Relevant bibliographies by topics / Smart vehicle

Academic literature on the topic 'Smart vehicle'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Smart vehicle"

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Bharathi, V. C. "Smart Parking System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1823–26. http://dx.doi.org/10.22214/ijraset.2021.36746.

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In the modern age, many people have vehicles. Vehicle is now a primary need. Every place is under process of urbanization. There are many supermarkets and shopping centers etc. There are many creative places where people used to go for refreshing and relaxation. All these places are full of with people so they need a parking space where people can park their vehicles safely and easily. Every parking area needs a website or system that records the detail of vehicles to give the parking facility. With the help of iot based system we can deliver a good service to users/people who wants to park their vehicles into organization’s premises. Present days in parking areas they just maintain the vehicles just with tokens and they have records of vehicle details in books so that during some critical situations like police enquiry of terrorist car or vehicle missing that case it is difficult to find the details of particular vehicle. But with our parking management system it is easy to find within 1 to 2 seconds. By parking the vehicle in public place the vehicle can be claimed by other person but in this case there is no such problem and no need to give fine for anything we can park our vehicle with securely.
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K R, Suma. "Smart Vehicle Sensor System." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 5, 2021): 4–7. http://dx.doi.org/10.22214/ijraset.2021.37004.

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Over recent years production of the vehicle around the world has increased rapidly, vehicle theft has become a shared concern for all citizens. Security and safety have always become a necessity. However, present anti-theft systems lack the tracking and monitoring function. The Wi-Fi module enabled cost-effective solution has been made to protect the vehicles. This paper attempts to utilize two physically disjoint units in conjunction with each other, to provide a fool-proof mechanism against vehicle theft. A prototype has been made using Arduino and Wi-Fi module. Android smartphones are used to design for the user interface that allows access of the vehicle to an intended person only.
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Leeks, Harry. "Smart Electric Vehicle Charging." ITNOW 61, no. 4 (2019): 12–13. http://dx.doi.org/10.1093/itnow/bwz092.

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Abstract What does IT have to do with the charging of electric vehicles? In this article, Harry Leeks, a graduate IT Analyst at National Grid, explains how IT plays a pivotal role in the electric vehicle charging market.
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YAO, Yiwei, Yongjun WANG, Xiaoyan ZHANG, Xingwang YANG, Shaobo QIU, Weimin HE, Hongjian LI, and Jun LI. "Smart vehicle for smart city." SCIENTIA SINICA Informationis 46, no. 5 (May 1, 2016): 551–59. http://dx.doi.org/10.1360/n112015-00294.

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Narmadha, R., R. Madhav, D. Barath, S. Kiruthika, and J. Keerthana. "Smart Moving Vehicle Detection System." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1758–63. http://dx.doi.org/10.1166/jctn.2020.8438.

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In Vehicle detection is a computer skill that determines the locations, direction and speed of running vehicles in arbitrary (digital) images. Using vehicle features and ignores anything else, such as buildings, trees and bodies. Vehicle detection is currently an active research area in the computer vision community. Automobile localization and detection are frequently the primary step in bids such as face gratitude, video observation, vehicle computer interface and image database administration. Speed and tracking vehicle shapes is a prerequisite for recognition and/or vehicle features analysis, although it is often assumed that a normalized moving vehicle image is available. Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. In this paper, a machine learning algorithm created for analyzing vehicle detection, travel direction and speed measurement.
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Phillips, Anthony M., Ryan A. McGee, Johannes G. Kristinsson, and Hai Yu. "Smart, Connected and Electric." Mechanical Engineering 135, no. 03 (March 1, 2013): S4—S9. http://dx.doi.org/10.1115/1.2013-mar-4.

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This article introduces opportunities that are seen at the intersection of electrification, connectivity, and smart controls in the automobile industry. Computational Intelligence provides the vehicle the ability to reason, adapt, and learn based on historical usage data, the present operating conditions, and the predicted future states. Modern automobiles continue to grow in complexity and sophistication. Electrified powertrains now provide vastly improved fuel efficiency by utilizing high-voltage systems to overcome some of the shortcomings of traditional combustion engines. Smart controls have enabled a wealth of new vehicle features ranging from automatic climate control to vehicle dynamic control. Vehicle connectivity, having already empowered the driver through infotainment and telematics, now promises new computing resources and information that can be leveraged directly for improved vehicle performance. At the intersection of these three vehicle mega trends lies a field that is rich for development. In the future, drivers will benefit in everything from enhanced drivability to more durable vehicles.
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Ravi Kumar Palla, Chonika Molli, Gowthami Narayanasetti, Dwarakesh Malla, Sai kiran Katiki, and Venkata Mani Gopal. "Vehicle parking: A smart solution." Global Journal of Engineering and Technology Advances 13, no. 3 (December 30, 2022): 066–71. http://dx.doi.org/10.30574/gjeta.2022.13.3.0209.

