BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification

2026-07-11

AI Summary

This work proposes the first surrogate model that integrates a variational autoencoder (VAE) with a sparse multi-task Gaussian process (GP) to address the high computational cost of physics-based battery degradation simulations, which hinders efficient exploration of capacity fade across diverse operating conditions. The VAE maps voltage-capacity curves into a low-dimensional latent space, where the effects of cycle count and charge C-rate on degradation trajectories are jointly modeled. A sparse multi-task GP then enables continuous, uncertainty-aware predictions in this latent space. Evaluated under protocol-level leave-one-out validation, the model accurately generates smooth degradation curves for both observed and unobserved charge C-rates and provides physically consistent uncertainty estimates of state-of-health (SOH).

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.

Problem

• battery degradation
• uncertainty quantification
• surrogate modeling
• long-horizon prediction
• charging protocols

Innovation

• Variational Autoencoder
• Gaussian Process
• Battery Degradation Modeling
• Uncertainty Quantification
• Surrogate Modeling