Hussein, 2014 - Google Patents
Capacity fade estimation in electric vehicle li-ion batteries using artificial neural networksHussein, 2014
View PDF- Document ID
- 9721774573158385215
- Author
- Hussein A
- Publication year
- Publication venue
- IEEE Transactions on Industry Applications
External Links
Snippet
In this paper, an artificial neural network (ANN) based approach is proposed to estimate the capacity fade in lithium-ion (Li-ion) batteries for electric vehicles (EVs). Besides its robustness, stability, and high accuracy, the proposed technique can significantly improve …
- 229910001416 lithium ion 0 title abstract description 25
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
- G01R31/3679—Various constructional arrangements for determining battery ageing or deterioration, e.g. state-of-health (SoH), state-of-life (SoL)
-
- 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
- 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
-
- 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
-
- 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
-
- 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
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hussein | Capacity fade estimation in electric vehicle li-ion batteries using artificial neural networks | |
| Kim et al. | Online SOC and SOH estimation for multicell lithium-ion batteries based on an adaptive hybrid battery model and sliding-mode observer | |
| Huang et al. | A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature | |
| Chaoui et al. | State of charge and state of health estimation for lithium batteries using recurrent neural networks | |
| Gholizadeh et al. | Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model | |
| Ramadan et al. | Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis | |
| Chaoui et al. | Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks | |
| Chen et al. | Robust adaptive sliding-mode observer using RBF neural network for lithium-ion battery state of charge estimation in electric vehicles | |
| Duong et al. | Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares | |
| Fotouhi et al. | Lithium–sulfur battery state-of-charge observability analysis and estimation | |
| Awadallah et al. | Accuracy improvement of SOC estimation in lithium-ion batteries | |
| Aung et al. | State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter (Sqrt-UKFST) in nanosatellite | |
| KR101846690B1 (en) | System and Method for Managing Battery on the basis of required time for Charging | |
| Watrin et al. | Multiphysical lithium-based battery model for use in state-of-charge determination | |
| Dai et al. | ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries | |
| Lashway et al. | Adaptive battery management and parameter estimation through physics-based modeling and experimental verification | |
| Hossain et al. | A parameter extraction method for the li-ion batteries with wide-range temperature compensation | |
| Stroe et al. | Lithium-ion battery dynamic model for wide range of operating conditions | |
| Hussein | Kalman filters versus neural networks in battery state-of-charge estimation: A comparative study | |
| Eddahech et al. | Adaptive voltage estimation for EV Li-ion cell based on artificial neural networks state-of-charge meter | |
| 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 | |
| Chen et al. | Sliding mode observer for state of charge estimation based on battery equivalent circuit in electric vehicles | |
| Hussein | Derivation and comparison of open-loop and closed-loop neural network battery state-of-charge estimators | |
| El Lakkis et al. | Combined battery SOC/SOH estimation using a nonlinear adaptive observer |