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The system proposed in this paper provides an effective solution in finding the vacant space and manages the vehicles entering in and out of parking area. The system consists of Arduino Uno, Liquid Crystal Display (LCD), Servo motor and Infrared (IR) Sensors. Two IR Sensors placed just before and after the entrance of the gate which detects the motion of the vehicle either entering or leaving the gate or slot area. The Arduino Uno, a microcontroller counts the number of available slots based on the entry or exit of the vehicle and the state of each slot i.e., either the slot is empty or full and provides a feedback which is displayed on the LCD. This whole process makes the system fully automated and thus provides a solution for vehicle parking in a smart way. The proposed automated system causes the driver to navigate in reaching to the vacant slot using display thus reduces search time. The proposed system can be implemented at places where heavy number of vehicles needs to be parked.
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Gupta, Rajesh Kumar, L. N. Padhy, and Sanjay Kumar Padhi. "Smart Driving System for Improving Traffic Flow." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 236. http://dx.doi.org/10.23956/ijarcsse/v7i7/0174.

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Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay. For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.
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RAM VARA PRASAD, B., CH PRASANTHI, G. JYOTHIKA SANTHOSHINI, K. J. S. V. KRANTI KUMAR, and K. YERNAIDU. "SMART ELECTRICAL VEHICLE." i-manager's Journal on Digital Signal Processing 8, no. 1 (2020): 7. http://dx.doi.org/10.26634/jdp.8.1.17347.

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Balog, Michal, Žofia Šimeková, and Pavol Semančo. "Smart Vehicle Railroad." Applied Mechanics and Materials 708 (December 2014): 148–52. http://dx.doi.org/10.4028/www.scientific.net/amm.708.148.

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Radio Frequency Identification (RFID) technology introduces the way of automated data collection, and processing to improve accuracy of processed data. In the present time possibilities in development and application of RFID technologies are almost limitless. Increasing expansion of RFID technology in almost any industry, where the RFID tag can be put on any product or material or component is an evidence of the previous assertion. Apart from technical aspects, i.e. security, the financial effect of the RFID technology implementation is also relevant in rail freight transport. Using this technology to keep records of technical condition of the wagons can be avoided a huge disaster caused by lack of knowledge about technical condition of the wagon. It can bring the return of initial investment and cost reduction.
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Dissertations / Theses on the topic "Smart vehicle"

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Mustafa, Mustafa Asan. "Smart Grid security : protecting users' privacy in smart grid applications." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/smart-grid-security-protecting-users-privacy-in-smart-grid-applications(565d4c36-8c83-4848-a142-a6ff70868d93).html.

