Highlights A data-driven approach for classifying cell chemistries of lithium-ion batteries for improved second-life and recycling assessment is introduced. Synthetical open circuit voltage data is generated by an electrochemical model with varying degradation states. Different machine learning models are tested for comparison.
When analyzing a battery with an unknown cell chemistry, the test boundaries are mainly determined by the cathode composition of the cell. For example, an LFP cell is usually safely operatable in a voltage range between 2.0 V and 3.6 V. An NMC cell on the other hand generally allows for cycling between 2.5 V and 4.2 V.
The resulting values are shown in Table 4. Particularly the high recall values for NMC batteries indicate that the model is sensitive for correctly identifying most NMC cells. As a misclassification of NMC cells would be the economical worst-case scenario, these results are promising regarding a real-world application of the method.
Here, cell chemistry identification can still be carried out as LFP cells are usually still safely operatable even at a voltage above 4 V and deep discharging only leads to severe degradation mechanisms but not to hazardous failure .
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