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

2026-03-17

AI Summary

Existing methods struggle to efficiently and accurately predict the coupled electrochemical–thermal–mechanical multiphysics evolution in lithium-ion batteries with diverse geometric configurations. This work proposes a neural fiber bundle mapping framework that models multiphysics evolution as mappings on fiber bundles over a geometric base manifold, thereby decoupling physical laws from geometric complexity. The approach achieves, for the first time, strong operator continuity and low-error transferability across arbitrary battery geometries, enabling high-fidelity, long-term stable spatiotemporal predictions. Experimental results demonstrate cross-configuration prediction accuracy with normalized mean absolute error below 1%, a two-order-of-magnitude reduction in computational cost, and successful optimization of a novel battery design that increases energy density by 38% while satisfying thermal safety constraints.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Learning with Manifolds
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• multiphysics prediction
• lithium-ion battery
• cell geometry
• scalability
• thermal safety

Innovation

• Neural Bundle Map
• multiphysics prediction
• geometric decoupling
• scalable battery modeling
• few-shot transfer learning