Modular and Reconfigurable Platform as New Philosophy for the Development of Updatable Vehicular Electronics

[EN] A new conception in the development of Electronic Control Units (ECUs), which are also called On-Board Units (OBUs), is discussed in this paper from an ontological vision oriented to the compatibility of vehicles with future technologies in the automotive field. This work also provides a new methodology in the design of On-Board vehicle units. The proposed technique is based on the concept of modular electronic units that can change their functionality depending on the modules they are consisted of. The study was initially designed at the theoretical level, analysing the problems in the sector in the face of the coexistence between vehicles today and those that are bound to appear in the near future, and that will incorporate capabilities making them connected and even autonomous. Additionally, a fully operational prototype has been developed so as to ascertain the possibilities of the proposed solution.[ES] Se presenta una nueva concepción en el desarrollo de Unidades Electrónicas de Control (ECU), también denominadas Unidades de a Bordo (OBU), desde una visión ontológica orientada en la compatibilización de los vehículos con las futuras tecnologías emergentes en el campo de la automoción. Se comienza por un estudio teórico que analiza la problemática en el sector del transporte que va a presentar la convivencia entre los vehículos actuales y los que van a ir apareciendo en el futuro; y que vendrán influenciados por conceptos tales como los vehículos conectados o los vehículos autónomos. Este artículo también aporta una nueva metodología en el diseño de unidades vehiculares de a bordo, basada en el concepto de unidades electrónicas modulares que definen su funcionalidad en base a los módulos que le sean acoplados. Adicionalmente se ha desarrollado un prototipo completo y totalmente funcional con el fin de analizar las posibilidades de la solución propuesta.Este trabajo ha sido realizado parcialmente gracias al apoyo recibido mediante la resolución del 31/07/2014, publicada por la Universidad de Castilla-La Mancha, que establece las bases reguladoras de la convocatoria para contratos predoctorales con objeto de preparar nuevos investigadores bajo el Plan Propio de I+D+i. [2014/10340]Cañas, V.; García, A.; De Las Morenas, J.; Blanco, J. (2019). Plataforma Modular Reconfigurable como Nueva Filosofía para el Desarrollo de Electrónica Vehicular Actualizable. Revista Iberoamericana de Automática e Informática. 16(2):200-211. https://doi.org/10.4995/riai.2018.9863SWORD20021116

Rancang Bangun Sistem Perpesanan OBU (On Board Unit) Untuk Intelligent Transport System Di Surabaya

Intelligent Transport System (ITS) diharapkan dapat mengoptimalkan kondisi angkutan umum saat ini yang belum terintegrasi melalui jaringan TIK serta dapat menanggulangi kemacetan yang selama ini menjadi permasalahan di kota-kota besar khususnya Surabaya. Salah satunya adalah dengan penggunaan On Board Unit (OBU) yang ditempatkan pada setiap armada Angkutan Masal Cepat yang diterapkan di kota Surabaya. OBU sendiri merupakan sebuah sistem ITS yang diletakkan di armada, berfungsi sebagai sebuah sistem pengendali masukan dan keluaran yang terkait fungsional manajemen armada, pendapatan (tiket), lalu lintas dan sistem darurat. Tugas akhir ini bertujuan untuk merancang serta membuat sebuah prototype sistem yang digunakan untuk proses komunikasi yang terintegrasi antara OBU (client) dengan CCROOM (server). Pada pengerjaan tugas akhir ini dilakukan dua tahap pada proses perancangan dan implementasi, yaitu pertama membuat arsitektur jaringan keseluruhan dan aplikasi sistem yang di dalamnya terdapat fitur-fitur pendukung untuk digunakan pada sistem komunikasi angkutan massal cepat (AMC). Aplikasi pengiriman data dari client-server dibuat menggunakan software Borland Delphi 6. Jaringan yang digunakan adalah jaringan seluler dengan menggunakan provider Tri sebagai paket data, pengiriman informasi dari armada melalui jaringan seluler mengirimkan paket data menuju tower terdekat dan di alirkan ke router hingga di terima di server. Pengujian sistem komunikasi ini dilakukan dengan 2 macam pengujian, yaitu pengujian sistem client-server beserta fitur-fitur yang ada di dalamnya seperti e-ticketing, emergency button, login logut serta pengujian kualitas jaringan pada saat sistem sedang berjalan. ====================================================================================== Intelligent Transport system (ITS) is expected to optimized the public transportation condition which right now is not integrated yet through ICT network, also to overcome the traffic jam that has been a problem in big cities especially Surabaya. One of them is the use of OBI which is placed in every Fast Mass Transportation vehicle which is applied in Surabaya. OBU it self is a ITS system that is placed in the vehicle, functioning as an input and output controlling system related to vehicle fungsional manangement, income (ticket), traffic and emergency system. This thesis purpose is to design and make the system prototype that can be use for communication process whoch ksnintegrated between OBU (client) with CCROOM (server) In the making of this thesis, 2 step as done in the designing and implementation process, first is the making of all network architecture and system application which includes supporting features to be use in the AMC communication. Data sending application from client-server is made using Borland Delphi 6 software. The network used is a cellular network using Tri provider as a data packet, sending information from fleets over cellular networks to send data packets to the nearest tower and routed to the router until received on the server The testing of this communication system is done by 2 types of testing, which is client-server system testing as well as the features inside it like e-ticketing, emergency buton, login logout aswell as network quality testing when the system is workin

