ELECTROCHEMICAL SYSTEMS ENGINEERING: MODELING, CONTROL, AND DEGRADATION ANALYSIS
Abstract
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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References
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Copyright (c) 2026 Yui Nakamura, Li Wei, Arfan Arfan
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