非定常劣化下でのバッテリ劣化予測のためのワールドモデル
World Model for Battery Degradation Prediction Under Non-Stationary Aging
リチウムイオン電池の劣化予測をワールドモデル問題として定式化し、各サイクルの電圧・電流・温度データを潜在状態に符号化して将来のSOH軌道を予測する手法を提案。直接回帰と比較して軌道予測誤差を半減させ、電気化学的知識を損失に組み込むことで劣化膝部の予測精度が向上することを示した。
著者: Kai Chin Lim, Khay Wai See
分類: cs.LG, eess.SY
原文アブストラクト
Degradation prognosis for lithium-ion cells requires forecasting the state-of-health (SOH) trajectory over future cycles. Existing data-driven approaches can produce trajectory outputs through direct regression, but lack a mechanism to propagate degradation dynamics forward in time. This paper formulates battery degradation prognosis as a world model problem, encoding raw voltage, current, and temperature time-series from each cycle into a latent state and propagating it forward via a learned dynamics transition to produce a future trajectory spanning 80 cycles. To investigate whether electrochemical knowledge improves the learned dynamics, a Single Particle Model (SPM) constraint is incorporated into the training loss. Three configurations are evaluated on the Severson LiFePO4 (LFP) dataset of 138 cells. Iterative rollout halves the trajectory forecast error compared to direct regression from the same encoder. The SPM constraint improves prediction at the degradation knee where the resistance to SOH relationship is most applicable, without changing aggregate accuracy.