Samanta et al., 2023 - Google Patents
Combined data driven and online impedance measurement-based lithium-ion battery state of health estimation for electric vehicle battery management systemsSamanta et al., 2023
- Document ID
- 8518475022205616986
- Author
- Samanta A
- Huynh A
- Shrestha N
- Williamson S
- Publication year
- Publication venue
- 2023 IEEE Applied Power Electronics Conference and Exposition (APEC)
External Links
Snippet
Impedance measurement-based lithium-ion battery state of health (SOH) estimation technique is the most accurate technique compared to the model-based and data-driven techniques. Typically, electrochemical impedance spectroscopy (EIS) is used to measure …
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
- G01R31/3662—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
- G01R31/3648—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
- G01R31/3665—Various constructional arrangements whereby the type of battery is of primary emphasis, e.g. determining the type of 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
- G01R31/3627—Testing, i.e. making a one-time determination of some variables, e.g. testing ampere-hour charge capacity
-
- 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]
-
- 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
- Y02E60/122—Lithium-ion batteries
-
- 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
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
-
- 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
- G01R31/02—Testing of electric apparatus, lines or components, for short-circuits, discontinuities, leakage of current, or incorrect line connection
- G01R31/024—Arrangements for indicating continuity or short-circuits in electric apparatus or lines, leakage or ground faults
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xiong et al. | A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries | |
Wang et al. | Lithium-ion battery temperature on-line estimation based on fast impedance calculation | |
Piłatowicz et al. | A critical overview of definitions and determination techniques of the internal resistance using lithium-ion, lead-acid, nickel metal-hydride batteries and electrochemical double-layer capacitors as examples | |
Farmann et al. | Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles | |
Lashway et al. | Adaptive battery management and parameter estimation through physics-based modeling and experimental verification | |
Mingant et al. | EIS measurements for determining the SOC and SOH of Li-ion batteries | |
Hossain et al. | A parameter extraction method for the Thevenin equivalent circuit model of Li-ion batteries | |
Varnosfaderani et al. | Online impedance spectroscopy estimation of a battery | |
Soto et al. | Noninvasive aging analysis of lithium-ion batteries in extreme cold temperatures | |
Galeotti et al. | Diagnostic methods for the evaluation of the state of health (SOH) of NiMH batteries through electrochemical impedance spectroscopy | |
Guoliang et al. | State of charge estimation for NiMH battery based on electromotive force method | |
Lyu et al. | A fast time domain measuring technique of electrochemical impedance spectroscopy based on FFT | |
Zhou et al. | Battery state of health estimation using the generalized regression neural network | |
Ranieri et al. | Electronic module for the thermal monitoring of a Li-ion battery cell through the electrochemical impedance estimation | |
Zhao et al. | Investigation into impedance measurements for rapid capacity estimation of lithium-ion batteries in electric vehicles | |
Luo et al. | AC impedance technique for dynamic and static state of charge analysis for Li-ion battery | |
Cui et al. | Prediction model and principle of end-of-life threshold for lithium ion batteries based on open circuit voltage drifts | |
Samanta et al. | Combined data driven and online impedance measurement-based lithium-ion battery state of health estimation for electric vehicle battery management systems | |
Vatani et al. | State of health prediction of li-ion batteries using incremental capacity analysis and support vector regression | |
Cai et al. | D-ukf based state of health estimation for 18650 type lithium battery | |
Kulkarni et al. | Li-ion battery digital twin based on online impedance estimation | |
Sun et al. | Estimation of Lithium Primary Battery Capacity Based on Pulse Load Test | |
Denisov et al. | Automated excitation signal generation system for time-domain impedance spectroscopy | |
Barcellona et al. | Analysis of the lithium-ion batteries resistance hysteresis phenomenon | |
Stroe et al. | Electric circuit modeling of lithium-sulfur batteries during discharging state |