Next Article in Journal
Estimating the Charging Profile of Individual Charge Sessions of Electric Vehicles in The Netherlands
Previous Article in Journal
Application of Driverless Electric Automated Shuttles for Public Transport in Villages: The Case of Appelscha
Article Menu

Export Article

Open AccessArticle
World Electr. Veh. J. 2018, 9(2), 16; https://doi.org/10.3390/wevj9020016

Online Multi Chemistry SoC Estimation Technique Using Data Driven Battery Model Parameter Estimation

ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Paper presented at EVS30 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, Stuttgart, Germany, 9–11 October 2017.
Paper presented at EVS30 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, Stuttgart, Germany, 9–11 October 2017.
*
Author to whom correspondence should be addressed.
Received: 8 May 2018 / Revised: 15 June 2018 / Accepted: 20 June 2018 / Published: 22 June 2018
Full-Text   |   PDF [1958 KB, uploaded 22 June 2018]   |  

Abstract

Kalman filters have shown to be a very accurate and robust method for State of Charge estimation. However, their performance depends heavily on the accuracy of the used battery model and its parameters. These battery model parameters have shown to vary with the State of Health, cell chemistry, temperature and load current. This paper studies a data driven battery model parameter estimation technique based on the recursive least squares method as an alternative to extensively characterizing every cell of interest with time-consuming test procedures. The performance of two commonly used electrical models is compared and extensively validated on three different cell chemistries (Nickel Cobalt Manganese, Lithium Iron Phosphate and Lithium Titanate Oxide), under load conditions of varying dynamic nature representative for electric vehicle (EV) applications, using a Dynamic Discharge Pulse Test (DDPT) and the Worldwide harmonized Light vehicles Test Procedure (WLTP). The developed model is able to identify and update battery model parameters online, for three different chemistries, potentially reducing offline characterization efforts and allowing monitoring of battery electrical behavior and state estimation over its entire lifetime. View Full-Text
Keywords: battery model; battery management system; electric vehicle; modeling; state of charge battery model; battery management system; electric vehicle; modeling; state of charge
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

De Sutter, L.; Nikolian, A.; Timmermans, J.-M.; Omar, N.; Van Mierlo, J. Online Multi Chemistry SoC Estimation Technique Using Data Driven Battery Model Parameter Estimation. World Electr. Veh. J. 2018, 9, 16.

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.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
World Electr. Veh. J. EISSN 2032-6653 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top