A Digital Twin Model for Battery Management Systems: Concepts, Algorithms, and Platforms

Mi, Zhou; Lu, Bai; Lei, Jiaxuan; Wang, Yibin; Li, Heng

doi:10.1007/978-981-16-6963-7_102

Cited by 2 publications

(1 citation statement)

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“…For instance, DT technology is used to optimize real-time disassembly strategies for end-of-life LIBs [28], production and value chain efficiency [29], and management systems [30]. DTs have also been employed to predict future battery behavior [31], the partial discharge voltage curve [32], the maximum available capacity of LIBs in EVs [33], the remaining useful cycle life [34], and the capacity decay and safety hazards in LIBs [35]. Additionally, DTs allow for real-time analysis and testing of LIB characteristics, offering a versatile approach to testing different configurations and control strategies without physical prototypes [36].…”

Section: Digital Twin Of Batteriesmentioning

confidence: 99%

A Digital Twin Framework for Simulating Distributed Energy Resources in Distribution Grids

Værbak,

Billanes,

Jørgensen

et al. 2024

Energies

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As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework’s ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade.

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