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Smart Grid (SG) is an electrical grid enhanced with information and communication technology capabilities, so it can support two-way electricity and communication flows among various entities in the grid. The aim of SG is to make the electricity industry operate more efficiently and to provide electricity in a more secure, reliable and sustainable manner. Automated Meter Reading (AMR) and Smart Electric Vehicle (SEV) charging are two SG applications tipped to play a major role in achieving this aim. The AMR application allows different SG entities to collect users’ fine-grained metering data measured by users’ Smart Meters (SMs). The SEV charging application allows EVs’ charging parameters to be changed depending on the grid’s state in return for incentives for the EV owners. However, both applications impose risks on users’ privacy. Entities having access to users’ fine-grained metering data may use such data to infer individual users’ personal habits. In addition, users’ private information such as users’/EVs’ identities and charging locations could be exposed when EVs are charged. Entities may use such information to learn users’ whereabouts, thus breach their privacy. This thesis proposes secure and user privacy-preserving protocols to support AMR and SEV charging in an efficient, scalable and cost-effective manner. First, it investigates both applications. For AMR, (1) it specifies an extensive set of functional requirements taking into account the way liberalised electricity markets work and the interests of all SG entities, (2) it performs a comprehensive threat analysis, based on which, (3) it specifies security and privacy requirements, and (4) it proposes to divide users’ data into two types: operational data (used for grid management) and accountable data (used for billing). For SEV charging, (1) it specifies two modes of charging: price-driven mode and price-control-driven mode, and (2) it analyses two use-cases: price-driven roaming SEV charging at home location and price-control-driven roaming SEV charging at home location, by performing threat analysis and specifying sets of functional, security and privacy requirements for each of the two cases. Second, it proposes a novel Decentralized, Efficient, Privacy-preserving and Selective Aggregation (DEP2SA) protocol to allow SG entities to collect users’ fine-grained operational metering data while preserving users’ privacy. DEP2SA uses the homomorphic Paillier cryptosystem to ensure the confidentiality of the metering data during their transit and data aggregation process. To preserve users’ privacy with minimum performance penalty, users’ metering data are classified and aggregated accordingly by their respective local gateways based on the users’ locations and their contracted suppliers. In this way, authorised SG entities can only receive the aggregated data of users they have contracts with. DEP2SA has been analysed in terms of security, computational and communication overheads, and the results show that it is more secure, efficient and scalable as compared with related work. Third, it proposes a novel suite of five protocols to allow (1) suppliers to collect users accountable metering data, and (2) users (i) to access, manage and control their own metering data and (ii) to switch between electricity tariffs and suppliers, in an efficient and scalable manner. The main ideas are: (i) each SM to have a register, named accounting register, dedicated only for storing the user’s accountable data, (ii) this register is updated by design at a low frequency, (iii) the user’s supplier has unlimited access to this register, and (iv) the user cancustomise how often this register is updated with new data. The suite has been analysed in terms of security, computational and communication overheads. Fourth, it proposes a novel protocol, known as Roaming Electric Vehicle Charging and Billing, an Anonymous Multi-User (REVCBAMU) protocol, to support the priced-driven roaming SEV charging at home location. During a charging session, a roaming EV user uses a pseudonym of the EV (known only to the user’s contracted supplier) which is anonymously signed by the user’s private key. This protocol protects the user’s identity privacy from other suppliers as well as the user’s privacy of location from its own supplier. Further, it allows the user’s contracted supplier to authenticate the EV and the user. Using two-factor authentication approach a multi-user EV charging is supported and different legitimate EV users (e.g., family members) can be held accountable for their charging sessions. With each charging session, the EV uses a different pseudonym which prevents adversaries from linking the different charging sessions of the same EV. On an application level, REVCBAMU supports fair user billing, i.e., each user pays only for his/her own energy consumption, and an open EV marketplace in which EV users can safely choose among different remote host suppliers. The protocol has been analysed in terms of security and computational overheads.
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Malmgren, Andreas. "Visual Vehicle Identification Using Modern Smart Glasses." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172428.

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In recent years wearable devices have been advancing at a rapid pace and one of the largest growing segments is the smart glass segment. In this thesis the feasibility of today’s ARM-based smart glasses are evaluated for automatic license plate recognition (ALPR). The license plate is by far the most prominent visual feature to identify a spe- cific vehicle, and exists on both old and newly produced vehicles. This thesis propose an ALPR system based on a sequence of vertical edge detection, a cascade classifier, verti- cal and horizontal projection as well as a general purpose optical character recognition library. The study further concludes that the optimal input resolution for license plate detection using vertical edges is 640x360 pixels and that the license plate need to be at least 20 pixels high or the characters 15 pixels high in order to successfully segment the plate and recognize each character. The separate stages were successfully implemented into a complete ALPR system that achieved 79.5% success rate while processing roughly 3 frames per second when running on a pair of Google Glass.
Under de senaste åren har området wearables avancerat i snabb takt, och ett av de snabbast växande segmenten är smarta glaögon. I denna examensuppsats utvärderas lämpligheten av dagens ARM-baserade smarta glasögon med avseende på automatisk registreringsskyltigenkänning. Registreringsskylten är den i särklass mest framträdande visuella egenskapen som kan användas för att identifiera ett specifikt fordon, och den finns på både gamla och nyproducerade fordon. Detta examensarbete föreslår ett system för automatisk registreringsskyltigenkänning baserat på en följd av vertikal kantdetektering, en kaskad av boostade klassificerare, vertikal och horisontell projektion samt ett optiskt teckenigenkänningsbibliotek. Studien konstaterar vidare att den optimala upplösningen för registreringsskyltdetektion med hjälp av vertikala kanter på smarta glasögonär 640x360 pixlar och att registreringsskylten måste vara minst 20 pixlar hög eller tecknen 15 pixlar höga för att registreringsskylten framgångsrikt skall kunna segmenteras samt tecken identifieras. De separata stegen implementerades framgångsrikt till ett system för automatisk registreringsskyltigenkänning på ett par Google Glass och lyckades känna igen 79,5% av de testade registreringsskyltarna, med en hastighet av ungefär 3 bilder per sekund.
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Moghaddam, Zeinab. "Smart charging strategies for electric vehicle charging stations." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2215.

