Machine learning for investigating the relative importance of electrodes’ N:P areal capacity ratio in the manufacturing of lithium-ion battery cells
Mona Faraji Niri, Geanina Apachitei, Michael Lain, Mark Copley, James Marco
Abstract
This work studies the impact of the ratio between the areal capacity of Graphite anode to NMC622 cathode for Lithium-ion batteries compared to the electrode characteristics of thickness, mass loading and cathode areal capacity, on their electrochemical properties. The influence of factors on energy capacity and gravimetric capacity at various Crates starting from C/20 up to 10C is quantified by combining experiments obtained via design of experiment techniques, machine learning modelling and explanation techniques. The results highlight that the performance at all Crates is highly affected by all features however their relative importance, and the linearity and nonlinearity of the dependencies is quite unique for each Crate capacity. N:P ratio is showing a relatively smaller effect on electrochemical performance compared to thickness, mass loading of active material and cathode areal capacity. It is also concluded that while the impact of N:P ratio is almost linear at lower Crates, it is nonlinear with a local optimum at medium and high Crates. This study offers a methodology for smart selection of a ratio between anode and cathode aerial capacity for a balanced performance of cells at all Crates.