Kwak et al., 2019 - Google Patents
Parameter identification and SOC estimation of a battery under the hysteresis effectKwak et al., 2019
- Document ID
- 163002444197049815
- Author
- Kwak M
- Lkhagvasuren B
- Park J
- You J
- Publication year
- Publication venue
- IEEE Transactions on Industrial Electronics
External Links
Snippet
This article concerns the estimation algorithms of battery's parameters and state of charge (SOC) and it is twofold. First, we present a simple variable length block wise least square estimation algorithm by considering locally linear SOC and open circuit voltage (OCV) …
- 230000000694 effects 0 title abstract description 23
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
- G01R31/3675—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
- G01R31/3644—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
- G01R31/3606—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]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kwak et al. | Parameter identification and SOC estimation of a battery under the hysteresis effect | |
Chen et al. | State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter | |
Li et al. | Constrained ensemble Kalman filter for distributed electrochemical state estimation of lithium-ion batteries | |
Ruan et al. | State of health estimation of lithium-ion battery based on constant-voltage charging reconstruction | |
Li et al. | A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique | |
Guha et al. | Online estimation of the electrochemical impedance spectrum and remaining useful life of lithium-ion batteries | |
Corno et al. | Electrochemical model-based state of charge estimation for Li-ion cells | |
Aung et al. | State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter (Sqrt-UKFST) in nanosatellite | |
Lotfi et al. | Reduced-order electrochemical model-based SOC observer with output model uncertainty estimation | |
Zhang et al. | Robust and adaptive estimation of state of charge for lithium-ion batteries | |
Xiong et al. | A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles | |
Vasebi et al. | A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications | |
CN1883097B (en) | Method for calculating power capability of battery packs | |
Locorotondo et al. | Online identification of thevenin equivalent circuit model parameters and estimation state of charge of lithium-ion batteries | |
An et al. | State of energy estimation for lithium-ion battery pack via prediction in electric vehicle applications | |
Eddahech et al. | Adaptive voltage estimation for EV Li-ion cell based on artificial neural networks state-of-charge meter | |
Taborelli et al. | State of charge estimation using extended Kalman filters for battery management system | |
Meng et al. | Comparative study of lithium‐ion battery open‐circuit‐voltage online estimation methods | |
Jiang et al. | Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery | |
Enache et al. | Comparative study for generic battery models used for electric vehicles | |
Duan et al. | Online parameter identification and state of charge estimation of battery based on multitimescale adaptive double Kalman filter algorithm | |
Asghar et al. | Simulation study on battery state of charge estimation using Kalman filter | |
Rosca et al. | On-line parameter, state-of-charge and aging estimation of Li-ion batteries | |
Zhang et al. | State-of-charge estimation of the lithium-ion battery using neural network based on an improved thevenin circuit model | |
Omiloli et al. | State of charge estimation based on a modified extended Kalman filter. |