ENERGETIC: Next Generation Battery Management System Based on Data Rich Digital Twin

欧盟到2050年实现气候中立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。能源储存提高了电网的灵活性，并允许更高水平的可再生能源渗透，通过利用第二代电池的方式创造一个脱碳和更电气化的社会。电池管理在确保电池高效安全运行方面起着至关重要的作用。然而，当前的电池管理系统(BMS)通常依赖于半经验电池模型(如等效电路模型)和有限的测量数据。因此，能源项目旨在开发下一代电池管理系统，以优化电池在第一次(运输)和第二次生命(静止)中的系统利用率，从而实现更可靠、更强大和更安全的操作。活力项目为翻译增强型传感技术领域做出了贡献，利用了边缘和云计算支持的多种人工智能模型。Energy的愿景不仅包括对带有数字孪生兄弟的锂离子电池的剩余使用寿命进行监测和预测，还包括通过研究可解释的人工智能模型仔细检查退化原因进行诊断。这涉及多物理和数据驱动模型的传感、组合和验证、人工智能信息融合、实时测试和智能数字双胞胎开发等新技术的开发。基于坚实和跨学科的合作伙伴联盟，活力研发项目在软件和硬件层面上开发创新的物理和基于数据的方法，以确保在所有操作模式下电池系统的优化和安全使用。

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 prognosisthe 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.