The need for early identification of degradation-induced structural changes in lithium-ion batteries
Research gap analysis derived from 3 computer_science papers in our local library.
The gap
The paper identifies the need for early identification of degradation-induced structural changes in lithium-ion batteries. Traditional methods for assessing battery health often rely on electric measurements, which may not be sufficient for accurate health assessment. There is a need for a non-destructive method for structural health classification of lithium-ion batteries.
Evidence profile
Sourced from the stated research gap and future-work section of the source papers, classified as general, all from Batteries. Those papers have been cited 1 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Deep Learning-Based Image Classification of 18650 Lithium-Ion Battery Structural Health Using X-Ray Micro-Computed Tomography (2026) · Batteries · doi
The paper identifies the need for early identification of degradation-induced structural changes in lithium-ion batteries. Traditional methods for assessing battery health often rely on electric measurements, which may not be sufficient for accurate health assessment. There is a need for a non-destructive method for structural health classification of lithium-ion batteries.
generalstated research gapKeywords: paper identifies need early identification degradation-induced structural changes - State-of-Health and Remaining-Useful-Life Estimation of Lithium-Ion Batteries Using Axial-Embedding Transformer–Bidirectional Long Short-Term Memory Optimized by an Improved Newton–Raphson-Based Optimizer (2026) · Batteries · cited 1× · doi
Existing methods for estimating the state of health and predicting the remaining useful life of lithium-ion batteries have limitations. The need for a reliable and accurate method for estimating the state of health and predicting the remaining useful life of lithium-ion batteries. The importance of considering global dependencies among multidimensional health features and local temporal dynamics in battery degradation curves.
generalstated research gapKeywords: existing methods estimating state health predicting remaining useful - A Multidimensional Classification Framework for Second-Life Lithium-Ion Batteries (2026) · Batteries · doi
validating the framework using real battery datasets - integrating quantitative diagnostic parameters - connecting the classification results with lifetime prediction, safety analysis, and life-cycle sustainability assessment - development of chemistry-specific quantitative scoring criteria - integration with lifetime prediction, battery-passport data, and life-cycle sustainability assessment
generalfuture-work sectionevidence 5/5Keywords: validating framework using real battery datasets integrating quantitative
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