اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDesign of Fast Response Smart Electric Vehicle Charging Infrastructure
90
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The response time of the smart electrical vehicle (EV) charging infrastructure is the key index of the system performance. The traffic between the smart EV charging station and the control center dominates the response time of the smart charging stations. To accelerate the response of the smart EV charging station, there is a need for a technology that collects the information locally and relays it to the control center periodically. To reduce the traffic between the smart EV charger and the control center, a Power Information Collector (PIC), capable of collecting all the meters power information in the charging station, is proposed and implemented in this paper. The response time is further reduced by pushing the power information to the control center. Thus, a fast response smart EV charging infrastructure is achieved to handle the shortage of energy in the local grid.
قيم البحث
اقرأ أيضاً
Electric Vehicles (EVs) can help alleviate our reliance on fossil fuels for transport and electricity systems. However, charging millions of EV batteries requires management to prevent overloading the electricity grid and minimise costly upgrades tha
t are ultimately paid for by consumers. Managed chargers, such as Vehicle-to-Grid (V2G) chargers, allow control over the time, speed and direction of charging. Such control assists in balancing electricity supply and demand across a green electricity system and could reduce costs for consumers. Smart and V2G chargers connect EVs to the power grid using a charging device which includes a data connection to exchange information and control commands between various entities in the EV ecosystem. This introduces data privacy concerns and is a potential target for cyber-security attacks. Therefore, the implementation of a secure system is crucial to permit both consumers and electricity system operators to trust smart charging and V2G. In principle, we already have the technology needed for a connected EV charging infrastructure to be securely enabled, borrowing best practices from the Internet and industrial control systems. We must properly adapt the security technology to take into account the challenges peculiar to the EV charging infrastructure. Challenges go beyond technical considerations and other issues arise such as balancing trade-offs between security and other desirable qualities such as interoperability, scalability, crypto-agility, affordability and energy efficiency. This document reviews security and privacy topics relevant to the EV charging ecosystem with a focus on smart charging and V2G.
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-ti
me monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
The number of electric vehicles (EVs) is expected to increase. As a consequence, more EVs will need charging, potentially causing not only congestion at charging stations, but also in the distribution grid. Our goal is to illustrate how this gives ri
se to resource allocation and performance problems that are of interest to the Sigmetrics community.
EVs (Electric Vehicles) represent a green alternative to traditional fuel-powered vehicles. To enforce their widespread use, both the technical development and the security of users shall be guaranteed. Privacy of users represents one of the possible
threats impairing EVs adoption. In particular, recent works showed the feasibility of identifying EVs based on the current exchanged during the charging phase. In fact, while the resource negotiation phase runs over secure communication protocols, the signal exchanged during the actual charging contains features peculiar to each EV. A suitable feature extractor can hence associate such features to each EV, in what is commonly known as profiling. In this paper, we propose EVScout2.0, an extended and improved version of our previously proposed framework to profile EVs based on their charging behavior. By exploiting the current and pilot signals exchanged during the charging phase, our scheme is able to extract features peculiar for each EV, allowing hence for their profiling. We implemented and tested EVScout2.0 over a set of real-world measurements considering over 7500 charging sessions from a total of 137 EVs. In particular, numerical results show the superiority of EVScout2.0 with respect to the previous version. EVScout2.0 can profile EVs, attaining a maximum of 0.88 recall and 0.88 precision. To the best of the authors knowledge, these results set a new benchmark for upcoming privacy research for large datasets of EVs.
The anticipated increase in the number of plug-in electric vehicles (EV) will put additional strain on electrical distribution circuits. Many control schemes have been proposed to control EV charging. Here, we develop control algorithms based on rand
omized EV charging start times and simple one-way broadcast communication allowing for a time delay between communication events. Using arguments from queuing theory and statistical analysis, we seek to maximize the utilization of excess distribution circuit capacity while keeping the probability of a circuit overload negligible.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
