TY - JOUR
T1 - Digital twin of electric vehicle battery systems
T2 - Comprehensive review of the use cases, requirements, and platforms
AU - Naseri, Farshid
AU - Gil Arboleda, Santiago
AU - Barbu, Corneliu
AU - Cetkin, E.
AU - Yarimca, G.
AU - Jensen, A.C.
AU - Larsen, Peter Gorm
AU - Gomes, Cláudio
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - Artificial intelligence (AI)
KW - Battery management system (BMS)
KW - Battery passport
KW - Battery recycling
KW - Digital twin (DT)
KW - Electric vehicle (EV)
KW - Fault diagnosis
KW - Internet-of-things (IoT)
KW - Machine learning (ML)
KW - Predictive maintenance
KW - Remaining useful life (RUL)
KW - Second-life
KW - Software architecture
UR - http://www.scopus.com/inward/record.url?scp=85152416376&partnerID=8YFLogxK
U2 - 10.1016/j.rser.2023.113280
DO - 10.1016/j.rser.2023.113280
M3 - Journal article
SN - 1364-0321
VL - 179
JO - Renewable and Sustainable Energy Reviews
JF - Renewable and Sustainable Energy Reviews
M1 - 113280
ER -