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  • Foundation DT for EVs
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Dual Digital Twin for Batteries

 A novel Digital Twin (DT) architecture leveraging edge and cloud computing for real-time battery system monitoring and control. This architecture seamlessly integrates advanced battery physics, machine learning techniques, Internet of Things (IoT), and edge computing to enhance performance and reliability. This project is funded by  Agency for Science, Technology and Research, Singapore.

Research

Cloud-edge Collaboration with Incremental Learning in EV Batteries

 This paper presents Dual Digital Twin, the next level of digital twin, in the presence of two levels of communication availability, for battery system real-time monitoring and control in electric vehicles.  We design the physics-informed update of the neural network using the Lyapunov stability theorem to enhance the synchronization with the physical battery behavior 

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