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

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 -