Probabilistic Modelling of the Nonlinear Mapping between Voltammograms and the Grain Size of the Electrode Material in Li-Ion Batteries Using Optimally Tuned Gaussian Process Regression Models

The electrochemical response, and hence the performance, of lithium-ion batteries (LIBs) is heavily influenced by the microstructure of their electrode material, particularly grain size. Thus, a model that correlates electrode microstructure with electrochemical response can aid in optimizing these characteristics for better battery performance. However, no existing studies address this modeling problem. This study fills this gap by using data-driven probabilistic modeling to map the electrochemical response (voltammograms) to electrode grain size. The methodology is demonstrated on six datasets containing voltammograms from in-silico cyclic voltammetry experiments on LiMn 2 O 4 electrode particles. Feature vectors created using seven features of the voltammogram were combined with the corresponding known grain size values to construct data-driven probabilistic models via Gaussian process regression (GPR) with five different kernels for each dataset. The hyperparameters of the kernels used in these GPR models were optimized using grid-search. Performance evaluation through bias-variance analysis demonstrated strong predictive accuracy of the developed models. Additionally, the comparisons across datasets and kernels showed the Gaussian and Rational Quadratic kernels’ effectiveness in modeling various relationships. The obtained results substantiate the efficacy of the proposed approach, revealing significant insights and encouraging further exploration.