ReviewMaterials (Basel, Switzerland)2025
State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning.
Review in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- An intelligent cloud-based battery management framework integrating GRU state-of-health estimation and double DQN charging optimisation.Scientific reports · 2026Article
- Deep learning-based battery health prediction for enhancing electric vehicle performance.Scientific reports · 2026Article
- Fascinating Frontier, Nanoarchitectonics, as Method for Everything in Materials Science.Materials (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current research focuses on health indicators associated with decreased capacity and increased internal resistance. However, due to the complex degradation mechanisms of lithium-ion batteries, the relationship between these mechanisms and health indicators has not been fully explored. This paper reviews a large number of literature sources. We discuss the application scenarios of different health factors, providing a reference for selecting appropriate health factors for state estimation. Additionally, the paper offers a brief overview of the models and machine learning algorithms used for health state estimation. We also delve into the application of health indicators in the health status assessment of battery management systems and emphasize the importance of integrating health factors with big data platforms for battery status analysis. Furthermore, the paper outlines the prospects for future development in this field.
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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.