Yes—but “entropy measurement” can mean several different research methods, and none is a standardized universal battery-health test. Researchers have studied how a cell’s thermodynamic entropy profile changes with state of charge and aging, how temperature patterns can reveal entropy-related behavior, and how an information-theoretic entropy-weighting algorithm can combine other health estimates. Results apply to the specific cells and protocols tested, not automatically to every lithium-ion battery.
What “entropy” means in battery-health research
In this context, the word refers to three distinct ideas. A thermodynamic entropy profile describes how a cell’s entropy-related behavior varies with state of charge. Entropy generation is a thermodynamic quantity explored as a way to characterize degradation. Entropy weighting, by contrast, is an algorithmic technique for assigning weights to estimates from other indicators; it does not directly measure a cell’s thermodynamic entropy.
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Keeping these meanings separate matters: evidence for one approach does not validate the others.
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How researchers estimate a thermodynamic entropy profile
A cell’s entropy profile varies with state of charge and may change as the cell ages. Researchers have investigated those changes for state-of-health (SoH) or state-of-charge estimation. Traditional potentiometric or calorimetric characterization across a full state-of-charge range can take time and requires controlled conditions.
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Thermal signatures during low-rate cycling
A 2025 study by Jinsong Hua, Preben J.S. Vie, and Julia Wind compared accelerated potentiometry with a method that estimates the continuous entropy variation from outer-surface temperature changes during low-rate charging or discharging. The authors suggest low current rates such as C/20, C/10, or C/5 so reversible heat contributes more strongly, and thermal insulation to reduce heat loss. They checked the entropy-profile result using coupled electrochemical and three-dimensional thermal modeling; the reported comparison showed good agreement in the time variation of surface temperature. Read the 2025 study.
This is a laboratory characterization approach, not a matter of placing an ordinary temperature logger on a battery and reading off its health. The cited work calls for specialized instrumentation and insulation, and does not establish consumer-device specifications.
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Faster potentiometric estimation
A 2024 study examined an NCR 18650 Panasonic cylindrical cell at different cycle numbers. Its method charges after pulse discharging to partly reduce voltage-relaxation effects. The authors reported an estimation time approximately 4.5 times shorter than their traditional comparison method. That figure belongs to this cell and protocol; it is not a general speed advantage for every battery or test setup. Read the 2024 study.
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A 2018 study by Yao Wu and Andreas Jossen proposed a thermal feature based on the distance between two observable cooling regions in a temperature-variation-rate curve during constant-current charging. They called the feature tmin and reported a linear relationship with SoH in their tested commercial pouch-cell data. They specified that the proposed gauge depends on electrode active-material loss being negligible.
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Across 544 aging data units, the authors reported a product-moment correlation coefficient of 0.954 between their feature and SoH. This describes correlation in the study’s data and protocol—not universal accuracy, proof of causation, or a validated threshold that can be applied to any cell chemistry. Read the 2018 study.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How entropy weighting can support a different kind of estimate
A 2023 framework by Ning He and colleagues uses incremental-capacity-curve features as health indicators, compressed sensing to address information loss and noise at low sampling frequencies, and estimates from Gaussian-process regression and a discrete aging model. An entropy-weight method combines those capacity estimates.
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The paper reports average errors of 2.5% on open NASA battery data and 2.6% in laboratory tests on two standard 18650 cells. These results describe the framework’s capacity-estimation performance on those datasets; they are not results from directly measuring thermodynamic entropy, nor a common benchmark for comparison with the thermal or potentiometric studies. Read the 2023 study.
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- It establishes research potential: entropy profiles and entropy-linked thermal behavior can carry information associated with state of charge or aging under studied conditions.
- It does not establish one standard test: the reviewed studies use different methods, cells, protocols, and validation designs. They do not establish a universal entropy threshold or cross-chemistry procedure for deciding whether a battery is healthy or failed.
- It does not show replacement of routine diagnostics: the evidence here does not demonstrate that entropy methods replace capacity, resistance, or battery-management-system measurements.
- It does not make temperature alone conclusive: thermal results depend on the charging or discharging protocol, heat-loss control, cell construction, and the assumptions of the specific method.
A separate 2024 thermodynamic paper reports a relationship between degradation rate and entropy-generation rate, presenting entropy generation as a degradation-characterization approach. Its abstract-level description does not establish a practical diagnostic protocol. Read the 2024 paper.
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