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Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, consumer concerns over the limited availability of charging stations and long charging/waiting times are major contributors to the slow uptake of plug-in electric vehicles (PEVs) in many countries. To address the consumer concerns, many countries have undertaken projects to deploy a network of both fast and slow charging stations, commonly known as electric vehicle charging networks. While a large electric vehicle charging network will certainly be helpful in addressing PEV owners' concerns, the full potential of this network cannot be realised without the implementation of smart charging strategies. For example, the charging load distribution in an EV charging network would be expected to be skewed towards stations located in hotspot areas, instigating longer queues and waiting times in these areas, particularly during afternoon peak traffic hours. This can also lead to a major challenge for the utilities in the form of an extended PEV charging load period, which could overlap with residential evening peak load hours, increasing peak demand and causing serious issues including network instability and power outages. This thesis presents a smart charging strategy for EV charging networks. The proposed smart charging strategy finds the optimum charging station for a PEV owner to ensure minimum charging time, travel time and charging cost. The problem is modelled as a multi-objective optimisation problem. A metaheuristic solution in the form of ant colony optimisation (ACO) is applied to solve the problem. Considering the influence of pricing on PEV owners' behaviour, the smart charging strategy is then extended to address the charging load imbalance problem in the EV network. A coordinated dynamic pricing model is presented to reduce the load imbalance, which contributes to a reduction in overlaps between residential and charging loads. A constraint optimization problem is formulated and a heuristic solution is introduced to minimize the overlap between the PEV and residential peak load periods. In the last part of this thesis, a smart management strategy for portable charging stations (PCSs) is introduced. It is shown that when smartly managed, PCSs can play an important role in the reduction of waiting times in an EV charging network. A new strategy is proposed for dispatching/allocating PCSs during various hours of the day to reduce waiting times at public charging stations. This also helps to decrease the overlap between the total PEV demand and peak residential load.
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Ozen, Etkin. "Design Of Smart Controllers For Hybrid Electric Vehicles." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606540/index.pdf.

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This thesis focuses on the feasibility of designing a commercial hybrid electric vehicle (HEV). In this work, relevant system models are developed for the vehicle including powertrain, braking system, electrical machines and battery. Based on these models ten different HEV configurations are assembled for detailed assessment of fuel consumption. This thesis also proposes a smart power management strategy which could be applied to any kind of HEV configuration. The suggested expert system deals with the external information about the driving conditions and modes of the driver as well as the internal states of the internal combustion engine efficiency and the state of charge of the battery, and decides on the power distribution between two different power supplies based on the predefined algorithms. The study illustrates the characteristics of the powertrain components for various HEV configurations. The work also shows the power flow of HEV configurations with the developed smart power management system and therefore, the effectiveness of power management strategies has been evaluated in detail.
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Bönström, Daniel. "Smartphone application in PhoneGap : M2C’s electric vehicle smart charger." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-32452.

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Aloqaily, Osama. "Charging and Discharging Algorithms for Electric Vehicles in Smart Grid Environment." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34562.

