A Comprehensive Study of Lithium-Ion Battery Modeling, Health Estimation Technique

As a critical technology in the transition to greener energy systems, lithium-ion batteries (LIBs) have found applications in electric vehicles (EVs), grid-scale energy storage, and portable devices. They are the ideal option for modern energy storage because of their high energy density, efficiency, and scalability; yet, problems including limited lifespan, safety risks, and performance degradation still pose significant barriers to wider adoption. To address these issues, exact health assessment methods including State of Charge (SOC), State of Health (SOH), State of Energy (SOE), and Remaining Useful Life (RUL) as well as accurate modeling of electrochemical, thermal, and aging behaviors are required. Additionally, robust lifecycle management strategies are required. This paper offers a complete evaluation of battery modeling techniques, including hybrid data-driven methodologies, electrochemical, equivalent circuit, and thermal approaches. The application of state estimation methods in real-time battery management systems is also investigated. Furthermore, analyzed are lifecycle optimization strategies such as temperature regulation, charging control, predictive diagnostics, and the expanding application of digital twin technologies. By integrating existing research findings, industry practices, and future possibilities, the study identifies knowledge gaps and offers a path for the development of intelligent, scalable, and sustainable battery management solutions for next-generation energy systems.