Wognsen et al., 2015 - Google Patents
A score function for optimizing the cycle-life of battery-powered embedded systemsWognsen et al., 2015
View PDF- Document ID
- 3663637766694364049
- Author
- Wognsen E
- Haverkort B
- Jongerden M
- Hansen R
- Larsen K
- Publication year
- Publication venue
- Formal Modeling and Analysis of Timed Systems: 13th International Conference, FORMATS 2015, Madrid, Spain, September 2-4, 2015, Proceedings 13
External Links
Snippet
An ever increasing share of embedded systems is powered by rechargeable batteries. These batteries deteriorate with the number of charge/discharge cycles they are subjected to, the so-called cycle life. In this paper, we propose the wear score function to compare and …
- 238000005457 optimization 0 abstract description 9
Classifications
-
- 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
- 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
- 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
-
- 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
- H01M10/46—Accumulators structurally combined with charging apparatus
-
- 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
- H01M10/05—Accumulators with non-aqueous electrolyte
- H01M10/052—Li-accumulators
-
- 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
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wognsen et al. | A score function for optimizing the cycle-life of battery-powered embedded systems | |
Kim et al. | Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search | |
El Mejdoubi et al. | Lithium-ion batteries health prognosis considering aging conditions | |
US10101406B2 (en) | Method and apparatus for estimating state of battery | |
Zhang et al. | Comprehensive dynamic battery modeling for PHEV applications | |
Hu et al. | Charging time and loss optimization for LiNMC and LiFePO4 batteries based on equivalent circuit models | |
Dini et al. | Review on modeling and soc/soh estimation of batteries for automotive applications | |
US9882409B2 (en) | System and method for correcting SOC of battery | |
EP3605126B1 (en) | Apparatus and method for estimating soc of battery | |
CN110007240A (en) | A kind of lithium ion battery residual life prediction technique | |
US20230349977A1 (en) | Method and apparatus for estimating state of health of battery | |
Vetter et al. | Rechargeable batteries with special reference to lithium-ion batteries | |
Lami et al. | Minimizing the state of health degradation of Li-ion batteries onboard low earth orbit satellites | |
CN111426960A (en) | Energy storage lithium battery charge state monitoring method and device | |
Bashir et al. | A Review of Battery Management System and Modern State Estimation Approaches in Lithiumion Batteries for Electric Vehicle | |
Mandli et al. | Fast computational framework for optimal life management of lithium ion batteries | |
Li et al. | An equivalent circuit model of li-ion battery based on electrochemical principles used in grid-connected energy storage applications | |
Kottas et al. | Intelligent power management: Promoting power-consciousness in teams of mobile robots | |
Li | Adaptive model-based state monitoring and prognostics for lithium-ion batteries | |
Oyewole | Optimal model reduction of lithium-ion battery systems using particle swarm optimization | |
Lucchetta et al. | Battery State of Charge estimation using a Machine Learning approach | |
Donadee et al. | Estimating the rate of battery degradation under a stationary Markov operating policy | |
Pinter et al. | Comparative analysis of rule-based and model predictive control algorithms in reconfigurable battery systems for ev fast-charging stations | |
Shekar | Real-Time Estimation of State-of-Charge Using Particle Swarm Optimization on the Electro-Chemical Model of a Single Cell | |
Wognsen et al. | A Score Function for State of Charge Profiles for Rechargeable Batteries |