Optimizing EV Battery Models for Full Vehicle Simulations

The increasing adoption of electric vehicles (EVs) demands accurate yet computationally efficient battery models that can be integrated into full vehicle simulations. At the cell level, mechanical battery models often employ fine-scale elements to capture localized deformation and failure phenomena. While such detailed discretization enables high-fidelity predictions, it also imposes significant computational costs that become prohibitive when scaling up to pack-level or full-vehicle crash and durability simulations. This research addresses the challenge by systematically simplifying cell-level mechanical models to reduce computational burden while preserving predictive accuracy. We propose an approach in which larger elements and reduced complexity representations are introduced without compromising the model’s ability to replicate experimentally observed behaviors. The methodology emphasizes model validation against targeted loading conditions, ensuring that the essential mechanics of cell deformation are retained. The developed model was evaluated and validated along three directions, with particular focus on cylindrical and hemispherical punch loadings that replicate key deformation scenarios relevant to battery safety assessment. Comparisons between high-fidelity models, simplified models, and experimental results demonstrate that the proposed simplifications significantly decrease computational effort while maintaining strong agreement with experimental data. This balance between efficiency and accuracy enables practical integration of battery mechanical models into full vehicle simulations, where computational resources must be distributed across multiple subsystems. The outcomes highlight a clear pathway for bridging detailed cell mechanics with system-level performance assessments. By advancing simplified yet validated modeling strategies, this work supports the development of more robust and efficient simulation tools, accelerating the evaluation of EV safety and performance under real-world loading scenarios.