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.
Highlights
Lithium-ion batteries (LIBs) are currently the most widely used type of battery for electromotive applications and are seen as the most promising candidate for the realisation of a comprehensive electro-mobility
Nonlinear Frequency Response Analysis (NFRA) data limited to meaningful frequency ranges are processed to build the degradation model, which is implemented as a support vector regression (SVR) approach
Prior to NFR data analysis and the development of the degradation model, appropriate frequency ranges for the relevant processes had to be identified in the NFR spectrum of the ageing training data sets [24,26]
Summary
Lithium-ion batteries (LIBs) are currently the most widely used type of battery for electromotive applications and are seen as the most promising candidate for the realisation of a comprehensive electro-mobility. Various LIB degradation processes, which lead to a distinct decrease of the maximal usable capacity C as well as an increase of the internal ohmic resistance R of the LIB, can be distinguished [1,2,3]. In the field of e-mobility, for instance, the LIB degradation results in a loss of driving range per charge in electric vehicles (EVs) [2] and in the deterioration of the coulombic efficiency η of LIBs. Ageing processes occurring at the negative and the positive electrodes differ significantly and are usually differentiated in the literature [1]. Ageing processes in the electrolyte and the separator mostly result, respectively, from reactions with the electrodes and reactions at the electrode–electrolyte interface [1].
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