ArticleNature communications2023
Realistic fault detection of li-ion battery via dynamical deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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Who cites it
14 citing papers in PubMed.
- Multi-model collaborative early warning for battery systems with integrated fiber optic sensors.Nature communications · 2026Article
- Giving AI a 'case library' to diagnose battery failures.National science review · 2026Article
- Universal Battery Capacity Degradation Forecasting Driven by Foundation Models Across Diverse Chemistries and Conditions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advancing battery failure diagnosis by knowledge-augmented large language models.National science review · 2026Article
- Advancing battery research through large language models: A review.Innovation (Cambridge (Mass.)) · 2026Review
- Hydraulic support pressure prediction via deep learning with multilevel temporal feature integration.Scientific reports · 2025Article
- Privacy-preserving collaborative battery fault warning for massive electric vehicles by heterogeneous data from charging stations.Nature communications · 2025Article
- Deep learning predicts real-world electric vehicle direct current charging profiles and durations.Nature communications · 2025Article
- Voltage faults diagnosis for lithium-ion batteries in electric vehicles using optimized graphical neural network.Scientific reports · 2025Article
- A physics-informed and data-driven framework for robotic welding in manufacturing.Nature communications · 2025Article
- Data-driven energy management for electric vehicles using offline reinforcement learning.Nature communications · 2025Article
- Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions.Nature communications · 2025Article
- Multi-modal framework for battery state of health evaluation using open-source electric vehicle data.Nature communications · 2025Article
- Pathway decisions for reuse and recycling of retired lithium-ion batteries considering economic and environmental functions.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.
Identifiers
What OpenQuestion holds
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.