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

2026-07-16

AI Summary

Accurate estimation of battery state of health (SOH) is critical for effective control, maintenance, and longevity in battery management systems, particularly within intelligent interconnected systems where estimation errors can propagate widely. To address this challenge, this work proposes TIDE—a novel framework that uniquely integrates knowledge-guided degradation priors, monotonic residual modeling, and context-aware learning, while introducing symbolic distillation to achieve model-level interpretability. The synergistic three-component architecture jointly optimizes estimation accuracy, reliability, and interpretability, preserving component-wise transparency without compromising performance. Experimental results demonstrate that TIDE improves average SOH estimation accuracy by 19.7% over baseline methods, substantially reduces violations of aging consistency constraints, and thereby significantly enhances the practicality and robustness of SOH estimation in real-world applications.

Abstract

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.

Problem

• battery health estimation
• trustworthiness
• interpretability
• battery degradation
• intelligent connected systems

Innovation

• symbolic distillation
• knowledge-guided prior
• monotone residual modeling
• contextual learning
• interpretable battery degradation estimation