Phd Studentship: AI-powered, Smart, and Sustainable E-bike Charging

With the increasing popularity of electric bikes (e-bikes), the demand for their efficient and sustainable charging stations is more critical than ever. This project aims to address these needs by developing a system that not only monitors and manages the operation of e-bike charging stations with energy supply from solar energy, but also ensures their safety and sustainability. The project focuses on combining innovative technologies such as remote monitoring, large language models, machine learning, blockchain, and eco-accounting to enhance the efficiency, security, and sustainability of e-bike charging stations. To achieve this goal, the candidate is expected to conduct the following: 

(1) Remote condition monitoring system. Develop a system to manage the working conditions of e-bike charging stations across multiple locations. This includes enabling real-time monitoring of electrical energy generated by solar panels, stored in battery power storage systems, and utilised for charging e-bikes and e-scooters. 

(2) Large Language Models (LLMs) for fault diagnostics. Utilise LLMs to analyse and diagnose working status of charging stations, with focus on battery’s state of heath, electric energy flows, and fault logs. Apply AI agents by integrating modern communication protocols to ensure reliable data transmission between IoT devices, LLM servers, and data repositories.

(3) Machine learning for predictive maintenance. Development of machine/deep learning methods to detect fault, provide early warning and reporting, and forecast lifetime trend of batteries, to support predictive maintenance and improve energy management of charging stations. 

(4) Blockchain infrastructure. Implement advanced distributed ledger technologies and representational state transfer interface, to enhance rapid response and resilience against unauthorized modifications of charging station data and ensure secure operation. 

(5) Environmental life cycle assessment (LCA). The LCA method ‘Product Environment Footprint (PEF)’ will be utilised to assess the environmental impact and sustainability of the e-bike charging station and related components/products such as the batteries used to store the electricity obtained from the solar panels.  

This PhD research is part of a project supported by the European Commission’s Horizon Europe programme.

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