Prototype Vehicle Research Articles

Design and Fabrication of an Acetylene Fuel Vehicle

Abstract: The transition towards sustainable transportation necessitates exploring alternative fuels with reduced environmental impacts. Acetylene has emerged as a promising candidate due to its clean combustion characteristics and high energy density. This abstract outline the design and fabrication process of an acetylene fuel vehicle, focusing on its feasibility and performance. The design phase involves meticulous material selection to ensure compatibility with acetylene's properties while prioritizing safety measures. Key components such as the fuel storage system, combustion chamber, and propulsion system are engineered to maximize energy conversion efficiency and minimize emissions. Safety protocols for acetylene handling and storage are integrated into the design to mitigate risks associated with its high reactivity. Fabrication incorporates advanced manufacturing techniques like additive manufacturing and precision machining to create a robust vehicle prototype. The integration of selected components is optimized to enhance structural integrity and overall performance. Rigorous testing procedures are conducted to validate the vehicle's functionality, including performance metrics, emissions analysis, and safety evaluations. The outcomes of this study provide valuable insights into the feasibility of acetylene as a sustainable transportation fuel. The design and fabrication processes contribute to ongoing research efforts aimed at reducing the carbon footprint of the transportation sector. The results also offer a pathway for future developments in alternative fuel technologies, driving towards a more environmentally friendly and energy-efficient transportation ecosystem.

PRORETA 5 – building blocks for automated urban driving enhancing city road safety

Abstract In the joint research project PRORETA 5, building blocks for automated driving in urban areas have been developed, implemented, and tested. The developed blocks involve an object tracking for cars, bicycles, and pedestrians that feeds a multimodal object prediction which is able to predict the traffic participants’ most likely trajectories. Then, an anytime tree-based planning algorithm calculates the vehicle’s desired path. Finally, logic-based safety functions ensure a collision-free trajectory for the ego vehicle. The mentioned building blocks were integrated and tested in a prototype vehicle in urban scenarios. Furthermore, a novel general framework for specifying and testing traffic rule compliance has been developed. In this paper, the automated driving concept of PRORETA 5 is introduced and the developed methods are briefly explained.

Approaching open innovation in customization frameworks for product prototypes with emphasis on quality and life cycle assessment (QLCA)

This paper introduces an innovative model designed to predict the optimal product prototype within the framework of sustainable development. The model takes into account both quality and environmental impact throughout the entire life cycle of the product, with a specific focus on light electric vehicles. By incorporating customer expectations using the Weighted Sum Model, the model assesses vehicle quality based on the importance of various criteria. Employing life cycle evaluations guided by the Pareto-Lorenz rule, environmental impact for vehicle prototypes is modeled. The resulting Quality-Environmental Indicator of LCA (QLCA) facilitates a qualitative environmental analysis, aiding in the selection of prototypes that align with customer expectations. The model proves effective in guiding decisions related to product development, providing a comprehensive approach to evaluating product value. Notable contributions to the scientific community include the introduction of the QLCA model, which integrates quality and environmental assessments, and underscores the significance of open innovation, sustainable development, and sensitivity analysis for organizational adaptation. Future research directions involve adapting the model to larger customer samples, exploring production costs, and assessing its applicability across diverse industries and contexts to enhance its universality and practical implications.

Development of a Hydrogen Fuel Cell Prototype Vehicle Supported by Artificial Intelligence for Green Urban Transport

In the automotive sector, the zero emissions area has been dominated by battery electric vehicles. However, prospective users cite charging times, large batteries, and the deployment of charging stations as a counter-argument. Hydrogen will offer a solution to these areas, in the future. This research focuses on the development of a prototype three-wheeled vehicle that is named Neumann H2. It integrates state-of-the-art energy storage systems, demonstrating the benefits of solar-, battery-, and hydrogen-powered drives. Of crucial importance for the R&D platform is the system’s ability to record its internal states in a time-synchronous format, providing valuable data for researchers and developers. Given that the platform is equipped with the ROS2 Open-Source interface, the data are recorded in a standardized format. Energy management is supported by artificial intelligence of the “Reinforcement Learning” type, which selects the optimal energy source for operation based on different layers of high-fidelity maps. In addition to powertrain control, the vehicle also uses artificial intelligence to detect the environment. The vehicle’s environment-sensing system is essentially designed to detect, distinguish, and select environmental elements through image segmentation using camera images and then to provide feedback to the user via displays.

