWh. The efficiency metrics ranged from 2.5 km/kWh to 3.0 km/kWh, and were directly affected by the battery properties and improvements in power electronics. Moreover, there was a little change in the link between temperature variations (ambient temperature ranging from 23°C to 29°C and battery temperature from 32°C to 40°C) and efficiency. This highlights the system's sensitivity to external variables. In summary, this relationship between battery characteristics, power electronics control, and environmental conditions in determining the efficiency of electric vehicles (EVs). The results emphasize the importance of customized setups and control techniques based on model predictive control (MPC) in optimizing energy use and increasing the distance electric cars can travel. These findings provide valuable knowledge for the development of sustainable transportation solutions in the electric vehicle industry.

Machine learning-based surrogates for eVTOL performance prediction and design optimization

<p>Predicting the performance of different electric vertical take-off and landing (eVTOL) vehicle designs is paramount to vehicle manufacturers and hobbyists. These vehicles' maximum flight time (endurance) and maximum flight distance (range) depend on design and operational parameters relating to their structure, propulsion system, payload, and mission profile. In recent years, sophisticated physics-based models have been developed to estimate and optimize their aerodynamic, propulsion, and electrical performance. Integrating and simulating those models can closely estimate a vehicle's endurance and range. However, this demands advanced knowledge of different subsystems utilized and extensive computational resources limiting the wide-scale utilization of such models. This paper showcases the development and implementation of a framework to train simpler machine learning-based surrogates. The surrogate models are trained on a limited number of eVTOL performance estimates generated by physics-based models and can mimic them accurately. Forty-seven thousand eVTOL vehicle designs were simulated to generate the training data for various machine-learning models. These include several decision tree models, K-nearest neighbor models, linear regression models, and a multi-perceptron neural network model. Vehicle design and operational parameters such as propeller size, payload mass, drag coefficient, velocity, and motor and battery parameters are used as features, and vehicle endurance and range estimates are used as targets. Compared to the alternative approaches, these surrogate models are computationally very efficient and easy to understand and use. Testing on hold-out datasets shows excellent performance, with multiple models having a mean average percentage error of less than 2% in estimating vehicle endurance and range.</p>

Ocena zwłoki rejestracji zamrożonej ramki na podstawie wartości napięcia sieci pojazdu

Assessment of frozen frame recording delay on the basis of vehicle power voltage Freeze frames are recorded at a changeable delay dependent on the system in which they were recorded, type of failure or vehicle operation parameters. This delay is sometimes difficult to assess, which makes it difficult for the reconstructionists to qualify the data from the frozen frame as useful in the process of event reconstruction. In the paper a method of assessing the recording delay of a frozen frame based on the vehicle power voltage is proposed.

Machine learning-based estimation of gaseous and particulate emissions using internally observable vehicle operating parameters

Measuring vehicular emissions is crucial for emission management and air quality control. However, conventional measurement equipment is costly and requires continuous maintenance. This study aims to address these challenges by introducing an Artificial Neural Network (ANN)-based methodology that using internally observable vehicle operating parameters to estimate emissions of particulate number (PN), nitrogen oxide (NOx), carbon monoxide (CO), and carbon dioxide (CO2). The research was conducted in three steps: (1) analyzing the correlations between vehicle operating parameters and exhaust emissions through emission map analysis, (2) developing and validating artificial neural networks (ANNs), and (3) examining the impact of vehicle driving conditions on emission predictions. The ANNs were rigorously validated across various operating and ambient conditions, including different temperatures, cold and warmed-up starts, and various driving scenarios. The prediction accuracy for CO2 is higher than other exhaust gases, with an error of <1% in all validation cases. The mean prediction error for NOx was 12.5%, with observed variations in accuracy depending on dynamic and thermal conditions. The ANN model accurately predicted the influence of thermal conditions on CO emissions, which exhibit a significant increase at lower temperatures.

