Rezvanizaniani et al., 2014 - Google Patents
Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobilityRezvanizaniani et al., 2014
View PDF- Document ID
- 10664963492468526060
- Author
- Rezvanizaniani S
- Liu Z
- Chen Y
- Lee J
- Publication year
- Publication venue
- Journal of power sources
External Links
Snippet
As hybrid and electric vehicle technologies continue to advance, car manufacturers have begun to employ lithium ion batteries as the electrical energy storage device of choice for use in existing and future vehicles. However, to ensure batteries are reliable, efficient, and …
- 230000036541 health 0 title abstract description 38
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
- 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
-
- 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
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating condition, e.g. level or density of the electrolyte
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rezvanizaniani et al. | Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility | |
Hu et al. | State estimation for advanced battery management: Key challenges and future trends | |
Elmahallawy et al. | A comprehensive review of lithium-ion batteries modeling, and state of health and remaining useful lifetime prediction | |
Hannan et al. | A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations | |
Chen et al. | State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter | |
Tan et al. | Real-time state-of-health estimation of lithium-ion batteries based on the equivalent internal resistance | |
Lipu et al. | A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations | |
Farmann et al. | A comprehensive review of on-board State-of-Available-Power prediction techniques for lithium-ion batteries in electric vehicles | |
Li et al. | On state-of-charge determination for lithium-ion batteries | |
Wang et al. | Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator | |
Ungurean et al. | Battery state of health estimation: a structured review of models, methods and commercial devices | |
Takyi‐Aninakwa et al. | A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries | |
Zhang et al. | Battery modelling methods for electric vehicles-A review | |
Zhang et al. | A multi-fault diagnosis method for lithium-ion battery pack using curvilinear Manhattan distance evaluation and voltage difference analysis | |
Qiu et al. | A novel entropy-based fault diagnosis and inconsistency evaluation approach for lithium-ion battery energy storage systems | |
Samadani et al. | A review study of methods for lithium-ion battery health monitoring and remaining life estimation in hybrid electric vehicles | |
Blanco et al. | An Equivalent Circuit Model With Variable Effective Capacity for $\hbox {LiFePO} _ {4} $ Batteries | |
Li et al. | Lithium-ion battery management system for electric vehicles | |
Priya et al. | State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques | |
Peng et al. | Real-time state of charge estimation of the extended Kalman filter and unscented Kalman filter algorithms under different working conditions | |
Wei et al. | Global sensitivity analysis for impedance spectrum identification of lithium-ion batteries using time-domain response | |
Yang et al. | Remaining Useful Life Prediction of Lithium-ion Batteries with Limited Degradation History Using Random Forest | |
Zhang et al. | Multi-step fast charging based state of health estimation of lithium-ion batteries | |
Huang et al. | Electrochemical model-based aging characterization of lithium-ion battery cell in electrified vehicles | |
Vatani et al. | Cycling lifetime prediction model for lithium-ion batteries based on artificial neural networks |