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Electrochemical Impedance Spectroscopy measurements and simulations are performed on a nickel manganese cobalt oxide (NMC)/graphite pouch cell. A physico-chemical continuum battery model is extended by a physical ageing model including a Solid Electrolyte Interphase. The model assumes a loss of electrochemically active surface area at anode and cathode as well as a growth of solid electrolyte interphase (SEI) layer thickness. These ageing parameters have been adjusted with an algorithm to achieve agreement between simulated and measured spectra. The results for a 28 mAh pouch cell show that the ageing model is suitable to correlate the change of the impedance spectrum with the degree of degradation of the cell. In detail, SEI thickness is shown to increase by 45 nm, while the anode and cathode loose 20 % and 57 % of their electrochemically active surface area, respectively. In addition, deviating measurement conditions and the end of life of the cell can be indicated by the parameter identification algorithm. Furthermore, it is demonstrated, that the change of the high and low frequency semicircles can be assigned to the anode SEI and cathode respectively.

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Estimation of the State-of-Health (SOH) of Lithium-ion Batteries (LIBs) is commonly conducted using in-situ measurement methods, such as Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) as well as impedance based techniques. In this study, we present an alternative method for SOH estimation: The nonlinear dynamic measurement method Nonlinear Frequency Response Analysis (NFRA) is shown to be able to estimate capacity fade of LIBs due to loss of active material. Capacity loss correlates with the quotient of the root mean square of the second and the third harmonic response for different excitation amplitudes in the frequency range sensitive to electrochemical reactions at approximately 1 Hz. The results of the experimental cycle-aging study are validated and further analyzed by using a reaction model containing Butler-Volmer kinetics with a dynamic charge balance of the electrode. Simulations show that the NFR quotient and capacity fade due to loss of specific surface area correlate exactly. We identify the NFR quotient as a reliable, easily measurable parameter for the diagnosis of the SOH of LIBs. Therefore, this study reveals a novel approach for SOH estimation of LIBs based on dynamic analysis with NFRA.

Abstract:In this study, we show an effective data-driven identification of the State-of-Health of Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on support vector regression is derived from highly informative Nonlinear Frequency Response Analysis data sets. First, an ageing test of a Lithium-ion battery at 25 • C is presented and the impact of relevant ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from 1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to standard measurement protocols. Next, informative features are extracted and used to parametrise the support vector regression model for the State of Health degradation. The performance of the degradation model is validated with additional cells and validation data sets, respectively. We show that the degradation model accurately predicts the State of Health values. Validation data demonstrate the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries in general.

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