Estimating the remaining useful life of lithium-ion batteries is crucial for their application as energy storage devices in stationary and automotive applications. It is therefore important to understand battery degradation based on chemistry, usage patterns, and operating environment. Different degradation mechanisms that affect performance and durability of lithium-ion batteries have been identified over the past decades. Amongst them, the solid-electrolyte interface (SEI) layer growth has been observed to be the most influential cause of capacity fading. In this paper, we introduce for the very first time, a framework that evaluates the predictive ability of physics-based macroscopic models in capturing battery dynamics as function of their state-of-health (SoH). Using data from accelerated aging experiments, we identify the applicability conditions of classical electrochemical models. This analysis is performed using a phase diagram approach that involves parameters controlling the micro-scale dynamics inside the lithium-ion cell.
- Dynamic Systems and Control Division
Preliminary Investigation of Provability of Li-Ion Macroscale Models Subject to Capacity Fade
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Arunachalam, H, Battiato, I, & Onori, S. "Preliminary Investigation of Provability of Li-Ion Macroscale Models Subject to Capacity Fade." Proceedings of the ASME 2016 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation. Minneapolis, Minnesota, USA. October 12–14, 2016. V001T08A002. ASME. https://doi.org/10.1115/DSCC2016-9736
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