UK-Australia Centre in a Secure Internet of Energy: Supporting Electric Vehicle Infrastructure at the "Edge" of the Grid

英国-澳大利亚安全能源互联网中心：支持电网“边缘”的电动汽车基础设施

• 批准号: EP/W003325/1
• 负责人: Rajiv Ranjan
• 金额: $ 192.54万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW003325%2F1
• 关键词: UK; Australia; Centre; Secure; Internet

The Internet of Energy (IoE) is a paradigm towards achieving a "zero-carbon" society by optimising electrical energy usage, especially for emerging loads such as Electric Vehicles. The paradigm is a recognition that integrating the internet of things with energy sources and demand loads, enables real-time processing of data streams to support actionable decision support. The aim of this centre-to-centre collaboration is to conduct fundamental multi-disciplinary research in the cyber resilience of future IoE systems. As electric vehicles are likely to make the greatest use of battery capacity in the future, they will play a key role in the IoE infrastructures. According to the "Global EV Outlook 2020" report (https://www.iea.org/reports/global-evoutlook-2020, International Energy Agency), Electric Vehicle sales topped 2.1M globally in 2019, surpassing 2018 - already a record year - to boost the stock to 7.2M electric cars. As technological progress in the electrification of two/three-wheelers, buses and trucks advances and the market for them grows, electric vehicles are expanding significantly. This growth is further amplified through government regulations, e.g. phasing out of diesel and petrol vehicles. This percentage is also likely to grow both in the United Kingdom and Australia. To meet climate-change goals, half of UK cars must be electric by 2030 (according to the UK government). Similarly, the Australian government (https://www.infrastructureaustralia.gov.au/) predicts that by 2040, electric vehicles (EVs) are projected to account for 70% to 100% of new vehicle sales. To meet the demand of the growing EV population, UK and Australian governments are ramping up the installation of charging infrastructure. For example, there are now more than 35,000 charge point connectors across the UK in over 13,000 locations - with around 7,000 charge point connectors added in 2020 alone. This makes electrical vehicles significant energy consumers in the IoE, with their batteries also providing the potential for energy storage in times of emergency or unexpected surges in demand. However, this benefit can only be effectively realised if we can secure the interaction between Electric Vehicles (EVs), charging infrastructure and the national grid. Since 2016, the number of cyber incidents involving vehicles has increased by 605%, with incident rates doubling on a year to year basis (according to 2020 Upstream security's global automotive cybersecurity report). The target of such cyber-attacks is not only private EVs but also commercial EVs. This proposal combines workstreams on attack modelling, data synthesis, attack generation and validation of these using testbeds across the UK and Australia. A simulator will be developed to support a number of "what-if" investigations in cyber resilience for EVs to be carried out. Partners in this proposal have expertise in cybersecurity, power electronics, electrical vehicles, artificial intelligence and distributed computing, and have extensive prior experience in multi-site collaborations. The IoE (cyber-physical) security theory developed in this project will also contribute to accelerated adoption of EV energy prosumers at the edge of the power grid.This proposal will also provide an opportunity for experienced and early career researchers to work collectively on the challenges identified above. A "future leaders" training programme will be developed as part of this proposal to create an "ideas exchange" community across students and academic faculty between the UK and Australian partners. Our industry partners will also be engaged through workshops and "sandpit" events to identify use cases that have industry relevance and which could provide the basis for future startups (in collaboration with entrepreneurship teams at our institutions). The shared testbeds and simulation environment developed will also provide a legacy on completion of this work.

