An improvement of equivalent circuit model for state of health estimation of lithium-ion batteries based on mid-frequency and low-frequency electrochemical impedance spectroscopy. (October 2022)

• Record Type: Journal Article
• Authors: Chang, Chun; Wang, Shaojin; Tao, Chen; Jiang, Jiuchun; Jiang, Yan; Wang, Lujun
• Is Part Of: Measurement. Volume 202(2022)
• Publication Date: 2022-10
• Subjects: Electrochemical impedance spectroscopy -- Lithium-ion battery -- Equivalent circuit model -- Model Improvement -- State of health; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111795
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The proposed fusion resistor R SC takes into account the difference between the fitted and real values of the ECM parameters, and this difference is greatly reduced by the proposed MLECM as an approach, and the fitting effect and success rate are optimized and improved. In this study, the CECM is optimized by discarding the high-frequency region and some of the mid-frequency parts of the circuit components and fusing them to form a completely new resistor. The method for estimating SOH based on the MLECM is obtained, and the model loading of MLECM is reduced by 69.57% relative to ECM. In this study, two different battery datasets are used, taking into account the variability brought by different SOC, different battery types, different charge/discharge rates and different temperatures for battery aging. The method is applicable to the range of 25–45 °C where the power battery temperature is located during the actual driving of the vehicle, and provides a new idea for the establishment of the EIS ECM for electric vehicle lithium-ion batteries. Abstract: Electrochemical impedance spectroscopy (EIS) is a non-invasive, information-rich measurement method. The biggest advantage is that it is possible to identify and analyze the battery state using a suitable equivalent circuit model (ECM) without the need for complete knowledge of the battery's past operation. Conventional equivalent circuit models (CECMs) achieve a high degree of accuracy by identifying model parameters with relatively fixed circuit components. However, CECM method fitting process may suffer from fitting failure and fitting error, resulting in poor estimation accuracy. To solve this problem, it is crucial to establish a suitable ECM with good fitting effect and high accuracy. Accordingly, this study proposes a method of mid-frequency and low-frequency domain ECM (MLECM) based on fusion SEI film resistance and charge transfer resistance. Firstly, two model building methods are presented. Then, by fitting and analyzing the model parameters of two different types of batteries, we conclude that MLECM has the advantages of fewer parameters and better parameter fitting. Finally, a method of power battery state of health (SOH) estimation based on the improved model is proposed by MLECM and the mathematical model of SOH. Validated by two datasets of experiments with different types of batteries, the results show that the maximum RMSE of the proposed estimation method is only 1.38% in the two datasets. And the average root mean squared error (RMSE) of MLECM is reduced by 0.708% compared to CECM, while the computational load of the former is reduced by 69.57% compared to the latter. Compared with the CECM method, the MLECM has high estimation accuracy, high applicability and low computational load.