ArticleNature communications2026
Multi-model collaborative early warning for battery systems with integrated fiber optic sensors.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Multi-model collaborative early warning for battery systems with integrated fiber optic sensors.Nature communications · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The safety assessment of battery systems is constrained by sparse temperature measurements and monolithic diagnostic frameworks in conventional battery management systems (BMS). To address this, we propose a multi-model collaborative thermal-runaway early-warning framework using distributed temperature sensing. An integrated sensing scheme increases temperature sensing points within the battery pack from 32 to 540 with minimal wiring complexity and is validated in real-world vehicle operation. We establish a functional relationship between temperature Shannon entropy (SE) and average temperature variation to derive a dynamic warning boundary incorporating error tolerance. Integrating this boundary with thresholds for temperature, temperature change rate, and SE change rate enables collaborative early warning. Compared with conventional BMS warning benchmarks, the framework provides maximum warning lead times of 706 s in laboratory modules and 554 s in real-world vehicle operation. These findings demonstrate the potential of distributed sensing and collaborative diagnostics for proactive thermal-runaway risk identification.
Identifiers
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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.