Scientometric Analysis and Systematic Review of Battery State of Health Estimation Methods for Wheelchair Applications

Accurate estimation of the state of health (SOH) of lithium-ion batteries is vital for ensuring safety, reliability, and optimal performance especially in wheelchairs and across a range of applications—including electric vehicles and renewable energy systems. As battery management systems (BMSs) become increasingly sophisticated, a deeper understanding of SOH estimation methodologies and their practical implications is essential. This study presents a comprehensive scientometric and systematic review of SOH estimation research, offering a dual-perspective analysis of the field’s evolution, methodologies, and future directions. Using the Scopus database, 3711 peer-reviewed publications from 2010 to 2024 were analyzed. Scientometric analysis, conducted via VOSviewer, mapped the intellectual structure of the domain, revealing key research trends, co-citation networks, influential publications, keyword clusters, and geographic research distribution. The systematic review complements this with a detailed classification of SOH estimation approaches, including direct measurement techniques, model-based methods, data-driven strategies, hybrid frameworks, adaptive filtering, differential analysis, and performance-based metrics. This integrated methodology bridges theoretical development with real-world implementation, identifying research gaps such as scalability, real-time applicability, and interdisciplinary integration. The novelty of this work lies in its combined quantitative and qualitative assessment, which not only synthesizes existing knowledge but also charts a clear roadmap for advancing SOH estimation in next-generation BMS. This review serves as a foundational reference for researchers, developers, and practitioners aiming to enhance the reliability and intelligence of battery health monitoring systems.