Driver-less Automated Solar Powered Electric Vehicle Prototype With Sensor Control

A solar powered electric vehicle has been designed in order to create the modern day transportation evolution. It uses renewable energy sources such as sunlight which never ends until the presence of atmosphere and it also the future of energy savings and reduce carbon emission, so it helps to prevent global warming. It has the feature of solar PV cell which absorbs the sunlight and recharge the battery of these prototype, when the sunlight is present it uses the sunlight for travelling and also recharge the battery and when the sunlight is not present the vehicle still moves with the help of battery. The battery can charge two ways either the help of solar PV or with the help of electricity. If there neither presence of solar PV nor the battery charge then it uses the adaptable jumper wire to connect with charging station and recharge its battery. It has another feature sensor and receiver, it is directly taken instruction using sensor and receiver so it is completely encrypted system that connects user and prototype without the interference of any third party application.

Detection of Potential Faults in the Electricity Distribution Network Using Unmanned Aerial Vehicles and Thermal Cameras Through Deep Learning Methods

In this article, a software-based unmanned aerial vehicle (UAV) prototype has been developed for exploration purposes, replacing the current manual inspection of energy transmission lines (ETLs) by human teams. The UAV's capabilities address the limitations of human viewing angles, ensuring more efficient exploration activities. Real-time exploration is now achieved through data collected with UAVs and processed using Convolutional Neural Networks (CNNs) and image processing techniques. The UAV’s path and detected objects are autonomously processed in the Cartesian coordinate system. The autonomous conductor tracking system provides bidirectional tracking of conductors in the distribution network through color space range and Faster R-CNN conductor detection. Incorporating a proportional-integral-derivative (PID) design, the UAV is capable of operating in adverse weather conditions. An additional overheating warning system protects equipment in the electricity distribution network (EDN) from potential issues related to uneven load distribution, economic life, and labor errors. Through modeling studies with convolutional neural networks and field data, impressive F-1 scores have been achieved for detecting various anomalies: 77% for trees in contact with the line, 67% for concrete equipment exceeding its economic life, 88% for bird nests, 55% for cracked porcelain insulators, 89% for foreign objects, and 97% for nonuse of original fuses.

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process.

Transferability Assessment of OBD-Related Calibration and Validation Activities from the Vehicle to HiL Applications

With the Euro 7 pollutant emission legislation currently under discussion, advanced and more efficient exhaust aftertreatment systems are being developed. The technologies required for these are leading to an increase in the number of components and control systems requiring diagnoses strategies under the on-board diagnostics (OBD) legislation. With concurrent shorter development times and significant reductions in budgets allocated to conventional powertrain development, challenges in the field of OBD calibration and verification are already rising sharply. In response to these challenges, hardware-in-the-loop (HiL) approaches have been successfully introduced to support and replace conventional development methods. The use of complex simulation models significantly improves the quality of calibrations while minimizing the number of required prototype vehicles and test resources, thus reducing development costs. This paper presents a feasibility study for moving OBD-related calibration and validation tasks from the vehicle to a HiL platform. In this context, the calibration and verification process of an active diagnostic for monitoring the condition of the three-way catalyst (TWC) and the oxygen sensors in the exhaust aftertreatment system is presented. It is shown that all relevant signals are simulated with sufficient accuracy to ensure a robust transfer from the vehicle to a HiL test bench. Special attention is given to the simulation of aged components and their influence on the emission behavior of the system. Furthermore, it is discussed that transferring OBD tasks from the vehicle to the HiL test bench could result in significant savings in development time and a reduction in the number of physical prototype vehicles and test resources required.

Practice of methods for remote control of walking robotic mini-dredgers moving along the bottom

The results of experimental testing of methods for remote control of underwater walking mini-dredgers are discussed. Tests were carried out in real water conditions on the basis of a prototype of a 6-legged underwater walking vehicle. During the tests, technological operations for clearing and deepening small channels and eriks, as well as the extraction of sapropel from the bottom of the lake, were simulated. During the experiments, a mini-dredger has been controlled using video information of on-board video cameras. Due to poor visibility in the zone of hydraulic erosion of bottom soil, management was carried out in conditions of an incomplete and ambiguous understanding of the external environment. Local positioning was carried out according to natural landmarks. It is shown that fairly accurate remote control of the movement of a mini-dredger can be organized on the basis of public satellite maps of the area, even in conditions of a lack of sensory information. Moreover, the operation of an underwater dredger can be realized with minimal external control influences. The research results may be demanded in the development of walking robotic systems intended for the rehabilitation of degrading small water bodies and for other underwater technical work.

An Optimization Model for the Optimal Control of the Oxygen Excess Ratio in a Hydrogen Fuel Cell System

This paper focuses on the control of a fuel cell system, for which an optimization model is proposed to control the oxygen excess ratio in order to tackle the fuel cell starvation phenomenon and increase the net power production of the system. The optimization problem considers the detailed nonlinear dynamics of the stack and the air supply sub-system as constraints and upper and lower bounds to consider the technical limitations in the system’s operation. The goal is to maintain the oxygen excess ratio at its reference value, using the compressor input voltage as a control variable, under several load variations of the current demand, considered as the external noise to the system. The proposed nonlinear optimization problem has been applied to a 75 kW stack used in the Ford P2000 fuel cell prototype vehicles, and results have been compared to the proportional-integral (PI) control technique. The proposed control approach has shown a significant enhancement in the control performance of the oxygen excess ratio.

