Project54 vehicle telematics for remote diagnostics, fleet management and traffic monitoring

The Project54 system was developed to introduce advanced technologies into the operations of the New Hampshire Department of Safety and other law enforcement agencies. The application of computing, sensing and telecommunication technologies within the Project54 system enables advanced telematics services that can provide benefits to vehicle operators, fleet managers and the public. This thesis describes the implementation of remote diagnostics and fleet management services for the Project54 system and investigates the use of radar equipped police vehicles as traffic probes. Aftermarket diagnostic hardware has been integrated in the Project54 system and software applications have been developed to control the hardware and record diagnostic information. An electronic data entry form has been created for tracking vehicle operating expenses and a vehicle status reporting system is described. Additionally, a traffic congestion scoring method using information from traffic radar units is presented

OwnGarage: Sistema Informático para Automação de Tarefas de Manutenção em Automóveis

A indústria automóvel da atualidade presenteia-nos com automóveis cada vez mais digitais, fornecendo aos utilizadores novas funcionalidades, para que o conforto e a comodidade na condução destes seja melhorada e mais segura. Com esta digitalização torna-se importante a obtenção de dados telemétricos dos automóveis seja para a otimização de frotas e custos, quer para a deteção precoce de possíveis avarias. A inovação, não é apenas no que foi referido anteriormente, mas sim também na parte da engenharia automóvel. Isto significa que toda a engenharia que envolve a conceção dum veículo é de elevada eficiência, robustez e precisão, resultante em larga medida da evolução tecnológica atualmente presenciada. Os veículos de agora emitem menos gases poluentes, fazem melhores consumos, e os seus chassis e sistemas de segurança (quer sejam ativos ou passivos) são desenhados e desenvolvidos para proteger ao máximo todos os ocupantes do veículo em caso de sinistro. Com o desenvolvimento do sistema OwnGarage pretende-se trazer mais comodidade ao utilizador do veículo, ao utilizar um dispositivo móvel para interação com o veículo, notificando o utilizador previamente das tarefas de manutenção inerentes a este seu veículo. Uma das partes deste sistema informático consistiu numa aplicação cliente para smartphones, a qual permitirá a recolha de dados de interesse de um automóvel com auxílio dum sistema embebido (baseado na plataforma Raspberry Pi) e que comunica com os sistemas de controlo do automóvel através do protocolo CAN (Controller Area Network). O sistema permite a recolha de dados de interesse de veículo, tais como velocidade, consumos, e quilometragem, permitindo que o utilizador receba proactivamente notificações relativas a tarefas de manutenção do veículo. Adicionalmente uma aplicação web permite a visualização (em forma gráfica) de alguns dados acima referidos, detalhes dos veículos e das reparações efetuadas. Uma outra parte, foi o desenvolvimento da plataforma web direcionada para as oficinas mecânicas, a qual se tornou útil neste contexto pois permitiu uma maior eficiência e rapidez na gestão e registo das reparações automóveis. Contudo, em relação ao sistema embebido surgiram várias dificuldades na obtenção de diversos dados devido à falta de segurança dos sistemas internos nos automóveis. Apesar destas dificuldades, os resultados obtidos nos vários testes que foram efetuados sobre o sistema revelaram-se positivos, o que significa uma boa implementação das funcionalidades desenvolvidas

Mansell power lifter actuator failure analysis

The Mansell Power Lifter is a critical component of the Mansell Infant Retrieval System. It functions as an electrically powered stretcher which can be raised or lowered through the use of two independant DC-motor driven linear actuators. The Mansell Infant Retrieval System is a key piece of medical equipment used widely to transport critically ill infants. Over a number of years there have been reports of the linear actuators used in the Mansell Power Lifter failing unexpectedly. Due to the high reliability requirements of the Mansell Power Lifter as a medical device any failures are unacceptable, and as a result the manufacturer of the Mansell Power lifter has sought to identify the cause of the failures. The aim of this project was to attempt to identify the mechanism and cause of the actuator failures and if possible propose a potential solution. In order to identify the failure mechanism and root cause, it was determined that data would need to be collected about the usage and operational characteristics of the linear actuators. After analysis of the situation, it was decided to construct an autonomous data collection device to collect real world usage data from the actuators. An integrated, microcontroller based, data logger device was designed and developed which could be placed on a Mansell Power Lifter and record operational data without interfering with normal operation. Analysis of the collected data was performed to identify any operational characteristics which might lead to failures. While an actual failure of the linear actuators was not observed during the testing period, the data obtained showed that the actuators were being subjected to a large number of short, sharp movements on a regular basis, which could be contributing to the limit switches in the actuators jamming. Since the definitive cause of the actuator failures could not be confirmed, given the data obtained, it was not possible to propose a guaranteed solution to the problem. Despite this, a number of potential solutions were proposed, which given further testing and analysis, could be implemented to help prevent actuator failures in the future

