A5018038426- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Fuel Cells and Related Materials
- Electric Vehicles and Infrastructure
- Advancements in Solid Oxide Fuel Cells
- Reliability and Maintenance Optimization
- Extraction and Separation Processes
- Fault Detection and Control Systems
- Recycling and Waste Management Techniques
- Electrocatalysts for Energy Conversion
- Machine Learning in Materials Science
- VLSI and Analog Circuit Testing
- Electronic and Structural Properties of Oxides
- Machine Learning and ELM
- Energy Load and Power Forecasting
- Advanced battery technologies research
- Machine Fault Diagnosis Techniques
- Green IT and Sustainability
- Advanced Data Processing Techniques
- Cruise Tourism Development and Management
- Hydropower, Displacement, Environmental Impact
- Transboundary Water Resource Management
- Advanced Memory and Neural Computing
- Industrial Vision Systems and Defect Detection
National Renewable Energy Laboratory
2020-2025
Boston University
2018-2019
Worcester Polytechnic Institute
2013-2014
Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on “knees” in lithium-ion aging trajectories. We first definitions for knees and three classes of “internal state trajectories” (termed snowball, hidden, threshold trajectories) cause a knee. then discuss six knee “pathways”, including lithium plating, electrode saturation, resistance growth, electrolyte additive depletion,...
The energy transition will require a rapid deployment of renewable (RE) and electric vehicles (EVs) where other transit modes are unavailable. EV batteries could complement RE generation by providing short-term grid services. However, estimating the market opportunity requires an understanding many socio-technical parameters constraints. We quantify global battery capacity available for storage using integrated model incorporating future deployment, degradation, participation. include both...
Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which inherently interpretable and computationally fast, ideal for use in battery controllers, technoeconomic multi-objective optimizations. For batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on surface dominates fade. This is often modeled using physically informed equations, such as square-root time predicting solvent-diffusion limited SEI...
Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized extrapolating degradation trends from accelerated tests to real-world scenarios. Identifying models with high accuracy and low uncertainty is crucial ensuring that model extrapolations believable, however, it difficult compose accurately predict multivariate data trends; a review cycling literature reveals wide variety functional relationships. Here, machine-learning...
Predicting battery capacity from impedance at varying temperature and state of charge using machine learning Gasper et al. demonstrate prediction electrochemical spectroscopy data recorded under conditions charge.A variety methods for featurization are tested several machine-learning model architectures to rigorously investigate the limits monitor health.
Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small sets, this procedure can be conducted manually; however, it is not feasible manually define a proper ECM extensive sets with wide range EIS Automatic identification would substantially...
Abstract Recycling Li‐ion batteries from electric vehicles is critical for reducing costs and supporting the development of a domestic battery supply chain. Direct recycling cathodes, like LiNi x Mn y Co z O 2 (NMC), attractive due to its low cost, energy use, emissions compared traditional techniques. However, comprehensive understanding active material properties at end‐of‐life needed guide direct processes performance‐dependent reuse applications. Here, NMC an commercial pouch cell...
This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large set reveals temperature-, charge C-rate-, depth-of-discharge-, state of (SOC)-dependent degradation patterns that would be unobserved in smaller test matrix. Model structure is informed by incremental analysis shows loss lithium inventory cathode-material as the dominant mechanisms....
ADVERTISEMENT RETURN TO ISSUEPREVEnergy FocusNEXTA Minimal Information Set To Enable Verifiable Theoretical Battery ResearchAashutosh MistryAashutosh MistryChemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Aashutosh Mistryhttps://orcid.org/0000-0002-4359-4975, Ankit VermaAnkit VermaEnergy Conversion Storage Systems Center, Renewable Energy Golden, Colorado 80401, Vermahttps://orcid.org/0000-0002-7610-8574, Shashank...
Metallic contaminants pose a significant challenge to the viability of directly recycling Li-ion batteries. To date, few strategies exist selectively remove metallic impurities from mixtures shredded end-of-life material (black mass; BM) without concurrently damaging structure and electrochemical performance target active material. We herein present tailored methods ionize two major contaminants—Al Cu—while retaining representative cathode (LiNi 0.33 Mn Co O 2 ; NMC-111) intact. This BM...
Abstract A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, process accelerated. However, questions on validity projecting models trained limited or simple cycling profiles, constant current cycling, real‐world scenarios with complex loads remains. Here, we present ability predict performance less than 1.2 % mean absolute percent error when cells aged using...
Maintaining safe operating conditions is a key challenge for high-performance lithium-ion battery applications. The lithium-plating reaction remains risk during charging, but limited studies consider the highly variable charging possible in commercial cells. Here we combine pseudo-2D electrochemical modeling with data visualization methods to reveal important relationships between measurable cell voltage and difficult-to-predict Li-plating onset criteria. An extensively validated model used...
Spent primary alkaline batteries present an unused source of secondary metals in Europe and the US, with at least 300,000 metric tons being landfilled each year. While battery recycling programs exist, current hydrometallurgical pyrometallurgical processes are not profitable when used for dedicated recycling, so industry growth is difficult. A novel mechanical separation process consisting shredding, baking, magnetic separation, specific gravity was developed to recycle one ton per hour...