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Power demands will increase day-by-day because of widely adopting of Plug-in Electric Vehicles (PEVs) in the world and growing population. Finding and managing additional power resources for upcoming demands is a challenge. Renewable power is one of the alternatives. However, to manage and control renewable resources, we need suitable Energy Storage System (ESS). PEVs have a large battery pack that is used mainly to supply electric motor. Moreover, PEV battery could be used as an ESS to store power at a certain time and use it at another time. Nevertheless, it can play the same role with electric power grids, so it can store power at a time and return it at another time. This role might help the grid to meet the growing demands. In this thesis, we propose a charging and discharging coordination algorithm that effectively addresses the problem of power demand on peak time using the PEV’s batteries as a backup power storage, namely, Flexible Charging and Discharging (FCD) algorithm. The FCD algorithm aims to manage high power demands at peak times using Vehicle to Home (V2H) technologies in Smart Grid and PEV’s batteries. Intensive computer simulation is used to test FCD algorithm. The FCD algorithm shows a significant reduction in power demands and total cost, in proportion to two other algorithms, without affecting the performance of the PEV or the flexibility of PEV owner’s trip schedule.
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Kaur, Amardeep. "Vehicle positioning using image processing." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Kaur_09007dcc80665391.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2009.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 27, 2009) Includes bibliographical references (p. 72-74).
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Zulkanthiwar, Amey. "VEHICLE INFORMATION SYSTEM USING BLOCKCHAIN." CSUSB ScholarWorks, 2019. https://scholarworks.lib.csusb.edu/etd/899.

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The main purpose of a vehicle information system using blockchain is to create a transparent and reliable information system which will help consumers buy a vehicle; it is a vehicle information system. The blockchain system will create a time sequence chain of events database for each vehicle from the original sale. It will include insurance, vehicle repair, and vehicle resale. This project is mainly divided into three parts. Part one is used by the administration who will create the blockchain and will give authentication to a different organization to create the blockchain. Part two will be used by the Organization to create a block in the blockchain. Part three will be used by customers who want to get information about the vehicle.
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Atterby, Alfred, Jakub Bluj, and Elias Sjögren. "Potential for electric vehicle smart charging station expansion at Fyrisskolan." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352636.

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The purpose of this bachelor thesis is to investigate the potential for electric vehicle charging at the high school Fyrisskolan, located in central Uppsala. The idea relies on charging electric vehicles (EV:s) outside of the hours of peak power consumption of the school which in this report is assumed to be solved by a suitable smart charger. In this project, various stochastic models are built to simulate solar energy production and school energy consumption using data collected from various sources. This generated data along with  driving distances and EV:s energy consumptions are used to calculate the available energy for EV charging. The available energy is then used to distinguish a minimal, mean and maximal amount of cars that could potentially be charged outside Fyrisskolan for each chosen month. The data collected is taken from December, March and June. Calculations and simulations are done in MATLAB. Results show that with available energy outside the peak energy consumption hours, there is a possibility to charge around 104 EV:s in one work day. The main conclusion is that there is not only a big potential to expand the charging of EV:s outside the school by installing smart charging stations in a technical view, but also a desire from employees at the school and neighbours living near it, to charge their future electric vehicles.
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Gao, Shuang, and 高爽. "Design, analysis and control of vehicle-to-grid services." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/197100.

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There are unique challenges and opportunities related to the integration of electric vehicles into the future power grid, especially the modern distribution grid since electric vehicle (EV) charging facilities and fast-charging stations are usually tied to low-voltage and medium-voltage power networks. The grid-connected EVs, if properly controlled, can operate as distributed energy storage and provide various ancillary services, such as peak shaving, fast-response reserve capacity, frequency regulation, voltage control and reactive supports. The purpose of this thesis is to integrate EVs to the power grid and provides suitable ancillary services to improve the grid reliability and stability. The larger future penetration of EVs and renewable energies is also taken into account to develop the vehicle-to-grid (V2G) control scheme with the constraints of EV charging and communication infrastructures. The main contents include: V2G mathematical model and system configuration; impact evaluation of EV integration and the V2G control framework; energy scheduling of EVs integration; V2G dynamic regulation services; control method of EV aggregator for dispatching a fleet of EVs; and the evaluation of V2G control scheme and hardware-in-the-loop experimental system design. 　　In the thesis, the impact of EV charging demand on the conventional distribution grid is firstly estimated to reveal the negative effects of the arbitrary EV charging and the necessity to control the EV charging process. The potential benefits EVs can bring into the power grid support are discussed and a V2G control framework is proposed to perform the V2G optimization and various regulation services. The current power electronics applied EV charging facilities and communication network are integrated into the V2G operation in the future distribution grid with microgrid and smaller installation of renewable generation units. 　　Next, mathematical model of V2G power control is formulated. Two optimization methods are proposed to schedule the EV charging and discharging energy to minimize the power losses and the operating cost while satisfying the mobility needs and the power system limitations. Subsequently, the dynamic regulation of V2G power is investigated to unleash the potential of EVs to provide multiple ancillary services simultaneously. In addition to V2G optimal energy scheduling, EVs can also be employed for dynamic power regulation which requires the fast response to the instantaneous imbalance between the power supply and demand. V2G power is controlled to mitigate the power fluctuation caused by the intermittent wind energy resources, and thus stabilize the system frequency and voltage. Finally, an EV-centric hybrid energy storage system is proposed, which combines the merits of V2G operation and superconducting magnetic energy storage (SMES) to enhance the power quality and system frequency stability. The critical issues in V2G applications are summarized in the end.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Books on the topic "Smart vehicle"

