Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life

Recycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage. However, an often-overlooked issue is the sometimes-unknown cell chemistry of batteries entering the end-of-life. In this work, a machine learning based approach for the identification of lithium-ion battery cathode chemistries is presented. First, an initial measurement boundary determination is introduced. Using the Python Battery Mathematical Modelling (PyBaMM) framework, synthetical partial open circuit voltage (OCV) charge and discharge curves are generated with an electrochemical single particle model for three different cathode chemistries and the initial state of charge and state of health values as well as the initial capacities are varied. The dV/dQ characteristics are chosen as features and four machine learning algorithms are trained on different lengths of OCV curves. The trade-off between achievable accuracy and the number of OCV steps showed that an increasing accuracy correlates with a higher step number. While extremely small charge and discharge capacities per step did not yield sufficient testing accuracies, capacities starting from 0.2 Ah per step up to 0.6 Ah per step showed increasingly good results with an accuracy of up to 89.3 % for 0.5 Ah and 15 OCV steps. Additionally, the approach was validated by classifying experimental data. The results especially demonstrate the effectiveness of the approach to distinguish between lithium iron phosphate (LFP) and lithium nickel manganese cobalt (NMC) cells.