Leveraging Digital Twin Technology for Battery Management: A Case Study Review
The increasing complexity of battery management systems (BMS) has led to challenges processing the vast amounts of data required for accurate real-time monitoring and control. Existing BMS frameworks, which rely heavily on artificial intelligence (AI), often struggle with data limitations that impac...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10845776/ |
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| author | Judith Nkechinyere Njoku Ebuka Chinaechetam Nkoro Robin Matthew Medina Cosmas Ifeanyi Nwakanma Jae-Min Lee Dong-Seong Kim |
| author_facet | Judith Nkechinyere Njoku Ebuka Chinaechetam Nkoro Robin Matthew Medina Cosmas Ifeanyi Nwakanma Jae-Min Lee Dong-Seong Kim |
| author_sort | Judith Nkechinyere Njoku |
| collection | DOAJ |
| description | The increasing complexity of battery management systems (BMS) has led to challenges processing the vast amounts of data required for accurate real-time monitoring and control. Existing BMS frameworks, which rely heavily on artificial intelligence (AI), often struggle with data limitations that impact the precision of state estimates, ultimately affecting battery performance and safety. The integration of digital twin (DT) technology has been proposed to address these challenges. DTs create virtual representations of physical battery systems, enabling enhanced monitoring, predictive maintenance, and optimized performance through advanced AI algorithms. This study presents a comprehensive exploration of DT technology for BMS. First, we review the fundamental concepts, including DTs’ definitions, roles, and high-level architecture in battery management. Second, we examine research and industry-based case studies to identify the necessary technologies and tools for developing robust battery DTs. We propose a detailed framework for integrating DTs with existing BMS infrastructure, focusing on scalability, cost-effectiveness, and practical implementation strategies. Finally, we discuss the open research challenges and future opportunities in the field, emphasizing the potential impact of DTs on the evolution of BMSs. |
| format | Article |
| id | doaj-art-04f1a3f43e62421aa834c2839393979a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-04f1a3f43e62421aa834c2839393979a2025-02-05T00:01:11ZengIEEEIEEE Access2169-35362025-01-0113213822141210.1109/ACCESS.2025.353183310845776Leveraging Digital Twin Technology for Battery Management: A Case Study ReviewJudith Nkechinyere Njoku0https://orcid.org/0000-0002-2294-9204Ebuka Chinaechetam Nkoro1Robin Matthew Medina2Cosmas Ifeanyi Nwakanma3https://orcid.org/0000-0003-3614-2687Jae-Min Lee4https://orcid.org/0000-0001-6885-5185Dong-Seong Kim5https://orcid.org/0000-0002-2977-5964Kumoh National Institute of Technology, Gumi, North Gyeongsang, South KoreaKumoh National Institute of Technology, Gumi, North Gyeongsang, South KoreaKumoh National Institute of Technology, Gumi, North Gyeongsang, South KoreaComputer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USAKumoh National Institute of Technology, Gumi, North Gyeongsang, South KoreaKumoh National Institute of Technology, Gumi, North Gyeongsang, South KoreaThe increasing complexity of battery management systems (BMS) has led to challenges processing the vast amounts of data required for accurate real-time monitoring and control. Existing BMS frameworks, which rely heavily on artificial intelligence (AI), often struggle with data limitations that impact the precision of state estimates, ultimately affecting battery performance and safety. The integration of digital twin (DT) technology has been proposed to address these challenges. DTs create virtual representations of physical battery systems, enabling enhanced monitoring, predictive maintenance, and optimized performance through advanced AI algorithms. This study presents a comprehensive exploration of DT technology for BMS. First, we review the fundamental concepts, including DTs’ definitions, roles, and high-level architecture in battery management. Second, we examine research and industry-based case studies to identify the necessary technologies and tools for developing robust battery DTs. We propose a detailed framework for integrating DTs with existing BMS infrastructure, focusing on scalability, cost-effectiveness, and practical implementation strategies. Finally, we discuss the open research challenges and future opportunities in the field, emphasizing the potential impact of DTs on the evolution of BMSs.https://ieeexplore.ieee.org/document/10845776/Digital twinbattery managementbatterystate estimationartificial intelligence |
| spellingShingle | Judith Nkechinyere Njoku Ebuka Chinaechetam Nkoro Robin Matthew Medina Cosmas Ifeanyi Nwakanma Jae-Min Lee Dong-Seong Kim Leveraging Digital Twin Technology for Battery Management: A Case Study Review IEEE Access Digital twin battery management battery state estimation artificial intelligence |
| title | Leveraging Digital Twin Technology for Battery Management: A Case Study Review |
| title_full | Leveraging Digital Twin Technology for Battery Management: A Case Study Review |
| title_fullStr | Leveraging Digital Twin Technology for Battery Management: A Case Study Review |
| title_full_unstemmed | Leveraging Digital Twin Technology for Battery Management: A Case Study Review |
| title_short | Leveraging Digital Twin Technology for Battery Management: A Case Study Review |
| title_sort | leveraging digital twin technology for battery management a case study review |
| topic | Digital twin battery management battery state estimation artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10845776/ |
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