The study of battery performance in connection to manufacturing parameters is one of the main focus of the current research. As of right now, a number of researchers have shown that modeling pipelines that use physics-based generated data to train AI surrogate models may also be created by utilizing the advantages of AI tools. However, integrating these two tools to create novel physics-informed ML approaches can also be a valuable strategy to optimize training data requirements, as we can lower the amount of bias and increase accuracy by using physics equations as biases. As a perspective, the utilization of the discussed approaches as a way to perform quality control for recycled electrode materials or hybrid electrodes containing both pristine active materials and recycled ones is proposed as an interesting topic of research.