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
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Batteries |
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| Online Access: | http://www.mdpi.com/2313-0105/4/2/19 |
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| author | Roxana-Elena Tudoroiu Mohammed Zaheeruddin Sorin-Mihai Radu Nicolae Tudoroiu |
| author_facet | Roxana-Elena Tudoroiu Mohammed Zaheeruddin Sorin-Mihai Radu Nicolae Tudoroiu |
| author_sort | Roxana-Elena Tudoroiu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T13:18:11Z |
| format | Article |
| id | doaj.art-f431216d7e67476fb3ee14e37007ac5c |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-12-14T13:18:11Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-f431216d7e67476fb3ee14e37007ac5c2022-12-21T23:00:01ZengMDPI AGBatteries2313-01052018-04-01421910.3390/batteries4020019batteries4020019Real-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 StudyRoxana-Elena Tudoroiu0Mohammed Zaheeruddin1Sorin-Mihai Radu2Nicolae Tudoroiu3Department of Mathematics and Informatics, University of Petrosani, Petrosani 332006, RomaniaDepartment of Building, Civil and Environmental Engineering, University Concordia from Montreal, Montreal, QC H3G 1M8, CanadaDepartment of Control, Computers, Electrical and Power Engineering, University of Petrosani, Petrosani 332006, RomaniaDepartment of Engineering Technologies, John Abbott College, Saint-Anne-de-Bellevue, QC H9X 3L9, CanadaThe 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.http://www.mdpi.com/2313-0105/4/2/19state-of-chargestate estimationextended Kalman filterPI observer state estimatorhybrid electric vehiclebattery management systemLi-Ion batteryequivalent circuit model |
| spellingShingle | Roxana-Elena Tudoroiu Mohammed Zaheeruddin Sorin-Mihai Radu Nicolae Tudoroiu 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 state-of-charge state estimation extended Kalman filter PI observer state estimator hybrid electric vehicle battery management system Li-Ion battery equivalent circuit model |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | state-of-charge state estimation extended Kalman filter PI observer state estimator hybrid electric vehicle battery management system Li-Ion battery equivalent circuit model |
| url | http://www.mdpi.com/2313-0105/4/2/19 |
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