Vehicle Performance Evaluation Research Articles

Innovation and future development direction of hydrogen fuel cell vehicles

With the increasing problem of global climate change, reducing greenhouse gas emissions has become an urgent issue for all countries to solve. The pollution of the Earth is gradually becoming severe. The extensive use of fossil fuels is a major cause of this problem. In this critical situation, energy with the least carbon emissions should be used to minimize air pollution and the harm caused by it. Hydrogen energy is currently a clean energy alternative that can achieve low carbon emissions. As a new energy vehicle, hydrogen fuel cell vehicles have the advantages of being clean, efficient, and environmentally friendly. Compared with traditional fuel vehicles, hydrogen fuel cell vehicles only produce water and a small amount of oxygen during operation, and do not emit harmful substances such as carbon dioxide, which helps to significantly reduce carbon emissions in the transportation sector. So, hydrogen fuel cell vehicles are a viable and clean solution. In this paper, the performance evaluation, cost analysis, and future impact of hydrogen-powered vehicles are analyzed, and the future development trend of hydrogen energy battery vehicles is discussed. As a new energy vehicle, hydrogen fuel cell vehicles have important strategic significance and market prospects. The purpose of this paper is to provide a useful reference for the development of China's new energy vehicle industry through the study of hydrogen fuel cell vehicles.

Performance Evaluation and Wing Deformation Analysis of Flapping-Wing Aerial Vehicles with Varying Flapping Parameters and Patterns

There has been significant interest in the field of bio-inspired robotics, particularly in the development of flapping-wing robots from micro to bird size. Most flapping robots use lever-crank mechanisms or servomotors as wing flapping mechanisms. Servomotor-based flapping has the advantage of being able to generate various flapping patterns according to amplitude, offset, frequency, waveform, and other factors. However, it is not clear how these factors affect thrust generation. Therefore, this study focuses on the force generation and power consumption in different flapping patterns as well as the wing deformation during the flapping motion to provide some insights into the performance improvement. The results showed that the response characteristics of the actuators caused the thrust to saturate at high frequencies, and that sinusoidal pattern could generally achieve good performance and efficiency.

Efficiency Analysis of Powertrain for Internal Combustion Engine and Hydrogen Fuel Cell Tractor According to Agricultural Operations.

As interest in eco-friendly work vehicles grows, research on the powertrains of eco-friendly tractors has increased, including research on the development of eco-friendly vehicles (tractors) using hydrogen fuel cell power packs and batteries. However, batteries require a long time to charge and have a short operating time due to their low energy efficiency compared with hydrogen fuel cell power packs. Therefore, recent studies have focused on the development of tractors using hydrogen fuel cell power packs; however, there is a lack of research on powertrain performance analysis considering actual working conditions. To evaluate vehicle performance, an actual load measurement during agricultural operation must be conducted. The objective of this study was to conduct an efficiency analysis of powertrains according to their power source using data measured during agricultural operations. A performance evaluation with respect to efficiency was performed through comparison and an analysis with internal combustion engine tractors of the same level. The specifications of the transmission for hydrogen fuel cell and engine tractors were used in this study. The power loss and efficiency of the transmission were calculated using ISO 14179-1 equations, as shown below. Plow tillage and rotary tillage operations were conducted for data measurement. The measurement system consists of four components. The engine data load measurement was calculated using the vehicle's controller area network (CAN) data, the axle load was measured using an axle torque meter and proximity sensors, and fuel consumption was measured using the sensor installed on the fuel line. The calculated capacities, considering the engine's fuel efficiency for plow and rotary tillage operations, were 131.2 and 175.1 kWh, respectively. The capacity of the required power, considering the powertrain's efficiency for hydrogen fuel cell tractors with respect to plow and rotary tillage operations, was calculated using the efficiency of the motor, inverter, and power pack, and 51.3 and 62.9 kWh were the values obtained, respectively. Considering these factors, the engine exhibited an efficiency of about 47.9% compared with the power pack in the case of plow tillage operations, and the engine exhibited an efficiency of about 29.3% in the case of rotary tillage operations. A hydrogen fuel cell tractor is considered suitable for high-efficiency and eco-friendly vehicles because it can operate on eco-friendly power sources while providing the advantages of a motor.

