Prognosis of Multivariate Battery State of Performance and Health via Transformers

• URL: http://arxiv.org/abs/2309.10014v1
• Date: Mon, 18 Sep 2023 15:04:40 GMT
• Title: Prognosis of Multivariate Battery State of Performance and Health via Transformers
• Authors: Noah H. Paulson, Joseph J. Kubal, Susan J. Babinec
• Abstract summary: Batteries are an essential component in a deeply decarbonized future. Understanding battery performance and "useful life" as a function of design and use is of paramount importance. We present a first step in that direction via deep transformer networks for the prediction of 28 battery state of health descriptors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Batteries are an essential component in a deeply decarbonized future. Understanding battery performance and "useful life" as a function of design and use is of paramount importance to accelerating adoption. Historically, battery state of health (SOH) was summarized by a single parameter, the fraction of a battery's capacity relative to its initial state. A more useful approach, however, is a comprehensive characterization of its state and complexities, using an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open circuit voltages, and microstructure metrics. Indeed, predicting across an extensive suite of properties as a function of battery use is a "holy grail" of battery science; it can provide unprecedented insights toward the design of better batteries with reduced experimental effort, and de-risking energy storage investments that are necessary to meet CO2 reduction targets. In this work, we present a first step in that direction via deep transformer networks for the prediction of 28 battery state of health descriptors using two cycling datasets representing six lithium-ion cathode chemistries (LFP, NMC111, NMC532, NMC622, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions versus battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for an LFP fast-charging dataset) illustrates the promise of deep learning towards providing deeper understanding and control of battery health.

Related papers

• OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes [44.16223940507546]
  Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future. Finding highly ion-conductive materials is time-consuming and resource-intensive. OBELiX is a database of synthesized solid electrolyte materials and experimentally measured room temperature ionic conductivities.
  arXiv  Detail & Related papers  (2025-02-20T03:59:35Z)
• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  Superionic materials are essential for advancing solid-state batteries, which offer improved energy density and safety. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. We propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Transformer-based Capacity Prediction for Lithium-ion Batteries with Data Augmentation [0.0]
  Lithium-ion batteries are pivotal to technological advancements in transportation, electronics, and clean energy storage. Current methods for estimating the capacities fail to adequately account for long-term temporal dependencies of key variables. We develop a transformer-based battery capacity prediction model that accounts for both long-term and short-term patterns in battery data.
  arXiv  Detail & Related papers  (2024-07-22T20:21:40Z)
• 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)
• Photovoltaic power forecasting using quantum machine learning [32.73124984242397]
  Predicting solar panel power output is crucial for advancing the energy transition but is complicated by the variable and non-linear nature of solar energy. Our study introduces a suite of solutions centered around hybrid quantum neural networks designed to tackle these complexities. The first proposed model, the Hybrid Quantum Long Short-Term Memory, surpasses all tested models by over 40% lower mean absolute and mean squared errors. The second proposed model, Hybrid Quantum Sequence-to-Sequence neural network, once trained, predicts photovoltaic power with 16% lower mean absolute error for arbitrary time intervals without the need for prior meteorological data.
  arXiv  Detail & Related papers  (2023-12-27T02:37:46Z)
• Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks [4.249657064343807]
  Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation. This paper proposes a two-stage RUL prediction scheme for Lithium-ion batteries using a-temporal attention network (ST-MAN)
  arXiv  Detail & Related papers  (2023-10-29T07:32:32Z)
• 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)
• 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)
• 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)
• 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.