Simulation analysis on vertical vehicle dynamics of three in-wheel motor drive configurations

The hub-driven technology provides several remarkable benefits to overcome some of today’s challenges in electrification of the transport sector. Although there are advantages by using in-wheel motors, their application results in increased unsprung masses, which have a negative impact on ride comfort and road holding of the vehicle. A novel but unexplored concept to inhibit the negative effects of the wheel hub motor is the two-stage-suspension structure. To investigate this in-wheel motor design and to compare it with established concepts for reducing the negative effects of the unsprung masses, three full-vehicle models were established. The vehicle models are based on a two-stage-suspension structure, a design where the motor functions as a tuned mass damper and a conventional in-wheel motor design. The suspension parameters of the three in-wheel motor configurations were further optimized using a genetic algorithm with respect to several vertical vehicle performance parameters. Subsequently, the full-vehicle models of the in-wheel motor configurations were compared by simulation in numerous different driving situations regarding their vertical vehicle dynamics. The results demonstrate that the two-stage-suspension causes an increase of the pitching and vertical vehicle body acceleration in several driving conditions, while the acceleration of the motor in general and the roll acceleration of the vehicle body especially during cornering maneuvers can be reduced significantly.

Experimental study on wheel-rail rolling contact fatigue damage starting from surface defects under various operational conditions

Surface defects in different railway lines experience different wheel-rail rolling contact actions owing to various railway line characteristics and vehicle operation parameters. This study focused on the evolution of defective wheel-rail rolling contact fatigue (RCF) behaviour under different operation conditions. Conical defects were created using an indentation apparatus. Wheel-rail RCF tests were performed using a twin-disc testing machine. Various operational conditions were simulated considering different slip ratios and wheel linear velocities on uphill and flat lines, as well as different vehicle axle loads and train configurations. The results indicate that surface damage, crack depths, and wear debris size increased only with decreasing wheel linear velocities, whereas the crack angles increased only with decreasing slip ratio. Meanwhile, both the wear rate and oxidation degree of wear debris were simultaneously affected by the slip ratio and wheel linear velocities. Under rolling conditions on flat lines, vehicle axle loads had a negligible influence on the wheel-rail RCF behaviour.

Povećanje efikasnosti guseničnog vozila optimizacijom prenosnih odnosa i strategije promene stepena prenosa

Introduction/purpose: Tracked vehicles play a vital role across various domains, from military operations to construction and agriculture. This study focuses on improving the efficiency of tracked vehicles by optimizing both gear ratios and gear-shifting strategies while preserving other performance aspects. Methods: The optimization process involves a genetic algorithm for determining optimal gear ratios, considering performance constraints. Furthermore, the paper introduces a gear-shifting optimization algorithm aimed at enhancing fuel economy to the maximum, while allowing for a valid comparison between two sets of gear ratios. Results: Optimizing gear ratios leads to substantial reductions in fuel consumption, as the engine operates within more efficient regions. Additionally, the optimized gear-shifting strategy further enhances efficiency, resulting in a fuel consumption reduction exceeding 12%, when combined with the optimized gear ratios. Conclusions: This paper offers a direct and robust approach for optimizing powertrain gear ratios and gear-shifting strategies in tracked vehicles. The results demonstrate significant improvements in fuel efficiency without compromising other critical vehicle performance parameters.

Application for testing and optimising the performance of electric power systems in a hybrid “MELEX” vehicle

This paper presents an application for monitoring power systems in a Melex type vehicle. The application is intended to optimise the energy use of the modified hybrid drive system used on this vehicle. It was written in Python programming language and is used to display vehicle operating parameters on a touch screen located on the control panel. A system for monitoring voltages and currents, of the electrical part of the drive train of a Melex vehicle, was designed and built, together with a data acquisition system. Tests were carried out on the basis of the completed measuring system, a selected section of which is presented in this paper.