Design and Implementation of a Car’s Black Box System using Arduino

A black box system (BBS) in a car is crucial for recording and analyzing critical data to enhance safety, investigate accidents, and improve vehicle performance. This research presents a BBS developed using Arduino for cars, aimed at using the power of modern technology for comprehensive data capture and analysis in vehicular contexts. The BBS, or Event Data Recorder (EDR), is an essential component for enhancing road safety, accident analysis, and overall vehicle performance evaluation. The proposed system uses Arduino, a versatile and cost-effective microcontroller platform, to create a robust and customizable solution. It integrates various sensors and data acquisition modules to collect critical data points, including speed, acceleration, GPS coordinates, engine performance, and vehicle diagnostics. The architecture of the system and its smooth integration into automobiles are described in this article through detailed hardware and software design. Data retrieval and analysis are made possible by the system's user-friendly interface, which helps with fleet management, driver behaviour analysis, and accident investigation. This paper addresses the importance of data privacy and security while highlighting technological improvements. It proposes measures to ensure that personal data is managed responsibly and in accordance with legal requirements. In conclusion, a major advancement in improving road safety and vehicle monitoring has been made with the integration of Arduino technology into the car's BBS. Considering data security and privacy, this system provides users with an extensive set of facts to enable them to make well-informed decisions.

Study on physiological representation of passenger cognitive comfort: An example with overtaking scenarios

During autonomous driving, the subjective cognition of scenario risks may lead to cognitive discomfort of occupants. This is a new concept of ride comfort for vehicles, which is being included in the performance evaluation of autonomous vehicles. The observation methods, generation mechanisms, prediction methods, and optimization methods of this cognitive discomfort remain to be explored. In order to quantify the risk cognition of passengers and evaluate cognitive comfort, this paper proposes a physiological representation method using overtaking scenarios as an example. Firstly, physiological data of passengers are collected through road test, and 19 physiological indicators are extracted, including 6 galvanic skin response (GSR) indicators, 5 electrocardiogram indicators, and 8 eye movement indicators. Secondly, the physiological indicator set related to the subjective evaluation results of comfort is constructed through correlation analysis. Finally, a multiple linear regression model is established to represent the level of passenger discomfort, with physiological indicators as inputs and subjective comfort indicators as outputs. The indicators that contribute significantly to the output results in the model are the maximum amplitude of GSR, the rise time of GSR, the maximum pupil diameter, and the average fixation time. The validation results indicate that the overall accuracy of the model is 86.90 %.

Multi criteria analysis of performance evaluation of launch vehicles in the space industry

Multi criteria analysis of performance evaluation of launch vehicles in the space industry

Design and Analysis of Solar Car Chassis for WSC 2023

The World Solar Challenger is a biennial global competition that plays a crucial role in testing and advancing the technological limits of solar-powered vehicles, particularly in the context of diverse travel conditions. The competition features three categories: Challenger Class, Cruiser Class, and Adventure Class. In 2023, Siam Technology University participated with the STC-4 model in the Cruiser Class. This research contributed to the vehicle's design, structural integrity, safety, and performance evaluation, essential for passing the scrutineering tests. Researchers were involved in shaping the vehicle's aerodynamics, analyzing structural aspects, and designing safety features compliant with World Solar Challenge 2023 requirements. The goal was to ensure the vehicle's safe participation and successful operation under varied conditions. The designed solar-powered vehicle, STC-4, aimed to address shortcomings from previous competitions. The structural framework, utilizing AISI 4130 steel, met safety standards, with a final weight of 258 kg. Structural testing ensured that the displacement did not exceed 25 mm. Finite Element Analysis confirmed the structural integrity, aligning with the team's objectives. In conclusion, this research significantly contributed to the design and safety aspects of solar-powered vehicles, as demonstrated by the successful participation of STC-4 in the World Solar Challenge 2023 Cruiser Class. The findings highlight advancements in structural design, safety measures, and performance capabilities, emphasizing the importance of continuous innovation in solar vehicle technology.

Performance Evaluation of New Energy Vehicles with Human-Machine Interaction in Intelligent Transportation Systems

An intelligent transport system is a sophisticated platform developed to provide new solutions related to transport administration, enabling people to be increasingly aware and make infrastructures secure, more synchronized, and more sophisticated utilization. Energy vehicles in intelligent transportation have additional positive benefits to society. However, handling mobility, connectivity, and communication infrastructure are significant challenges in integrating the energy vehicle in intelligent transport systems. This paper proposed an energy vehicle with a human interaction framework (EVHIF) to enhance the intelligent transportation system. The EVHIF provides human-machine interaction through a voice-over interface, and the natural language processing made the interface more flexible. The voice-over interaction framework adopts the advanced user interface, mobility problems and becomes more accessible. Static language program modifies the user voice interaction to the energy vehicles.
 If correctly designed and executed, ITS can save a great deal of time, money, and even lives. ITS encompasses the information analysis and data transmission programs that enable the operation and maintenance of surface transportation modes to increase the safety and efficiency of conveying people and commodities. People, safety officers, and government also use its benefits.
 Experimentation is done by focusing on the performance of EVHIF in an intelligent transportation system. Evaluation results prove that EVHIF is efficient at 98.56% and advisable for transportation systems with advanced interaction frameworks.

Assessing the adequacy of transportation overall vehicle effectiveness for sustainable road transportation

Organizations in today’s supply chains strive for transportation activities optimization. However, transportation is a significant environmental impact activity. Particularly, road transportation is the highest emission rate source and the most widespread modality for last-mile delivery. In this context, the use of performance management tools, such as key performance indicators (KPIs), is a strategy to reach both economic-operative and environmental benefits. Among all KPIs, overall equipment effectiveness (OEE) is one of the most suitable KPIs to measure the utilization of an industrial asset. In the transportation sector, a variant of the OEE, known as the transportation overall vehicle effectiveness (TOVE), is used to define the performance of vehicle distribution activities, such as road transportation for last-mile delivery and urban logistics. Although TOVE is effective for evaluating vehicle performance in terms of administrative availability, operating availability, performance, and quality, the indicator does not take into the environmental impact related to road transportation activities. Literature has proposed several formulations to quantify transport carbon emissions, most of which are linear relationships to the distance travelled. However, these models are not suitable for assessing the TOVE performance of road transportation activities. This paper aims to compare the performance of last-mile delivery in terms of TOVE and carbon emissions evaluated with a distance travelled formulation in two different scenario systems. The comparison shows the inadequacy of TOVE in terms of environmental sustainability, as maximizing road transport performance while ignoring the environmental dimension excludes the minimization of CO2 emissions. Therefore, the foundation for future developments of TOVE for sustainable road transportation can be established from this divergence.

Raspberry Pi-Based Low-Cost System for Electrical Parameters Gathering and Analysis for Performance Evaluation of Electric Vehicles

In recent years, both the manufacture and sales of electric vehicles have significantly grown compared to traditional vehicles. This growth has favored the creation of tools that measure and evaluate this class of vehicles’ mechanical and electrical operation quickly, simply, and efficiently. Nevertheless, the measurement of electric-vehicle parameters can be a challenge due to the fact that the required DAQ equipment is expensive and thus of limited availability. This work describes a low-cost data acquisition system to simplify and reduce the cost of measuring the current and voltage parameters of electric vehicles. The proposed system is based on a Raspberry PI 3b+ microcomputer and it was evaluated during different driving cycles. The results were compared with the data obtained using a professional Dewesoft Sirius system for validation. The Raspberry Pi-based low-cost system achieved comparable performance to commercial systems (with a measurement error of less than 1%), despite having a lower sampling rate. Furthermore, unlike other systems, the proposed platform is an easy-to-set universal solution that can store the measured data, display them in real-time, graph them, and export the information for analysis with other specialized statistical tools. The main contribution of this work lies in the fact that the proposed low-cost DAQ system can be easily cloned, and the components cost of the prototype is about 10 times less than the price of the commercial systems.

Energy Efficient Platooning of Connected Electrified Vehicles Enabled by a Mixed Hybrid Electric Powertrain Architecture

The platooning of electrified vehicles can improve vehicle performance by enhancing energy efficiency and safety. Nevertheless, studies focusing on control strategies for platoons usually ignore or over-simplify the powertrain in the vehicle dynamics model. Advanced powertrain control strategies for electrified vehicles show that efficient operation of the powertrain depends on the current vehicle status, such as the torque demand, vehicle speed, and battery state of charge. In addition, the operation of individual powertrains can affect platoon-level performance, and the relationship between powertrain operation and vehicle status can be highly nonlinear. In studies of platoons, the evaluation of vehicle performance usually requires the vehicle to converge to a static operating point or to follow a simple operating pattern. Ideal cases can help in the understanding and improvement of vehicle performance but may be less beneficial for applications in realistic cases that are more complex, e.g. in military operations. In this study, a platooning optimization problem is formulated for optimal performance between two locations, given the terrain grade and soil properties along the path. A high-fidelity powertrain model is constructed and embedded in a detailed platoon model. The drive schedule is specifically designed for a platoon of vehicles with an innovative hybrid electric powertrain to achieve minimum total energy consumption. Results show that the approach is able to reduce energy consumption and headway keeping errors.

