Trends and Future Perspectives of Internet of Things (IoT) in Battery Performance Monitoring: A Bibliometric Study

• Published in: 2024 IEEE International Conference on Applied Electronics and Engineering, ICAEE 2024
• Main Author: Supian M.F.I.M.; Redzuan S.M.; Halim F.H.A.; Aziz A.M.A.; Talib M.A.; Idros M.F.M.; Razak A.H.A.; Halim A.K.; Abidin A.F.; Junid S.A.M.A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204762036&doi=10.1109%2fICAEE62924.2024.10667514&partnerID=40&md5=8b4a55e2ef619dba4eaf894b46533fbd

This bibliometric study investigates the evolving research landscape of IoT applications in battery performance monitoring, focusing on trends, key contributors, and emerging themes. By analyzing 102 peer-reviewed publications from 2010 to 2024 retrieved from the Scopus database, the study identifies significant growth in research activity, particularly post-2014, with an emphasis on IoT-enabled solutions for optimizing battery management systems (BMS) and enhancing the efficiency of energy storage systems. The study employs ScientoPy and VOSviewer to conduct a comprehensive analysis, highlighting the dominance of conference papers, particularly from 2017 onwards, and the significant contributions of specific keywords such as 'IoT,' 'battery management system,' and 'state of charge,' reflecting the field's shift towards advanced IoT-driven methodologies. The citation analysis underscores the field's focus on integrating IoT into smart microgrids, electric vehicles, and energy-efficient systems, with the most cited works demonstrating the critical impact of IoT in these areas. The study also maps global research contributions, identifying India as the leading contributor, followed by significant research outputs from Indonesia and China. The findings provide valuable insights for future research, emphasizing the need to explore advanced IoT applications, including AI-driven analytics and real-time monitoring, to address emerging challenges in battery performance monitoring and enhance the reliability and sustainability of energy management systems. © 2024 IEEE.