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

Danko et al., 2019 - Google Patents

Overview of batteries State of Charge estimation methods

Danko et al., 2019

View PDF
Document ID
4015896599269418492
Author
Danko M
Adamec J
Taraba M
Drgona P
Publication year
Publication venue
Transportation Research Procedia

External Links

Snippet

In the present, electric and hybrid electric vehicles are more and more common. Usually, the main source of energy are batteries. To achieve optimal utilization and protection of batteries, battery management is used. This battery management system prevents batteries …
Continue reading at www.sciencedirect.com (PDF) (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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage for electromobility
    • Y02T10/7005—Batteries
    • Y02T10/7011—Lithium ion battery
    • 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
    • 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
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging several batteries simultaneously or sequentially
    • H02J7/0021—Monitoring or indicating circuits
    • 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
    • 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
    • H01M10/05—Accumulators with non-aqueous electrolyte
    • H01M10/052—Li-accumulators

Similar Documents

Publication Publication Date Title
Danko et al. Overview of batteries State of Charge estimation methods
Azis et al. State of charge (SoC) and state of health (SoH) estimation of lithium-ion battery using dual extended kalman filter based on polynomial battery model
Song et al. Improved SOC estimation of lithium-ion batteries with novel SOC-OCV curve estimation method using equivalent circuit model
Kim Nonlinear state of charge estimator for hybrid electric vehicle battery
Watrin et al. Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation
Chang The state of charge estimating methods for battery: A review
Chen et al. Robust adaptive sliding-mode observer using RBF neural network for lithium-ion battery state of charge estimation in electric vehicles
Tang et al. A balancing current ratio based state-of-health estimation solution for lithium-ion battery pack
Huang et al. Fast Charge Strategy Based on the Characterization and Evaluation of LiFePO $ _ {\bm 4} $ Batteries
US7525285B2 (en) Method and system for cell equalization using state of charge
Yatsui et al. Kalman filter based state-of-charge estimation for lithium-ion batteries in hybrid electric vehicles using pulse charging
Goud et al. An online method of estimating state of health of a Li-ion battery
Kim et al. Adaptive battery state-of-charge estimation method for electric vehicle battery management system
Samadani et al. A review study of methods for lithium-ion battery health monitoring and remaining life estimation in hybrid electric vehicles
Qiu et al. Battery hysteresis modeling for state of charge estimation based on Extended Kalman Filter
Hussein et al. State-of-charge estimation for a single Lithium battery cell using Extended Kalman Filter
Baba et al. State of charge estimation of lithium-ion battery using Kalman filters
Han et al. Modeling for lithium-ion battery used in electric vehicles
Qin et al. State of charge estimation for lithium-ion batteries based on NARX neural network and UKF
Wang et al. State-of-charge estimation of lithium-ion batteries based on multiple filters method
Han et al. State of Charge estimation of Li-ion battery in EVs based on second-order sliding mode observer
Kim et al. Online state of charge and electrical impedance estimation for multicell lithium-ion batteries
Chaoui et al. State of charge estimation of LiFePO 4 batteries with temperature variations using neural networks
Kim et al. Design of State of Charge and Health Estimation for Li-ion Battery Management System
Dong et al. A method for peak power prediction of series-connected lithium-ion battery pack using extended Kalman filter