[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zequera et al., 2023 - Google Patents

Data Science-based Techniques for Modelling and Diagnostics of Battery Cells Based on End-of-Life criteria

Zequera et al., 2023

Document ID
5375313146531954426
Author
Zequera R
Rassõlkin A
Vaimann T
Kallaste A
Publication year
Publication venue
2023 International Conference on Electrical Drives and Power Electronics (EDPE)

External Links

Snippet

This research work focuses on implementing Data Science techniques for Battery Energy Storage Systems (BESSs) according to Health and Charge indicators based on End-of-Life (EOL) criteria. A simple Equivalent Circuit Model (ECM) is implemented to illustrate the …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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)

Similar Documents

Publication Publication Date Title
Rauf et al. Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling
Semeraro et al. Battery monitoring and prognostics optimization techniques: Challenges and opportunities
Liu et al. State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning
Xie et al. Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries
Shah et al. State of charge, remaining useful life and knee point estimation based on artificial intelligence and Machine learning in lithium-ion EV batteries: A comprehensive review
Bamati et al. Lithium-ion batteries long horizon health prognostic using machine learning
Bai et al. Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering
Zhang et al. Improved LSTM based state of health estimation using random segments of the charging curves for lithium-ion batteries
Lin et al. Lithium-ion batteries SOH estimation with multimodal multilinear feature fusion
Guo et al. An optimal relevance vector machine with a modified degradation model for remaining useful lifetime prediction of lithium-ion batteries
Li et al. A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression
Beltran et al. Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves
Zhao et al. Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization
Wang et al. An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm
Singh et al. A genetic algorithm and RNN-LSTM model for remaining battery capacity prediction
Han et al. Integration of long-short term memory network and fuzzy logic for high-safety in a FR-ESS with degradation and failure
Zequera et al. Data Science-based Techniques for Modelling and Diagnostics of Battery Cells Based on End-of-Life criteria
Sharma et al. State of Health Estimation for Li-Ion Battery Using Machine Learning
Aliberti et al. Comparative analysis of neural networks techniques for lithium-ion battery SOH estimation
Degla et al. An enhanced neural network application for a lithium-ion battery pack state-of-health estimator
Basia et al. Comparison of data driven algorithms for SoH estimation of Lithium-ion batteries
Zhang et al. Prediction of Lithium-ion battery's remaining useful life based on relevance vector machine
Jameel et al. Predicting batteries second-life state-of-health with first-life data and on-board voltage measurements using support vector regression
Arandhakar et al. State of charge estimation of lithium ion battery for electric vehicle using cutting edge machine learning algorithms: A review
Can Investigation of the aging of lithium-ion batteries