Integration of Autonomous Driving Functionalities for End-of-Line Production Transport Scenarios

Autonomous driving functionalities are increasingly being integrated into nowadays cars, which presents an opportunity for Automotive manufacturers to use these functionalities as part of the production process. This paper presents a case study of the integration of autonomous driving functionalities in a prototype vehicle for end-of-line production transport.The vehicle is equipped with sensor instrumentation, processing systems, and actuators to achieve automated driving, and the Robot Operating System (ROS) was utilized to achieve navigation on a pre-established route within a factory hall. The vehicle was also tested on a test track to emulate outdoor post-production testing stations. Complementary developed functions allow the vehicle to be effectively tracked and monitored inside and outside the demo factory.The use of autonomous driving functionalities in end-of-line production transport presents an opportunity for car manufacturers to streamline their production process. The successful integration of these functionalities in the vehicle using ROS and real-time tracking validates their use in a smart automotive factory. Further research and development in this area can lead to improved production efficiency and safety.

The effect of chassis weight optimization on the carbon footprint of the electric prototype vehicle

Electrification of vehicles has become increasingly widespread lately. It aims to reduce carbon emissions globally. Another step, namely reducing vehicle weight, is expected to reduce energy consumption during the operation. A vehicle part that can be reduced in weight is the chassis. This research compares the carbon footprint between the stock chassis and the lightweight version. The lightweight chassis requires additional energy during its fabrication. Life cycle analysis (LCA) is conducted to calculate the carbon footprint of each chassis. Material loss and manufacturing time are the main differences in the footprint. Manufacturing strategy is important in order to minimize the emission of the process. The lightweight chassis can reduce CO2 emission by 11% assuming 200 hours of operation. Therefore, optimization of the weight significantly reduces the emission.

A Systematic Mapping of Autonomous Vehicle Prototypes: Trends and Opportunities

A Systematic Mapping of Autonomous Vehicle Prototypes: Trends and Opportunities

Design, Simulation, and Prototype of an 18-Wheeler Electric Vehicle with Range Extension using Solar PV and Regenerative Braking

In response to California's initiative to propel the advancement of hybrid and electric vehicles toward achieving zero emissions, this study undertakes the design of a hybrid 18-wheeler aligning with the state's stringent standards. The motivation stems from the imperative need to address the research and development in the domain of hybrid/electric class 8 vehicles, specifically focusing on the pivotal segment of 18-wheelers. The research team developed a realistic theoretical design, verified it using MATLAB Simulink simulation, and ultimately built a prototype. The truck's novel features, namely, 60% electric power, regenerative braking, and solar PV for range extension, are included in the design and partially implemented in the prototype. Two motors, a DC and an AC, are used as the prototype drives where the system control is mostly automatic. Simulation of the electric 18-wheeler design shows that the model works properly. It follows the speed command closely while the acceleration and deceleration behaviors are normal. Testing of the prototype shows that it functions appropriately. The DC motor speed can be regulated over a wide speed range while the AC motor can run at two different speeds as designed. The prototype microcontroller logic is followed, ensuring safe operation of the solar PV system and the battery, and effective control of the motors. Overall, the project succeeded in achieving a harmonious blend of simulation, design, and physical implementation. It can be used as an engineering and public education tool. Further, by exploring cutting-edge technologies such as regenerative braking and solar power for truck range extension, the project contributes to raising vehicle efficiency and finding sustainable transportation solutions, which make the transportation sector more friendly to the environment.

Adversarial deep reinforcement learning based robust depth tracking control for underactuated autonomous underwater vehicle

In this paper, an adversarial deep reinforcement learning-based control method is proposed to address the issue of robust depth tracking of an underactuated autonomous underwater vehicle in the presence of intrinsic coupled dynamics and external disturbances. First, long-short-term-memory neural network is presented to memorize and predict the changes in the state of vehicle, and a cascaded multilayer perception projects the output into action space of vehicle. Subsequently, adversarial deep reinforcement learning scheme is applied to the training of control agent by introducing an adversary which counteracts the control behavior, whereby the agent is enabled to learn the control strategy in different distributions of state transition. For evaluation of the performance, a control agent is pre-trained in simulation environment based on the reliable digital model of a real vehicle, and the simulation environment is paced at one iteration per second to align with real-time operations to ensure the portability of training result. Furthermore, the training cost is also extremely reduced. Finally, experiments are conducted with time-varying disturbances to further prove the feasibility of the proposed learning-based control scheme on a prototype of underwater vehicle in towing tank. Moreover, comparative experiment results show the better robustness performance of the learning-based control agent than that of classic line-of-sight based proportional–integral–derivative and adaptive line-of-sight based proportional–integral–derivative controllers in different scenarios.

