A Compression Force Prediction Model for Mechanically Constrained Lithium-Ion Batteries

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.