The crash safety of lithium-ion traction batteries is a relevant concern for electric vehicles. Current passive safety strategies of traction batteries usually come at the cost of their volumetric or gravimetric energy density. This work analyses the influence of the variables cell selection and orientation within the traction battery on the crash safety of an electric-powered two-wheeler. These two variables do not negatively influence the traction battery’s volumetric or gravimetric energy density in the design process. Metamodels and numerical simulations are used to evaluate the crash safety of an electric-powered two-wheeler’s traction battery in a potentially dangerous crash scenario. The influence of the variable’s cell selection and orientation is evaluated through the internal short circuit risk of the integrated cells. The comparison of the metamodels shows that the cell orientation reduces the internal short circuit risk by up to 51% on average in the analysed crash scenario. The cell selection reduces it only up to 21% on average. The results show that crash safety can be increased in the design process, and a combination with the current protection strategies can increase crash safety further.

Lithium-ion batteries (LIBs) in electric vehicle battery packs are assembled under mechanical constraints that exert compression force during operation. During operation, battery cells undergo volumetric changes that cause thickness variations, known as battery swelling. The resulting swelling behavior under mechanical constraints generates force fluctuation. The prediction for force magnitude is critical, as insufficient force compromises electrical contact and structural stability, while excessive force accelerates structural degradation and increases safety risks. However, force sensors have not been widely implemented due to high costs, integration complexity, and reliability concerns, which limits the practical application of force prediction. In this study, we present a coupled electrochemical–mechanical model for predicting compression forces aligning with battery swelling behavior without requiring real-time force sensors during operation. The model predicts the evolution of compression force at an initial external pressure of 0.201 MPa, achieving a mean absolute percentage error (MAPE) of 4.48% during 1C discharge and 7.02% under multi-phase driving cycle conditions, compared with experimental results. The developed framework addresses the existing gap in predicting compression force for mechanically constrained LIBs. The proposed modeling approach enables real-time force monitoring in battery modules, thereby enhancing safety assessment capabilities through force predictions and optimizations.