Engineering topics

Babinec, Susan J.

Publications and source records attributed to Babinec, Susan J..

6 records

DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗

DOE OSTI2024

Stable, Impermeable Hexacyanoferrate Anolyte for Nonaqueous Redox Flow Batteries

Redox-active molecules, or redoxmers, in nonaqueous redox flow batteries often suffer from membrane crossover and low electrochemical stability. Transforming inorganic polyionic redoxmers established for aqueous batteries into nonaqueous candidates is an attractive strategy to address these challenges. Here, in this study, we demonstrate such tailoring for hexacyanoferrate (HCF) by pairing the anions with tetra-n-butylammonium cation (TBA + ). TBA 3 HCF has good solubility in acetonitrile and >1 V lower redox potential vs the aqueous counterpart; thus, the familiar aqueous catholyte becomes a new nonaqueous anolyte. The lowering of redox potential correlates with replacement of water by acetonitrile in the solvation shell of HCF, which can be traced to H-bond formation between water and cyanide ligands. Symmetric flow cells indicate exceptional stability of HCF polyanions in nonaqueous electrolytes and Nafion membranes completely block HCF crossover in full cells. Ion pairing of metal complexes with organic counterions can be effective for developing promising redoxmers for nonaqueous flow batteries.

25 ENERGY STORAGE↗

DOE OSTI2024

Multivariate prognosis of battery advanced state of health via transformers

De-risking energy storage investments necessary to meet CO 2 reduction targets requires a deep understanding of the connections between battery health, design, and use. The historical definition of the battery state of health (SOH) as the percentage of current versus initial capacity is inadequate for this purpose, motivating an expanded SOH consisting of an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open-circuit voltages, and microstructure metrics. In this work, we introduce deep transformer networks for the simultaneous prognosis of 28 battery SOH descriptors using two cycling datasets representing six lithium-ion cathode chemistries, multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions for battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for a lithium-iron-phosphate fast-charging dataset) illustrates the promise of deep learning toward providing enhanced understanding and control of battery health.

25 ENERGY STORAGE↗

DOE OSTI2024

Electrochemical cells and methods of manufacturing thereof

An electrochemical cell includes a housing, a positive electrode substrate disposed within a first electrode chamber of the housing, a negative electrode substrate disposed within a second electrode chamber of the housing, and a separator may be disposed within the housing between the first electrode chamber and the second electrode chamber. A method further includes pumping a manufacturing electrolyte through the positive electrode portion around the positive electrode substrate. The method further includes applying a first electrical signal to the positive electrode substrate so as to electrochemically fabricate one or both of an active material the negative electrode substrate to form a negative electrode and/or an active material on the positive electrode substrate, thereby forming a positive electrode.

Lopes, Pietro Papa↗

DOE OSTI2022

Feature engineering for machine learning enabled early prediction of battery lifetime

Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. Here, a mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.

25 ENERGY STORAGE↗

DOE OSTI2021

TEMPO allegro: liquid catholyte redoxmers for nonaqueous redox flow batteries

Redoxmers are organic active molecules storing energy in redox flow batteries (RFBs). Liquid redoxmers represent an extreme scenario where maximum concentration may be achieved by minimizing supporting solvents, thus maximizing the energy density of RFBs. Herein, a series of (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO)-based high potential (catholyte) liquid redoxmers, TEMPO-EG1, TEMPO-EG2, and TEMPO-EG3, were developed by incorporating polyethylene glycol (PEG) chains. Such modifications not only afford dramatic physical changes from solid to liquid and full miscibility in acetonitrile, but also impact the redox behavior. DFT calculations indicate that the incorporated PEG chains impact the charge distribution, which may account for the electrochemical changes. Importantly, compared to our previous liquid catholytes, the new redoxmers exhibit lower viscosity, which is desired for enhancing high concentration cycling performance. Furthermore, by using a hybrid flow cell, TEMPO-EG1 demonstrated more than 70% capacity retention over 100 cycles at 0.1 M and 66% capacity retention at 0.5 M, affording excellent cyclability at various concentrations. The study exemplifies how molecular engineering tuned the rheological properties of redoxmers, such as viscosity, to improve the high concentration cycling performance of RFBs, which may represent a promising avenue for a high energy density and low-cost flow battery system.

25 ENERGY STORAGE↗