Rapid failure mode classification and quantification in batteries: A deep learning modeling framework

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The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety

Donal P. Finegan et al.

Summary: The article discusses how to improve the accuracy of battery failure prediction by designing experiments, processing data, applying data prediction methods, and using physics knowledge to reduce the number of experiments and costs.

JOULE (2021)

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Article Electrochemistry

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Paul Gasper et al.

Summary: The article introduces a novel procedure for automatically identifying reduced-order degradation models, which are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping, demonstrating that previously known physical relationships can be empirically rediscovered using machine learning.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2021)

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Article Chemistry, Physical

Identifying Chemical Factors Affecting Reaction Kinetics in Li-air Battery via ab initio Calculations and Machine Learning

Aiping Wang et al.

Summary: The research reveals that regulating the structure of discharge products and selecting appropriate solvents can accelerate the reaction kinetics between redox mediators and discharge products.

ENERGY STORAGE MATERIALS (2021)

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Article Chemistry, Physical

Extended cycle life implications of fast charging for lithium-ion battery cathode

Tanvir R. Tanim et al.

Summary: The study found that while cathode issues are minimal in early cycling of extreme fast charging, they begin to accelerate in later life with distinct cracking identified as a fatigue mechanism. The bulk structure of cathodes remains intact, but there is particle surface reconstruction observed, which has a less pronounced effect on cathode aging compared to cracking.

ENERGY STORAGE MATERIALS (2021)

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Review Chemistry, Physical

A Review of Existing and Emerging Methods for Lithium Detection and Characterization in Li-Ion and Li-Metal Batteries

Partha P. Paul et al.

Summary: This review discusses various approaches used to detect and characterize the formation of Li in batteries, each technique has its unique advantages and limitations towards solving part of the puzzle of battery degradation. Multimodal characterization holds promise for addressing concerns in the implementation of the next generation of batteries in the transportation sector.

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Article Chemistry, Physical

How Machine Learning Will Revolutionize Electrochemical Sciences

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Summary: The development time of electrochemical systems is limited by the identification of new materials and understanding their electrochemical response. To shorten this cycle, machine learning can be used for data-driven predictions.

ACS ENERGY LETTERS (2021)

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Battery production design using multi-output machine learning models

Artem Turetskyy et al.

Summary: The paper presents a multi-output approach for battery production design based on data-driven models predicting final product properties from intermediate product features. Establishing a systematic approach is necessary to gain an in-depth understanding of the interlinkage of LiB cell production processes with product quality and performance.

ENERGY STORAGE MATERIALS (2021)

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Summary: The study shows that under certain conditions, automatic differentiation is almost always correct, with only inputs of probability zero leading to errors.

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Directionality of thermal gradients in lithium-ion batteries dictates diverging degradation modes

Rachel Carter et al.

Summary: The intentional application of interelectrode thermal gradients (ITGs) accelerates capacity loss in 35°C cells, with the directionality of the thermal gradient determining the degradation mode. Experimental results showed that when the positive electrode is warmer than the negative electrode, the capacity loss is 77%, while when the negative electrode is warmer, the capacity loss is 100%.

CELL REPORTS PHYSICAL SCIENCE (2021)

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Article Energy & Fuels

Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries

Sheng Shen et al.

APPLIED ENERGY (2020)

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Closed-loop optimization of fast-charging protocols for batteries with machine learning

Peter M. Attia et al.

NATURE (2020)

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Intrinsic Kinetic Limitations in Substituted Lithium-Layered Transition-Metal Oxide Electrodes

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JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2020)

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Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries

Andrea Gayon-Lombardo et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions

Yuliya Preger et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2020)

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Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives

Rui Xiong et al.

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Machine learning assisted materials design and discovery for rechargeable batteries

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Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Matthieu Dubarry et al.

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Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning

Joonki Hong et al.

APPLIED ENERGY (2020)

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Fingerprinting Redox Heterogeneity in Electrodes during Extreme Fast Charging

Aashutosh Mistry et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2020)

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Battery Lifetime Prognostics

Xiaosong Hu et al.

