Next Article in Journal
3-D FEM Analysis, Prototyping and Tests of an Axial Flux Permanent-Magnet Wind Generator
Previous Article in Journal
Modelling and Simulation of Electric Vehicle Fast Charging Stations Driven by High Speed Railway Systems
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Energies 2017, 10(9), 1266; doi:10.3390/en10091266

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

1
Department of Mechanical Engineering, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA
2
Energy Systems and Power Electronics Laboratory, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA
This paper is an extended version of our paper published in Rahman, M.A.; Anwar, S.; Izadian, A. Electrochemical model based fault diagnosis of a lithium ion battery using multiple model adaptive estimation approach. In Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain, 17–19 March 2015.
*
Author to whom correspondence should be addressed.
Received: 27 June 2017 / Revised: 17 August 2017 / Accepted: 18 August 2017 / Published: 25 August 2017
View Full-Text   |   Download PDF [1946 KB, uploaded 25 August 2017]   |  

Abstract

Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system. View Full-Text
Keywords: electrochemical model; lithium-ion batteries; fault diagnosis; MMAE; PDAE observer; battery management system electrochemical model; lithium-ion batteries; fault diagnosis; MMAE; PDAE observer; battery management system
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).

Share & Cite This Article

MDPI and ACS Style

Rahman, M.A.; Anwar, S.; Izadian, A. Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC. Energies 2017, 10, 1266.

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