Next Article in Journal
Study on the Thermal Distribution Characteristics of High-Speed and Light-Load Rolling Bearing Considering Skidding
Next Article in Special Issue
Numerical Investigation of Energy Saving Characteristic in Building Roof Coupled with PCM Using Lattice Boltzmann Method with Economic Analysis
Previous Article in Journal
Micromechanism of the Dispersion Behavior of Polymer-Modified Rejuvenators in Aged Asphalt Material
Previous Article in Special Issue
Power to Fuels: Dynamic Modeling of a Slurry Bubble Column Reactor in Lab-Scale for Fischer Tropsch Synthesis under Variable Load of Synthesis Gas
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(9), 1592; https://doi.org/10.3390/app8091592

A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries

School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
*
Authors to whom correspondence should be addressed.
Received: 22 August 2018 / Revised: 1 September 2018 / Accepted: 6 September 2018 / Published: 8 September 2018
(This article belongs to the Special Issue Clean Energy and Fuel (Hydrogen) Storage)
Full-Text   |   PDF [5592 KB, uploaded 10 September 2018]   |  

Abstract

In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC). In the proposed model, the solid-phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks. To improve the global accuracy of the model, a subarea parameter-identification method based on particle swarm optimization is proposed, in order to determine the optimal model parameters in the entire SOC area. Then, an SOC estimator is developed based on extended kalman filter. The comparative study shows that a model considering solid-phase diffusion with two RC networks is the best choice. Finally, experimental results show that the accuracy of the proposed model is one times higher than that of the traditional ECM in the low SOC area, and is able to estimate SOC with errors less than 1% in the entire SOC area. Furthermore, estimation results of two types of batteries under two working conditions indicate that the developed model and SOC estimator have satisfactory global accuracy and guaranteed robustness with low computational complexity, which can be applied in real-time situations. View Full-Text
Keywords: lithium-ion batteries; simplified electrochemical model; state of charge estimator; extended kalman filter lithium-ion batteries; simplified electrochemical model; state of charge estimator; extended kalman filter
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

Share & Cite This Article

MDPI and ACS Style

Lai, X.; Qin, C.; Gao, W.; Zheng, Y.; Yi, W. A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries. Appl. Sci. 2018, 8, 1592.

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]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top