Combinatorial atomistic-to-AI prediction and experimental validation of heating effects in 350 F supercapacitor modules

Abstract

Abstract Accurately predicting thermal behavior is critically important in the real-world thermal management of supercapacitor modules with ultrahigh power and discharging current. In this work, an artificial intelligence approach based on the improved multiscale coupled electro-thermal model is employed for the first time to accurately predict the thermal behavior of a 350 F supercapacitor module under air-cooling conditions. Different from previous work that used commercial cells, the 350 F supercapacitors are fabricated from our proprietary pilot-scale production line. This approach provides a platform to precisely measure the structural parameters, electrical and thermal properties of electrodes and electrolytes (e.g., the temperature/current dependent equivalent series resistance and axial/radial thermal characteristics), which can improve the model for characterizing the irreversible heat generation and thermal transport processes. In particular, coupled with molecular dynamics simulations, the molecular origin of entropy is revealed via probing the atomic-level information (e.g., 1D/2D electric double-layer structure, electrical field/potential distributions, areal capacitance, and diffusion kinetics) to accurately predict the reversible heat generation. As a consequence, the deviation between our improved model and experimental results is substantially reduced to below 5%. A deep neural network based on the long short-term memory (LSTM) approach is trained to build a temperature database for practical supercapacitor modules under different operating conditions (including charging/discharging currents, cooling airflow rates, and cycle duration). This work demonstrates the potential of LSTM in predicting the thermal behavior, which can be broadly used for industry-relevant thermal management applications.