[Submitted on 11 Jul 2026]
Title:BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification
View a PDF of the paper titled BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification, by Raghvender Raghvender and 4 other authors
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Abstract: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.
Comments: 17 pages, 9 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.11943 [cs.LG]
(or arXiv:2607.11943v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.11943
arXiv-issued DOI via DataCite
Submission history
From: Arnaud Demortiere Dr. [view email] [v1] Sat, 11 Jul 2026 09:48:36 UTC (12,891 KB)
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