This project focuses on the development of a train energy saving algorithm optimisation and a Large Language Model (LLM) based assisted driving system. The system aims to optimise the energy consumption of railway trains and improve the driving experience through a combination of energy saving algorithms and LLM-based interaction systems. The energy-saving optimisation algorithm focuses on building an efficient algorithm based on convex optimisation and mixed integer linear programming. Taking into account factors such as train speed, traction and braking forces, the algorithm aims to minimise energy consumption while ensuring safety and meeting schedule requirements, thus maximising energy efficiency. The interactive system, leveraging the natural language understanding capabilities of LLM, enables real-time interaction with train drivers. This system is capable of not only understanding the train driver’s natural language inputs in real-time, including questions, instructions, and feedback, but also analyzing the train’s operational data and external environmental information promptly. Based on these inputs, it can predict potential risks and challenges, generate precise optimization suggestions, and provide drivers with coping strategies and recommendations.
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