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Schmidt, Gerhard, Huseyin Abut, Kazuya Takeda, and John H. L. Hansen, eds. Smart Mobile In-Vehicle Systems. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9120-0.

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Smart highways, smart cars. Boston: Artech House, 1995.

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P, Pauwelussen J., and Pacejka H. B, eds. Smart vehicles. Lisse, Netherlands: Swets & Zeitlinger, 1995.

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Vahidinasab, Vahid, and Behnam Mohammadi-Ivatloo, eds. Electric Vehicle Integration via Smart Charging. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05909-4.

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Alam, Mohammad Saad, and Mahesh Krishnamurthy. Electric Vehicle Integration in a Smart Microgrid Environment. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780367423926.

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Kranitz, Michael Scott. Look before you lease: Secrets to smart vehicle leasing. Dublin, Ohio: Dublin Financial Press, 1995.

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Käppler, Wolf Dieter. Smart Vehicle Handling - Test und Evaluation in der Fahrzeugtechnik. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46417-5.

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Dailey, Daniel J. Smart Trek: A model deployment initiative. [Olympia, Wash.]: Washington State Dept. of Transportation, 2001.

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P, Haselkorn Mark, Washington (State). Dept. of Transportation., Washington State Transportation Center, and Washington State Transportation Commission, eds. Bellevue Smart Traveler: Design, demonstration and assessment. [Olympia, Wash.]: Washington State Dept. of Transportation, 1995.

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William, Mouyos, and United States. National Aeronautics and Space Administration., eds. X-33/RLV: System Health Management/Vehicle Health Management. [Washington, DC: National Aeronautics and Space Administration, 1998.

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Book chapters on the topic "Smart vehicle"

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Gurmani, Muhammad Salman, and Dietmar P. F. Möller. "Mechanism Protecting Vehicle-to-Vehicle Communication." In Smart Technologies, 335–43. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7139-4_26.

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Tapak, Peter, Michal Kocur, Matej Rabek, and Juraj Matej. "Smart Vehicle Inspection." In Computer Aided Systems Theory – EUROCAST 2022, 384–91. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-25312-6_45.

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Angkititrakul, Pongtep, John H. L. Hansen, Sangjo Choi, Tyler Creek, Jeremy Hayes, Jeonghee Kim, Donggu Kwak, Levi T. Noecker, and Anhphuc Phan. "UTDrive: The Smart Vehicle Project." In In-Vehicle Corpus and Signal Processing for Driver Behavior, 55–67. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-79582-9_5.

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Florea, Mihai, Valerian Croitorescu, and Mircea Oprean. "Smart Solutions for Vehicle Chassis." In Proceedings of the European Automotive Congress EAEC-ESFA 2015, 233–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27276-4_21.

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Yoga Sasidhar Reddy, S., Ch Amarnath, K. Surya Teja, M. Sridhar, and Surendra Kumar Bitra. "Smart Vehicle and Smart Parking System Using IOT." In Cybernetics, Cognition and Machine Learning Applications, 71–77. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1484-3_9.

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Malhotra, Abhishek, and Hardil Kanabar. "Adaptive Vehicle Safety and Collision Warning System Using DSRC for Heavy-Duty Vehicle." In IOT with Smart Systems, 437–43. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3945-6_42.

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Hornyák, Olivér, and George Farid Alkhoury. "Smart Contracts in the Automotive Industry." In Vehicle and Automotive Engineering 3, 148–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9529-5_13.

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Ben-Romdhane, Hajer, and Saoussen Krichen. "An Efficient Hybrid Evolutionary Algorithm for the Smart Vehicle Routing Problem." In Smart Technologies for Smart Cities, 197–213. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39986-3_10.

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Barrero, Lucía, Rodrigo Viera, Franco Robledo, Claudio Risso, and Sergio Nesmachnow. "Hybrid GRASP+VND for Flexible Vehicle Routing in Smart Cities." In Smart Cities, 240–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96753-6_17.

