[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Shen et al., 2018 - Google Patents

Accurate state of charge estimation with model mismatch for li-ion batteries: A joint moving horizon estimation approach

Shen et al., 2018

Document ID
5551825543722900812
Author
Shen J
Shen J
He Y
Ma Z
Publication year
Publication venue
IEEE Transactions on Power Electronics

External Links

Snippet

The accurate state of charge (SOC) estimation plays a significant role in charge/discharge control, balance control, and safe management of lithium-ion batteries (LIBs). However, due to the model mismatch issues, either from battery inconsistency or battery dynamic …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • G01R31/3644—Various constructional arrangements
    • G01R31/3648—Various constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • G01R31/3651—Software aspects, e.g. battery modeling, using look-up tables, neural networks
    • 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
    • G01R31/3644—Various constructional arrangements
    • G01R31/3662—Various constructional arrangements involving measuring the internal battery impedance, conductance or related variables
    • 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
    • G01R31/3644—Various constructional arrangements
    • G01R31/3675—Various constructional arrangements for compensating for temperature or ageing
    • 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
    • G01R31/3606—Monitoring, i.e. measuring or determining some variables continuously or repeatedly over time, e.g. current, voltage, temperature, state-of-charge [SoC] or state-of-health [SoH]
    • 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
    • H01M10/44—Methods for charging or discharging
    • H01M10/446—Initial charging measures
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Shen et al. Accurate state of charge estimation with model mismatch for li-ion batteries: A joint moving horizon estimation approach
Li et al. A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique
Yang et al. A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles
Chen et al. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter
Takyi‐Aninakwa et al. A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries
Meng et al. A simplified model-based state-of-charge estimation approach for lithium-ion battery with dynamic linear model
Song et al. Current profile optimization for combined state of charge and state of health estimation of lithium ion battery based on Cramer–Rao bound analysis
Zheng et al. Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles
Tian et al. State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer
Wang et al. Revisiting the state-of-charge estimation for lithium-ion batteries: A methodical investigation of the extended Kalman filter approach
Deng et al. Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery
Zhang et al. Lithium-ion battery pack state of charge and state of energy estimation algorithms using a hardware-in-the-loop validation
Tang et al. Joint estimation of state-of-charge and state-of-health for all cells in the battery pack using “leader-follower” strategy
CN107690585B (en) Method and apparatus for determining the state of health and state of charge of a lithium sulfur battery
Xu et al. Hierarchical estimation model of state-of-charge and state-of-health for power batteries considering current rate
Zhang et al. Real-time capacity estimation of lithium-ion batteries utilizing thermal dynamics
Song et al. Model-based health diagnosis for lithium-ion battery pack in space applications
Wang et al. Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method
Nejad et al. Online battery state of power prediction using PRBS and extended Kalman filter
US20060111870A1 (en) Method and system for joint battery state and parameter estimation
Saleem et al. Online reduced complexity parameter estimation technique for equivalent circuit model of lithium-ion battery
Snoussi et al. Unknown input observer design for lithium-ion batteries SOC estimation based on a differential-algebraic model
Shen et al. Online state of charge estimation of lithium-ion batteries: A moving horizon estimation approach
Ding et al. Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter
Wang et al. Identification of fractional-order equivalent circuit model of lithium-ion battery for improving estimation of state of charge