Indirect monitoring of fast-charge cycling behavior of an energy-storage device-analysis of ambient temperature variations

This paper demonstrates that reanalyzing ambient temperature data from a certified laboratory report allows for the indirect extraction of key fast-charging metrics—such as cycle count, period, and asymmetry—revealing that an unstated device successfully completed 338 rapid charge/discharge cycles at a 3C rate without detectable thermal degradation.

The Gist

Imagine you are sitting in a quiet library, trying to read a book. Suddenly, you notice the air conditioning vent above you is humming a very specific, rhythmic tune. You don't know what's causing the hum, but you realize that if you listen closely enough, you can figure out exactly what the machine doing the humming is up to, even though you can't see it.

That is essentially what this paper is about.

The Mystery in the Air

The author, Pertti Tikkanen, was looking at a standard lab report about a new type of battery (a "Donut Lab" solid-state battery). The report was mostly about how well the battery held its charge over 10 days. However, the report included a graph of the room temperature inside the test chamber (a fume hood).

In the original report, the scientists just said, "Oh, the room temperature is wobbling a bit because other batteries are being tested nearby." They treated it as background noise.

Tikkanen looked at that "noise" and thought, "Wait a minute. That's not just noise; that's a secret code."

The Detective Work: Listening to the "Hum"

Think of the room temperature sensor as a microphone and the other batteries being tested as musicians playing in the next room. Even though the walls (the air in the room) dampen the sound, the rhythm is still there.

By using some clever math (specifically, looking at the patterns in the temperature waves), Tikkanen was able to "listen" to the other batteries and figure out exactly what they were doing, without ever touching them or seeing them.

Here is what he discovered from the temperature "hum":

1. The Rhythm: The other batteries were charging and discharging in a perfect loop. One full loop took about 40 minutes.
2. The Speed: They were being charged and drained very fast (3 times faster than a normal slow charge).
3. The Secret Pause: The charging part took about 22 minutes, and the draining part took 18 minutes. That 4-minute difference was a "breather" or a pause built into the test. It's like a runner sprinting, then taking a specific 4-second pause before sprinting back.
4. The Endurance: The most impressive part? The batteries did this 338 times in a row over 10 days.

The "Silent" Proof of Health

Usually, to know if a battery is breaking down, you have to open it up or hook up special sensors to it. But this battery was being tested in a different room.

When a lab report documenting 10 days of temperature data became public, Tikkanen spotted the hidden periodic signal within just three days — drawing on decades of experience as an experimental nuclear physicist. If the battery were getting sick or breaking down, the temperature waves would have gotten messy, smaller, or changed their rhythm.

Instead, the waves stayed perfectly steady the whole time.

• Analogy: Imagine a drummer playing a beat for 10 hours straight. If the drummer is getting tired, their hands might slow down, or the beat might get sloppy. But in this case, the drummer played the exact same beat, with the exact same energy, for 338 rounds. This proved the battery was incredibly healthy and stable.

Why This Matters

This paper is a bit of a "Sherlock Holmes" story for engineers. It shows that you don't always need expensive, invasive tools to check on a machine. Sometimes, just watching the ambient temperature (the air around it) is enough to tell you:

• How many times it was used.
• How fast it was working.
• Whether it was getting tired or broken.

In short: The author took a graph that everyone else ignored, treated it like a musical score, and discovered that a hidden battery had performed a marathon of 338 high-speed sprints without ever showing a sign of fatigue. It's a clever way of "eavesdropping" on technology to learn its secrets.

Technical Summary

1. Problem Statement

Thermal characterization of energy storage devices (batteries) is critical for understanding degradation, validating thermal management, and ensuring safety. Standard methods (thermocouples, IR cameras, calorimeters) require physical access to the device and dedicated instrumentation, which can be intrusive or impossible in certain testing environments.

The specific problem addressed in this study is the lack of analysis regarding "noise" in ambient temperature data. In a previously published certified laboratory report (VTT Technical Research Centre of Finland) regarding the self-discharge of a Donut Lab Solid-State Battery, variations in the fume hood's ambient temperature were dismissed as irrelevant interference caused by "other cells being cycled simultaneously." The author hypothesized that these dismissed temperature fluctuations actually contained rich, extractable data regarding the cycling behavior of those unmonitored "other" devices.

2. Methodology

The study employs a non-invasive, passive re-analysis of existing public data without modifying the original experimental setup or the devices.

