@article{
 title = {State-of-Health identification of Lithium-ion batteries based on Nonlinear Frequency Response Analysis: First steps with machine learning},
 type = {article},
 year = {2018},
 keywords = {Battery degradation,Lithium-ion batteries,Machine learning; support vector regression,Nonlinear frequency response analysis,State-of-health},
 volume = {8},
 id = {d3dd673c-834f-3960-9251-d442008c2523},
 created = {2021-10-26T13:36:47.230Z},
 file_attached = {false},
 profile_id = {46206c9e-d69a-378c-9478-6dd168f65080},
 group_id = {cc302413-c146-306b-8008-abbf67f3b420},
 last_modified = {2021-10-26T13:36:47.230Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Harting2018},
 private_publication = {false},
 abstract = {© 2018 by the authors. 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.},
 bibtype = {article},
 author = {Harting, N. and Schenkendorf, R. and Wolff, N. and Krewer, U.},
 doi = {10.3390/app8050821},
 journal = {Applied Sciences (Switzerland)},
 number = {5}
}

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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.","bibtype":"article","author":"Harting, N. and Schenkendorf, R. and Wolff, N. and Krewer, U.","doi":"10.3390/app8050821","journal":"Applied Sciences (Switzerland)","number":"5","bibtex":"@article{\n title = {State-of-Health identification of Lithium-ion batteries based on Nonlinear Frequency Response Analysis: First steps with machine learning},\n type = {article},\n year = {2018},\n keywords = {Battery degradation,Lithium-ion batteries,Machine learning; support vector regression,Nonlinear frequency response analysis,State-of-health},\n volume = {8},\n id = {d3dd673c-834f-3960-9251-d442008c2523},\n created = {2021-10-26T13:36:47.230Z},\n file_attached = {false},\n profile_id = {46206c9e-d69a-378c-9478-6dd168f65080},\n group_id = {cc302413-c146-306b-8008-abbf67f3b420},\n last_modified = {2021-10-26T13:36:47.230Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Harting2018},\n private_publication = {false},\n abstract = {© 2018 by the authors. 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