Design of an AI- Enabled Digital Twin for Battery Optimisation

用于电池优化的人工智能数字孪生设计

• 批准号: 2599170
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2599170
• 关键词: Design; AI; Enabled; Digital; Twin

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.

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. 主要目的是使用现实世界中的电池数据集通过循环中的硬件设计数字双胞胎，并集成机器学习技术，以根据关键指标（例如健康状况，充电状态和剩余使用寿命）进行预测和优化电池性能。