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Batteries 2018, 4(2), 19; https://doi.org/10.3390/batteries4020019

Real-Time Implementation of an Extended Kalman Filter and a PI Observer for State Estimation of Rechargeable Li-Ion Batteries in Hybrid Electric Vehicle Applications—A Case Study

1
Department of Mathematics and Informatics, University of Petrosani, Petrosani 332006, Romania
2
Department of Building, Civil and Environmental Engineering, University Concordia from Montreal, Montreal, QC H3G 1M8, Canada
3
Department of Control, Computers, Electrical and Power Engineering, University of Petrosani, Petrosani 332006, Romania
4
Department of Engineering Technologies, John Abbott College, Saint-Anne-de-Bellevue, QC H9X 3L9, Canada
*
Author to whom correspondence should be addressed.
Received: 8 March 2018 / Revised: 2 April 2018 / Accepted: 4 April 2018 / Published: 10 April 2018
(This article belongs to the Special Issue Battery Integration and Operation in Electro-Mobile Applications)

Abstract

The Li-Ion battery state-of-charge estimation is an essential task in a continuous dynamic automotive industry for large-scale and successful marketing of hybrid electric vehicles. Also, the state-of-charge of any rechargeable battery, regardless of its chemistry, is an essential condition parameter for battery management systems of hybrid electric vehicles. In this study, we share from our accumulated experience in the control system applications field some preliminary results, especially in modeling, control and state estimation techniques. We investigate the design and effectiveness of two state-of-charge estimators, namely an extended Kalman filter and a proportional integral observer, implemented in a real-time MATLAB environment for a particular Li-Ion battery. Definitely, the aim of this work is to find the most suitable estimator in terms of estimation accuracy and robustness to changes in initial conditions (i.e., the initial guess value of battery state-of-charge) and changes in process and measurement noise levels. By a rigorous performance analysis of MATLAB simulation results, the potential estimator choice is revealed. The performance comparison can be done visually on similar graphs if the information gathered provides a good insight, otherwise, it can be done statistically based on the calculus of statistic errors, in terms of root mean square error, mean absolute error and mean square error. View Full-Text
Keywords: state-of-charge; state estimation; extended Kalman filter; PI observer state estimator; hybrid electric vehicle; battery management system; Li-Ion battery; equivalent circuit model state-of-charge; state estimation; extended Kalman filter; PI observer state estimator; hybrid electric vehicle; battery management system; Li-Ion battery; equivalent circuit model
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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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Tudoroiu, R.-E.; Zaheeruddin, M.; Radu, S.-M.; Tudoroiu, N. Real-Time Implementation of an Extended Kalman Filter and a PI Observer for State Estimation of Rechargeable Li-Ion Batteries in Hybrid Electric Vehicle Applications—A Case Study. Batteries 2018, 4, 19.

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