新能源汽车与储能产业的快速发展对动力电池健康管理提出了更高要求,健康状态的精准预测成为保障系统安全性和经济性的核心挑战。当前预测方法面临动态工况适应性不足、微观衰退机理表征困难以及工程落地成本高昂的三重瓶颈。因此,本文针对多物理场特征协同提取机制与轻量化深度学习框架展开深入研究,旨在建立兼具实时响应能力与工业级精度的新型预测范式,为电池全生命周期管理提供理论突破方向。

The rapid development of new energy vehicles and energy storage industries has imposed higher demands on power battery health management. Accurate prediction of State of Health has become a core challenge in ensuring system safety and economic viability. Current prediction methods face three major bottlenecks: insufficient adaptability to dynamic operating conditions, difficulties in characterizing micro-level degradation mechanisms, and high implementation costs. Therefore, this paper conducts in-depth research on a multi-physics field feature collaborative extraction mechanism and a lightweight deep learning framework. The goal is to establish a novel prediction paradigm that combines real-time responsiveness with industrial-grade accuracy, providing a theoretical breakthrough direction for battery lifecycle management.