Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems

• Published in: Energy and AI 2020-08, Vol.1, p.100016, Article 100016
• Main Authors: Wu, Billy, Widanage, W. Dhammika, Yang, Shichun, Liu, Xinhua
• Format: Article
• Language: English
• ISSN: 2666-5468; 2666-5468
• DOI: 10.1016/j.egyai.2020.100016

•Challenges and opportunities in battery modelling and control are reviewed.•Battery diagnostic approaches are reviewed and emerging new data types identified.•Application of machine learning towards batteries are identified and reviewed.•A perspective and framework on the integration of models, data and artificial intelligence is presented towards the creation of a battery digital twin. Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future. [Display omitted]