Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, Levenberg–Marquardt algorithm, and multiphase exponential regression model

The state of health (SOH) of lithium-ion (Li + ) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the Levenberg–Marquardt (LM) algorithm, and multiphase exponential...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications Vol. 33; no. 4; pp. 1193 - 1206
Main Authors: Ossai, Chinedu I., Egwutuoha, Ifeanyi P.
Format: Journal Article
Language:English
Published: London Springer London 01.02.2021
Springer Nature B.V
Subjects:
Algorithms
Anomalies
Artificial Intelligence
Battery cycles
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Electrical measurement
Image Processing and Computer Vision
Life cycle assessment
Lithium
Lithium-ion batteries
Model testing
Multiphase
Original Article
Probability and Statistics in Computer Science
Real time
Rechargeable batteries
Regression models
Segmentation
Transition probabilities
Lithium-ion battery
Temperature
Levenberg–Marquardt algorithm
State of health
Voltage
Multiphase exponential regression
Anomaly detection
ISSN:0941-0643, 1433-3058
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The state of health (SOH) of lithium-ion (Li + ) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the Levenberg–Marquardt (LM) algorithm, and multiphase exponential regression (MER) model to determine SOH of the Li + batteries. By determining the changepoint boundaries using the characteristic values such as voltage transition rate (VTR), temperature transition rate (TTR), and charge capacities of the Li + battery at the changepoint timestamps, we determined the parametric values of the biphasic MER. The characteristic transition rate values, which depend on the transition probabilities of the rolling standard deviations of the measured voltage and temperature, were later utilized with the matching charge capacities to model various training–testing dataset combinations. This helped to estimate the SOH of the battery at different life-cycle phases. This study also developed a technique for real-time estimation of the remaining useful life of the battery by using the MER model parameters, VTR, and TTR which were previously unseen parametric values of the Li + battery. The result obtained from the proposed model indicates that our technique will be effective for online SOH estimation of Li + batteries.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05031-1