Degradation Self-Supervised Learning for Lithium-ion Battery Health Diagnostics

• URL: http://arxiv.org/abs/2503.08083v1
• Date: Tue, 11 Mar 2025 06:29:13 GMT
• Title: Degradation Self-Supervised Learning for Lithium-ion Battery Health Diagnostics
• Authors: J. C. Chen,
• Abstract summary: Health evaluation for lithium-ion batteries (LIBs) typically relies on constant charging/discharging protocols.<n>In this study, a novel training strategy for estimating LIB health based on the paradigm of self-supervised learning is proposed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Health evaluation for lithium-ion batteries (LIBs) typically relies on constant charging/discharging protocols, often neglecting scenarios involving dynamic current profiles prevalent in electric vehicles. Conventional health indicators for LIBs also depend on the uniformity of measured data, restricting their adaptability to non-uniform conditions. In this study, a novel training strategy for estimating LIB health based on the paradigm of self-supervised learning is proposed. A multiresolution analysis technique, empirical wavelet transform, is utilized to decompose non-stationary voltage signals in the frequency domain. This allows the removal of ineffective components for the health evaluation model. The transformer neural network serves as the model backbone, and a loss function is designed to describe the capacity degradation behavior with the assumption that the degradation in LIBs across most operating conditions is inevitable and irreversible. The results show that the model can learn the aging characteristics by analyzing sequences of voltage and current profiles obtained at various time intervals from the same LIB cell. The proposed method is successfully applied to the Stanford University LIB aging dataset, derived from electric vehicle real driving profiles. Notably, this approach achieves an average correlation coefficient of 0.9 between the evaluated health index and the degradation of actual capacity, demonstrating its efficacy in capturing LIB health degradation. This research highlights the feasibility of training deep neural networks using unlabeled LIB data, offering cost-efficient means and unleashing the potential of the measured information.

Related papers

• Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction [40.96453902709292]
  We propose pretraining strategies that leverage a phenomenon in real-world applications. In medicine, components of the same biological signaling pathways frequently influence both baseline risk and treatment response. We use this structure to incorporate "side information" and develop models that can exploit synergies between risk prediction and causal effect estimation.
  arXiv  Detail & Related papers  (2025-05-01T05:12:14Z)
• Robust Confinement State Classification with Uncertainty Quantification through Ensembled Data-Driven Methods [39.27649013012046]
  We develop methods for confinement state classification with uncertainty quantification and model robustness.<n>We focus on off-line analysis for TCV discharges, distinguishing L-mode, H-mode, and an in-between dithering phase (D)<n>A dataset of 302 TCV discharges is fully labeled, and will be publicly released.
  arXiv  Detail & Related papers  (2025-02-24T18:25:22Z)
• 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.<n>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)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations. We incorporate elements modeling effects to better align simulated data with real-world measurements. The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence [33.12963161545068]
  Causal machine learning holds promise for estimating individual treatment effects from complex data.<n>We propose PermuCATE, an algorithm based on the Conditional Permutation Importance (CPI) method.<n>We empirically demonstrate the benefits of PermuCATE in simulated and real-world health datasets.
  arXiv  Detail & Related papers  (2024-08-23T11:44:07Z)
• HarmonICA: Neural non-stationarity correction and source separation for motor neuron interfaces [37.0279893661798]
  We propose a potential solution, using an unsupervised learning algorithm to blindly correct for the effects of latent processes which drive the signal non-stationarities. The proposed design, HarmonICA, sidesteps the identifiability problems of nonlinear ICA. We test HarmonICA on both invasive and non-invasive recordings both simulated and real.
  arXiv  Detail & Related papers  (2024-06-28T00:08:13Z)
• Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training [19.606703130917325]
  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.
  arXiv  Detail & Related papers  (2024-01-30T14:47:15Z)
• Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling [12.367920799620965]
  This paper presents aging and reconditioning experiments of 62 commercial high-energy type lithium iron phosphate (LFP) cells. The relatively large-scale data allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity.
  arXiv  Detail & Related papers  (2023-09-21T17:15:10Z)
• Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off [39.061605300172175]
  We show that managing the temporal resolution can improve policy evaluation efficiency in LQR systems with finite data. These findings show that managing the temporal resolution can provably improve policy evaluation efficiency in LQR systems with finite data.
  arXiv  Detail & Related papers  (2022-12-17T20:45:34Z)
• 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)
• 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)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

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.