Leveraging Digital Twin Technology for Battery Management: A Case Study Review
Judith Nkechinyere Njoku, Ebuka Chinaechetam Nkoro, Robin Matthew Medina, Cosmas Ifeanyi Nwakanma, Jae‐Min Lee, Dong‐Seong Kim
Abstract
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.