Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training

• URL: http://arxiv.org/abs/2402.00068v3
• Date: Tue, 19 Nov 2024 05:08:44 GMT
• Title: Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training
• Authors: Yuyuan Feng, Guosheng Hu, Xiaodong Li, Zhihong Zhang,
• Abstract summary: Health modeling of lithium-ion batteries (LIBs) is crucial for safe and efficient energy management and carries significant socio-economic implications. We introduce a practical Test-Time Training framework, BatteryTTT, which adapts the model continually using each target data (UTD) collected amidst degradation. To fully utilize each UTD, BatteryTTT integrates the inherent physical laws of modern LIBs into self-supervised learning, termed Physcics-Guided Test-Time Training.
• Score: 19.606703130917325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Health modeling of lithium-ion batteries (LIBs) is crucial for safe and efficient energy management and carries significant socio-economic implications. Although Machine Learning (ML)-based State of Health (SOH) estimation methods have made significant progress in accuracy, the scarcity of high-quality LIB data remains a major obstacle. Existing transfer learning methods for cross-domain LIB SOH estimation have significantly alleviated the labeling burden of target LIB data, however, they still require sufficient unlabeled target data (UTD) for effective adaptation to the target domain. Collecting this UTD is challenging due to the time-consuming nature of degradation experiments. To address this issue, we introduce a practical Test-Time Training framework, BatteryTTT, which adapts the model continually using each UTD collected amidst degradation, thereby significantly reducing data collection time. To fully utilize each UTD, BatteryTTT integrates the inherent physical laws of modern LIBs into self-supervised learning, termed Physcics-Guided Test-Time Training. Additionally, we explore the potential of large language models (LLMs) in battery sequence modeling by evaluating their performance in SOH estimation through model reprogramming and prefix prompt adaptation. The combination of BatteryTTT and LLM modeling, termed GPT4Battery, achieves state-of-the-art generalization results across current LIB benchmarks. Furthermore, we demonstrate the practical value and scalability of our approach by deploying it in our real-world battery management system (BMS) for 300Ah large-scale energy storage LIBs.

Related papers

• Investigating Data Pruning for Pretraining Biological Foundation Models at Scale [47.09153330837959]
  We propose a post-hoc influence-guided data pruning framework tailored to biological domains.<n>Our framework consistently outperforms random selection baselines under an extreme pruning rate of over 99 percent.<n>These findings underscore the potential of influence-guided data pruning to substantially reduce the computational cost of BioFM pretraining.
  arXiv  Detail & Related papers  (2025-12-15T02:42:52Z)
• 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)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring [0.2094545406831271]
  We propose a novel SOH prediction framework (SOH-KLSTM) using Kolmogorov-Arnold Network (KAN)-Integrated Candidate Cell State in LSTM for Li batteries Health Monitoring.<n>This hybrid approach combines the ability of LSTM to learn long-term dependencies for accurate time series predictions with KAN's non-linear approximation capabilities to effectively capture complex degradation behaviors in Lithium batteries.
  arXiv  Detail & Related papers  (2025-08-31T19:31:45Z)
• Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life [1.2603104712715607]
  We propose a hybrid survival analysis framework integrating survival data reconstruction, survival model learning, and survival probability estimation. Our approach transforms battery voltage time series into time-to-failure data using path signatures. Experiments conducted on the Toyota battery and NASA battery datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2025-03-17T02:49:34Z)
• Degradation Self-Supervised Learning for Lithium-ion Battery Health Diagnostics [0.0]
  Health evaluation for lithium-ion batteries (LIBs) typically relies on constant charging/discharging protocols. In this study, a novel training strategy for estimating LIB health based on the paradigm of self-supervised learning is proposed.
  arXiv  Detail & Related papers  (2025-03-11T06:29:13Z)
• Battery State of Health Estimation Using LLM Framework [0.0]
  This study introduces a transformer-based framework for estimating the State of Health (SoH) of lithium titanate (LTO) battery cells. We demonstrate the impact of charge durations on energy storage trends and apply Differential Voltage Analysis (DVA) to monitor capacity changes. Our model achieves superior performance, with a Mean Absolute Error (MAE) as low as 0.87% and varied latency metrics.
  arXiv  Detail & Related papers  (2025-01-30T03:55:56Z)
• 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)
• ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning [0.0]
  This work establishes a foundational battery degradation model, providing reliable forecasts across a range of battery types and operating conditions. Our novel framework uses contrastive learning to map the relationship between the underlying physical degradation parameters and observable operational quantities.
  arXiv  Detail & Related papers  (2025-01-17T12:13:04Z)
• AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge [68.39683427262335]
  Existing studies fail to guarantee contamination-free evaluation as newly collected data may contain pre-existing knowledge. We propose AntiLeak-Bench, an automated anti-leakage benchmarking framework.
  arXiv  Detail & Related papers  (2024-12-18T09:53:12Z)
• An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data [35.943089444017666]
  We propose an efficient method of contrastive pretraining tailored for long clinical timeseries data. Our model demonstrates the ability to impute missing measurements, providing clinicians with deeper insights into patient conditions.
  arXiv  Detail & Related papers  (2024-10-11T19:05:25Z)
• Improving Biomedical Entity Linking with Retrieval-enhanced Learning [53.24726622142558]
  $k$NN-BioEL provides a BioEL model with the ability to reference similar instances from the entire training corpus as clues for prediction. We show that $k$NN-BioEL outperforms state-of-the-art baselines on several datasets.
  arXiv  Detail & Related papers  (2023-12-15T14:04:23Z)
• Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix [59.55173022987071]
  We study the potential of semi-supervised learning for class-agnostic motion prediction. Our framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data. Our method exhibits comparable performance to weakly and some fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-13T09:32:50Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• A Mapping Study of Machine Learning Methods for Remaining Useful Life Estimation of Lead-Acid Batteries [0.0]
  State of Health (SoH) and Remaining Useful Life (RUL) contribute to enhancing predictive maintenance, reliability, and longevity of battery systems. This paper presents a mapping study of the state-of-the-art in machine learning methods for estimating the SoH and RUL of lead-acid batteries.
  arXiv  Detail & Related papers  (2023-07-11T10:41:41Z)
• DIICAN: Dual Time-scale State-Coupled Co-estimation of SOC, SOH and RUL for Lithium-Ion Batteries [6.930255986517943]
  A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper. The DIICAN method is validated on the Oxford battery dataset.
  arXiv  Detail & Related papers  (2022-10-20T14:42:20Z)
• Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction [2.670887944566458]
  We introduce a novel Transformer-based deep learning architecture which is able to simultaneously infer the ageing state from a limited number of voltage/current samples. Our experiments show that the trained model is effective for input current profiles of different complexities and is robust to a wide range of degradation levels.
  arXiv  Detail & Related papers  (2022-06-01T15:31:06Z)
• Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis [6.7143928677892335]
  Lithium-ion batteries (LIBs) are key to promoting electrification in the coming decades. Inadequate understanding of LIB degradation is an important bottleneck that limits battery durability and safety. Here, we propose hybrid physics-based and data-driven modeling for online diagnosis and prognosis of battery degradation.
  arXiv  Detail & Related papers  (2021-10-25T11:14:12Z)
• Machine learning pipeline for battery state of health estimation [3.0238880199349834]
  We design and evaluate a machine learning pipeline for estimation of battery capacity fade. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45%.
  arXiv  Detail & Related papers  (2021-02-01T13:50:56Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• 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)
• Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions [80.12620331438052]
  deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features. Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets. We argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance.
  arXiv  Detail & Related papers  (2020-06-25T08:46:37Z)

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.