Next Article in Journal
Hydrogen Storage in Pristine and d10-Block Metal-Anchored Activated Carbon Made from Local Wastes
Next Article in Special Issue
Comparative Study of a Fault-Tolerant Multiphase Wound-Field Doubly Salient Machine for Electrical Actuators
Previous Article in Journal
China’s Low-Carbon Scenario Analysis of CO2 Mitigation Measures towards 2050 Using a Hybrid AIM/CGE Model
Previous Article in Special Issue
The Three-Phase Power Router and Its Operation with Matrix Converter toward Smart-Grid Applications
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(5), 3556-3577; doi:10.3390/en8053556

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

1
National Engineering Laboratory for the Automotive Electronic Control Technology, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China
2
University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Academic Editors: Paul Stewart and Chris Bingham
Received: 13 January 2015 / Revised: 3 April 2015 / Accepted: 20 April 2015 / Published: 28 April 2015
(This article belongs to the Special Issue Electrical Power and Energy Systems for Transportation Applications)
View Full-Text   |   Download PDF [1626 KB, uploaded 28 April 2015]   |  

Abstract

Estimation of state of charge (SOC) is of great importance for lithium-ion (Li-ion) batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF) and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster. View Full-Text
Keywords: state of charge; lithium-ion battery; electric vehicles; nonlinear predictive filter state of charge; lithium-ion battery; electric vehicles; nonlinear predictive filter
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

Hua, Y.; Xu, M.; Li, M.; Ma, C.; Zhao, C. Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles. Energies 2015, 8, 3556-3577.

Show more citation formats Show less citations formats

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