State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model
State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a chal...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2013-01-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/6/1/444 |
| _version_ | 1798005245812408320 |
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| author | Hongjie Wu Shifei Yuan Chengliang Yin |
| author_facet | Hongjie Wu Shifei Yuan Chengliang Yin |
| author_sort | Hongjie Wu |
| collection | DOAJ |
| description | State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs. |
| first_indexed | 2024-04-11T12:36:08Z |
| format | Article |
| id | doaj.art-eb2fc18273954ea4955c49eca0321d6d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T12:36:08Z |
| publishDate | 2013-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-eb2fc18273954ea4955c49eca0321d6d2022-12-22T04:23:37ZengMDPI AGEnergies1996-10732013-01-016144447010.3390/en6010444State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery ModelHongjie WuShifei YuanChengliang YinState of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.http://www.mdpi.com/1996-1073/6/1/444state of chargeARX battery modelHPPC testlithium-ion batteryextended Kalman filter&#160 |
| spellingShingle | Hongjie Wu Shifei Yuan Chengliang Yin State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model Energies state of charge ARX battery model HPPC test lithium-ion battery extended Kalman filter&#160 |
| title | State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model |
| title_full | State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model |
| title_fullStr | State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model |
| title_full_unstemmed | State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model |
| title_short | State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model |
| title_sort | state of charge estimation using the extended kalman filter for battery management systems based on the arx battery model |
| topic | state of charge ARX battery model HPPC test lithium-ion battery extended Kalman filter&#160 |
| url | http://www.mdpi.com/1996-1073/6/1/444 |
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