Another relevant example was presented by Li et al., where a combination of techniques was done by training recurrent neural networks (RNN) by using a synthetic dataset coming from a P2D model, in order to predict SoC, and estimate the internal battery variables at different spatial locations . This was achieved by training on a large dataset coming from an electrochemicalthermal model. The goal of tracking internal concentrations and potentials of both electrodes based only on inputs of current, voltage and temperature was achieved, demonstrating the ease of use of these types of models for the final user.