The poster addresses the challenge of rapidly developing automotive traction batteries under tightening performance, cost, and sustainability constraints, while data and time are scarce in early development phases. It introduces an object-oriented framework that enables bottom-up design and evaluation of battery systems from cell to module to pack level, specifically tailored to early-stage concept work. The method automatically configures electrical topology, packaging, and key subsystems such as housing, cooling, and E/E components, and computes consistent key performance indicators (KPIs) including energy and power capability, dimensions, mass, and cell-to-pack volume efficiency factor.
To cope with varying data maturity, the framework can operate with minimal cell information, enriching it via physics-based correlations and a proprietary database of more than 1500 commercial cells, or it can directly use complete supplier data sheets. A scaling routine preserves electrochemical consistency while enabling virtual cell formats, allowing concept studies with chemistries and geometries that are not yet commercially available. All computations are algebraic, so large design spaces can be explored within minutes on standard hardware; each variant is also exported as a simplified color-coded 3D mesh model to support subsequent CAD-based refinement.
The framework’s capabilities are demonstrated in a layout study comparing NMC and LFP prismatic cells across five integration levels, from conservative small-module designs to highly integrated cell-to-pack and quasi-blade architectures within a fixed pack envelope representative of mid- to upper-class BEVs. Starting from a VDA PHEV2-based reference pack, module count is reduced, module housings are successively removed, and finally large-format cell-to-pack and quasi-blade concepts are realized, while meeting consistent safety and reliability requirements. The study assumes higher thermal robustness for LFP cells, enabling tighter packing and reduced thermal-propagation mitigation measures, and shows that pack-level energy-density differences between NMC and LFP shrink significantly compared with cell-level values when high integration densities are exploited.
The results quantify realistic cell-to-pack volume ratios up to nearly 60% and illustrate the trade-off between highly integrated, large custom cells—which offer superior energy density but higher supply and safety dependencies—and smaller, standardized cells, which provide modularity, reuse potential, and higher fault tolerance. Overall, the proposed framework delivers benchmark-consistent pack designs and serves as a decision-support tool that bridges early concept exploration with subsequent detailed CAD, cost, LCA, and safety analyses, rather than replacing conventional development workflows.