SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction

• URL: http://arxiv.org/abs/2510.10621v1
• Date: Sun, 12 Oct 2025 14:02:17 GMT
• Title: SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction
• Authors: Hanbing Liu, Yanru Wu, Yang Li, Ercan E. Kuruoglu, Xuan Zhang,
• Abstract summary: We propose a semi deep Gaussian process regression framework named SDG-L to give predictions based on the modeling of time series battery state data.<n>In experimental studies based on NASA dataset, our proposed method obtains an average test MSE error of 1.2%.
• Score: 14.25882750541692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping capacity with the growing number of charging/discharging cycles. An accurate capacity prediction is the key to ensure system efficiency and reliability, where the exploitation of battery state information in each cycle has been largely undervalued. In this paper, we propose a semiparametric deep Gaussian process regression framework named SDG-L to give predictions based on the modeling of time series battery state data. By introducing an LSTM feature extractor, the SDG-L is specially designed to better utilize the auxiliary profiling information during charging/discharging process. In experimental studies based on NASA dataset, our proposed method obtains an average test MSE error of 1.2%. We also show that SDG-L achieves better performance compared to existing works and validate the framework using ablation studies.

Related papers

• A Lightweight Transfer Learning-Based State-of-Health Monitoring with Application to Lithium-ion Batteries in Autonomous Air Vehicles [53.158733310637295]
  Transfer learning is a promising technique for leveraging knowledge from data-rich source working conditions.<n>Traditional TL-based state-of-health (SOH) monitoring is infeasible when applied in portable mobile devices.<n>This paper proposes a lightweight TL-based SOH monitoring approach with constructive transfer incremental learning (CITL)
  arXiv  Detail & Related papers  (2025-12-09T11:54:09Z)
• BITS for GAPS: Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates [45.88028371034407]
  We introduce the Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates (BITS for GAPS) framework.<n>BITS for GAPS supports serial hybrid modeling, where known physics governs part of the system.<n>We derive entropy-based acquisition functions that quantify expected information gain from candidate input locations.
  arXiv  Detail & Related papers  (2025-11-20T21:36:21Z)
• Knowledge-Aware Modeling with Frequency Adaptive Learning for Battery Health Prognostics [27.840159692578354]
  Karma is a knowledge-aware model with frequency-adaptive learning for battery capacity estimation and remaining useful life prediction.<n>A dual-stream deep learning architecture is developed, where one stream captures long-term low-frequency degradation trends and the other models high-frequency short-term dynamics.<n>Experiments show Karma's superior performance, achieving average error reductions of 50.6% and 32.6% over state-of-the-art algorithms for battery health prediction.
  arXiv  Detail & Related papers  (2025-10-03T09:24:38Z)
• State-of-Health Prediction for EV Lithium-Ion Batteries via DLinear and Robust Explainable Feature Selection [14.672460390509334]
  We propose an explainable, data-driven state-of-health (SOH) prediction framework tailored for electric vehicles (EVs)<n>We extract twenty meaningful features from voltage, current, temperature, and time profiles, and select key features using Pearson correlation and Shapley additive explanations (SHAP)<n>The SHAP-based selection yields consistent feature importance across multiple cells, effectively capturing cell-to-cell variability (CtCV)
  arXiv  Detail & Related papers  (2025-01-20T15:28:20Z)
• GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction [6.289972392749257]
  We propose GiNet, a gated recurrent units enhanced Informer network, for predicting battery's capacity.<n>GiNet achieves 0.11 mean absolute error for predicting the battery capacity in a sequence of future time slots without knowing the historical battery capacity.<n>It also outperforms the latest algorithms significantly with 27% error reduction on average compared to Informer.
  arXiv  Detail & Related papers  (2025-01-09T06:26:28Z)
• Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making [0.0]
  We develop machine learning-based models to predict and classify battery RUL.<n>The proposed TLE model consistently outperforms baseline models in RMSE, MAE, and R squared error.<n>XGBoost achieves an impressive 99% classification accuracy, validated through cross-validation techniques.
  arXiv  Detail & Related papers  (2024-09-26T15:08:38Z)
• Generating Comprehensive Lithium Battery Charging Data with Generative AI [24.469319419012745]
  This study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE) Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data.
  arXiv  Detail & Related papers  (2024-04-11T09:08:45Z)
• BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling [42.453194049264646]
  We introduce a machine learning-based approach for battery-type classification and address the problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge optimized with large-scale datasets. We conducted an experimental study and the results show that BatSort can achieve outstanding accuracy of 92.1% on average and up to 96.2%.
  arXiv  Detail & Related papers  (2024-04-08T18:05:24Z)
• Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries [1.2468700211588883]
  Accurately predicting the state of health (SOH) can not only ease the anxiety of users about the battery life but also provide important information for the management of the battery. This paper presents a prediction method for SOH based on Vision Transformer (ViT) model.
  arXiv  Detail & Related papers  (2022-09-07T16:54:15Z)
• Data Driven Prediction of Battery Cycle Life Before Capacity Degradation [0.0]
  This paper utilizes the data and methods implemented by Kristen A. Severson, et al, to explore the methodologies that the research team used. The fundamental effort is to find out if machine learning techniques may be trained to use early life cycle data in order to accurately predict battery capacity.
  arXiv  Detail & Related papers  (2021-10-19T01:35:12Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks [68.8204255655161]
  We build a Deep Forward Network for a lithium-ion battery and its performance assessment. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment.
  arXiv  Detail & Related papers  (2020-09-20T23:47:11Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)

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.