Application of Deep Learning to Optimize Gradient Porosity Profile for Improved Energy Density of Lithium-Ion Batteries

Lithium-ion batteries with high active material loading can yield a high energy density at low C-rates. However, the sluggish ion transport caused by longer and more tortuous pathways hinders high energy delivery when extracting high power. This study presents the implementation of neural networks to optimize the gradient active material distribution profile throughout the thickness of electrodes to enhance energy density. The profiles were randomly generated, while maintaining a constant average active material in each electrode. An electrochemical–thermal model was used to investigate the impact of different profiles. A neural network model was then developed to establish the connection between the profiles and the resulting energy density for various electrode thicknesses and C-rates, utilizing a limited amount of simulation data. The neural network model could replicate the performance of the electrochemical–thermal model, but with significantly reduced computational time. This enabled the possibility of efficiently exploring a vast number of candidate profiles to identify the most optimal one for each of the positive and negative electrodes. The results showed that the gradient profiles were mostly influenced by the average active material, rather than the thickness of the electrode. Finally, at high currents, the optimal gradient profiles increased the energy density by over four times compared to uniform electrodes.