State-of-the-Art Assessment of Smart Charging and Vehicle 2 Grid services

Electro-mobility – especially when coupled smartly with a decarbonised grid and also renewable distributed local energy generation, has an imperative role to play in reducing CO2 emissions and mitigating the effects of climate change. In parallel, the regulatory framework continues to set new and challenging targets for greenhouse gas emissions and urban air pollution. • EVs can help to achieve environmental targets because they are beneficial in terms of reduced GHG emissions although the magnitude of emission reduction really depends on the carbon intensity of the national energy mix, zero air pollution, reduced noise, higher energy efficiency and capable of integration with the electric grid, as discussed in Chapter 1. • Scenarios to limit global warming have been developed based on the Paris Agreement on Climate Change, and these set the EV deployment targets or ambitions mentioned in Chapter 2. • Currently there is a considerable surge in electric cars purchasing with countries such as China, the USA, Norway, The Netherlands, France, the UK and Sweden leading the way with an EV market share over 1%. • To enable the achievement of these targets, charging infrastructures need to be deployed in parallel: there are four modes according to IEC 61851, as presented in Chapter 2.1.4. • The targets for SEEV4City project are as follow: o Increase energy autonomy in SEEV4-City sites by 25%, as compared to the baseline case. o Reduce greenhouse gas emissions by 150 Tonnes annually and change to zero emission kilometres in the SEEV4-City Operational Pilots. o Avoid grid related investments (100 million Euros in 10 years) by introducing large scale adoption of smart charging and storage services and make existing electrical grids compatible with an increase in electro mobility and local renewable energy production. • The afore-mentioned objectives are achieved by applying Smart Charging (SC) and Vehicle to Grid (V2G) technologies within Operational Pilots at different levels: o Household. o Street. o Neighbourhood. o City. • SEEV4City aims to develop the concept of 'Vehicle4Energy Services' into a number of sustainable business models to integrate electric vehicles and renewable energy within a Sustainable Urban Mobility and Energy Plan (SUMEP), as introduced in Chapter 1. With this aim in mind, this project fills the gaps left by previous or currently running projects, as reviewed in Chapter 6. • The business models will be developed according to the boundaries of the six Operational Pilots, which involve a disparate number of stakeholders which will be considered within them. • Within every scale, the relevant project objectives need to be satisfied and a study is made on the Public, Social and Private Economics of Smart Charging and V2G. • In order to accomplish this work, a variety of aspects need to be investigated: o Chapter 3 provides details about revenue streams and costs for business models and Economics of Smart Charging and V2G. o Chapter 4 focuses on the definition of Energy Autonomy, the variables and the economy behind it; o Chapter 5 talks about the impacts of EV charging on the grid, how to mitigate them and offers solutions to defer grid investments; o Chapter 7 introduces a number of relevant business models and considers the Economics of Smart Charging and V2G; o Chapter 8 discusses policy frameworks, and gives insight into CO2 emissions and air pollution; o Chapter 9 defines the Data Collection approach that will be interfaced with the models; o Chapter 10 discusses the Energy model and the simulation platforms that may be used for project implementation