Experimental driving performance evaluation of battery-powered medium and heavy duty all-electric vehicles

As the push to reduce air pollutants and carbon dioxide for global warming continues, there is increasing interest in the development and deployment of battery electric vehicles for heavy-duty applications as one of the critical and challenging steps of a broader effort to fully electrify the transportation sector. This study evaluated and quantified the energy consumption rates and energy regeneration rates for three heavy-duty battery electric vehicles (a step van, a yard tractor, and a class 8 truck) using driving cycle tests with a heavy-duty vehicle chassis dynamometer. Information in this area is currently very limited, and yet it is critical to understanding how effective heavy-duty battery electric vehicles will be for a wide range of applications as the electrification of the heavy-duty vehicle market continues to expand. Energy consumption rates ranged from 0.37 and 2.71 kWh/km for most vehicle/cycle combinations. On a diesel liter energy-equivalent basis, the heavy-duty vehicles had approximately 3 to 6 times better energy consumption rates compared to comparable diesel vehicles, with weight normalized energy consumption rates also comparing favorably to the current and future greenhouse gas/fuel economy standards for equivalent diesel vehicles. Based on battery capacities ranging from 80 to 215 kWh, the vehicle ranges varied from 38.18 km to 171.2 km, with ranges of 115.9 km to 171.2 km for most types of driving and lower ranges of 38.18 to 42.20 km for sustained grade cycles. Overall, the advantage of improved energy consumption rates coupled with improvements that can be expected for battery technology and vehicle range, provide promise for expanded use of heavy-duty vehicles going into the future.

Multi criteria analysis of performance evaluation of launch vehicles in space industry

Multi criteria analysis of performance evaluation of launch vehicles in space industry

Coulombic efficiency estimation technique for eco-routing in electric vehicles

<abstract> <p>A battery source is an indispensable component in electric vehicles and its efficient usage results in improvement of battery life. The amount of energy stored in batteries, including their state of charge and efficiency, are critical metrics for evaluating vehicle performance. This article presents an automated motor-generator linked technique for estimating battery-related parameters, which is an essential aspect of eco-routing in electric vehicles. This system enables discharging of a battery at various current rates by constant resistive loads which are electronically applied. The charging process however uses a charger attached to various sensors for estimating the current. The coulomb counting method has been used which provides information of State of Charge and Coulombic Efficiency of the test battery. Since battery behavior is dynamic; the energy source was charged and discharged over numerous cycles at various current rates. The experimental set-up uses sensors for accurate data acquisition. In addition to the experimental set-up, an electrical circuit battery model has also been simulated in this work. All simulation as well as post processing tasks have been done in the Arduino-MATLAB environment. The main highlight of this system is that the system is portable, easy to operate, low cost and can be used to test all types of batteries. The proposed system is simple and has yielded good results. The battery parameters obtained prove to be vital for range determination in eco-routing of electric vehicles among other applications.</p> </abstract>

Vibration control of improved LQG for wheel drive electric vehicle based on uncertain parameters

To improve the ride comfort of wheel drive electric vehicle, a quarter dynamic model is established for vehicle active and passive suspension. Based on the response root mean square (RMS), the evaluation index of vehicle ride comfort is derived. According to the influence of uncertain parameters on system minimum RMS, the optimal vehicle parameters are determined. To avoid experience value of uncertain weight coefficient affecting the LQG control, analytic hierarchy process (AHP) is adopted to determine the weighting coefficient of vehicle performance evaluation index. And the LQG controller of vehicle active suspension is designed based on optimal control theory. Compared with the analysis results of passive suspension, the effectiveness of active control scheme is verified.

