Design of an AI- Enabled Digital Twin for Battery Optimisation

项目计划细节包括关键里程碑：*对数字孪生，硬件在环，电池建模和机器学习优化的综合背景研究*电池数据集评估*决定电池性能指标*数字孪生设计*集成机器学习以帮助关键决策过程。物联网和人工智能支持的硬件在循环研究中提出了一种机器学习解决方案，通过建模来优化电池。主要目标将是使用真实世界的电池数据集，通过硬件在环设计数字孪生，并集成机器学习技术，根据健康状态、充电状态和剩余使用寿命等关键指标预测和优化电池性能。

Details of Project Plan including key milestones:* Comprehensive background study into Digital Twinning, Hardware in the Loop, Battery Modelling and Machine learning Optimization * Assessment of battery dataset * Deciding battery performance metrics * Design of Digital Twin * Integrating Machine learning to aid key decision making processes A Comprehensive Integration of the novelty Digital Twining, IOT and AI-enabled Hardware in the Loop research to propose a Machine learning Solution to battery Optimization using modelling. The main objective will be to use a real-world battery dataset to design a digital twin through Hardware in the Loop, and integrating machine learning techniques to predict and optimize battery performance based on key metrics such as State of health, State of Charge and Remaining Useful life.