Desenvolvimento de uma interface de comunicação para motores de veículos pesados

O objetivo do presente trabalho é desenvolver uma interface que comunique com um veículo pesado adquirindo informação de sensores e que controle o regime do motor. Com este interface é possível analisar a condição de motores após o seu recondicionamento de modo a garantir a sua fiabilidade antes de ser reintroduzido no seu ciclo de utilização. Para tal, neste projeto, descodificam-se os protocolos de comunicação existentes nos veículos pesados, através da aplicação de métodos de engenharia inversa aos barramentos CAN. Estudam-se vários métodos de controlo do regime do motor e analisam-se quais os métodos mais eficazes. Desenvolve-se hardware que promova a ligação entre a informação presente nos barramentos CAN e um computador (porta série). Define-se um protocolo de comunicação dedicado e implementa-se este na comunicação entre o hardware e o computador. Recorre-se ao software LabVIEW® para o desenvolvimento da aplicação, executada num computador, que possibilita visualizar os dados adquiridos pelo hardware. Com esta aplicação, o utilizador controla o regime do motor através de um ambiente gráfico “user friendly”. No fim de cada ensaio gera-se um ficheiro de dados que contem toda a informação adquirida permitindo uma interpretação e análise à posteriori. Realizam-se vários testes ao sistema desenvolvido, tanto ao nível de hardware como de software, com o objetivo de validar as funções implementadas. Após a análise dos resultados obtidos verifica-se correto funcionamento da interface e da aplicação desenvolvida durante o projeto

Quantified vehicles: data, services, ecosystems

Advancing digitalization has shown the potential of so-called Quantified Vehicles for gathering valuable sensor data about the vehicle itself and its environment. Consequently, (vehicle) Data has become an important resource, which can pave the way to (Data-driven) Services. The (Data-driven Service) Ecosystem of actors that collaborate to ultimately generate services, has only shaped up in recent years. This cumulative dissertation summarizes the author's contributions and includes a synopsis as well as 14 peer-reviewed publications, which contribute to answer the three research questions.Die Digitalisierung hat das Potenzial für Quantified Vehicles aufgezeigt, um Sensordaten über das Fahrzeug selbst und seine Umgebung zu sammeln. Folglich sind (Fahrzeug-)Daten zu einer wichtigen Ressource der Automobilindustrie geworden, da sie auch (datengetriebene) Services ermöglichen. Es bilden sich Ökosysteme von Akteuren, die zusammenarbeiten, um letztlich Services zu generieren. Diese kumulative Dissertation fasst die Beiträge des Autors zusammen und enthält eine Synopsis sowie 14 begutachtete Veröffentlichungen, die zur Beantwortung der drei Forschungsfragen beitragen

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety

The average American spends around at least one hour driving every day. During that time the driver utilizes various sensors to enhance their commute. Approximately 77% of smartphone users rely on navigation apps daily. Consumer grade OBD dongles that collect vehicle sensor data to monitor safe driving habits are common. Existing sensing applications pertaining to our drive are often separate from each other and fail to learn from and utilize the information gained by other sensing streams and other drivers. In order to best leverage the widespread use of sensing capabilities, we have to unify and coordinate the different sensing streams in a meaningful way. This dissertation explores and validates the following thesis: Sensing the same phenomenon from multiple perspectives can enhance vehicle safety, security and transportation. First, it presents findings from an exploratory study on unifying vehicular sensor streams. We explored combining sensory data from within one vehicle through pairwise correlation and across multiple vehicles through normal models built with principal component analysis and cluster analysis. Our findings from this exploratory study motivated the rest of this thesis work on using sensor redundancy for CAN-bus injection detection and driving hazard detection. Second, we unify the phone sensors with vehicle sensors to detect CAN bus injection attacks that compromise vehicular sensor values. Specifically, we answer the question: Are phone sensors accurate enough to detect typical CAN bus injection attacks found in literature? Through extensive tests we found that phone sensors are sufficiently accurate to detect many CAN-bus injection attacks. Third, we construct GPS trajectories from multiple vehicles nearby to find stationary and mobile driving hazards such as a bicyclist on the side of the road. Such a tool will effectively extend the repertoire of current navigation assistant applications such as Google Maps which detect and warn drivers about upcoming stationary hazards. Finally, we present an easy-to-use tool to help developers and researchers quickly build and prototype data-collection apps that naturally exploit sensing redundancy. Overall, this thesis provides a unified basis for exploiting sensing redundancy existing inside a single vehicle as well as between different vehicles to enhance driving safety and security.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155154/1/arungan_1.pd

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance