Tracking the Evolution of Controllers in Battery Management Systems: A Scientometric Mapping of Research Themes and Gaps

The controller is a critical enabler of performance, safety, and efficiency in Battery Management Systems (BMS), yet systematic scientometric evidence on its research evolution remains sparse. Prior reviews have predominantly focused on battery chemistries, thermal management, or overall BMS architectures, leaving controller technologies underexplored. To bridge this gap, this study presents the first longitudinal scientometric and bibliometric mapping of controller-based BMS research, analyzing 103 Scopus-indexed publications from 2010 to 2024. Using Biblioshiny and VOSviewer, the study evaluates publication growth, influential sources, global collaboration networks, and thematic development. Results reveal exponential growth after 2021, driven primarily by conference proceedings and research on controller/microcontroller integration. India leads in publication output, while the United States exhibits the highest citation impact, and China demonstrates steady contributions. However, only 10.68% of publications involve international co-authorship, underscoring a significant collaboration gap that limits global knowledge exchange. Highly cited works highlight controllers' role in advancing SOC/SOH estimation, active balancing, and charging optimization. Keyword co-occurrence analysis identifies three dominant clusters: (i) SOC estimation and charging strategies for electric vehicles, (ii) controller-centric BMS for secondary batteries, and (iii) pack-level optimization in lithium-ion chemistries. Emerging trends point toward intelligent controllers incorporating AI, FPGA, and IoT-enabled real-time monitoring. The contribution of this study lies in consolidating four decades of fragmented research into a structured mapping, addressing critical gaps in prior bibliometric reviews, and offering future research directions centered on AI-driven, safety-certified, and globally collaborative controller architectures for next-generation BMS applications.