World Model for Battery Degradation Prediction Under Non-Stationary Aging

• URL: http://arxiv.org/abs/2603.10527v1
• Date: Wed, 11 Mar 2026 08:30:15 GMT
• Title: World Model for Battery Degradation Prediction Under Non-Stationary Aging
• Abstract summary: This paper formulates battery degradation prognosis as a world model problem.<n>It encodes raw voltage, current, and temperature time-series from each cycle into a latent state.<n>It propagates it forward via a learned dynamics transition to produce a future trajectory spanning 80 cycles.
• Score: 1.2891210250935148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks [2.3099144596725565]
  We propose a physics-informed deep learning approach for lithium-ion battery SOH prediction using incomplete discharge curves extracted from arbitrary voltage ranges.<n>The proposed method combines data-driven learning with physically motivated degradation dynamics to ensure consistent and reliable SOH estimation.
  arXiv  Detail & Related papers  (2026-08-14T10:34:13Z)
• PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction [2.9577202646353284]
  We develop a physics-informed differentiable degradation modeling framework for battery state of health prediction.<n> framework incorporates empirical Arrhenius degradation kinetics associated with solid electrolyte interphase growth and loss of lithium inventory into the training objective.
  arXiv  Detail & Related papers  (2026-07-31T07:19:26Z)
• Early Cycle Charge Trajectory Generative Prediction and Full Life Cycle Health Management of Iron-Chromium Flow Batteries Based on FlowBD-E1 [3.6963089752268687]
  FlowBD-E1, an early-cycle generative forecasting framework, predicts complete future charge V/I trajectories from only the first few cycles.<n>Results suggest that early-cycle trajectory generation can turn a short commissioning record into a long-horizon diagnostic signal for flow-battery management.
  arXiv  Detail & Related papers  (2026-07-28T09:02:14Z)
• AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries [47.27974914502511]
  We introduce a deep learning surrogate pipeline based on the Swin3D Transformer to predict discharge dynamics directly from data.<n>The proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high- throughput battery design and optimization.
  arXiv  Detail & Related papers  (2026-07-22T09:10:48Z)
• BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification [0.0]
  Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive.<n>We propose a hybrid physics-probabilistic learning framework for surrogate modeling of lithium-ion battery degradation trajectories at unseen charging rates.
  arXiv  Detail & Related papers  (2026-07-11T09:48:36Z)
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction [1.6874375111244329]
  This study proposes an Adaptive Ensemble Sparse Identification (AESI) framework that enhances the accuracy of reduced-order li-ion battery models.<n>The approach integrates an Extended Single Particle Model (ESPM) with an evolutionary ensemble sparse learning strategy to construct a robust hybrid model.<n> Evaluation across diverse operating conditions shows that the hybrid model (ESPM + AESI) improves the voltage prediction accuracy, achieving mean squared error reductions of up to 46% on unseen data.
  arXiv  Detail & Related papers  (2025-07-01T01:00:07Z)
• Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks [45.32169712547367]
  Proton exchange membrane (PEM) electrolyzers are pivotal for sustainable hydrogen production.<n>Their long-term performance is hindered by membrane degradation, which poses reliability and safety challenges.<n>Traditional physics-based models have been developed, offering interpretability but requiring numerous parameters that are often difficult to measure and calibrate.<n>This study presents the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation in PEM electrolyzers.
  arXiv  Detail & Related papers  (2025-06-19T15:46:49Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation [18.980782609740082]
  Multiple degradation patterns of a complete life cycle of a battery make it challenging to pursue transfer learning (TL) A transferable multi-stage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps. The proposed method outperforms its competitive algorithms in various transfer tasks for a run-to-failure benchmark with three batteries.
  arXiv  Detail & Related papers  (2022-09-01T02:59:46Z)
• Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization [16.637948430296227]
  Accurately estimating a battery's state of health (SOH) helps prevent battery-powered applications from failing unexpectedly. With the superiority of reducing the data requirement of model training for new batteries, transfer learning (TL) emerges as a promising machine learning approach. This paper proposes an interpretable TL-based SOH estimation method by exploiting the temporal dynamic to assist transfer learning.
  arXiv  Detail & Related papers  (2022-08-23T21:40:40Z)
• Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level [0.8137198664755597]
  Quantile Regression Forest (QRF) model is introduced to make cycle life range prediction with uncertainty quantified. Data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. The interpretability of the final QRF model is explored with two global model-agnostic methods.
  arXiv  Detail & Related papers  (2022-04-26T16:26:27Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping [13.19976118887128]
  State of health (SOH) estimation plays an essential role in battery-powered applications to avoid unexpected breakdowns due to battery capacity fading. This paper proposes an innovative cycle synchronization way to change the existing coordinate system using dynamic time warping. By exploiting the time information of the time series, the proposed method embeds the time index and the original measurements into a novel indicator to reflect the battery degradation status.
  arXiv  Detail & Related papers  (2021-09-28T02:53:54Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.