Pith Number

pith:7YLZ3EL2

pith:2026:7YLZ3EL2WUV5VCYJCMH4AZMDXV

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A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles

Bing Wang, Han Wang, Ying Wang

Tucker tensor decomposition combined with LSTM networks improves SOC prediction accuracy for electric vehicles by preserving temporal structure in compressed data.

arxiv:2605.13200 v1 · 2026-05-13 · cs.LG · cs.ET

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Claims

C1strongest claim

Tucker-LSTM outperforms the baseline on all metrics, with MSE dropping 70.5% (from 21.07 to 6.22), MAE improving 48.7% (from 3.37% to 1.73%), RMSE falling from 4.59% to 2.49%, and R² rising from 0.918 to 0.976.

C2weakest assumption

That Tucker decomposition reduces dimensionality while fully preserving the temporal structure and predictive information needed for accurate SOC forecasting on real EV data.

C3one line summary

Tucker-LSTM hybrid reduces MSE by 70.5% for EV battery SOC prediction versus standard LSTM on full-lifecycle field data.

References

98 extracted · 98 resolved · 0 Pith anchors

[1] A lithium-ion battery remaining useful life prediction model based on ceemdan data preprocessing and hssa-lstm-tcn, 2024

[2] Mechanistically guided residual learning for battery state monitoring throughout life, 2026

[3] Analysis of state-of-charge estimation methods for li-ion batteries considering wide temperature range, 2025

[4] A critical review of the state estimation methods of power batteries for electric vehicles, 2025

[5] Explainable real-time data driven method for battery electric model reconstruction via tensor train decomposition, 2025

Receipt and verification

First computed	2026-05-18T03:08:48.675503Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fe179d917ab52bda8b09130fc06583bd67a81a4da81f0e285ad78b1a758b0b7e

Aliases

arxiv: 2605.13200 · arxiv_version: 2605.13200v1 · doi: 10.48550/arxiv.2605.13200 · pith_short_12: 7YLZ3EL2WUV5 · pith_short_16: 7YLZ3EL2WUV5VCYJ · pith_short_8: 7YLZ3EL2

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7YLZ3EL2WUV5VCYJCMH4AZMDXV \
  | 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: fe179d917ab52bda8b09130fc06583bd67a81a4da81f0e285ad78b1a758b0b7e

Canonical record JSON

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    "abstract_canon_sha256": "700695f716775e3edb5f128caa70f98862d29d9622b5371a99e39a659e3e826d",
    "cross_cats_sorted": [
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T08:54:17Z",
    "title_canon_sha256": "37e5d6ea21d0d26de9e54ace5500e9ec006634e0a7d47f833f7c0e14915217c8"
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  "source": {
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