Abstract

Lithium plating is detrimental to battery operation from efficiency, ageing and safety perspectives. Lithium deposition at the anode Solid Electrolyte Interface (SEI) is known to occur when the local potential in the anode approaches 0V, and is commonly encountered during Direct Current Fast Charge (DCFC) scenarios at large Charge Rates. Recent studies have suggested a correlation between tortuosity of the porous electrode and plating risk. Since electrolyte tortuosity is determined by Active Material (AM) particle geometry and packing, optimal AM particle statistics are important for cell design from an Electrochemical performance perspective. Further, for a class of electrodes such as the graphite-based anode investigated here, Li intercalation swelling induces stresses due to mechanical constraints imposed by the cell-casing. Consequentially, performance degradation occurs over several charge/discharge cycles due to mechanical fatigue. Crucial to this context, particle shape and stress distribution are linked via current density distribution. Specifically, localization of current density due to local particle geometry and connectivity can result in stress-strain localization due to local Li intercalation behavior. Therefore, an investigation that involves both the electrochemical and mechanical aspects can better guide the microstructural cell design process. Towards this end, we investigate microstructural unit-cell geometry using STAR-CCM+. Key features of our solution approach include: 1. Microstructural Electrochemistry (MSE) simulations providing transient electro-chemical solution including Lithium/Lithium salt concentration throughout the domain. Plating risk can be identified with electric potential distribution. 2. Parameterized anode and cathode particle geometries to study different particle shape, orientation and connectivity statistics. 3. Using Finite Element Analysis (FEA) in STAR-CCM+ to predict mechanical deformation and stress solution based on local particle lithiation from the MSE solution. Simulations are carried out with a few parameterized geometries. The resulting FEA and MSE distributions then help to assess the tradeoffs involved in particle-shape optimization potentially leading towards an effective Multiphysics-based approach to plating mitigation.