NEXT GENERATION BATTERY MANAGEMENT SYSTEM BASED ON DATA RICH DIGITAL TWIN

基于数据丰富的数字孪生的下一代电池管理系统

• 批准号: 10069742
• 金额: $ 39.56万
• 依托单位: COVENTRY UNIVERSITY
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 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=10069742
• 关键词: NEXT; GENERATION; BATTERY; MANAGEMENT; SYSTEM

The EU roadmap towards a climate-neutral economy by 2050 sets ambitious decarbonisation targets that shall be achieved by a massive deployment of renewable energy sources. Energy storage improves grid flexibility and allows higher penetration levels of renewable energy sources to create a decarbonised and more electrified society by means of leveraging second-life batteries. Battery management plays an essential role by ensuring an efficient and safe battery operation. However, current battery management systems (BMS) typically rely on semi-empirical battery models (such as equivalent-circuit models) and on a limited amount of measured data. Therefore, ENERGETIC project aims to develop the next generation BMS for optimizing batteries’ systems utilisation in the first (transport) and the second life (stationary) in a path towards more reliable, powerful and safer operations. ENERGETIC project contributes to the field of translational enhanced sensing technologies, exploiting multiple Artificial Intelligence models, supported by Edge and Cloud computing. ENERGETIC’s vision not only encompasses monitoring and prognosis the remaining useful life of a Li-ion battery with a digital twin, but also encompasses diagnosis by scrutinising the reasons for degradation through investigating the explainable AI models. This involves development of new technologies of sensing, combination and validation of multiphysics and data driven models, information fusion through Artificial Intelligence, Real time testing and smart Digital Twin development. Based on a solid and interdisciplinary consortium of partners, the ENERGETIC R&D project develops innovative physics and data-based approaches both at the software and hardware levels to ensure an optimised and safe utilisation of the battery system during all modes of operation.

欧盟到2050年实现气候中性经济的路线图设定了雄心勃勃的脱碳目标，这些目标将通过大规模部署可再生能源来实现。储能提高了电网的灵活性，并允许更高水平的可再生能源渗透，通过利用二次电池来创造一个脱碳和更加电气化的社会。电池管理在确保电池高效、安全运行方面发挥着至关重要的作用。然而，当前的电池管理系统（BMS）通常依赖于半经验电池模型（例如等效电路模型）和有限量的测量数据。因此，BGETIC项目旨在开发下一代BMS，以优化电池系统在第一次（运输）和第二次（固定）中的利用率，从而实现更可靠，更强大和更安全的操作。ARGETIC项目致力于翻译增强传感技术领域，利用多种人工智能模型，由边缘和云计算支持。AIGETIC的愿景不仅包括监测和预测具有数字孪生的锂离子电池的剩余使用寿命，还包括通过调查可解释的AI模型来仔细检查退化原因的诊断。这涉及到开发新的传感技术，多物理场和数据驱动模型的组合和验证，通过人工智能进行信息融合，真实的时间测试和智能数字孪生开发。基于一个坚实的跨学科合作伙伴联盟，AEGETIC研发项目在软件和硬件层面开发了创新的物理和基于数据的方法，以确保在所有操作模式下优化和安全地使用电池系统。