A multi-factor model for range estimation in electric vehicles

Electric vehicles (EVs) are well-known for their challenges related to trip planning and energy consumption estimation. Range anxiety is currently a barrier to the adoption of EVs. One of the issues influencing range anxiety is the inaccuracy of the remaining driving range (RDR) estimate in on-board displays. RDR displays are important as they can help drivers with trip planning. The RDR is a parameter that changes under environmental and behavioural conditions. Several factors (for example, weather, and traffic) can influence the energy consumption of an EV that are not considered during the RDR estimation in traditional on-board computers or third-party applications, such as navigation or mapping applications. The need for accurate RDR estimation is growing, since this can reduce the range anxiety of drivers. One way of overcoming range anxiety is to provide trip planning applications that provide accurate estimations of the RDR, based on various factors, and which adapt to the users’ driving behaviour. Existing models used for estimating the RDR are often simplified, and do not consider all the factors that can influence it. Collecting data for each factor also presents several challenges. Powerful computing resources are required to collect, transform, and analyse the disparate datasets that are required for each factor. The aim of this research was to design a Multi-factor Model for range estimation in EVs. Five main factors that influence the energy consumption of EVs were identified from literature, namely, Route and Terrain, Driving Behaviour, Weather and Environment, Vehicle Modelling, and Battery Modelling. These factors were used throughout this research to guide the data collection and analysis processes. A Multi-factor Model was proposed based on four main components that collect, process, analyse, and visualise data from available data sources to produce estimates relating to trip planning. A proof-of-concept RDR system was developed and evaluated in field experiments, to demonstrate that the Multi-factor Model addresses the main aim of this research. The experiments were performed to collect data for each of the five factors, and to analyse their impact on energy consumption. Several machine learning techniques were used, and evaluated, for accuracy in estimating the energy consumption, from which the RDR can be derived, for a specified trip. A case study was conducted with an electric mobility programme (uYilo) in Port Elizabeth, South Africa (SA). The case study was used to investigate whether the available resources at uYilo were sufficient to provide data for each of the five factors. Several challenges were noted during the data collection. These were shortages of software applications, a lack of quality data, technical interoperability and data access between the data collection instruments and systems. Data access was a problem in some cases, since proprietary systems restrict access to external developers. The theoretical contribution of this research is a list of factors that influence RDR and a classification of machine learning techniques that can be used to estimate the RDR. The practical contributions of this research include a database of EV trips, proof-of-concept RDR estimation system, and a deployed machine learning model that can be accessed by researchers and EV practitioners. Four research papers were published and presented at local and international conferences. In addition, one conference paper was published in an accredited journal: NextComp 2017 (Appendix C), Conference Paper, Pointe aux Piments (Mauritius); SATNAC 2017 (Appendix F), Conference Paper, Barcelona (Spain); GITMA 2018 (Appendix B), Conference Paper, Mexico City (Mexico); SATNAC 2018 (Appendix G), Conference Paper, George (South Africa), and IFIP World Computer Congress 2018 (Appendix E), Journal Article