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@article{203300,
        author = {Mandala Trinadh and Singidi Sai Krishna Lahari and Siriki Santhisri and TADALA PUNITH SAI and D.Dileep Reddy and RSR Krishnam Naidu and M. DURGA NAGA RAJESH},
        title = {State of Health (SOH) Monitoring for Battery Management System (BMS) in Electric Vehicles},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {10785-10792},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203300},
        abstract = {The rapid global transition toward sustainable mobility has positioned Electric Vehicles (EVs) at the forefront of modern transportation. Central to EV performance, safety, and longevity is the Battery Management System (BMS), which acts as both a supervisor and caretaker for high-voltage battery packs. Among the critical parameters monitored by a BMS, the State of Health (SOH) stands out as a vital yet abstract metric that reflects battery degradation, remaining useful life, and overall operational reliability. Unlike voltage or current, SOH cannot be directly measured and must be inferred through advanced estimation techniques that correlate measurable physical quantities with internal battery aging. This paper presents a comprehensive framework for SOH monitoring in EVs, analysing model-based, data-driven, and hybrid estimation methodologies. It details the mathematical and algorithmic foundations of techniques such as Equivalent Circuit Models (ECMs), internal resistance and impedance tracking, Coulomb counting, Kalman filtering, neural networks, fuzzy logic, and ultrasonic wave detection. Furthermore, the study explores the integration of these SOH estimation methods within modern BMS architectures and examines how emerging EV platforms leverage artificial intelligence, cloud connectivity, wireless monitoring, and advanced thermal management to enhance battery health prognostics. By synthesizing experimental insights and contemporary industry practices, this work underscores the necessity of robust, multi-dimensional SOH assessment for optimizing EV performance, enabling predictive maintenance, extending battery lifespan, and supporting global decarbonization goals.},
        keywords = {Battery Management System (BMS), State of Health (SOH), State of Charge (SOC), Electric Vehicle (EV), Equivalent Circuit Model (ECM), Kalman Filter, Machine Learning, Ultrasonic Detection, Battery Degradation, Energy Storage Systems.},
        month = {May},
        }