Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression

The main problems of the EIS online application are that due to the battery's nonlinear characteristics, the amplitude of the square wave and multi-sine signals cannot be effectively equivalent to the sine wave of each frequency respectively. Besides, it also relies on proprietary equipment, including excitation sources and high-rate data acquisition equipment. We propose a technical route for online identification of Low-frequency EIS based on step wave conditions under the technical framework of 50ms communication interval between charger and BMS and 50Hz sampling frequency of BMS. Compared with square wave and multi-sine signals, the stress and response of each frequency step wave are equivalent to that of the sine wave. The market's mainstream bi-directional converter and BMS can realize the identification condition output and data acquisition. The identified Low-frequency EIS achieves 0.96 goodness-of-fit. The health indicators resolved from the actual angle can improve the correlation from a moderate correlation of 0.6 to a strong correlation of 0.8. Furthermore, the novel health indicators significantly improve the SOH estimation accuracy with R2 above 0.95, root mean squared error below 1% and mean absolute percentage error of about 0.9%. This method is an effective way to the active SOH detection for Li-ion battery, which is vital for the online SOH evaluation and early safety warning.