能源互联网（IoE）是通过优化电能使用来实现“零碳”社会的范例，特别是对于电动汽车等新兴负载。该范例是一种认识，即将物联网与能源和需求负载相集成，能够实时处理数据流以支持可操作的决策支持。这种中心间合作的目的是对未来万物联网系统的网络弹性进行基础的多学科研究。由于电动汽车未来可能会最大程度地利用电池容量，因此它们将在万物互联基础设施中发挥关键作用。根据《2020 年全球电动汽车展望》报告（https://www.iea.org/reports/global-evoutlook-2020，国际能源署），2019 年全球电动汽车销量突破 210 万辆，超过 2018 年，创历史新高年 - 将电动汽车库存增至 720 万辆。随着两轮/三轮车、公共汽车和卡车电气化技术的进步及其市场的增长，电动汽车正在大幅扩张。这种增长通过政府法规进一步放大，例如逐步淘汰柴油和汽油车辆。英国和澳大利亚的这一比例也可能会增长。为了实现气候变化目标，到 2030 年，英国一半的汽车必须是电动的（根据英国政府的说法）。同样，澳大利亚政府 (https://www.infrastructureaustralia.gov.au/) 预测，到 2040 年，电动汽车 (EV) 预计将占新车销量的 70% 至 100%。为了满足不断增长的电动汽车数量的需求，英国和澳大利亚政府正在加大充电基础设施的安装力度。例如，目前英国各地 13,000 多个地点有超过 35,000 个充电点连接器，仅 2020 年就新增了约 7,000 个充电点连接器。这使得电动汽车成为万物联网中的重要能源消耗者，其电池还可以在紧急情况或需求意外激增时提供能量存储潜力。然而，只有确保电动汽车（EV）、充电基础设施和国家电网之间的互动，才能有效实现这一效益。自2016年以来，涉及车辆的网络事件数量增加了605%，事件发生率逐年翻倍（根据2020年Upstream security全球汽车网络安全报告）。此类网络攻击的目标不仅是私人电动汽车，还包括商用电动汽车。该提案结合了攻击建模、数据合成、攻击生成以及使用英国和澳大利亚测试平台验证这些工作流。将开发一个模拟器来支持对电动汽车网络弹性进行的一系列“假设”调查。该提案中的合作伙伴拥有网络安全、电力电子、电动汽车、人工智能和分布式计算方面的专业知识，并且在多站点协作方面拥有丰富的经验。该项目中开发的 IoE（网络物理）安全理论也将有助于加速电网边缘的电动汽车能源产消者的采用。该提案还将为经验丰富的早期职业研究人员提供共同应对挑战的机会如上所述。作为该提案的一部分，将制定“未来领导者”培训计划，以在英国和澳大利亚合作伙伴之间创建一个跨学生和学术教师的“思想交流”社区。我们的行业合作伙伴还将通过研讨会和“沙坑”活动参与其中，以确定具有行业相关性的用例，并为未来的初创企业提供基础（与我们机构的创业团队合作）。开发的共享测试台和模拟环境也将在完成这项工作后提供遗产。

项目成果

Lime: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks

Lime：动态异构信息网络的低成本增量学习

• DOI: 10.1109/tc.2021.3057082
• 发表时间: 2021-02-11
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Hao Peng;Renyu Yang;Z. Wang;Jianxin Li;Lifang He;Philip S. Yu;A. Zomaya;R. Ranjan
• 通讯作者: R. Ranjan

FedMSA: A Model Selection and Adaptation System for Federated Learning

FedMSA：联邦学习的模型选择和适应系统

• DOI: 10.3390/s22197244
• 发表时间: 2022-09-24
• 期刊: Sensors (Basel, Switzerland)
• 作者: Sun R;Li Y;Shah T;Sham RWH;Szydlo T;Qian B;Thakker D;Ranjan R
• 通讯作者: Ranjan R

OsmoticGate: Adaptive Edge-Based Real-Time Video Analytics for the Internet of Things

OsmoticGate：物联网的基于边缘的自适应实时视频分析

• DOI: 10.1109/tc.2022.3193630
• 发表时间: 2023-04-01
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Bin Qian;Z. Wen;Junqi Tang;Ye Yuan;Albert Y. Zomaya;R. Ranjan
• 通讯作者: R. Ranjan

CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning

CorrDetector：使用集成深度学习从无人机图像中检测结构腐蚀的框架

• DOI: http://dx.10.1016/j.eswa.2021.116461
• 发表时间: 2022
• 期刊: Expert Systems with Applications
• 影响因子: 8.5
• 作者: Forkan A

Blockchain adoption: A study of cognitive factors underpinning decision making

区块链采用：支持决策的认知因素的研究

• DOI: 10.1016/j.chb.2022.107207
• 发表时间: 2022-06-01
• 期刊: Comput. Hum. Behav.
• 作者: D. Marikyan;S. Papagiannidis;O. Rana;R. Ranjan
• 通讯作者: R. Ranjan