Accelerating Battery Manufacturing Optimization by Combining Experiments, In Silico Electrodes Generation and Machine Learning

Abstract

Both the society and the market calls for safer, high-performing and cheap Li-ion batteries (LIBs) in order to speed up the transition from oil-based to electric-based economy. One critical aspect to be taken into account in this modern challenge is LIBs manufacturing process, whose optimization is time and resources consuming due to the several interdependent physicochemical mechanisms involved. In order to tackle rapidly this challenge, digital tools able to accelerate LIBs manufacturing optimization are crucially needed for both well assessed and recently discovered chemistries. The methodology presented here encompasses experimental characterizations, in silico generation of electrode mesostructures and machine learning algorithms to track the effect of manufacturing over a wide array of mesoscale electrode properties critically linked to the electrochemical performance. Particularly, features as the interconnectivity of the particles network, the electrolyte tortuosity and effective ionic conductivity, the percentage of current collector surface covered by either active material or carbon-binder domain particles and the active material surface in contact with electrolyte were analysed and discussed in detail. This approach was tested and validated for the case of LiNi1/3Mn1/3Co1/3O2-based cathodes calendering, proving its capability to ease the process parameters-electrode properties interdependencies analysis, paving the way to deeper understanding and then faster optimization of LIBs manufacturing.

Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

“…In microstructural design, few works have focussed on the optimisation of microstructures of porous energy materials in combination with their manufacture. [247][248][249][250] A digital twin of the manufacturing process is necessary at this stage to virtually predict the influence of the manufacturing parameters on the final microstructures of porous energy materials. Notably, stochastic grain stacking methods and mesoscopic physical models contribute to the development of a digital twin since they can output virtually realistic microstructures of porous energy materials.…”