State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations
Abstract The battery management system (BMS) in electric vehicles monitors the state of charge (SOC) and state of health (SOH) of lithium‐ion battery by controlling transient parameters such as voltage, current, and temperature prevents the battery from operating outside the optimal operating range....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2022-09-01
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| Series: | IET Electrical Systems in Transportation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/els2.12045 |
| _version_ | 1797430779867824128 |
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| author | Davoud Mahboubi Iraj Jafari Gavzan Mohammad Hassan Saidi Naghi Ahmadi |
| author_facet | Davoud Mahboubi Iraj Jafari Gavzan Mohammad Hassan Saidi Naghi Ahmadi |
| author_sort | Davoud Mahboubi |
| collection | DOAJ |
| description | Abstract The battery management system (BMS) in electric vehicles monitors the state of charge (SOC) and state of health (SOH) of lithium‐ion battery by controlling transient parameters such as voltage, current, and temperature prevents the battery from operating outside the optimal operating range. The main feature of the battery management system is the correct estimation of the SOC in the broad range of vehicle navigation. In this paper, to estimate real‐time of SOC in lithium‐ion batteries and overcome faults of Extended Kalman Filter (EKF), the Square‐Root Sigma Point Kalman Filter is applied on the basis of numerical approximations rather than analytical methods of EKF. For this purpose, the Hybrid Pulse Power Characterisation tests are combined with the non‐linear least square method that acquired the second‐order equivalent circuit model parameters. Then, the newly developed method is tested with an 18,650 cylindrical lithium‐ion battery with a nominal capacity of 2600 mAh in four different ambient temperatures. Finally, the accuracy and effectiveness of the two proposed methods are verified by comparing with results of pulse discharge and dynamic driving cycle tests. The comparison results indicate the error of the proposed algorithm is about 0.02 under the most test conditions. |
| first_indexed | 2024-03-09T09:32:29Z |
| format | Article |
| id | doaj.art-fe5e8b2d39cd4077abb02842cc5db462 |
| institution | Directory Open Access Journal |
| issn | 2042-9738 2042-9746 |
| language | English |
| last_indexed | 2024-03-09T09:32:29Z |
| publishDate | 2022-09-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Electrical Systems in Transportation |
| spelling | doaj.art-fe5e8b2d39cd4077abb02842cc5db4622023-12-02T03:27:52ZengHindawi-IETIET Electrical Systems in Transportation2042-97382042-97462022-09-0112316518010.1049/els2.12045State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variationsDavoud Mahboubi0Iraj Jafari Gavzan1Mohammad Hassan Saidi2Naghi Ahmadi3Faculty of Mechanical Engineering Semnan University Semnan IranFaculty of Mechanical Engineering Semnan University Semnan IranCenter of Excellence in Energy Conversion (CEEC) School of Mechanical Engineering Sharif University of Technology Tehran IranResearch and Development Department Mega Motor Company Tehran IranAbstract The battery management system (BMS) in electric vehicles monitors the state of charge (SOC) and state of health (SOH) of lithium‐ion battery by controlling transient parameters such as voltage, current, and temperature prevents the battery from operating outside the optimal operating range. The main feature of the battery management system is the correct estimation of the SOC in the broad range of vehicle navigation. In this paper, to estimate real‐time of SOC in lithium‐ion batteries and overcome faults of Extended Kalman Filter (EKF), the Square‐Root Sigma Point Kalman Filter is applied on the basis of numerical approximations rather than analytical methods of EKF. For this purpose, the Hybrid Pulse Power Characterisation tests are combined with the non‐linear least square method that acquired the second‐order equivalent circuit model parameters. Then, the newly developed method is tested with an 18,650 cylindrical lithium‐ion battery with a nominal capacity of 2600 mAh in four different ambient temperatures. Finally, the accuracy and effectiveness of the two proposed methods are verified by comparing with results of pulse discharge and dynamic driving cycle tests. The comparison results indicate the error of the proposed algorithm is about 0.02 under the most test conditions.https://doi.org/10.1049/els2.12045extended Kalman filterleast square methodlithium‐ion batterysquare‐root sigma point Kalman filterstate of charge (SOC) |
| spellingShingle | Davoud Mahboubi Iraj Jafari Gavzan Mohammad Hassan Saidi Naghi Ahmadi State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations IET Electrical Systems in Transportation extended Kalman filter least square method lithium‐ion battery square‐root sigma point Kalman filter state of charge (SOC) |
| title | State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations |
| title_full | State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations |
| title_fullStr | State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations |
| title_full_unstemmed | State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations |
| title_short | State of charge estimation for lithium‐ion batteries based on square root sigma point Kalman filter considering temperature variations |
| title_sort | state of charge estimation for lithium ion batteries based on square root sigma point kalman filter considering temperature variations |
| topic | extended Kalman filter least square method lithium‐ion battery square‐root sigma point Kalman filter state of charge (SOC) |
| url | https://doi.org/10.1049/els2.12045 |
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