Modeling, Model Calibration, and Characterization of Graphite Anodes from Coal Derived Carbon for Battery Applications

Most current production electric vehicles (EVs) contain cells with a battery pack or module in order to maintain electrical conductivity, prevent fatigue due to vibrations, and provide efficient cooling. Various module hardware has to be designed in order to facilitate the efficient rejection of heat and contain volume change due to operation and battery cell aging. As we see a shift towards EVs both globally and domestically, the time from concept development to vehicle production needs to rapidly decrease. The use of model-based engineering is critical to driving rapid design and virtual prototyping of electric vehicles to ensure that battery module components meet EV requirements.Currently, empirical data is used to characterize and parameterize battery models that are used to drive design decisions at the battery cell and battery module level. Typical vehicle level use profiles such as the Hybrid Pulse Power Characterization (HPPC) profile, constant rate discharge, constant rate charging, and US06 drive cycles are traditionally used to parameterize battery models. In this work, we discuss select cell level testing profiles, evaluate the electrochemical transport and reaction parameters that are estimated using a porous electrode model, and provide insight and recommendations based on data from coal derived graphite for battery applications.References Garrick, T. R., Gao, J., Yang, X., & Koch, B. J. (2021). “Modeling Electrochemical Transport within a Three-Electrode System.” Journal of The Electrochemical Society, 168(1), 010530. Lopata, J. S., Garrick, T. R., Wang, F., Zhang, H., Zeng, Y., & Shimpalee, S. (2023). “Dynamic Multi-Dimensional Numerical Transport Study of Lithium-Ion Battery Active Material Microstructures for Automotive Applications.” Journal of The Electrochemical Society, 170(2), 020530.

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.

Nanoscale Reconfigurable Si Transistors: From Wires to Sheets and Unto Multi‐Wire Channels

AbstractIn this work, bottom‐up Al–Si–Al nanowire (NW) heterostructures are presented, which act as a prototype vehicle toward top‐down fabricated nanosheet (NS) and multi‐wire (MW) reconfigurable field‐effect transistors (RFETs). Evaluating the key parameters of these transistors regarding the on‐ and off‐currents as well as threshold voltages for n‐ and p‐type operation exhibit a high degree of symmetry. Most notably also a low device‐to‐device variability is achieved. In this respect, the investigated Al–Si material system reveals its relevance for reconfigurable logic cells obtained from Si NSs. To show the versatility of the proposed devices, this work reports on a combinational wired‐AND gate obtained from a multi‐gate RFET. Additionally, up‐scaling the current is achieved by realizing a MW RFET without compromising reconfigurability. The Al–Si–Al platform has substantial potential to enable complex adaptive and self‐learning combinational and sequential circuits with energy efficient and small footprint computing paradigms as well as for native components for hardware security circuits.

About the Automation of an Autonomous Sail-propelled Search Drone

The paper presents the prototype of an autonomous sail-propelled vehicle, powered by sails and equipped with mini solar panels, intended for the surveillance of maritime space. Hardware and software aspects regarding the automation of this unmanned boat are addressed. Conceptual solutions have been identified to ensure short manufacturing times, component accessibility (price and availability on the free market), and high system reliability. The paper proposes an original solution to respond to at least two imperative needs: the safety and security of commercial and military navigation in the Black Sea area (faced with a tense geo-political climate) and the increased need to integrate European and global policies on reducing pollution and promoting renewable energy principles.

Linear permanent magnet electrodynamic suspension system: Dynamic characteristics, magnetic-mechanical coupling and filed test

This study investigates the dynamic electromagnetic characteristics and thermo-mechanical coupling effect of the linear permanent magnet electrodynamic suspension system (PMEDS) via high-speed test rig. Besides, a physical prototype weighing 1.5 tons along with a 50-m linear copper guideway is developed and tested. Firstly, the topology structure along with fundamental operation model is introduced, followed by establishing the analytical model. Subsequently, a multi-functional test rig is utilized to experimentally determine its speed characteristics, lateral stability, and vertical vibration, while also validating the simulation and analytical models. Additionally, thermo-mechanical coupling analysis is conducted through co-simulation, experimentation and calculation to determine the dependence of temperature on the levitation and drag forces. Finally, a prototype vehicle integrated with auxiliary components, is fabricated and tested on a copper guideway. The vehicle is suspended freely via levitation force in the air above 30 mm, with tested suspension height generally aligning with dynamic simulation results.