ArticleNature communications2025
Multi-modal framework for battery state of health evaluation using open-source electric vehicle data.
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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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
8 citing papers in PubMed.
- Multi-model collaborative early warning for battery systems with integrated fiber optic sensors.Nature communications · 2026Article
- Vision-powered generative paradigm for robust battery health prediction in real-world electric vehicles.Innovation (Cambridge (Mass.)) · 2026Article
- Adaptive battery state-of-health estimation for electric vehicles under diverse driving styles using reinforcement learning.Scientific reports · 2026Article
- A physics-guided temporal convolutional learning with XGBoost integration for degradation-aware and robust electric vehicle range prediction.Scientific reports · 2026Article
- Contactless Battery Sensing: A Survey.Sensors (Basel, Switzerland) · 2026Review
- From state estimation to active intelligence: safety-aware battery management for electric vehicles.Frontiers in chemistry · 2026Review
- Self-Powered Sensing System for Electric Vehicle Drive Condition Monitoring and Driving Condition Identification in Intelligent Electric Vehicles.Research (Washington, D.C.) · 2026Article
- Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning.ACS applied materials & interfaces · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation. Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems. Furthermore, we make the field data of these electric vehicles publicly available aiming to promote further research on the development of effective and reliable battery management systems for real-world vehicles.
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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.