Lithium plating (LP), as a specific degradation mechanism in lithium-ion batteries (LIBs), has been thoroughly investigated regarding formation conditions and potential safety hazards, but it is yet unknown how this effect influences the mechanical properties of batteries in the case of mechanical deformation. To address this issue, pouch cells used in EVs were artificially aged (AA) to a state of health of 80–82% in conditions that predominantly cause the formation of LP. These cells were subjected to a mechanical abuse load, and safety-relevant parameters, such as tolerated deformation level, failure force, and the process of thermal runaway (TR), were analyzed and compared with respective fresh (F) and aged cells of the same type. Complementary microscopy analyses were carried out to compare the found changed mechanical response with the different layer morphology caused by LP. The tests did exhibit a significantly different mechanical response of cells in the three states but also clearly altered short-circuiting behavior. The tolerated peak force at discharge state dropped by −28% and at charge state by −37% compared to fresh cells, while the deformation at failure slightly increased by +6% for the AA cells. A clear reduction in stiffness (−16%) of the LP cells was attributed to the formed layer, identified as mossy LP. The significantly stronger voltage drop at failure, seen for the LP cells, was associated with severe exothermal reactions of LP in contact with air and moisture during TR. This study revealed the strong influence of LP on the mechanical properties of LIBs. However, the transferability of the findings to other cell chemistries or formats is unclear, emphasizing the need for further investigations in this research field.

Despite the existence of computationally efficient tools, the effort for parametric investigations is currently high in industry. In this paper, within the context of Li-Ion batteries, an efficient meta-modelling approach based on the Proper Generalized Decomposition (PGD) is considered. From a suitable design of experiments, a parametric model is trained and then exploited to predict, in real time, the system response to a specific parameter combination. In particular, two different methods are considered, the sparse PGD (sPGD) and the anchored-ANOVA based one (ANOVA-PGD). As a use case for the method the dynamic indentation test of a commercial lithium-ion pouch cell with a cylindrical impactor is selected. The cell model considers a homogenised macroscopic structure suitably calibrated for explicit finite element simulations. Four parameters concerning the impactor are varied, both non-geometric (mass and initial velocity) and geometric (diameter and orientation). The study focuses on multi-dimensional outputs, such as curves and contour plots. Inspired by earlier studies, the sPGD is used to predict the force-displacement curves. As a further development, the impactor kinetic energy curve and the displacement contours are both predicted using its recently developed variant ANOVA-PGD. Moreover, a novel curve alignment technique based on the Gappy Proper Orthogonal Decomposition (Gappy-POD) is suggested here. The meta-model is compared to the results of an FE simulation and the resulting deviations are then discussed.

To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required parameters are calibrated against several characteristic cell experiments. As a result, it is hardly possible to describe the behavior of the individual battery components, which reduces the level of detail. In this work, a new data-driven material model is presented, which not only provides the homogenized behavior but also information about the components. For this purpose, a representative volume element (RVE) of the cell structure is created. To determine the constitutive material models of the individual components, different characterization tests are performed. A novel method for carrying out single-layer compression tests is presented for the characterization in the thickness direction. The parameterized RVE is subjected to a large number of load cases using first-order homogenization theory. This data basis is used to train an artificial neural network (ANN), which is then implemented in commercial FEA software LS-DYNA R9.3.1 and is thus available as a material model. This novel data-driven material model not only provides the stress–strain relationship, but also outputs information about the condition of the components, such as the thinning of the separator. The material model is validated against two characteristic cell experiments. A three-point-bending test and an indentation test of the cell is used for this purpose. Finally, the influence of the architecture of the neural network on the computational effort is discussed.

Lithium-ion cells can be considered a laminate of thin plies comprising the anode, separator, and cathode. Lithium-ion cells are vulnerable toward out-of-plane loading. When simulating such structures under out-of-plane mechanical loads, subordinate approaches such as shells or plates are sub-optimal because they are blind toward out-of-plane strains and stresses. On the other hand, the use of solid elements leads to limitations in terms of computational efficiency independent of the time integration method. In this paper, the bottlenecks of both (implicit and explicit) methods are discussed, and an alternative approach is shown. Proper generalized decomposition (PGD) is used for this purpose. This computational method makes it possible to divide the problem into the characteristic in-plane and out-of-plane behaviors. The separation of space achieved with this method is demonstrated on a static linearized problem of a lithium-ion cell structure. The results are compared with conventional solution approaches. Moreover, an in-plane/out-of-plane separated representation is also built using proper orthogonal decomposition (POD). This simply serves to compare the in-plane and out-of-plane behaviors estimated by the PGD and does not allow computational advantages relative to conventional techniques. Finally, the time savings and the resulting deviations are discussed.

Li-ion pouch cells can be understood as a thin-layer laminate. Pressure loads normal to the flat electrodes can lead to failure or thermal runaway. However, detailed models for the investigation of transverse pressure loads, in which each layer is resolved, are computationally intensive. In this paper two approaches for time-efficient detailed explicit finite element models of a cell are outlined: In both approaches, modelling of interlaminar contacts is omitted for reasons of time efficiency. A discrete approach is also used to further increase the computational efficiency. In the first variant, the behaviour in thickness direction is generated by characterising each component separately. The second approach is based on the behaviour of the entire cell. The detailed build-up of both approaches allows the application of a failure modelling of the separator layers. This will be used as a short-circuit criterion. It is a very realistic approach, since a short circuit also occurs in the real cell as soon as a separator layer fails. Both cell models are validated against cell tests. Both modelling approaches showed only minor deviations from the validation data. This relates to both the force-displacement curves and the prediction of the internal short circuit.