Research, development and production costs prediction parametric model for future civil hypersonic aircraft

Assessing the economic viability of new high-speed systems concepts since the early design phases is crucial for the success of future hypersonic vehicles including cruisers, reusable access-to-space and re-entry systems. Besides literature reports few parametric cost models for high-speed vehicles, all of them makes exclusively use of mass as parameter and none of the models moves beyond the vehicle level. This paper describes a new parametric cost estimation model which moves beyond the state-of-the-art methodologies (1) by integrating vehicle design and operational parameters (in addition to the mass) as cost drivers for the prediction of the vehicle life-cycle cost, (2) by introducing prediction margins accounting for the uncertainties on the data-driven correlations, (3) by providing a first estimate of the costs of every on-board subsystem, including combined cycle engines and multi-functional subsystems, (4) by increasing the granularity of the analysis up to technology level, thus providing a valuable support to Technology Roadmaping activities. The parametric cost estimation model has been refined and exploited in the context of the Horizon 2020 STRATOFLY project, where the technological, operational, environmental, and economic viability of a Mach 8 waverider concept have been investigated.

Dynamic response assessment and multi-objective optimization of organic Rankine cycle (ORC) under vehicle driving cycle conditions

Organic Rankine cycle (ORC) can effectively utilize the waste heat energy of internal combustion (IC) engine. The ORC system has obvious large time-varying characteristic, strong fluctuation, and hysteresis under the synergistic influence of frequent fluctuation of vehicle speed and operating parameters in the system. The power system, waste heat recovery system, transmission system, and other subsystem models are constructed in this study. The key components are verified. An integrated system model of IC engine–ORC for driving cycle is constructed on the basis of the subsystem model. The dynamic response of ORC system is evaluated under different road conditions. The synergistic influence of multiple variables on system performance is analyzed. Obvious strong coupling and nonlinear characteristics are observed among different performances of ORC system under road conditions. Therefore, a multi-objective cooperative optimization framework of ORC system considering actual road conditions is proposed on the basis of multidimensional data. In the process of collaborative optimization, thermal efficiency and emissions of CO2 equivalent show a clear trade-off under different road conditions. The limit value of thermal efficiency under new European driving cycle is also 6.48% higher than that under worldwide harmonized light vehicles test cycle. Frequent fluctuation of vehicle speed is not conducive to obtaining the limit value of thermal efficiency. The evaluation and optimization of the comprehensive performance of ORC system under complex road conditions can provide a reference for the practical application of vehicle ORC system.

Simulation Study to Predict Generation Power of a Vehicle Photovoltaic System

The integration of solar photovoltaic (PV) power generation technology into electric vehicle (EV) charging systems is of great significance, and it is very important to analyze the influencing factors of vehicle operating parameters on the generation power of vehicle PV system. In this paper, a model is developed to predict the power generated by vehicle PV system through a combination of a Fluent simulation study and Simulink modeling. Then the generation power of vehicle PV system under 7 different vehicle speeds in different months in Northeast China is analyzed. Through the Spearman correlation analysis and multilevel function fitting of the data, the mathematical model of a vehicle PV system power is proposed. Its variation with vehicle speed is obtained, as affected by solar radiation intensity and ambient temperature. According to the mathematical model analysis, the relevant vehicle characteristics and rules for vehicle PV systems were obtained. The simulation results show that the generation power of vehicle PV system in Northeast China increases significantly with the increase of vehicle speed in summer, but does not change significantly with the increase of vehicle speed in winter.

Preliminary Selection of Road Test Sections for High-Mobility Wheeled Vehicle Testing under Proving Ground Conditions

Before being introduced into the maintenance system, each vehicle must undergo a series of tests, including durability tests. Computer models and whole vehicles on test stands can be used to identify vehicle performance parameters, which requires access to special software and test stands. However, due to the size of the vehicle or access to test stands, experimental tests must often be carried out on proving grounds; while generating the most complete set of loads, this is much more time-consuming. Therefore, methods are sought to indicate whether testing at the proving ground can be accelerated, for example, by increasing the speed of driving or using test road sections that generate more severe loads. This paper presents a method for evaluating the suitability of selected test road sections for durability tests of a special high-mobility wheeled vehicle. The method is based on the Basquin model, which consider the fatigue strength of materials, and rainflow load cycle counting method combined with the P-M damage summation method. Evaluation of test road selection was supplemented with analysis of travel speed distributions determined using the Beta statistic distribution. The presented model was used to evaluate the ability to accelerate a mileage test of an 8 × 8 vehicle. While certain data mentioned in the article remain classified and are not able to be presented in full, the author has attempted to provide a comprehensive background of the analyses conducted and the data used to illustrate them.

