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Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms

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Transportation electrification has been fueled by recent advancements in the technology and manufacturing of battery systems, but the industry yet is facing serious challenges that could be addressed using cutting-edge digital technologies. One such novel technology is based on the digital twining of battery systems. Digital twins (DTs) of batteries utilize advanced multi-layer models, artificial intelligence, advanced sensing units, Internet-of-Things technologies, and cloud computing techniques to provide a virtual live representation of the real battery system (the physical twin) to improve the performance, safety, and cost-effectiveness. Furthermore, they orchestrate the operation of the entire battery value chain offering great advantages, such as improving the economy of manufacturing, re-purposing, and recycling processes. In this context, various studies have been carried out discussing the DT applications and use cases from cloud-enabled battery management systems to the digitalization of battery testing. This work provides a comprehensive review of different possible use cases, key enabling technologies, and requirements for battery DTs. The review inclusively discusses the use cases, development/integration platforms, as well as hardware and software requirements for implementation of the battery DTs, including electrical topics related to the modeling and algorithmic approaches, software architectures, and digital platforms for DT development and integration. The existing challenges are identified and circumstances that will create enough value to justify these challenges, such as the added costs, are discussed.

Original languageEnglish
Article number113280
JournalRenewable and Sustainable Energy Reviews
Number of pages23
Publication statusPublished - Jun 2023

    Research areas

  • Artificial intelligence (AI), Battery management system (BMS), Battery passport, Battery recycling, Digital twin (DT), Electric vehicle (EV), Fault diagnosis, Internet-of-things (IoT), Machine learning (ML), Predictive maintenance, Remaining useful life (RUL), Second-life, Software architecture

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