• Data Source: Temperature traces were extracted from Figures 2, 3, and 4 of a publicly available VTT laboratory report [3].
• Extraction Process:
  • Graphs were converted from PDF to SVG format using Inkscape.
  • Path data was parsed using AI (Anthropic Claude) to digitize the temperature curves.
  • Data was resampled to a uniform interval of $\approx 16$ seconds ($f_s \approx 3.75$ mHz) to facilitate Fourier analysis.
• Signal Processing:
  • Drift Removal: A clamped cubic spline was fitted to remove slow baseline drifts (caused by long-term environmental changes). For segments with interruptions, a reference sensor on the primary test cell was linearly scaled and subtracted to isolate the ambient signal.
  • Segmentation: Five distinct time segments were analyzed from a single 254-hour record: two high-resolution "zoomed" segments (ZE, ZL) and three "overview" segments (OE, OI, OL).
  • Spectral Analysis: One-sided Discrete Fourier Transform (DFT) was applied to identify dominant frequencies and harmonic structures.
  • Peak Detection: Algorithms located peaks and troughs to calculate charge/discharge durations and amplitude stability.

3. Key Contributions

• Proof of Concept for Indirect Monitoring: Demonstrates that a standard fume hood air-temperature sensor, acting as a low-pass thermal filter, can resolve individual charge/discharge half-cycles of a co-located device.
• Extraction of Hidden Parameters: Successfully extracted previously unpublished operational data from "noise," including cycle counts, cycle periods, and protocol asymmetries.
• Non-Invasive Degradation Assessment: Showed that thermal stability over hundreds of cycles can be assessed without attaching sensors to the device under test.
• Harmonic Analysis of Asymmetric Cycling: Provided a mathematical framework linking the alternating charge/discharge periods to a specific harmonic series in the frequency domain.

4. Key Results

The analysis of the ambient temperature signal revealed the following characteristics of the "other" device (Donut Lab Solid-State Battery) undergoing simultaneous testing:

• Cycle Parameters:
  • Total Cycles: Approximately 338 full charge/discharge cycles were completed during the 254-hour run.
  • Cycle Rate: The device was cycled at a 3C rate.
  • Cycle Period: The full cycle duration was stable at $\approx 40$ minutes.
  • Half-Cycle Asymmetry:
    • Charge phase ($T_{charge}$): $\approx 22$ minutes.
    • Discharge phase ($T_{discharge}$): $\approx 18$ minutes.
    • Difference ($\Delta$): A consistent 4-minute difference ($T_{charge} - T_{discharge}$) was observed across all segments.
• Thermal Signature:
  • The temperature oscillation amplitude was $\approx 1^\circ$C.
  • The Fourier spectrum showed a clean harmonic series ($f_1, 2f_1, 3f_1$) with a fundamental frequency corresponding to the 40-minute cycle. The dominant peak at $\approx 20$ minutes was identified as the second harmonic, not a fundamental oscillation.
• Stability and Degradation:
  • Amplitude and Period Stability: Over the 254-hour run (covering $\approx 330$ cycles), the cycle period and thermal amplitude remained stable.
  • No Degradation: There was no detectable thermal signature of device degradation (e.g., increasing internal resistance or thermal runaway) in the ambient signal.
  • Relaxation Pause: The consistent 4-minute difference between charge and discharge times suggests an intentional relaxation pause in the cycling protocol, rather than just the Constant Voltage (CV) tail of a CCCV protocol.

5. Significance

This paper fundamentally shifts the perspective on environmental monitoring in battery testing:

1. Passive Diagnostics: It proves that "background" temperature data in shared testing environments (like fume hoods) is a valuable, untapped resource for monitoring device health and cycling protocols without additional hardware.
2. Protocol Inference: It allows researchers to reverse-engineer cycling protocols (e.g., identifying relaxation pauses or asymmetry) solely from thermal data.
3. Scalability: The method requires only commodity temperature sensors and standard signal processing, making it highly scalable for monitoring large banks of batteries where individual sensor access is impractical.
4. Validation of Solid-State Batteries: The specific re-analysis provided evidence that the Donut Lab Solid-State Battery V1 can sustain 338 fast-charging cycles (3C) at room temperature without thermal degradation, a significant finding for the viability of solid-state technology.

Conclusion: The study demonstrates that with rigorous signal processing, ambient temperature variations can serve as a high-fidelity proxy for direct device monitoring, revealing detailed cycling dynamics and confirming the thermal robustness of the tested energy-storage device.