Influence of Engine Electronic Management Fault Simulation on Vehicle Operation.

The preparation of the fuel mixture of a conventional internal combustion engine is currently controlled exclusively electronically. In order for the electrical management of an internal combustion engine to function properly, it is necessary that all its electronic components work flawlessly and fulfill their role. Failure of these electronic components can cause incorrect fuel mixture preparation and also affect driving safety. Due to the effect of individual failures, it has a negative impact on road safety and also negatively affects other participants. The task of the research is to investigate the effect of the failure of electronic engine components on the selected operating characteristics of a vehicle. The purpose of this article is to specify the extent to which a failure of an electronic engine component may affect the operation of a road vehicle. Eight failures of electronic systems (sensors and actuators) were simulated on a specific vehicle, with a petrol internal combustion engine. Measurements were performed in laboratory conditions, the purpose of which was to quantify the change in the operating characteristics of the vehicle between the faulty and fault-free state. The vehicle performance parameters and the production of selected exhaust emission components were determined for selected vehicle operating characteristics. The results show that in the normal operation of vehicles, there are situations where a failure in the electronic system of the engine has a significant impact on its operating characteristics and, at the same time, some of these failures are not identifiable by the vehicle operator. The findings of the publication can be used in the drafting of legislation, in the field of production and operation of road vehicles, and also in the mathematical modeling of the production of gaseous emissions by road transport.

A Novel Loss Tolerant Data Transmission Schemes for Airborne Telemetry System of a Long Range Aerospace Vehicle

The on-board telemetry system of an aerospace vehicle sends the vehicle performance parameters to the ground receiving station at all instances of its trajectory. During the course of its trajectory, the communication channel of a long range vehicle, experiences various phenomena such as plume attenuation, stage separation, manoeuvring of a vehicle and RF blackout, causing loss of valuable telemetry data. The loss of communication link is inevitable due to these harsh conditions even when using the space diversity of ground receiving systems. Conventional telemetry systems do not provide redundant data for long range aerospace vehicles. This research work proposes an innovative delay data transmission, frame switchover and multiple frames data transmission schemes to improve the availability of telemetry data at ground receiving stations. The proposed innovative schemes are modelled using VHDL and extensive simulations have been performed to validate the results. The functionally simulated net list has been synthesised with 130 nm ACTEL flash based FPGA and verified on telemetry hardware.

Development of a cold-start emission model for diesel vehicles using an artificial neural network trained with real-world driving data

During the cold start and warm-up phase, modern vehicles emit considerable amounts of pollutants due to the incomplete combustion and deteriorated performance of aftertreatment devices. In terms of emission modeling, there have been many attempts to estimate cold start emission such as cold-hot conversion factor, regression model, and physis-based model. However, as the emission characteristic become complicated due to the adoption of aftertreatment devices and various emission control strategies for the strengthened emission regulations, the conventional cold start emission models do not always show satisfactory performances. In this study, artificial neural networks were used to predict the cold start emissions of carbon dioxide, nitrogen oxides, carbon monoxide, and total hydrocarbon of diesel passenger vehicles. We used real-world driving data to train neural networks as an emission prediction tool. Through machine leaning, numerous trainable variables of neural networks were properly adjusted to predict cold start emissions. For input variables of the ANN model, the velocity, vehicle specific power, engine speed, engine torque, and engine coolant temperature were used. The proposed ANN models accurately predicted sharp increases in carbon monoxide, hydrocarbon, and nitrogen oxides during the cold start phase. In addition to the quantitative estimations, the correlations between the operating variables and exhaust gas emissions were visually described in the form of emission maps. The emission map graphically showed the emission levels according to the vehicle and engine operating parameters.

