NDLI: State-of-charge estimation of lithium-ion batteries using composite multi-dimensional features and a neural network

Content Provider	IET Digital Library
Author	Li, Jianhua Liu, Mingsheng
Abstract	A novel method that uses composite multi-dimensional features data to estimate the state of charge (SOC) of a battery is presented to address the shortcomings of using single-dimensional feature data. Two types of data, the terminal voltage and the terminal current, which can be obtained directly by measuring, are selected as low-dimensional feature data. The open-circuit voltage (OCV), as high-dimensional feature data, cannot be directly measured, and can be used to estimate the SOC by the OCV-SOC method. Thus, in this study, the second-order RC equivalent model of a battery is used and the OCV is identified online by the forgetting factor recursive least-squares algorithm. The proposed method is implemented by first using a feed-forward neural network, followed by a time-series neural network. The dynamic stress test and urban dynamometer driving schedule discharging profiles are applied to train and test the two neural networks. The experimental results show that the proposed method can estimate the SOC more accurately than neural networks using only single-dimensional feature data. Moreover, the time series neural network can overcome the shortcomings of traditional estimation methods.
Starting Page	1470
Ending Page	1478
Page Count	9
ISSN	17554535
Volume Number	12
e-ISSN	17554543
Issue Number	Issue 6, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-pel/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-pel.2018.6144
Journal	IET Power Electronics
Publisher Date	2019-02-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Battery Battery Management System Battery Powered Vehicle Dynamometers Feed-forward Neural Network High-dimensional Feature Data Interpolation And Function Approximation Least Squares Approximation Lithium-ion Battery Low-dimensional Feature Data Multidimensional Feature Data Neural Computing Technique Neural Nets Numerical Analysis OCV-SOC Method Open Circuit Voltage Secondary Cell Single-dimensional Feature Data State-of-charge Estimation Statistics Terminal Voltage Time Series Time Series Neural Network Time-series Neural Network Traditional Estimation Method
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Relaxation model of the open-circuit voltage for state-of-charge estimation in lithium-ion batteries

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

State-of-charge estimation for lithium-ion battery pack using reconstructed open-circuit-voltage curve

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery

State of charge estimation based on extened Kalman filter algorithm for Lithium-Ion battery

A State-of-Charge and Capacity Estimation Algorithm for Lithium-ion Battery Pack Utilizing Filtered Terminal Voltage

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times

State-of-charge estimation of lithium-ion batteries using composite multi-dimensional features and a neural network

Similar Documents

Relaxation model of the open-circuit voltage for state-of-charge estimation in lithium-ion batteries

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

State-of-charge estimation for lithium-ion battery pack using reconstructed open-circuit-voltage curve

A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature

A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery

State of charge estimation based on extened Kalman filter algorithm for Lithium-Ion battery

A State-of-Charge and Capacity Estimation Algorithm for Lithium-ion Battery Pack Utilizing Filtered Terminal Voltage

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times

State-of-charge estimation of lithium-ion batteries using composite multi-dimensional features and a neural network