Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries
In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjus...
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| Format: | Article |
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MDPI AG
2016-06-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/6/472 |
| _version_ | 1798002572995330048 |
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| author | Zhongwei Deng Lin Yang Yishan Cai Hao Deng |
| author_facet | Zhongwei Deng Lin Yang Yishan Cai Hao Deng |
| author_sort | Zhongwei Deng |
| collection | DOAJ |
| description | In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance (R). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R. Combining the positive strategy (i.e., online identification) and negative strategy (i.e., fuzzy model), a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePO4 batteries. |
| first_indexed | 2024-04-11T11:54:26Z |
| format | Article |
| id | doaj.art-943eba2b4b0a4b85a458c400463036b4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T11:54:26Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-943eba2b4b0a4b85a458c400463036b42022-12-22T04:25:13ZengMDPI AGEnergies1996-10732016-06-019647210.3390/en9060472en9060472Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion BatteriesZhongwei Deng0Lin Yang1Yishan Cai2Hao Deng3School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaIn the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance (R). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R. Combining the positive strategy (i.e., online identification) and negative strategy (i.e., fuzzy model), a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePO4 batteries.http://www.mdpi.com/1996-1073/9/6/472battery management systemintrinsic model errorparameter reliability criterionfuzzy adaptive extended Kalman filterstate of charge |
| spellingShingle | Zhongwei Deng Lin Yang Yishan Cai Hao Deng Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries Energies battery management system intrinsic model error parameter reliability criterion fuzzy adaptive extended Kalman filter state of charge |
| title | Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries |
| title_full | Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries |
| title_fullStr | Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries |
| title_full_unstemmed | Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries |
| title_short | Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries |
| title_sort | online identification with reliability criterion and state of charge estimation based on a fuzzy adaptive extended kalman filter for lithium ion batteries |
| topic | battery management system intrinsic model error parameter reliability criterion fuzzy adaptive extended Kalman filter state of charge |
| url | http://www.mdpi.com/1996-1073/9/6/472 |
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