Chi-Jyun KoChun-Wei LuKUO-CHING CHENCHIH-HUNG CHEN2024-08-262024-08-262024-08https://www.scopus.com/record/display.uri?eid=2-s2.0-85200107690&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/720377Highly sensitive (HS) electrochemical parameters can serve as battery aging indicators, deserving thorough examination. To identify these parameters and determine their cycle evolution across a wide range of battery state of health, this study conducts a three-stage analysis. First, after a detailed evaluation, a global sensitivity analysis is performed to evaluate the sensitivity values of ten selected parameters across five intervals of discharge voltage curves simulated through the Doyle-Fuller-Newman theory. Such analysis facilitates the classification of these parameters into HS and low sensitive (LS) groups. Next, a deep learning (DL) model is developed for parameter identification, employing as minimal data as possible, specifically only partial discharge curves and their corresponding first-order derivatives, as inputs, while the electrochemical parameters are generated as outputs. It is found that utilizing the model built with the HS parameters achieves a remarkably low mean absolute percentage error (MAPE) of 0.25 % for output prediction. On the contrary, adopting the LS parameters yields a MAPE of 13.30 %, highlighting the significant impact of parameter sensitivity on the model outputs. Lastly, the well-trained DL model is directly applied to identify the five HS parameters pertaining to real-world commercial batteries, whose state of health spans approximately from 97 % down to 55 %. With the experimentally recorded partial discharge voltage data, the model generates the cycle evolutions of the HS parameters, revealing a noticeable decrease in their values ranging from 12 % to 40 % with aging.falseLithium-ion batteryParameter identificationDeep neural networkParameter sensitivity[SDGs]SDG7[SDGs]SDG11journal article10.1016/j.ensm.2024.1036652-s2.0-85200107690