JOULE (2020)

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Investigation of Path-Dependent Degradation in Lithium-Ion Batteries

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BATTERIES & SUPERCAPS (2020)

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Pathways for practical high-energy long-cycling lithium metal batteries

Jun Liu et al.

NATURE ENERGY (2019)

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Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson et al.

NATURE ENERGY (2019)

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Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2019)

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Battery health prediction under generalized conditions using a Gaussian process transition model

Robert R. Richardson et al.

JOURNAL OF ENERGY STORAGE (2019)

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Yi Li et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

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A deep learning method for online capacity estimation of lithium-ion batteries

Sheng Shen et al.

JOURNAL OF ENERGY STORAGE (2019)

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Extreme Fast Charge Challenges for Lithium-Ion Battery: Variability and Positive Electrode Issues

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2019)

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Review Electrochemistry

Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries

Jorn M. Reniers et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2019)

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Rationalizing the interphase stability of Li|doped-Li7La3Zr2O12via automated reaction screening and machine learning

Bo Liu et al.

JOURNAL OF MATERIALS CHEMISTRY A (2019)

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A review on the key issues of the lithium ion battery degradation among the whole life cycle

Xuebing Han et al.

ETRANSPORTATION (2019)

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Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

M. Schimpe et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2018)

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Review Chemistry, Physical

Calendar aging of commercial Li-ion cells of different chemistries - A review

Matthieu Dubarry et al.

CURRENT OPINION IN ELECTROCHEMISTRY (2018)

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Article Chemistry, Physical

A novel mechanistic modeling framework for analysis of electrode balancing and degradation modes in commercial lithium-ion cells

Stefan Schindler et al.

JOURNAL OF POWER SOURCES (2017)

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Article Chemistry, Physical

State of health battery estimator enabling degradation diagnosis: Model and algorithm description

Matthieu Dubarry et al.

JOURNAL OF POWER SOURCES (2017)

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Article Chemistry, Physical

Degradation diagnostics for lithium ion cells

Christoph R. Birkl et al.

JOURNAL OF POWER SOURCES (2017)

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Article Electrochemistry

Oxygen Release and Its Effect on the Cycling Stability of LiNixMnyCozO2 (NMC) Cathode Materials for Li-Ion Batteries

Roland Jung et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2017)

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Path dependence of lithium ion cells aging under storage conditions

Laisuo Su et al.

JOURNAL OF POWER SOURCES (2016)

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Review Electrochemistry

Review-Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

Thomas Waldmann et al.

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Investigation of path dependence in commercial lithium-ion cells for pure electric bus applications: Aging mechanism identification

Zeyu Ma et al.

JOURNAL OF POWER SOURCES (2015)

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In-Situ Detection of Lithium Plating Using High Precision Coulometry

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2015)

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Cell degradation in commercial LiFePO4 cells with high-power and high-energy designs

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JOURNAL OF POWER SOURCES (2014)

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Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application

Wladislaw Waag et al.

APPLIED ENERGY (2013)

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Synthesize battery degradation modes via a diagnostic and prognostic model

Matthieu Dubarry et al.

JOURNAL OF POWER SOURCES (2012)

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Investigation of path dependence in commercial lithium-ion cells chosen for plug-in hybrid vehicle duty cycle protocols

Kevin L. Gering et al.

JOURNAL OF POWER SOURCES (2011)

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Aging of a Commercial Graphite/LiFePO4 Cell

M. Safari et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2011)

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Incremental capacity analysis and close-to-equilibrium OCV measurements to quantify capacity fade in commercial rechargeable lithium batteries

Matthieu Dubarry et al.

ELECTROCHEMICAL AND SOLID STATE LETTERS (2006)

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Article Chemistry, Physical

An accelerated calendar and cycle life study of Li-ion cells

I Bloom et al.

JOURNAL OF POWER SOURCES (2001)

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Rapid failure mode classification and quantification in batteries: A deep learning modeling framework

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