<![CDATA[The global transition toward sustainable energy infrastructure relies heavily on the reliability and longevity of electrochemical energy storage systems. However, conventional management strategies often struggle with the highly non-linear dynamics and unobservable internal degradation mechanisms of these devices. This research addresses the critical need for advanced systems engineering by evaluating a physics-based framework for real-time modeling, state-aware control, and non-invasive degradation analysis. The study aims to optimize the balance between operational performance and capacity retention through the implementation of reduced-order Doyle-Fuller-Newman models. Utilizing a multi-physics experimental design, forty lithium-ion cells were subjected to high-rate cycling while monitored by an adaptive observer-based controller. Results demonstrate that the physics-based approach achieves a 75% reduction in state-of-estimation error compared to empirical models, while significantly mitigating internal resistance growth. Furthermore, the “health-aware” control strategy successfully improved capacity retention by 7.2% over 1,000 cycles by preemptively preventing lithium plating thresholds. This research concludes that internal state visibility is a prerequisite for achieving maximum electrochemical utilization. The findings provide a scalable blueprint for the next generation of resilient battery management systems, asserting that the integration of multi-scale physical models into control architectures is essential for securing the future of global energy storage.]]>
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