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Heidenreich, Philipp, and Abdelhak M. Zoubir. "Computational Aspects of Maximum Likelihood DOA Estimation of Two Targets with Applications to Automotive Radar." In Smart Mobile In-Vehicle Systems, 3–18. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9120-0_1.

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Conference papers on the topic "Smart vehicle"

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Kim, Taekjung, Byungwook Jin, Si-Ho Cha, and Moon-Seog Jun. "A Study on Issuance of Secure Vehicle Certificate for Vehicle to Vehicle Communications in Internet of Vehicles." In Smart Information Technology 2016. Science & Engineering Research Support soCiety, 2016. http://dx.doi.org/10.14257/astl.2016.142.21.

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Pahadiya, Pallavi, Rajni Gupta, R. B. Patel, and B. P. Singh. "Smart Vehicle System." In INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN SCIENCE AND TECHNOLOGY (ICM2ST-10). AIP, 2010. http://dx.doi.org/10.1063/1.3526178.

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Khan, M. Al Rizwan, B. Gowtham, A. S. Akash Saravanan, R. Arun Bharathi, and A. Elakya. "Smart Electric Vehicle." In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE, 2019. http://dx.doi.org/10.1109/icaccs.2019.8728534.

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Komanecky, Mark R., and David M. Claus. "IVHS Applications of Smart Cards." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912848.

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Wang, Yubin. "Smart Vehicle Status Service Guarantee Framework Integrating Vehicle-Machine Smart Modules." In 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2023. http://dx.doi.org/10.1109/idciot56793.2023.10053469.

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Senthilnathan, A., R. Manohar, J. Mohanavel, A. Omesh Heman Kumar, and R. Saravana Kumar. "Smart hybrid electric vehicle." In 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT). IEEE, 2017. http://dx.doi.org/10.1109/igeht.2017.8094071.

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Kessels, J. T. B. A., J. H. M. Martens, P. P. J. van den Bosch, and W. H. A. Hendrix. "Smart vehicle powernet enabling complete vehicle energy management." In 2012 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, 2012. http://dx.doi.org/10.1109/vppc.2012.6422771.

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Sulic, E., B. Pell, S. John, Rahul K. Gupta, W. Rowe, K. Ghorbani, and K. Zhang. "Performance of Embedded Multi-Frequency Communication Devices in Smart Composite Structures." In ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2008. http://dx.doi.org/10.1115/smasis2008-402.

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Lately, there has been an increased demand for vehicle manufacturers to incorporate a large number of communication, security, guidance and entertainment devices in their new vehicle models. In recent decades, the list has expanded from the AM and FM radio antennas to include GPS, mobile phone, collision avoidance radar, Digital Radio and Digital TV antennas. In addition, new technologies such as vehicle to vehicle and vehicle to road side communication are being implemented at 5.9 GHz in the next generation of vehicles. In the past the AM/FM antenna was typically a mast antenna protruding from the vehicle’s exterior, recently however, the trend has been to limit the visibility of vehicular antennas as much as possible to improve vehicle design and aerodynamics. This has lead to integration of antennae so that they become a seamless part of the vehicle structure. This paper reports on a parametric study of embedding an antenna in a polymeric composite substrate in relation to several material processing and coating parameters.
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Bello, Olumide, and Landon Onyebueke. "Optimization of Smart Grid Solar Energy Application." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-36791.

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This paper presents an approach to modeling of renewable energy integration into Smart Grid for Electric Vehicle charging applications. Integration of renewable energy sources to smart grid is not only the key to smart Electric Vehicle charging but also the most efficient way to manage the distributed energy resources. It enables the ability to control, ease the peak load impacts, and protect distribution network components from being overloaded by Electric Vehicles. Thus, the electricity generation and consumption is managed in more cost effective way. The developed model is a grid connected solar-assisted Electric Vehicle charging station, with battery bank. It generates electricity using solar photovoltaic (PV) arrays to augment the electricity used to charge the electric vehicles. The battery bank stores electricity from the grid and discharges the stored energy during periods of peak charging demand. Optimization of the model was done by developing a program written in Visual Basic 2012. The computational results show the economic advantages of this model as well as the anticipated benefits of the smart grid for reduced peak loads, and increased efficiency.
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Sataraddi, Mamata J., and Mahabaleshwar S. Kakkasageri. "BDI agent based dynamic routing scheme for vehicle-to-vehicle communication in VANETs." In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon). IEEE, 2017. http://dx.doi.org/10.1109/smarttechcon.2017.8358468.

