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Outlines of the multiphysics behaviors upon penetration and the comparisons of experiment and simulation.

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Dendrite growth and crack propagation are two major hurdles on the road towards the large‐scale commercialization of lithium metal all‐solid‐state batteries (ASSBs). Due to the high multiphysics coupled nature of the underlying dendrite growth mechanism, understanding it has been difficult. Herein, for the first time, an electrochemical‐mechanical model is established that directly couples dendrite growth and crack propagation from a physics‐based perspective at the cell level. Results reveal that overpotential‐driven stress propels a crack to penetrate through the solid electrolyte, creating vacancies for dendrite growth, leading to the short circuit of the battery. Thus, high lithiation/charging rate and low conductivity of electrolytes can accelerate the electrochemical failure of the battery. It is further discovered that Young's modulus ELLZO of the electrolyte has competing contributions to the fracture and dendrite growth; specifically, when ELLZO = 40–100 GPa, the short circuit is triggered early. A larger toughness value hinders the crack propagation and mitigates the Li dendrite growth. The developed multiphysics model provides an in‐depth understanding of the coupling of crack propagation and dendrite growth within ASSBs and an insightful mechanistic design guidance map for robust and safe ASSB cells.

Despite the huge expansion of electric vehicle sales in the market, customers are discouraged by the possible catastrophic consequences brought by the safety issues of lithium-ion batteries, such as internal...

With the increasing number of electric vehicles, inevitable crash accidents, vibration and foreign objective penetration potentially generate catastrophic consequences such as fire or explosion. Unlike traditional engineering materials or structures, LIBs exhibit multiphysical behaviors including mechanical deformation/failure, thermal conduction, series of electrochemical and chemical reactions, upon mechanical abusive loading. Therefore, developing computational frameworks capable of describing multiphysical behaviors of cylindrical batteries in crash safety design of electric vehicles based on commercially available platform is in pressing need. In this paper, based on the widely used LS-DYNA software platform, a multiphysics model with the comprehensive coupling of mechanical, battery, short-circuit, exothermic and thermal models are established. Models are validated by the in-house designed experiments. Further, parametric studies based on the established model demonstrates that a larger indentor leads to a later onset of internal short circuit (ISC) for LIBs but result in a higher peak battery temperature. On the other hand, an ISC will be triggered early if the compressive loading is applied near the ends of battery cells. This study provides an accessible, fast and accurate computational framework for safety design, assessment and improvement of lithium-ion batteries and electric vehicles in harsh mechanical scenarios.

Inevitable safety issues have pushed battery engineers to become more conservative in battery system design; however, battery‐involved accidents still frequently are reported in headlines. Identifying, understanding, and predicting safety risks have become priorities to further accelerate technology and industry development. However, diverse loading scenarios, significantly varied stress‐induced short circuit mechanisms, and highly coupled mechanical–electrochemical safety behaviors have remained grand challenges. Herein, the safety risk is termed as the probability of the mechanical triggering of an internal short circuit, to reflect the safety related behaviors of lithium‐ion batteries. Based on a mechanical model and experimental results, a sufficient dataset is generated consisting of strain states and their corresponding safety risks, covering both cylindrical and pouch cells, various states of charges, and loading conditions. Machine‐learning tools combined with the established finite element mechanical model are applied to predict the safety risks of the cells. The results achieve a high level of accuracy on the test data (the relative error of the average short circuit prediction deviation is less than 6.2%.). This work underpins the safety risk concept and highlights the promise of physics combined with data‐driven modeling methodology to predict the safety behaviors of energy storage systems.