Pith Number

pith:IIB4HCXZ

pith:2026:IIB4HCXZJZZGRXP26HINOVQJ3U

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Causal Anomaly Detection for Lithium-Ion Battery Degradation

Dieter W. Heermann, Hagen Heermann

Causal anomaly detection on routine battery measurements identifies degradation up to 402 cycles before standard failure indicators.

arxiv:2605.17334 v1 · 2026-05-17 · cond-mat.mtrl-sci · cond-mat.stat-mech · cs.LG · physics.comp-ph

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Claims

C1strongest claim

The Magnitude-shift class achieves 100% detection across all seven tested cells spanning LFP (MIT–Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries, with a lead time of up to 402 cycles before conventional capacity-threshold failure on gradual-fade cells.

C2weakest assumption

The assumption that the twelve anomaly scores derived from causal graph discovery and k-nearest-neighbour transfer entropy on per-cycle time series reliably reflect physical degradation processes (rather than dataset-specific artifacts), which is taken to be supported by the EIS correlation on one additional NMC cell.

C3one line summary

CausalHealth detects lithium-ion battery degradation with 100% sensitivity and up to 402-cycle lead time using causal anomaly scores from voltage, current, temperature, and resistance time series across seven cells.

References

23 extracted · 23 resolved · 1 Pith anchors

[1] Closed- loop optimization of fast-charging protocols for batteries with machine learning 2020 · doi:10.1038/s41586-020-1994-5

[2] Degradation diagnostics for lithium ion cells 2017 · doi:10.1016/j.jpowsour.2016.12.011

[3] Learning Phrase Representations using 2014 · doi:10.3115/v1/d14-1179

[4] Electrolyticconductivityandglasstransitiontemperature as functions of salt content, solvent composition, and temperature for LiPF6 in propylene carbonate–ethylene carbonate 2004 · doi:10.1021/je034259d

[5] Exploring the emerging role of large language models in smart grid cybersecurity: A survey of attacks, detection mechanisms, and mitigation strategies 2022 · doi:10.3389/fenrg

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Receipt and verification

First computed	2026-05-20T00:03:52.676083Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4203c38af94e7268ddfaf1d0d75609dd23020b3cb4ccfeea4f29292f4b992821

Aliases

arxiv: 2605.17334 · arxiv_version: 2605.17334v1 · doi: 10.48550/arxiv.2605.17334 · pith_short_12: IIB4HCXZJZZG · pith_short_16: IIB4HCXZJZZGRXP2 · pith_short_8: IIB4HCXZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/IIB4HCXZJZZGRXP26HINOVQJ3U \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4203c38af94e7268ddfaf1d0d75609dd23020b3cb4ccfeea4f29292f4b992821

Canonical record JSON

{
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    "abstract_canon_sha256": "436c0d5aad3f8f4e8ecfc6f2c9553a90b287fb11ef76079ab54db5db513e8ad0",
    "cross_cats_sorted": [
      "cond-mat.stat-mech",
      "cs.LG",
      "physics.comp-ph"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2026-05-17T08:53:16Z",
    "title_canon_sha256": "67ef734c1e101476a9001b1bca8170d03554374fffb2fefb70e5104f7509b999"
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    "version": 1
  }
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