[go: up one dir, main page]

Bourelly et al., 2023 - Google Patents

Ga-based features selection for electro-chemical impedance spectroscopy on lithium iron phosphate batteries

Bourelly et al., 2023

View PDF
Document ID
8101572805904240669
Author
Bourelly C
Vitelli M
Milano F
Molinara M
Fontanella F
Ferrigno L
Publication year
Publication venue
2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)

External Links

Snippet

Online and real-time estimation of the State of Charge (SoC) of batteries is an issue that affects several applications where energy storage systems are used. Among the most effective techniques for estimating the SoC, we find those based on Electrochemical …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/28Testing of electronic circuits, e.g. by signal tracer
    • G01R31/317Testing of digital circuits
    • G01R31/3181Functional testing
    • G01R31/3183Generation of test inputs, e.g. test vectors, patterns or sequence
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
    • G01R31/3644Various constructional arrangements
    • G01R31/3648Various constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • G01R31/3651Software aspects, e.g. battery modeling, using look-up tables, neural networks
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/28Testing of electronic circuits, e.g. by signal tracer
    • G01R31/2832Specific tests of electronic circuits not provided for elsewhere
    • G01R31/2836Fault-finding or characterising
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
    • G01R31/3644Various constructional arrangements
    • G01R31/3662Various constructional arrangements involving measuring the internal battery impedance, conductance or related variables
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483Physical analysis of biological material
    • G01N33/487Physical analysis of biological material of liquid biological material

Similar Documents

Publication Publication Date Title
Faraji-Niri et al. Accelerated state of health estimation of second life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine learning techniques
Zhang et al. Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks
Yang et al. Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model
Li et al. State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression
You et al. Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach
Zhou et al. State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression
Kim et al. Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction
Heinrich et al. Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data
Jorge et al. Time series feature extraction for lithium-ion batteries state-of-health prediction
Pizarro-Carmona et al. GA-based approach to optimize an equivalent electric circuit model of a Li-ion battery-pack
Li et al. State of health estimation of lithium-ion batteries using EIS measurement and transfer learning
Zhao et al. State-of-health estimation with anomalous aging indicator detection of lithium-ion batteries using regression generative adversarial network
Wong et al. Li-ion batteries state-of-charge estimation using deep lstm at various battery specifications and discharge cycles
Narayanan et al. Machine learning-based model development for battery state of charge–open circuit voltage relationship using regression techniques
Wang et al. An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm
Buchicchio et al. Uncertainty characterization of a CNN method for Lithium-Ion Batteries state of charge estimation using EIS data
Bourelly et al. Ga-based features selection for electro-chemical impedance spectroscopy on lithium iron phosphate batteries
Zhao et al. Data-driven battery health prognosis with partial-discharge information
CN115575841A (en) Retired battery health state evaluation method and system based on electrochemical impedance spectrum
Ghosh et al. An evolving quantum fuzzy neural network for online state-of-health estimation of Li-ion cell
Bourelly et al. Eis-based soc estimation: A novel measurement method for optimizing accuracy and measurement time
Temiz et al. State of charge and temperature-dependent impedance spectra regeneration of lithium-ion battery by duplex learning modeling
Zhang et al. Early-stage lifetime prediction for lithium-ion batteries: Linear regression-ensemble learning hybrid model based on impedance spectroscopy geometry
Dai et al. State-of-health estimation of lithium-ion batteries using multiple correlation analysis-based feature screening and optimizing echo state networks with the weighted mean of vectors
Mustafa et al. SoC estimation on Li-ion batteries: A new EIS-based dataset for data-driven applications