Next Article in Journal
Using CPE Function to Size Capacitor Storage for Electric Vehicles and Quantifying Battery Degradation during Different Driving Cycles
Next Article in Special Issue
Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications
Previous Article in Journal
Auto-Mapping and Configuration Method of IEC 61850 Information Model Based on OPC UA
Previous Article in Special Issue
Modeling of a Pouch Lithium Ion Battery Using a Distributed Parameter Equivalent Circuit for Internal Non-Uniformity Analysis
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2016, 9(11), 900; doi:10.3390/en9110900

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

1
National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
2
Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
3
Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Rui Xiong, Hailong Li and Joe (Xuan) Zhou
Received: 19 August 2016 / Revised: 19 October 2016 / Accepted: 26 October 2016 / Published: 1 November 2016
(This article belongs to the Special Issue Advanced Energy Storage Technologies and Their Applications (AESA))
View Full-Text   |   Download PDF [3710 KB, uploaded 1 November 2016]   |  

Abstract

A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation. View Full-Text
Keywords: electrochemical process analysis; SOC-OCV modeling; SOC estimation; lithium-ion batteries electrochemical process analysis; SOC-OCV modeling; SOC estimation; lithium-ion batteries
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, C.; Jiang, J.; Zhang, L.; Liu, S.; Wang, L.; Loh, P.C. A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery. Energies 2016, 9, 900.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top