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Reports on the topic "Smart vehicle"

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Kevin Morrow, Dimitri Hochard, and Jeff Wishart. Vehicle to Electric Vehicle Supply Equipment Smart Grid Communications Interface Research and Testing Report. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1034806.

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Kwiat, Paul, Eric Chitambar, Andrew Conrad, and Samantha Isaac. Autonomous Vehicle-Based Quantum Communication Network. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-020.

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Quantum communication was demonstrated using autonomous vehicle-to-vehicle (V2V), as well as autonomous vehicle-to-infrastructure (V2I). Supporting critical subsystems including compact size, weight, and power (SWaP) quantum sources; optical systems; and pointing, acquisition, and tracking (PAT) subsystems were designed, developed, and tested. Novel quantum algorithms were created and analyzed, including quantum position verification (QPV) for mobile autonomous vehicles. The results of this research effort can be leveraged in support of future cross-platform, mobile quantum communication networks that provide improved security, more accurate autonomous sensors, and connected quantum computing nodes for next-generation, smart-infrastructure systems.
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Roth, Christian. Design of the In-vehicle Experience. SAE International, June 2022. http://dx.doi.org/10.4271/epr2022012.

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The in-vehicle experience, both physical and digital, is increasingly the differentiating factor between vehicles. Since touch displays, smart surfaces, and internet connectivity are present in most vehicle segments, the growing resemblance of in-vehicle experiences with mobile experiences leads to user expectations on par with smartphones. While manufacturers are faced with providing suitable service offerings that are safe to use, they must also identify services to exclude or limit, without encouraging drivers to resort back to their mobile devices. This increasingly complex in-vehicle experience design process is being shaped by new stakeholders, including operating system providers and application developers. Design of the In-vehicle Experience examines the challenging and changing relationships between manufacturers (that lack in software development and mobile experience design skills) and new stakeholders (that lack the decades of experience designing for the driving context). The report also discusses augmenting and expanding existing guidelines and best practices to address the challenges of modern in-vehicle experience design.
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Wei, Dong, Harry Haas, and Paul Terricciano. VersiCharge-SG - Smart Grid Capable Electric Vehicle Supply Equipment (EVSE) for Residential Applications. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1234438.

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Shladover, Steven E., and Jeffrey B. Greenblatt. Connected and Automated Vehicle Concept Dimensions and Examples DOE SMART Mobility, Task 7A1.1. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1412520.

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Lave, Matthew Samuel, and Christian Birk Jones. Smart Electric Vehicle Charging for a Reliable and Resilient Grid (Sandia National Laboratories). Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1572597.

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Campbell, James. Final Technical Report-WestSmart EV: Western Smart Plug-in Electric Vehicle Community Partnership. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1760465.

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Bennett, Brion. Results from Operational Testing of the Siemens Smart Grid-Capable Electric Vehicle Supply Equipment. Office of Scientific and Technical Information (OSTI), May 2015. http://dx.doi.org/10.2172/1202889.

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Bennett, Brion. Results from the Operational Testing of the Eaton Smart Grid Capable Electric Vehicle Supply Equipment. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1186750.

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Li, Lingxi, Yaobin Chen, Renren Tian, Feng Li, Howell Li, and James R. Sturdevant. An Integrated Critical Information Delivery Platform for Smart Segment Dissemination to Road Users. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317440.

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An integrated critical information delivery platform for smart segment dissemination to road users was developed. A statewide baseline milepost geodatabase was created at 0.1-mile resolution with tools, protocols, and interfaces that allow other data sources to be efficiently utilized. A variety of data sources (e.g., INRIX, CARS, Doppler, camera images, connected vehicle data, automated vehicle location) were integrated into existing and new dashboards for stakeholders to monitor roadway conditions and after-action reviews. Additionally, based on these data sources, algorithms were developed and an API was created to identify hazardous road conditions when the location of the end-user mobile device was given. Message delivery schemes were successfully implemented to issue alerts to drivers, which were integrated with two in-vehicle smartphone applications. The performance of the integrated platform was evaluated using both the driving simulator and a number of simulated and on-road tests. The results demonstrated the system was able to disseminate data in real-time using the developed platform.
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