Alternating Current Impedance Probing Capacity of Lithium‐Ion Battery by Gaussian Process Regression. Issue 8 (21st June 2022) - Zhu, Jiangong; Zhang, Qianqian; Mereacre, Liuda; Wang, Xueyuan; Jiang, Bo; Dai, Haifeng; Wei, Xuezhe; Knapp, Michael; Ehrenberg, Helmut
Abstract : Alternating current (AC) impedance is an important and promising feature for lithium‐ion battery state estimation and prediction. Herein, a new battery capacity estimation method using AC impedance with Gaussian process regression (GPR) is proposed. A bunch of high‐energy 18 650‐type batteries with a nominal capacity of 3.5 Ah are cycled at 25, 35, and 45 °C until the capacity drops below 2.6 Ah. Two single‐frequency points are found which are highly correlated with the battery residual capacity regardless of the cycling temperatures. Machine learning methods are used to probe the battery capacity with the real and imaginary impedance of two single‐frequency points. The best model achieves a test root‐mean‐squared error of 0.5% (17.36 mAh) with GPR. This work provides a new perspective to predicting the complex dynamical behavior of batteries by combining electrochemical impedance with data‐driven methods. Abstract : The study finds that the battery impedance at two single‐frequency points is highly correlated with its capacity regardless of the cycling temperatures. Machine learning methods are used to probe the battery capacity with the real and imaginary impedance. It provides a new perspective to predicting the battery degradation by combining electrochemical impedance with data‐driven methods.