Zhang et al., 2019 - Google Patents
State of charge estimation for lithium battery based on adaptively weighting cubature particle filterZhang et al., 2019
View PDF- Document ID
- 1513234028338124714
- Author
- Zhang K
- Ma J
- Zhao X
- Zhang D
- He Y
- Publication year
- Publication venue
- Ieee Access
External Links
Snippet
Accurate estimation of lithium battery state of charge is very important for ensuring the operation of battery management system, realizing the energy management strategy of electric vehicles, reducing mileage anxiety and promoting the sustainable development of …
- 239000002245 particle 0 title abstract description 45
Classifications
-
- 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
-
- 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
-
- 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
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies related to electric vehicle charging
- Y02T90/12—Electric charging stations
- Y02T90/128—Energy exchange control or determination
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | State of charge estimation for lithium battery based on adaptively weighting cubature particle filter | |
Shu et al. | An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation | |
Yang et al. | Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey | |
Meng et al. | An overview and comparison of online implementable SOC estimation methods for lithium-ion battery | |
Wang et al. | Probability based remaining capacity estimation using data-driven and neural network model | |
Wang et al. | An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect | |
Li et al. | Comparative study of the influence of open circuit voltage tests on state of charge online estimation for lithium-ion batteries | |
Wang et al. | Adaptive robust unscented Kalman filter-based state-of-charge estimation for lithium-ion batteries with multi-parameter updating | |
Li et al. | Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter | |
Li et al. | Review of lithium-ion battery state of charge estimation | |
Solomon et al. | State of charge estimation of Lithium-ion battery using an improved fractional-order extended Kalman filter | |
Zhao et al. | State of charge estimation based on a new dual-polarization-resistance model for electric vehicles | |
Li et al. | A novel state estimation approach based on adaptive unscented Kalman filter for electric vehicles | |
Xu et al. | State of charge estimation for liquid metal battery based on an improved sliding mode observer | |
Shao et al. | On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter | |
Samadani et al. | A review study of methods for lithium-ion battery health monitoring and remaining life estimation in hybrid electric vehicles | |
Shi et al. | Electric vehicle battery remaining charging time estimation considering charging accuracy and charging profile prediction | |
Liu et al. | A review of multi-state joint estimation for lithium-ion battery: Research status and suggestions | |
Duan et al. | Online parameter identification and state of charge estimation of battery based on multitimescale adaptive double Kalman filter algorithm | |
Li et al. | Multi-state joint estimation for a lithium-ion hybrid capacitor over a wide temperature range | |
Ouyang et al. | Prognostics and health management of lithium-ion batteries based on modeling techniques and Bayesian approaches: A review | |
Wang et al. | Identification of fractional-order equivalent circuit model of lithium-ion battery for improving estimation of state of charge | |
Takyi-Aninakwa et al. | A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries | |
Wei et al. | State-of-charge estimation for lithium-ion batteries based on temperature-based fractional-order model and dual fractional-order kalman filter | |
Liu et al. | Dynamic adaptive square-root unscented Kalman filter and rectangular window recursive least square method for the accurate state of charge estimation of lithium-ion batteries |