TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation

📄 arXiv: 2607.14640v1 📥 PDF

作者: Wen Yang Tan, Jiawei Li, Fang Liu, Wei Zhang, Sumei Sun, Peng Cheng Wang, Elisa Y. M. Ang

分类: cs.LG

发布日期: 2026-07-16

备注: 8 pages, 11 figures, WI-IAT 2026

💡 一句话要点

提出TIDE以解决电池健康估计的信任性与可解释性问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 电池健康估计 可解释性 信任性 上下文学习 符号蒸馏 机器学习 电池管理

📋 核心要点

  1. 现有电池健康估计方法在准确性、信任性和可解释性方面存在不足，影响实际应用效果。
  2. TIDE通过结合电池领域知识与操作测量，采用三组件架构，提升电池健康估计的可靠性与可解释性。
  3. 实验结果显示，TIDE在估计准确性上较基线提升19.7%，并有效减少了老化一致性违规现象。

📝 摘要（中文）

电池健康估计是电池管理的基础，不准确的健康状态可能影响控制、维护和服务寿命。在智能连接系统中，估计误差可能在互联设备和下游决策中传播。本文提出TIDE，一个可信且可解释的电池退化估计器，旨在实现可靠的电池健康估计。TIDE结合了电池领域知识与操作测量，通过知识引导的退化先验、单调残差组件和上下文学习组件，提升了准确性、信任性和可解释性。实验表明，TIDE在估计准确性上显著提升，平均提高19.7%。

🔬 方法详解

问题定义：本文旨在解决电池健康估计中的信任性和可解释性问题。现有方法往往缺乏准确性和透明度，导致在智能连接系统中的应用受到限制。

核心思路：TIDE通过将电池领域知识与操作测量结合，采用知识引导的退化先验、单调残差和上下文学习组件，来实现更高的准确性和可解释性。

技术框架：TIDE的整体架构包括三个主要模块：知识引导的退化先验用于提供可信估计，单调残差组件用于实现可解释的老化一致性细化，上下文学习组件则捕捉电池特定的操作效应以提高准确性。

关键创新：TIDE的主要创新在于其知识引导的退化先验和单调残差建模，显著减少了老化一致性违规现象，支持可信的估计。

关键设计：在设计中，TIDE使用了特定的损失函数来优化各个组件的性能，并通过符号蒸馏技术将训练后的模型压缩为紧凑的符号代理，以提供简洁的模型级解释。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，TIDE在电池健康估计的准确性上较代表性基线平均提升19.7%。其知识引导的先验和单调残差建模有效减少了老化一致性违规现象，支持了可信的估计，并且模型的组件级解释能力增强了其实际应用价值。

🎯 应用场景

TIDE的研究成果在电池健康监测和智能连接系统的决策支持中具有广泛的应用潜力。通过提供可信且可解释的电池健康估计，TIDE能够帮助提升电池管理系统的效率和可靠性，进而延长电池的使用寿命并降低维护成本。

📄 摘要（原文）

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 that provides a concise model-level interpretation of its learned estimation logic. 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.