发表机构
CNRS（法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对长周期电池退化模拟计算昂贵的问题，提出BattVAE - GP框架，先利用VAE编码数据至潜在空间，再用GP训练并估计不确定性，能准确恢复轨迹，提供高效且具不确定性意识的长周期退化建模代理。
AI 中文摘要
基于物理的长周期电池退化模拟虽能提供机理见解，但计算成本高昂，限制了其在扩展循环寿命期间对运行条件的密集探索。本文提出了一种混合物理 - 概率学习框架，用于对未见过的充电速率下锂离子电池退化轨迹进行代理建模。首先将用PyBaMM中的DFN/P2D电化学模型生成的循环分辨退化数据转换为容量对齐的电压和导数特征，并使用变分自编码器（VAE）进行编码。然后在这个潜在空间中使用循环数和C速率作为输入变量训练稀疏多任务高斯过程（GP），提供潜在退化动态的连续插值以及后验不确定性估计。在协议级留出评估下，潜在空间GP能准确恢复未见过的C速率轨迹，并表现出与训练数据支持一致的不确定性行为。通过冻结的VAE解码器解码GP预测的潜在状态可产生平滑的电压 - 容量演变，而通过辅助潜在到健康状态（SOH）预测器对GP潜在后验进行蒙特卡罗传播可提供具有不确定性意识的SOH估计。所提出的BattVAE - GP框架为长周期退化建模提供了一种计算高效且具有不确定性意识的代理，为将电池健康预测扩展到更丰富的运行条件和未来模拟 - 实验融合提供了结构化基础。
英文摘要
Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive, limiting their use for dense exploration of operating conditions over extended cycle life. Here, we propose a hybrid physics-probabilistic learning framework for surrogate modeling of lithium-ion battery degradation trajectories at unseen charging rates. Cycle-resolved degradation data generated with a DFN/P2D electrochemical model in PyBaMM are first transformed into capacity-aligned voltage and derivative features and encoded using a Variational Autoencoder (VAE). The resulting two-dimensional latent space organizes degradation trajectories according to both cycle progression and charging protocol. A sparse multitask Gaussian process (GP) is then trained in this latent space using cycle number and C-rate as input variables, providing continuous interpolation of latent degradation dynamics together with posterior uncertainty estimates. Under protocol-level holdout evaluation, the latent-space GP accurately recovers unseen C-rate trajectories and exhibits uncertainty behavior consistent with the support of the training data. When queried at unseen interior C-rates, the model generates latent trajectories that remain coherently positioned between neighboring simulated protocols. Decoding the GP-predicted latent states through the frozen VAE decoder yields smooth voltage-capacity evolution, while Monte Carlo propagation of the GP latent posterior through an auxiliary latent to State of Health (SOH) predictor provides uncertainty-aware SOH estimates. The proposed BattVAE-GP framework therefore offers a computationally efficient and uncertainty-aware surrogate for long-horizon degradation modeling, providing a structured basis for extending battery health prediction toward richer operating conditions and future simulation-experiment fusion.
Comments17 pages, 9 figures
