Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456
Li et al., 2017 - Google Patents
[go: Go Back, main page]

Li et al., 2017 - Google Patents

A single particle model for lithium-ion batteries with electrolyte and stress-enhanced diffusion physics

Li et al., 2017

Document ID
9598349346723612901
Author
Li J
Lotfi N
Landers R
Park J
Publication year
Publication venue
Journal of The Electrochemical Society

External Links

Snippet

A low-order battery model has been developed that incorporates stress-enhanced diffusion and electrolyte concentration distribution into a modified single particle model. This model addresses two important challenges of battery modeling for Battery Management Systems …
Continue reading at iopscience.iop.org (other versions)

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage
    • Y02E60/12—Battery technology
    • Y02E60/122—Lithium-ion batteries
    • H—ELECTRICITY
    • H01—BASIC ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/05—Accumulators with non-aqueous electrolyte
    • H01M10/052—Li-accumulators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
    • G01N27/26—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/50—Fuel cells
    • H—ELECTRICITY
    • H01—BASIC ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells

Similar Documents

Publication Publication Date Title
Li et al. A single particle model for lithium-ion batteries with electrolyte and stress-enhanced diffusion physics
Nuroldayeva et al. State of health estimation methods for lithium‐ion batteries
Li et al. A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation
Fan et al. Modeling of Li-Ion cells for fast simulation of high C-rate and low temperature operations
Li et al. Effect of thermal gradients on inhomogeneous degradation in lithium-ion batteries
Samad et al. Battery capacity fading estimation using a force-based incremental capacity analysis
Shen et al. Least squares galvanostatic intermittent titration technique (LS-GITT) for accurate solid phase diffusivity measurement
Barai et al. Reduced order modeling of mechanical degradation induced performance decay in lithium-ion battery porous electrodes
Tanim et al. A temperature dependent, single particle, lithium ion cell model including electrolyte diffusion
Arunachalam et al. Full homogenized macroscale model and pseudo-2-dimensional model for lithium-ion battery dynamics: comparative analysis, experimental verification and sensitivity analysis
Sabatier et al. Lithium-ion batteries modeling: A simple fractional differentiation based model and its associated parameters estimation method
Pan et al. Novel non-destructive detection methods of lithium plating in commercial lithium-ion batteries under dynamic discharging conditions
Pang et al. A comprehensive physics-based equivalent-circuit model and state of charge estimation for lithium-ion batteries
Veth et al. 3D electro-thermal model approach for the prediction of internal state values in large-format lithium ion cells and its validation
Gao et al. Estimation of state-of-charge and state-of-health for lithium-ion degraded battery considering side reactions
Chen et al. Analysis and prediction of the discharge characteristics of the lithium–ion battery based on the Grey system theory
Chen et al. State of charge and state of health estimation for lithium-ion battery through dual sliding mode observer based on AMESim-Simulink co-simulation
Hamar et al. Anode potential estimation in lithium-ion batteries using data-driven models for online applications
Parmananda et al. Underpinnings of Multiscale Interactions and Heterogeneities in Li‐Ion Batteries: Electrode Microstructure to Cell Format
Pang et al. A composite state of charge estimation for electric vehicle lithium-ion batteries using back-propagation neural network and extended kalman particle filter
Peng et al. Real-time state of charge estimation of the extended Kalman filter and unscented Kalman filter algorithms under different working conditions
Xu et al. Order reduction of lithium-ion battery model based on solid state diffusion dynamics via large scale systems theory
Liu et al. A simplified fractional order modeling and parameter identification for lithium-ion batteries
Mellgren et al. Impedance as a tool for investigating aging in lithium-ion porous electrodes: I. physically based electrochemical model
Xu et al. A hybrid method for lithium-ion batteries state-of-charge estimation based on gated recurrent unit neural network and an adaptive unscented Kalman filter