A scalable neural bundle map for multiphysics prediction in lithium-ion battery across varying configurations

• URL: http://arxiv.org/abs/2603.17209v1
• Date: Tue, 17 Mar 2026 23:15:34 GMT
• Title: A scalable neural bundle map for multiphysics prediction in lithium-ion battery across varying configurations
• Abstract summary: We present a mathematically rigorous framework that reformulates multiphysics evolution as a bundle map over a geometric base manifold.<n>Our framework achieves high-fidelity-temporal predictions with a normalized mean absolute error of less than 1% across varying configurations.<n>It provides a foundational paradigm for the intelligent design and real-time monitoring of complex energy storage infrastructures.
• Score: 20.5206187385656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient and accurate prediction of Multiphysics evolution across diverse cell geometries is fundamental to the design, management and safety of lithium-ion batteries. However, existing computational frameworks struggle to capture the coupled electrochemical, thermal, and mechanical dynamics across diverse cell geometries and varying operating conditions. Here, we present a Neural Bundle Map (NBM), a mathematically rigorous framework that reformulates multiphysics evolution as a bundle map over a geometric base manifold. This approach enables the complete decoupling of geometric complexity from underlying physical laws, ensuring strong operator continuity across varying domains. Our framework achieves high-fidelity spatiotemporal predictions with a normalized mean absolute error of less than 1% across varying configurations, while maintaining stability during long-horizon forecasting far beyond the training window and reducing computational costs by two orders of magnitude compared with conventional solvers. Leveraging this capability, we rapidly explored a vast configurational space to identify an optimal battery design that yields a 38% increase in energy density while adhering to thermal safety constraints. Furthermore, the NBM demonstrates remarkable scalability to multi-cell systems through few-shot transfer learning, providing a foundational paradigm for the intelligent design and real-time monitoring of complex energy storage infrastructures.

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