Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect
Problem
Traditional trial-and-error methods are inadequate for predicting battery lifetime and guiding design of next-generation battery systems.
Why it matters
Better battery prognosis affects safety, cost and reliability of energy storage for vehicles and grids.
What the AI did
The paper reviews interpretable machine learning frameworks that balance predictive fidelity with physicochemical relevance for battery prognosis; it is a review, not a new system.
Results
not stated in source
Impact
The evidence only supports that interpretable ML is an emerging research direction for battery prognosis.
Record
| Venue | Advanced Energy Materials |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | T. Wang, Kun‐Yu Liu, Hong‐Jie Peng, Xinyan Liu |
| Citations | 20 (OpenAlex, at import time) |
| References | 151 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/aenm.202503067 |
| Open access | hybrid |
| Keywords | interpretable machine learning, battery prognosis, lifetime prediction, physics-informed ML, review |
Importance rating
significant — thousands to millions affected
Battery prognosis affects safety, cost and reliability of energy storage for vehicles and grids; abstract claims relevance to next-generation battery systems and sustainable energy but quantifies no scale.
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