Nothing Special   »   [go: up one dir, main page]

de Lima et al., 2021 - Google Patents

Data-driven state-of-charge estimation of the Panasonic 18650PF Li-ion cell using deep forward neural networks

de Lima et al., 2021

Document ID
13601584263678862036
Author
de Lima A
Salles M
Cardoso J
Publication year
Publication venue
2021 14th IEEE International Conference on Industry Applications (INDUSCON)

External Links

Snippet

The State-of-Charge (SOC) is a key parameter for the proper functioning of the Battery Management System (BMS) of lithium-ion (Li-ion) batteries, and indicates the amount of charge remaining in the battery. In this work, we present a novel empirical study for the data …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10Energy storage
    • Y02E60/12Battery technology
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/50Fuel cells
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01MPROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL INTO ELECTRICAL ENERGY
    • H01M8/00Fuel cells; Manufacture thereof
    • H01M8/04Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298Processes for controlling fuel cells or fuel cell systems
    • H01M8/04313Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/04537Electric variables
    • H01M8/04544Voltage
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01MPROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL INTO ELECTRICAL ENERGY
    • H01M10/00Secondary cells; Manufacture thereof
    • H01M10/42Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44Methods for charging or discharging
    • H01M10/446Initial charging measures

Similar Documents

Publication Publication Date Title
Wu et al. State of health estimation for lithium-ion batteries based on healthy features and long short-term memory
Shu et al. A flexible state-of-health prediction scheme for lithium-ion battery packs with long short-term memory network and transfer learning
Khalid et al. Prediction of li-ion battery state of charge using multilayer perceptron and long short-term memory models
Cui et al. A dynamic spatial-temporal attention-based GRU model with healthy features for state-of-health estimation of lithium-ion batteries
Bamati et al. Lithium-ion batteries long horizon health prognostic using machine learning
Sidhu et al. State of charge estimation of lithium-ion batteries using hybrid machine learning technique
Mamo et al. Long short-term memory with attention mechanism for state of charge estimation of lithium-ion batteries
Cui et al. State of health diagnosis and remaining useful life prediction for lithium-ion battery based on data model fusion method
Li et al. A novel state estimation approach based on adaptive unscented Kalman filter for electric vehicles
Wang et al. Health diagnosis for lithium-ion battery by combining partial incremental capacity and deep belief network during insufficient discharge profile
Lin et al. Lithium-ion batteries SOH estimation with multimodal multilinear feature fusion
Zhu et al. Battery voltage prediction using neural networks
Vilsen et al. Log-linear model for predicting the lithium-ion battery age based on resistance extraction from dynamic aging profiles
Varatharajalu et al. Electric vehicle parameter identification and state of charge estimation of Li-ion​ batteries: Hybrid WSO-HDLNN method
Zhang et al. Online state-of-health estimation for the lithium-ion battery based on an LSTM neural network with attention mechanism
Dewalkar et al. State of charge estimation system for electric vehicle batteries using ANN
de Lima et al. Data-driven state-of-charge estimation of the Panasonic 18650PF Li-ion cell using deep forward neural networks
Kumari et al. Recurrent Neural Network based Data-Driven SOC Estimation in Lithium-Ion Battery
Boujoudar et al. Lithium-Ion batteries modeling using NARX Nonlinear model
Zhang et al. The early prediction of lithium-ion battery remaining useful life using a novel long short-term memory network
Basia et al. Comparison of data driven algorithms for SoH estimation of Lithium-ion batteries
de Lima et al. State-of-charge estimation of a li-ion battery using deep learning and stochastic optimization
Mohanty et al. Effect of training algorithms in accurate state of charge estimation of lithium-ion batteries using NARX model
de Lima THE RELEVANCE OF THE OPTIMIZATION ALGORITHM ON THE DATA-DRIVEN ESTIMATION OF THE STATE-OF-CHARGE OF THE PANASONIC 18650PF LITHIUM-ION CELL USING DEEP FEEDFORWARD NEURAL NETWORKS
Huang et al. Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs