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Energies 2015, 8(8), 8594-8612; doi:10.3390/en8088594

A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles

1
College of Vehicle and Transportation Engineering, Henan University of Science and Technology, Luoyang 471023, China
2
National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
3
Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Omar Hegazy
Received: 30 April 2015 / Revised: 22 July 2015 / Accepted: 4 August 2015 / Published: 12 August 2015
(This article belongs to the Special Issue Advances in Plug-in Hybrid Vehicles and Hybrid Vehicles)
View Full-Text   |   Download PDF [855 KB, uploaded 12 August 2015]   |  

Abstract

Accurate state of charge (SoC) estimation of batteries plays an important role in promoting the commercialization of electric vehicles. The main work to be done in accurately determining battery SoC can be summarized in three parts. (1) In view of the model-based SoC estimation flow diagram, the n-order resistance-capacitance (RC) battery model is proposed and expected to accurately simulate the battery’s major time-variable, nonlinear characteristics. Then, the mathematical equations for model parameter identification and SoC estimation of this model are constructed. (2) The Akaike information criterion is used to determine an optimal tradeoff between battery model complexity and prediction precision for the n-order RC battery model. Results from a comparative analysis show that the first-order RC battery model is thought to be the best based on the Akaike information criterion (AIC) values. (3) The real-time joint estimator for the model parameter and SoC is constructed, and the application based on two battery types indicates that the proposed SoC estimator is a closed-loop identification system where the model parameter identification and SoC estimation are corrected mutually, adaptively and simultaneously according to the observer values. The maximum SoC estimation error is less than 1% for both battery types, even against the inaccurate initial SoC. View Full-Text
Keywords: electric vehicles; lithium-ion battery; real-time; state of charge; n-order RC model; Akaike information criterion electric vehicles; lithium-ion battery; real-time; state of charge; n-order RC model; Akaike information criterion
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).

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MDPI and ACS Style

Gao, J.; Zhang, Y.; He, H. A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles. Energies 2015, 8, 8594-8612.

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