EVOLVE: Electric Vehicles Point Location Optimisation via Vehicular Communications

EVOLVE：通过车辆通信优化电动汽车点位置

• 批准号: EP/X039161/1
• 负责人: Syed Ali Raza Zaidi
• 金额: $ 7.58万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX039161%2F1
• 关键词: EVOLVE; Electric; Vehicles; Point; Location

Recent developments in the Electric Vehicles (EV) technologies have made great strides to offer new modes of smart green urban mobility, where EVs are used as the main transportation mode replacing the conventional diesel and petrol powered vehicles. EVs are expected to play a key role in achieving ambitious targets of lowering greenhouse emission in the transportation sector. Given the EV penetration rates and future forecasts, it is essential to develop technologies that tackle charging management of EVs considering a holistic view of charging supply/delivery management for EVs, communication of EVs to the smart grid infrastructure system using 5G and beyond systems, smart grids and big data analytics for EVs optimise smart mobility and enhance the quality of experience for EV users. EVOLVE aims to exploit the scientific excellence and expertise of key academic and industrial players into a joint collaborative effort to design, develop and test various technologies that consider the holistic view of EV charging taking into account the view of technical and business stakeholders. EVOLVE will pursue innovation for advancing the technologies in EV charging ecosystem by orchestrating and managing the underlying networking and computational resources, design innovative algorithms using Artificial Intelligence (AI) and Machine Learning ML), developing communication protocols, prediction and optimising load in smart grids, developing software systems and user interfaces (UIs). EVOLVE aims to transcend analytical models and simulation-based validation and aims to deliver five proof-of-concept (PoC) demonstrations. EVOLVE project will provide a platform to foster a close collaboration between academia and industry partners providing each with a unique experience to create new knowledge, share know-how and skills development.

电动汽车（EV）技术的最新发展已经取得了长足的进步，以提供智能绿色城市移动的新模式，其中EV被用作替代传统柴油和汽油动力车辆的主要交通模式。预计电动汽车将在实现降低交通部门温室气体排放的宏伟目标方面发挥关键作用。考虑到电动汽车的普及率和未来预测，必须开发解决电动汽车充电管理的技术，考虑电动汽车充电供应/交付管理的整体观点，电动汽车与使用5G及更高系统的智能电网基础设施系统的通信，智能电网和电动汽车大数据分析优化智能移动性并提高电动汽车用户的体验质量。EVOLVE旨在利用主要学术和工业参与者的科学卓越性和专业知识，共同合作设计，开发和测试各种技术，这些技术考虑到技术和商业利益相关者的观点。EVOLVE将通过协调和管理底层网络和计算资源，使用人工智能（AI）和机器学习（ML）设计创新算法，开发通信协议，预测和优化智能电网中的负载，开发软件系统和用户界面（UI）来推动电动汽车充电生态系统的技术创新。EVOLVE旨在超越分析模型和基于模拟的验证，并旨在提供五个概念验证（proof-of-concept，缩写EVOLVE）演示。EVOLVE项目将提供一个平台，促进学术界和行业合作伙伴之间的密切合作，为每个人提供独特的经验，以创造新知识，分享专有技术和技能发展。

项目成果

Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

• DOI: 10.1109/access.2023.3246924
• 发表时间: 2023
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Abdelaziz Salama;Achilleas Stergioulis;A. Hayajneh;Syed Ali Raza Zaidi;D. McLernon;Ian Robertson

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

• DOI: 10.1049/smc2.12072
• 发表时间: 2023-11
• 期刊: IET Smart Cities
• 影响因子: 3.1
• 作者: A. Hayajneh;Sami A. Aldalahmeh;Feras Alasali;H. Al-Obiedollah;Sayed Ali Zaidi;Des McLernon

Autoencoder and Incremental Clustering-Enabled Anomaly Detection

• DOI: 10.3390/electronics12091970
• 发表时间: 2023-04
• 期刊: Electronics
• 影响因子: 2.9
• 作者: Andrew Charles Connelly;Syed Ali Raza Zaidi;D. McLernon

TinyML Empowered Transfer Learning on the Edge

• DOI: 10.1109/ojcoms.2024.3373177
• 发表时间: 2024
• 期刊: IEEE Open Journal of the Communications Society
• 影响因子: 7.9
• 作者: A. Hayajneh;Maryam Hafeez;Syed Ali Raza Zaidi;Des McLernon