Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.
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The Netherlands are one of the frontrunners in stimulating electric mobility in Europe when it comes to the charging infrastructure density and electric vehicle adoption. Municipalities play an instrumental role in the rollout of public charging infrastructure while they have little insight in the relevant key performance indicators of the charging infrastructure as a means to support effective decision making. This paper aims to contribute to providing a more thorough understanding of relevant key performance indicators for public charging infrastructure. An approach is presented that explores result and performance indicators to support policy makers optimizing the roll out of and improvement of the business case for charging infrastructure.
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As Vehicle-to-Everything (V2X) communication technologies gain prominence, ensuring human safety from radiofrequency (RF) electromagnetic fields (EMF) becomes paramount. This study critically examines human RF exposure in the context of ITS-5.9 GHz V2X connectivity, employing a combination of numerical dosimetry simulations and targeted experimental measurements. The focus extends across Road-Side Units (RSUs), On-Board Units (OBUs), and, notably, the advanced vehicular technologies within a Tesla Model S, which includes Bluetooth, Long Term Evolution (LTE) modules, and millimeter-wave (mmWave) radar systems. Key findings indicate that RF exposure levels for RSUs and OBUs, as well as from Tesla’s integrated technologies, consistently remain below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) exposure guidelines by a significant margin. Specifically, the maximum exposure level around RSUs was observed to be 10 times lower than ICNIRP reference level, and Tesla’s mmWave radar exposure did not exceed 0.29 W/m2, well below the threshold of 10 W/m2 set for the general public. This comprehensive analysis not only corroborates the effectiveness of numerical dosimetry in accurately predicting RF exposure but also underscores the compliance of current V2X communication technologies with exposure guidelines, thereby facilitating the protective advancement of intelligent transportation systems against potential health risks.
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In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
In september 2017 startten de lectoraten LEAN-World Class Performance en Automotive Research van de HAN University of Applied Sciences met het onderzoek ‘Werkplaats op Weg’ (cofinanciering door SIA middels het RAAK-MKB subsidieprogramma). Hierin werd de vraag beantwoord: “Wat betekenen alle technologische ontwikkelingen voor de gewenste inrichting van onze onderhoudsprocessen? Wat betekent dit voor acties die we nu en in de nabije toekomst moeten nemen?” De autowerkplaats van de toekomst zal - door innovaties in autotechnologieën, toenemende zorgen over het milieu en klimaat, en een veranderende toekomstvisie op mobiliteit - verschillen van huidige werkplaatsen. Deze ontwikkelingen leidden tot grote onzekerheid bij MKB-ondernemers, met name over de mogelijke effecten op de onderhoudsvraag van voertuigen. Werkplaats op Weg heeft het kennishiaat hieromtrent opgepakt. Op basis van specifieke casussen, interviews en praktijkonderzoeken zijn zes potentiële bedrijfstypes voor het MKB gedefinieerd. Deze zijn gelinkt aan de eerder beschreven technologische en maatschappelijke ontwikkelingen. De relevantste technologische ontwikkelingen die hierin centraal stonden zijn Connected, Autonomous, Shared en Electric Vehicles (CASE; zie figuur 1). De analyse heeft geleid tot concrete en toegankelijke aanbevelingen en online tools. Hiermee kunnen bedrijven binnen de sector hun eigen strategische keuzes maken met betrekking tot het uitvoeren en organiseren van werkzaamheden in hun werkplaats. Tevens is vastgesteld welke consequenties er zijn voor automotive opleidingen. Resultaten van het onderzoek zijn verzameld op de website: www.werkplaatsopweg.nl Figuur 1: Resultaten Werkplaats op Weg Met behulp van de Top-Up willen we onderzoeken hoe ondernemers, onderwijzers en onderzoekers om kunnen gaan met onverwachte, disruptieve veranderingen zoals de Coronacrisis, als aanvulling op de eerdere bevindingen die vooral gericht waren op het omgaan met verwachte technologische innovaties. Gezien de enorme en radicale impact van de huidige coronacrisis, is dit het perfecte moment om de sector extra aandacht en ondersteuning hiertoe aan te bieden.
With increasing penetration rates of driver assistance systems in road vehicles, powerful sensing and processing solutions enable further automation of on-road as well as off-road vehicles. In this maturing environment, SMEs are stepping in and education needs to align with this trend. By the input of student teams, HAN developed a first prototype robot platform to test automated vehicle technology in dynamic road scenarios that include VRUs (Vulnerable Road Users). These robot platforms can make complex manoeuvres while carrying dummies of typical VRUs, such as pedestrians and bicyclists. This is used to test the ability of automated vehicles to detect VRUs in realistic traffic scenarios and exhibit safe behaviour in environments that include VRUs, on public roads as well as in restricted areas. Commercially available VRU-robot platforms are conforming to standards, making them inflexible with respect to VRU-dummy design, and pricewise they are far out of reach for SMEs, education and research. CORDS-VTS aims to create a first, open version of an integrated solution to physically emulate traffic scenarios including VRUs. While analysing desired applications and scenarios, the consortium partners will define prioritized requirements (e.g. robot platform performance, dummy types and behaviour, desired software functionality, etc.). Multiple robots and dummies will be created and practically integrated and demonstrated in a multi-VRU scenario. The aim is to create a flexible, upgradeable solution, published fully in open source: The hardware (robot platform and dummies) will be published as well-documented DIY (do-it-yourself) projects and the accompanying software will be published as open-source projects. With the CORDS-VTS solution, SME companies, researchers and educators can test vehicle automation technology at a reachable price point and with the necessary flexibility, enabling higher innovation rates.
