发表机构
A*STAR（新加坡科技研究局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对电池健康估计问题，提出TIDE方法，结合电池领域知识与运行测量，通过知识引导退化先验、单调残差组件和上下文学习组件，提升估计准确性、可靠性与可解释性，经实验验证其有效性，支持智能互联系统的电池健康监测与决策。
AI 中文摘要
电池健康估计对于电池供电系统的管理至关重要，不准确的健康状态会影响控制、维护和使用寿命，在智能互联系统中更是如此，估计误差会传播。本文提出TIDE，一个用于可靠电池健康估计的可靠且可解释的电池退化估计器。TIDE联合考虑准确性、可靠性和可解释性，通过三组件架构结合电池领域知识与运行测量。一个知识引导的退化先验促进可靠估计，一个单调残差组件提供可解释的与老化一致的细化，一个上下文学习组件捕捉特定电池的运行效果以提高准确性。训练后的架构被蒸馏成一个紧凑的符号代理，提供其学习到的估计逻辑的简洁模型级解释。实验表明，TIDE实现了强大的估计准确性，平均比代表性基线提高了19.7%的整体估计保真度，其知识引导的先验和单调残差建模大大减少了与老化一致性的违反，支持可靠估计。同时，架构实现了组件级解释，而符号蒸馏提供了学习到的估计逻辑的紧凑模型级表示。这些结果支持TIDE在智能互联系统中用于电池健康监测和决策支持的实际应用。
英文摘要
Battery health estimation is fundamental for battery management in battery-powered systems, where inaccurate health states may affect control, maintenance, and service life. It becomes even more critical in intelligent connected systems, where estimation errors can propagate across interconnected devices and downstream decisions. In this paper, we propose TIDE, a trustworthy and interpretable battery degradation estimator for reliable battery health estimation. TIDE jointly considers accuracy, trustworthiness, and interpretability, which are all essential for practical deployment and downstream decision making. To realize these objectives, TIDE combines battery-domain knowledge with operational measurements in a three-component backbone. A knowledge-guided degradation prior promotes trustworthy estimation, a monotone residual component provides interpretable aging-consistent refinement, and a contextual learning component captures battery-specific operational effects for improved accuracy. The trained backbone is then distilled into a compact symbolic surrogate to provide model-level interpretability and support deployment. Experiments show that TIDE achieves strong estimation accuracy, improving overall estimation fidelity by an average of 19.7% over representative baselines. Its knowledge-guided prior and monotone residual modelling substantially reduce aging-consistency violations, supporting trustworthy estimation. Meanwhile, the backbone enables component-level interpretation, while symbolic distillation provides a compact model-level representation of the learned estimation logic. These results support the practical use of TIDE for battery health monitoring and decision support in intelligent connected systems.
Comments8 pages, 11 figures, WI-IAT 2026
