Mesoscale Effective Property Simulations Incorporating Conductive Binder

Abstract

Lithium-ion battery electrodes are composed of active material particles, binder, and conductive additives that form an electrolytefilled porous particle composite. The mesoscale (particle-scale) interplay of electrochemistry, mechanical deformation, and transport through this tortuous multi-component network dictates the performance of a battery at the cell-level. Effective electrode properties connect mesoscale phenomena with computationally feasible battery-scale simulations. We utilize published tomography data to reconstruct a large subsection (1000+ particles) of an NMC333 cathode into a computational mesh and extract electrode-scale effective properties from finite element continuum-scale simulations. We present a novel method to preferentially place a composite binder phase throughout the mesostructure, a necessary approach due difficulty distinguishing between non-active phases in tomographic data. We compare stress generation and effective thermal, electrical, and ionic conductivities across several binder placement approaches. Isotropic lithiation-dependent mechanical swelling of the NMC particles and the consideration of strain-dependent composite binder conductivity significantly impact the resulting effective property trends and stresses generated. Our results suggest that composite binder location significantly affects mesoscale behavior, indicating that a binder coating on active particles is not sufficient and that more accurate approaches should be used when calculating effective properties that will inform battery-scale models in this inherently multi-scale battery simulation challenge.

Controlling Binder Adhesion to Impact Electrode Mesostructures and Transport

“…The effective electronic conductivity is calculated by solving the electronic current conservation equation, where the CBD phase conductivity is set to σ cbd = 15.9 S m − 1, AM phase conductivity is set to 0.18 S m − 1, and the electrolyte is non-conducting. 50,60 As the volume fraction of the CBD phase is in-creased, the electronic conductivity expectedly increases, as shown by the variation of ratio σ eff /σ cbd , with the CBD volume fraction cbd in Figures 5(c) and (d). When the AM-CBD adhesion is low, the effective electronic conductivity decreases moderately with increasing CBD cohesion, as shown in Figure 5(c).…”

Mechanical, Electrical, and Ionic Behavior of Lithium‐Ion Battery Electrodes via Discrete Element Method Simulations

“…This additional part was simulated by taking into account the volumetric change of NMC particles and the evolution of their mechanical properties induced by the lithium concentration gradient. Although the volume increase of NMC particles was reported to be only around 1.54% when lithiated from x = 0.5 to x = 1, x being the stoichiometric value of lithium in NMC, it can trigger different phenomena . Recent investigations have shown that the stress induced upon lithiation may lead to particle fracture, structural disintegration, and even mechanical failure, which significantly decrease the electronic and ionic conduction, reduces cycle efficiency, and ultimately result in battery failure .…”

“…Within this framework, 2D particle‐resolved models have been developed, and furthermore, 3D battery models have been used to evaluate the empirical Bruggeman exponent and investigate the effect of the electrode microstructure on the battery performance . Trembacki et al used tomography data of an NMC‐based cathode to assemble a mesh and extract electrode‐scale effective properties via finite element continuum‐scale simulations considering the lithiation‐dependent mechanical swelling of the particles. Although the aforementioned studies have provided general guidelines regarding the impact of the microstructure, the great majority were solved either by finite element methods or finite volume methods.…”

Mesoscale Effects of Composition and Calendering in Lithium-Ion Battery Composite Electrodes

“…While some have attempted to physically predict the CBD phase [36,37], modelers typically use stochastic or algorithmic approaches to incorporate CBD into image-based mesoscale simulations [19,20,[38][39][40][41][42]. This includes work by the current authors, who have proposed a "binder bridge" CBD placement algorithm [43,44] designed to replicate the CBD phase that may arise from solvent drying and precipitation processes shown by Jaiser et al [14]. While our previous publications [43,44] established the binder bridge CBD algorithm and used it to calculate effective transport properties, it was only demonstrated for a single mesostructure.…”