Emergent Mind

Abstract

Advanced battery management is to lithium-ion battery systems as the brain is to the human body. Its performance rests on the use of battery models that are both fast and accurate. However, mainstream equivalent circuit models and electrochemical models have yet to meet this need well, due to struggle with either predictive accuracy or computational complexity. This problem has acquired urgency as some emerging battery applications running across broad current ranges, e.g., electric vertical take-off and landing aircraft, can hardly find usable models from the literature. Motivated to address the problem, we develop an innovative model in this study. Called BattX, the model is an equivalent circuit model but draws comparisons to a single particle model with electrolyte and thermal dynamics, thus combining their respective merits to be computationally efficient, accurate, and physically interpretable. The model design pivots on leveraging multiple circuits to approximate major electrochemical and physical processes in charging/discharging. Given the model, we develop a multipronged approach to design experiments and identify its parameters in groups from experimental data. Experimental validation proves that the BattX model is capable of accurate voltage prediction for charging/discharging across low to high C-rates.

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