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.

电动汽车技术（EV）技术的最新发展已取得了长足的进步，以提供新的智能绿色城市移动性，其中电动汽车被用作取代常规柴油和汽油动力车辆的主要运输模式。预计EV将在实现降低运输部门温室排放的雄心勃勃的目标方面发挥关键作用。鉴于电动汽车渗透率和未来预测，考虑到考虑为电动汽车的充电供应/交付管理的整体观点，使用5G和超越系统，智能电网，智能电网和大型数据分析EV优化智能机动性和增强EV EV质量的质量的智能网格分析，可以开发对电动汽车的充电管理的技术至关重要。 Evolve旨在利用主要的学术和工业参与者的科学卓越和专业知识，以共同的合作努力，以设计，开发和测试各种技术，这些技术考虑了技术和商业利益相关者的观点，这些技术考虑了EV充电的整体观点。 Evolve将通过协调和管理基础网络和计算资源，使用人工智能（AI）（AI）和机器学习ML设计创新的算法，开发通信协议，预测和优化智能网格系统，开发软件系统和用户接口（UIS）来实现创新来推进EV充电生态系统中的技术，设计创新的算法。 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（POC）演示。 Evolve项目将提供一个平台，以促进学术界与行业合作伙伴之间的密切合作，为每个人提供独特的体验，以创造新的知识，分享知识和技能发展。

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