ArticleNature communications2025
Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Multi-model collaborative early warning for battery systems with integrated fiber optic sensors.Nature communications · 2026Article
- Electrochemical-mechanical-thermal modeling for lithium-ion batteries.Innovation (Cambridge (Mass.)) · 2026Review
- Advancing battery failure diagnosis by knowledge-augmented large language models.National science review · 2026Article
- An Adaptive Refined Composite Multiscale Differential Symbolic Entropy Rooted in LSC-SAO and Its Application in Fault Diagnosis.Entropy (Basel, Switzerland) · 2026Article
- Natural-language-processing and safety-engineering-based fault identification technique for electrochemical ESSs.Innovation (Cambridge (Mass.)) · 2026Article
- Confining Li⁺ Solvation in Core-Shell Metal-Organic Frameworks for Stable Lithium Metal Batteries at 100 °C.Nano-micro letters · 2026Article
- High-fidelity hierarchical modeling of lithium-ion batteries: a cross-scale electrochemical-mechanical framework.Communications engineering · 2025Article
- Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions.Nature communications · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For the intricate and infrequent safety issues of batteries, online safety fault diagnosis over stochastic working conditions is indispensable. In this work, we employ deep learning methods to develop an online fault diagnosis network for lithium-ion batteries operating under unpredictable conditions. The network integrates battery model constraints and employs a framework designed to manage the evolution of stochastic systems, thereby enabling fault real-time determination. We evaluate the performance using a dataset of 18.2 million valid entries from 515 vehicles. The results demonstrate our proposed algorithm outperforms other relevant approaches, enhancing the true positive rate by over 46.5% within a false positive rate range of 0 to 0.2. Meanwhile, we identify the trigger probability for four safety fault samples, namely, electrolyte leakage, thermal runaway, internal short circuit, and excessive aging. The proposed network is adaptable to packs of varying structures, thereby reducing the cost of implementation. Our work explores the application of deep learning for real-state prediction and diagnosis of batteries, demonstrating potential improvements in battery safety and economic benefits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.