{"slug": "battvae-gp-generative-modeling-of-long-horizon-battery-degradation-with", "title": "BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification", "summary": "Researchers propose BattVAE-GP, a hybrid physics-probabilistic framework that uses a Variational Autoencoder and Gaussian process to model long-horizon battery degradation trajectories with uncertainty quantification, enabling computationally efficient surrogate modeling for unseen charging rates.", "body_md": "arXiv:2607.11943v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/battvae-gp-generative-modeling-of-long-horizon-battery-degradation-with", "canonical_source": "https://arxiv.org/abs/2607.11943", "published_at": "2026-07-15 04:00:00+00:00", "updated_at": "2026-07-15 04:21:04.163381+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["BattVAE-GP", "PyBaMM", "DFN/P2D"], "alternates": {"html": "https://wpnews.pro/news/battvae-gp-generative-modeling-of-long-horizon-battery-degradation-with", "markdown": "https://wpnews.pro/news/battvae-gp-generative-modeling-of-long-horizon-battery-degradation-with.md", "text": "https://wpnews.pro/news/battvae-gp-generative-modeling-of-long-horizon-battery-degradation-with.txt", "jsonld": "https://wpnews.pro/news/battvae-gp-generative-modeling-of-long-horizon-battery-degradation-with.jsonld"}}