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Yes—but entropy measurements are research tools for estimating battery health, not a standardized test that gives every lithium-ion battery a universal healthy-or-failed verdict. Researchers have studied thermodynamic entropy profiles, temperature patterns linked to entropy, and algorithms that use “entropy weighting” to combine other indicators. Those are distinct approaches, tested on particular cells and protocols.
What “entropy measurement” means in battery-health research
In thermodynamic studies, a battery’s entropy profile describes how entropy varies with its state of charge (SoC). Researchers investigate whether changes in that profile as a cell ages can help estimate its state of health (SoH) or SoC. Measuring a full profile with traditional potentiometric or calorimetric methods can take time and requires controlled conditions. Hua, Vie, and Wind’s 2025 study compares faster approaches to estimating it.
Other papers use related but different ideas. One infers a health indicator from the shape of a cell’s temperature response during charging; another uses an information-theoretic entropy-weighting step to combine capacity estimates. Neither should be mistaken for a direct measurement of the cell’s thermodynamic entropy.
How researchers estimate an entropy profile
Thermal signatures during low-rate cycling
Instead of relying only on voltage relaxation, Hua, Vie, and Wind estimate entropy variation from changes in a cell’s outer-surface temperature during low-rate charging or discharging. Their approach uses low current rates such as C/20, C/10, or C/5 so reversible heat has a larger relative contribution, and thermal insulation to reduce heat loss. The authors checked the resulting profile using coupled electrochemical and three-dimensional thermal modeling; their reported comparison showed good agreement in the time variation of surface temperature. This is a controlled measurement approach, not a demonstration that surface temperature alone can diagnose an arbitrary battery in everyday use. The study’s comparison of thermal signatures and accelerated potentiometry describes the method and its conditions.
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Faster potentiometric estimation
A separate 2024 study tested a faster potentiometric approach on an NCR 18650 Panasonic cylindrical cell. The authors pulse-discharged the cell before charging to partly reduce voltage-relaxation effects, then examined entropy changes at different cycle numbers. They reported that their method took approximately 4.5 times less time than their conventional comparison method. That figure belongs to their cell and protocol; it is not a general speed advantage for every battery or test setup. The study reports its protocol and comparison.
What temperature curves can indicate about health
Wu and Jossen proposed a temperature-derived feature for constant-current charging. They identified two cooling regions in a temperature-variation-rate curve and defined the distance between them as tmin. In their tested commercial pouch-cell data, the feature was linearly correlated with SoH, provided electrode active-material loss was negligible. Across 544 aging data units, they reported a product-moment correlation coefficient of 0.954. This is a correlation for that dataset and method—not a universal accuracy score, threshold, or proof that the temperature feature causes degradation. Wu and Jossen’s 2018 paper sets out the qualification and findings.
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The result makes temperature response a potentially useful research signal, but interpreting it depends on the test conditions and degradation mode. A temperature curve should not be treated as a stand-alone health verdict across different chemistries, cell formats, or aging histories.
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How entropy weighting differs from measuring entropy
He and colleagues’ 2023 framework combines capacity estimates derived from other health indicators and models. It extracts incremental-capacity-curve features, uses compressed sensing to address information loss and noise at low sampling frequencies, and combines estimates from Gaussian-process regression and a discrete aging model using an entropy-weight method.
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In this context, “entropy weight” names an algorithmic way of assigning weights; it is not a measurement of a battery’s thermodynamic entropy profile. The authors reported average errors of 2.5% on open NASA battery data and 2.6% in laboratory tests on two standard 18650 cells. Those results describe their fused capacity-estimation framework and its datasets, not a common benchmark for entropy-profile or temperature-curve methods. The 2023 framework paper describes its components and reported errors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence supports—and what it does not
- Supported: Entropy profiles can vary with SoC, and published studies report changes associated with aging. Researchers have investigated these signals for health or charge-state estimation.
- Supported within specific studies: Low-rate thermal signatures, faster potentiometry on a particular 18650 cell, and a temperature-curve feature on tested pouch cells have each shown promising results under their reported protocols.
- Not established: The studies do not supply one standardized, cross-chemistry entropy threshold or protocol that classifies any lithium-ion battery as healthy or failed.
- Not established: The cited findings do not show that entropy methods replace conventional capacity, resistance, or battery-management-system measurements, or that an ordinary consumer temperature logger can diagnose SoH by itself.
A separate 2024 thermodynamic paper describes a relationship between degradation rate and entropy-generation rate as a way to characterize degradation. Its abstract supports that research direction, but does not establish a broadly validated diagnostic procedure. The paper’s abstract presents the proposed relationship.
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