A Review of the Electric Vehicle Charging Techniques, Standards, Progression and Evolution of EV Technologies in Germany

Ahmad, Aqueel; Khan, Zeeshan Ahmad; Alam, Mohammad Saad; Khateeb, Siddique

doi:10.1080/23080477.2017.1420132

Cited by 97 publications

(38 citation statements)

References 71 publications

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“…For instance, [28] provides a survey of optimization and mathematical modelling-based solutions to EV operations and management, including EV charging. Aqueel et al [29] provided a review of various charging technologies and standards with a case study from Germany. The authors in [30] reviewed charging station location problem, which is concerned with the location and deployment of charging stations.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…Şarj cihazı, güç kaynağı ve araç arasında doğrudan temasın olduğu bir şarj sistemidir [17]. Bu şarj sistemi çeşitli güç faktörü düzeltmeli doğrultucu ve konnektörden oluşmaktadır.…”

Section: Kablolu şArj Yöntemi (Conductive Charging Methods)unclassified

“…Bataryaya gönderilen AA güç bataryayı şarj etmek ve manyetik rezonans sisteminin verimliliğini artırmak için kullanılmaktadır [28]. Bu sistemin frekansı 20-100kHz arasında [1] değişmektedir ve sistemdeki dönüştürücü %90'a varan verimle çalışabilmektedir [17]. Kablosuz şarj yöntemi statik ve dinamik şarj yöntemi olarak ikiye ayrılmaktadır.…”

Section: Kablosuz (Endüktif) şArj Yöntemi (Wireless Charging Methods)unclassified

Elektri̇kli̇ Araçlar İçi̇n Hizli Şarj İstasyonu Teknoloji̇leri̇

Kerem

Gürbak

2020

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Electric vehicle (EV) technology is a new technology aiming to reduce fossil fuel usages. This new technology has some problems including battery charging time and range. One of the problems, battery charging times (level 1 level 2, level 3, and level 4), was investigated in this study. While level 1 and level 2 refer to methods of slow charging, level 3 and level 4 refer to fast charging. Fast charging methods consist of three different topologies: topology with AC/ DC/ DC converters (vehicle-to-grid (V2G) charging stations and one-way charging stations as their subbranches), common AC-connected multi-point topology and transformerless topologies. Figure A. EV Charging Methods Among these topologies, it was concluded that the V2G method is an effective and reliable method in terms of adaptation to renewable energy sources, energy efficient use, easy installation and adaptation to both AC and DC systems. Purpose: It is aimed to determine fastest and the most efficient charging method for EV's. Hence the layout, functionality and importance of EV charging stations have been investigated. Theory and Methods: Topologies of EV charging stations were analyzed and compared in literature from various perspectives. In addition, tables were created to detect variations between charging levels and charging rates. Results: Fast charging stations have been observed to be vital for EV's to adapt to future technologies. The V2G fast charging topology has been observed to have a high energy efficiency and can operate in accordance with other renewable energy sources. Conclusion: Some fast charging stations are viewed as a threat to the electricity grids as having a charging capacity at MW levels. Among the steps that can be taken against this challenge are to reduce the problems caused by the demand for electricity, to preserve the charging station's common link point voltage and to send reactive power to the grid to avoid voltage instability. Another precaution is to use the V2G fast charging method. These stations must also be built in compliance with IEC requirements in terms of user/ network protection, device reliability and energy efficiency.

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“…when a car stops in a cross road, allowing also to charge wirelessly its batteries. 96 Another example of a promising ongoing investigation has recently demonstrated a prototype of large-area electronic carpet capable to harvest energy from human steps. 97 These technologies are relevant for e-skin, as mechanical energy could be harvested by robots or human movements to power the distributed electric components.…”

Section: Mechanical Energymentioning

confidence: 99%

Energy autonomous electronic skin

2019

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Energy autonomy is key to the next generation portable and wearable systems for several applications. Among these, the electronic-skin or e-skin is currently a matter of intensive investigations due to its wider applicability in areas, ranging from robotics to digital health, fashion and internet of things (IoT). The high density of multiple types of electronic components (e.g. sensors, actuators, electronics, etc.) required in e-skin, and the need to power them without adding heavy batteries, have fuelled the development of compact flexible energy systems to realize self-powered or energy-autonomous e-skin. The compact and wearable energy systems consisting of energy harvesters, energy storage devices, low-power electronics and efficient/wireless power transfer-based technologies, are expected to revolutionize the market for wearable systems and in particular for e-skin. This paper reviews the development in the field of self-powered e-skin, particularly focussing on the available energy-harvesting technologies, high capacity energy storage devices, and high efficiency power transmission systems. The paper highlights the key challenges, critical design strategies, and most promising materials for the development of an energy-autonomous e-skin for robotics, prosthetics and wearable systems. This paper will complement other reviews on e-skin, which have focussed on the type of sensors and electronics components.

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A Review of the Electric Vehicle Charging Techniques, Standards, Progression and Evolution of EV Technologies in Germany

Cited by 97 publications

References 71 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

Elektri̇kli̇ Araçlar İçi̇n Hizli Şarj İstasyonu Teknoloji̇leri̇

Energy autonomous electronic skin

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