State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization
Owing to the degradation of an echelon-use lithium-ion battery (EULIB), the Ohmic internal resistance (OIR) and actual capacity (AE) have both changed greatly, and the state of energy (SOE) can more accurately represent the state of a EULIB than the state of charge (SOC) because of the working volta...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1137358/full |
| _version_ | 1811155485525540864 |
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| author | Enguang Hou Zhen Wang Zhixue Wang Xin Qiao Guangmin Liu |
| author_facet | Enguang Hou Zhen Wang Zhixue Wang Xin Qiao Guangmin Liu |
| author_sort | Enguang Hou |
| collection | DOAJ |
| description | Owing to the degradation of an echelon-use lithium-ion battery (EULIB), the Ohmic internal resistance (OIR) and actual capacity (AE) have both changed greatly, and the state of energy (SOE) can more accurately represent the state of a EULIB than the state of charge (SOC) because of the working voltage. To improve the accuracy and adaptability of SOE estimation, in the paper, we study the energy state estimation of a EULIB. First, the four-order resistor–capacitance equivalent model of a EULIB is established, and an unscented transformation is introduced to further improve the estimation accuracy of the SOE. Second, a EULIB’s SOE is estimated based on adaptive unscented Kalman filter (AUKF), and the OIR and AE of a EULIB are estimated based on the AUKF. Third, a Takagi–Sugeno fuzzy model is introduced to optimize the OIR and AE of the EULIB, and the SOE estimation method is established based on an adaptive dual unscented Kalman filter (ADUKF). Through simulation experiments, verification, and comparison, energy decayed to 80%, 60%, and 40% of the rated energy, respectively, even with a large initial error; with the initial value of the SOE starting at 100%, 60%, or 20%, the estimated SOE can track the actual value. It can be seen that the method has a strong adaptive ability, and the estimation accuracy error is less than 1.0%, indicating that the algorithm has high accuracy. The method presented in this paper provides a new perspective for SOE estimation of EULIBs. |
| first_indexed | 2024-04-10T04:33:48Z |
| format | Article |
| id | doaj.art-58609a8deec643a3a8c9bd6561178c70 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T04:33:48Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-58609a8deec643a3a8c9bd6561178c702023-03-10T04:50:22ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-03-011110.3389/fenrg.2023.11373581137358State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimizationEnguang HouZhen WangZhixue WangXin QiaoGuangmin LiuOwing to the degradation of an echelon-use lithium-ion battery (EULIB), the Ohmic internal resistance (OIR) and actual capacity (AE) have both changed greatly, and the state of energy (SOE) can more accurately represent the state of a EULIB than the state of charge (SOC) because of the working voltage. To improve the accuracy and adaptability of SOE estimation, in the paper, we study the energy state estimation of a EULIB. First, the four-order resistor–capacitance equivalent model of a EULIB is established, and an unscented transformation is introduced to further improve the estimation accuracy of the SOE. Second, a EULIB’s SOE is estimated based on adaptive unscented Kalman filter (AUKF), and the OIR and AE of a EULIB are estimated based on the AUKF. Third, a Takagi–Sugeno fuzzy model is introduced to optimize the OIR and AE of the EULIB, and the SOE estimation method is established based on an adaptive dual unscented Kalman filter (ADUKF). Through simulation experiments, verification, and comparison, energy decayed to 80%, 60%, and 40% of the rated energy, respectively, even with a large initial error; with the initial value of the SOE starting at 100%, 60%, or 20%, the estimated SOE can track the actual value. It can be seen that the method has a strong adaptive ability, and the estimation accuracy error is less than 1.0%, indicating that the algorithm has high accuracy. The method presented in this paper provides a new perspective for SOE estimation of EULIBs.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1137358/fullstate of energy (SOE)echelon-use lithium-ion battery (EULIB)Takagi–Sugeno (TS) fuzzy optimizationfour-order resistor–capacitance equivalent model (FRCEM)adaptive dual unscented Kalman filter (ADUKF) |
| spellingShingle | Enguang Hou Zhen Wang Zhixue Wang Xin Qiao Guangmin Liu State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization Frontiers in Energy Research state of energy (SOE) echelon-use lithium-ion battery (EULIB) Takagi–Sugeno (TS) fuzzy optimization four-order resistor–capacitance equivalent model (FRCEM) adaptive dual unscented Kalman filter (ADUKF) |
| title | State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization |
| title_full | State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization |
| title_fullStr | State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization |
| title_full_unstemmed | State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization |
| title_short | State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization |
| title_sort | state of energy estimation of the echelon use lithium ion battery based on takagi sugeno fuzzy optimization |
| topic | state of energy (SOE) echelon-use lithium-ion battery (EULIB) Takagi–Sugeno (TS) fuzzy optimization four-order resistor–capacitance equivalent model (FRCEM) adaptive dual unscented Kalman filter (ADUKF) |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1137358/full |
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