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The performance of Lithium-Ion batteries (LIB’s) strongly depends on 3D microstructure and continued research is needed for the development and optimization of electrode designs to further reduce cost and improve performance and durability. In this work, a pore network modelling approach is presented to understand the structure-performance relationship of porous cathodes of LIB’s. It was demonstrated that pore network models can efficiently predict the rate-dependent capacity of an electrode using only a 3-phase tomogram as input. The developed modelling framework was used to perform structural analysis on two Li(Ni0.5Mn0.3Co0.2)O2 (NMC532) cathodes of different thickness and calendaring pressure and revealed important insights of microstructural heterogeneities inside porous structures, including spatial distribution of concentration, potential and state of lithiation in electrolyte, active material and carbon binder domain. The computational performance of the pore network model was analyzed, and excellent performance was demonstrated, taking hours instead of weeks for a similar direct numerical simulation. The novel modelling framework reported in this study will enable the study of local heterogeneities in other types of cathode material to help screen next-generation electrode designs, augmenting and informing time-consuming cell fabrication and laboratory testing.

Image processing of 3D tomographic images to extract structural information of porous materials has become extremely important in porous media research with the commoditization of x-ray tomography equipment to the lab scale. Extracted pore networks from image analysis techniques enable transport properties calculation for bigger domains at a very low computational cost, allowing substantial investigation of porous media. Consequently, they are being increasingly used to model Multiphysics transport processes in various energy storage devices like fuel cells and batteries, where the geometrical structure plays a vital role in electrochemical performance, but true pore-scale modeling is computationally infeasible. Traditionally, pore network modelling has been used to find electrochemical performance of Li Ion batteries by investigating pore phase interconnectivity in carbon electrodes. The actual lithium ion electrode, however, consists of active material, carbon binder and electrolyte filled pore phase. The interconnectivity of these phases influences the effective transport properties and hence electrochemical performance of cathode material. The presence of carbon binder phase not only reduces the SEI layer between active material and electrolyte phase but also influences tortuosity and effective electronic conductivity in the pore and solid phase respectively. The present work uses a pore network modelling frame work to investigate the effect of carbon binder phase on Lithium Ion battery cathode. We used actual three phase, X-ray tomography image of NMC-811 cathode material and studied its electrochemical performance with and without binder phase. Unlike previous models which compensate the importance of carbon binder as electrical conductor by assuming high electronic conductivity of active material, this study considers actual values of conductivities in all phases. Moreover, the impact of nanoporosity in the carbon binder phase was also explored and found to enhance the reaction rate compared to solid binder. The reduction in computational time achieved using the pore-network approach was so significant that adding additional physics and transient conditions could conceivably be included to increase the accuracy of the model, thereby providing more realistic simulations and point to the true limiting processes. The developed pore network model opens a new avenue for modelling complex electrochemical systems with less computational cost, enabling simulation of bigger electrode domains while keeping structural heterogeneities of material.

Pore-scale simulations of Li-ion battery electrodes were conducted using both pore-network modeling and direct numerical simulation. Ternary tomographic images of NMC811 cathodes were obtained and used to create the pore-scale computational domains. A novel network extraction method was developed to manage the extraction of N-phase networks which was used to extract all three phases of NMC-811 electrode along with their interconnections Pore network results compared favorably with direct numerical simulations (DNS) in terms of effective transport properties of each phase but were obtained in significantly less time. Simulations were then conducted with combined diffusion-reaction to simulate the limiting current behavior. It was found that when considering only ion and electron transport, the electrode structure could support current densities about 300 times higher than experimentally observed values. Additional case studies were conducted to illustrate the necessity of ternary images which allow separate consideration of carbon binder domain and active material. The results showed a 24.4% decrease in current density when the carbon binder was treated as a separate phase compared to lumping the CBD and active material into a single phase. The impact of nanoporosity in the carbon binder phase was also explored and found to enhance the reaction rate by 16.8% compared to solid binder. In addition, the developed technique used 58 times larger domain volume than DNS which opens up the possibility of modelling much larger tomographic data sets, enabling representative areas of typically inhomogeneous battery electrodes to be modelled accurately, and proposes a solution to the conflicting needs of high-resolution imaging and large volumes for image-based modelling. For the first time, three-phase pore network modelling of battery electrodes has been demonstrated and evaluated, opening the path towards a new modelling framework for lithium ion batteries. The described here was financially supported by the University of Engineering and Technology Lahore, Pakistan as well as the Natural Science and Engineering Research Council (NSERC) of Canada and in the UK by the Faraday Institution (EP/R042012/1 and EP/R042063/1). Pablo A. García-Salaberri thanks the support from the STFC Early Career Award (ST/R006873/1) during his stay at the Electrochemical Innovation Lab