Study of Driving Cycle of City Tour Bus Based on Coupled GA-K-Means and HMM Algorithms: A Case Study in Beijing

Driving cycle is an important indicator to evaluate vehicle performance and to measure fuel consumption. Its impact is not only on pollution emissions but also on asset management. Based on the real life detailed data from the city tour buses in Beijing, this paper proposes to couple a genetic algorithm based GA-K-means clustering with the Hidden Markov Model (HMM) to construct a city tour bus urban road driving cycle in Beijing. Compared to the standard driving cycle C-WTVC used for heavy commercial vehicle fuel consumption certification in China, our driving cycle model can reflect the real life situation more accurately. With our model, the influence of driving state and driving behavior on fuel consumption is also analyzed, and the fuel consumption estimation model under the corresponding driving cycle is constructed. To gain deeper insight and better fuel consumption, we further segment and construct the driving sub-cycles of peak and off-peak periods according to the difference in fuel consumption at different time periods in Beijing in September 2019. Through this fine-grain driving cycle, we can further improve the accuracy of the fuel consumption estimation model.

Influence of balanced suspension on handling stability and ride comfort of off-road vehicle

Handing stability and ride comfort, basic indexes to evaluate vehicle performance, usually cannot be guaranteed simultaneously. Given the contradiction between the two indexes, a new type of suspension – balanced suspension, has attracted wide attentions for years. Balanced suspensions are a device that converts the movement of a wheel into force at the other wheels through a mechanical or hydraulic structure, which might improve ride comfort/handling stability while maintaining the handling stability/ride comfort. As the hydraulically interconnected suspension and hydro-pneumatic suspension show disadvantages of high cost and high installation space requirement, a specific balanced suspension which is connected through a mechanical structure is presented and thoroughly analyzed in this paper. The balanced suspension connects the front and rear suspension motion of the vehicle by means of the lever mechanical connection structure to obtain the comprehensive performance of enhanced vehicle’s ride comfort and handling stability. The half-vehicle mathematical model for ride comfort and the multi-body dynamics model for handling stability are established for the comparison and analysis of the dynamic performance of vehicle when the balanced suspension on and off. In addition, experimental tests of the modified vehicle prototypes when the balanced suspension on and off on the ride comfort and handling stability are conducted. Similar with the simulation results, experimental tests show that the handling stability is nearly unchanged while the ride comfort improves about 15.9% when the balanced suspension is on.

Recent aerodynamic performance evaluation of commercial and non-commercial vehicles

The effect of aerodynamics of commercial and non-commercial are analyzed in this study and after analyzing it is founded that many researchers have not attempted much on the comparative study on two different types of commercial and non-commercial vehicle having a same turbulence model. Secondly, there is lack of numerical three dimensional aerodynamic flow analysis in the case of both commercial and non-commercial vehicles. Hence, there is requirement for numerically analyzing two commercial and non-commercial vehicles having the same turbulence model to better understand the aerodynamics of both commercial and non-commercial vehicles.

Unmanned Aerial Vehicle Communications: Path-Loss Modeling and Evaluation

Channel modeling plays an important role in unmanned aerial vehicle (UAV) communication system design and performance evaluation. In this article, we provide a brief review of the existing UAV channel modeling, emphasizing that UAV altitude is a significant factor in characterizing large-scale fading modeling. We then investigate the effect of UAV altitude on the system-level performance of various networks. Simulation results are provided to show the behavior of cellular-connected UAV networks with different channel models and conditions. In addition, we show that antenna design significantly affects system performance. (Table 1 summarizes the abbreviations used in the article.)

Machine to machine performance evaluation of grid-integrated electric vehicles by using various scheduling algorithms

Abstract For smart cities, electric vehicles (EVs) are promisingly considered as a striving industry due to its pollution-less behaviours and easy-to-maintain characteristics. A seamless management system is necessary to manage the energy between EV and various parties participating in the grid operation. To facilitate the energy system in a distributed and coordinated way, a machine-to-machine (M2M) system can be considered as the key component in future intelligent transportation systems. Due to the ubiquitous range and data speed, a fourth-generation (4G) cellular-based long-term evaluation (LTE) system inspires us to select it as a potential carrier for M2M communication. However, various simulation and analytical modelling end up with the conclusion that the maximum 250 EVs can be connected under an LTE base station. These limitations or scalability limits may result in a terrible mix-up in future smart cities for over dense roads. In this paper, we measured various M2M quality of services performance for exceeding the number of EVs by using three popular algorithms (proportional fair scheduling, modified largest weighted delay first scheduling and exponential scheduling). The result shows that the proportional fair scheduler has the highest packet loss ratio (PLR) and delay time as compared to other two schedulers.