Optimal Information Filtering for Robust Aerocapture Trajectory Generation and Guidance

Entry flight is a critical mission phase of planetary aeroassist problems. During atmospheric flight, aleatory-epistemic uncertainty and environmental factors reduce the accuracy of predicted future states for precision targeting. This problem has been approached historically with closed-loop guidance rooted in certainty equivalence. This property separates estimation and control problems, allowing each to be considered independently. In other concept studies, an observer model is neglected altogether in favor of assuming perfect state knowledge. However, a flight system will inevitably have imprecise state information and variability in its underlying dynamics and measurement models. Systemic uncertainty is a fundamental limitation of existing entry guidance approaches. This work seeks to overcome these challenges by posing aerocapture as a robust optimization problem. The cost objective of the maneuver is reformulated to account for uncertainty in atmospheric structure, vehicle performance parameters, and state estimation accuracy using an observer-based consider filter. An expected value performance cost is developed from anticipated measurement conditioning effects. A rapid solution methodology is illustrated using explicit integration strategies with a parameterized control structure. Results for a Mars aerocapture concept study show improvement in the postcapture orbit accuracy with low computational overhead.

Framework for Real-Time Monitoring of Battery Performance in Electric Vehicles and Locating Charging Facilities Nearby

Electric vehicles (EVs) are becoming more prevalent in the market. In a world where all vehicles are electric, it will be necessary to provide infrastructure on average highways that is similar to the energy consumption of the automobiles on that highway. As a result, a large transition from one type of energy carrier to electrical energy would be required. In comparison to where we are now, this translates into a significant increase in the carrying capacity of the power grid. Electric vehicles are in increasing demand because they have several advantages over gasoline-powered vehicles. On the other hand, limited battery power stations and a lack of infrastructure giving real-time vehicle performance parameters such as battery efficiency, durability, total distance travelled before being totally depleted, and so on should be considered. As a result, an embedded system is being developed in our project to address the aforementioned restrictions. The suggested research demonstrates all of the mathematical calculations of battery characteristics (including but not limited to battery efficiency and percentage durability), which aids in evaluating real-time batter performance. The system was tested and verified in laboratories using acceptable methodologies. In addition, a mobile application for the end-user is being created to provide information on battery parameters as well as the maximum distance it can travel till full discharge.

A comprehensive set of global scenarios of housing, mobility, and material efficiency for material cycles and energy systems modeling

Scenario-based assessments are a useful tool to explore unknown futures and inform decision makers and the public of the consequences of different courses of action. Scenario developments in industrial ecology have focused on disparate components of the socioeconomic metabolism and case studies, and few efforts of comprehensive and cumulative scenario formulation are documented. Many important, empirically derived relationships between material cycles, end-use services, and energy use are relevant to global scenario modeling efforts, for example, of integrated assessment models (IAMs), which do not routinely describe material cycles or the life-cycle impacts of various technology shifts. These inconsistent depictions of material cycles and their environmental impacts hinder the assessment of sustainable development strategies such as demand-side sufficiency, material efficiency, and energy efficiency. We developed three highly detailed scenarios covering 20 global regions to 2060 for the service provisioning of dwelling area and personal transport grounded in salient building and vehicle operation parameters. Our scenarios are based on, and interface with, the Low Energy Demand (LED) and Shared Socioeconomic Pathways (SSP1 and SSP2) narratives. The results comprise scenario-, region-, and period-specific narratives and corresponding parameter values, including per-capita floor space and vehicle stocks, building and vehicle archetype mixes, passenger-km, vehicle-km, vehicle occupancy rates, and implementation potentials of nine material efficiency strategies. The explicit storyline extension approach presented here is an alternative to the aggregate GDP-driven or historical trend extrapolations of service or energy demands. We describe the scenario formulation processes, resulting parameters, their applications, and offer an outlook for prospective sustainability models. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.