Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation
As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accur...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-06-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/8/6/5217 |
| _version_ | 1798039745506312192 |
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| author | Saeed Sepasi Leon R. Roose Marc M. Matsuura |
| author_facet | Saeed Sepasi Leon R. Roose Marc M. Matsuura |
| author_sort | Saeed Sepasi |
| collection | DOAJ |
| description | As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V. |
| first_indexed | 2024-04-11T21:57:59Z |
| format | Article |
| id | doaj.art-d216ebebf37448d78b0e93bed55209c9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:57:59Z |
| publishDate | 2015-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d216ebebf37448d78b0e93bed55209c92022-12-22T04:01:02ZengMDPI AGEnergies1996-10732015-06-01865217523310.3390/en8065217en8065217Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge EstimationSaeed Sepasi0Leon R. Roose1Marc M. Matsuura2Hawaii Natural Energy Institute, University of Hawaii at Manoa, 1680 East-West Road, Post 105, Honolulu, HI 96822, USAHawaii Natural Energy Institute, University of Hawaii at Manoa, 1680 East-West Road, Post 105, Honolulu, HI 96822, USAHawaii Natural Energy Institute, University of Hawaii at Manoa, 1680 East-West Road, Post 105, Honolulu, HI 96822, USAAs the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V.http://www.mdpi.com/1996-1073/8/6/5217Li-ion batteryaged cellstate of chargeextended Kalman filterfuzzy |
| spellingShingle | Saeed Sepasi Leon R. Roose Marc M. Matsuura Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation Energies Li-ion battery aged cell state of charge extended Kalman filter fuzzy |
| title | Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation |
| title_full | Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation |
| title_fullStr | Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation |
| title_full_unstemmed | Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation |
| title_short | Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation |
| title_sort | extended kalman filter with a fuzzy method for accurate battery pack state of charge estimation |
| topic | Li-ion battery aged cell state of charge extended Kalman filter fuzzy |
| url | http://www.mdpi.com/1996-1073/8/6/5217 |
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