A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias
Accurate estimation of the state of charge (SOC) of zinc–nickel single-flow batteries (ZNBs) is an important problem in battery management systems (BMSs). A nonideal electromagnetic environment will usually cause the measured signal to contain nonnegligible noise and bias. At the same time, due to t...
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MDPI AG
2022-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/4/1537 |
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| author | Yang Guo Ziguang Lu |
| author_facet | Yang Guo Ziguang Lu |
| author_sort | Yang Guo |
| collection | DOAJ |
| description | Accurate estimation of the state of charge (SOC) of zinc–nickel single-flow batteries (ZNBs) is an important problem in battery management systems (BMSs). A nonideal electromagnetic environment will usually cause the measured signal to contain nonnegligible noise and bias. At the same time, due to the influence of battery ageing, environmental temperature changes, and a complex reaction mechanism, it is difficult to establish a very accurate system model that can be applied to various complex working conditions. The unscented Kalman filter (UKF) is a widely used SOC estimation algorithm, but the UKF will reduce the estimation accuracy and divergence under the influence of inaccurate model and sensor errors. To improve the performance of the UKF, a robust desensitized unscented Kalman filter (RDUKF) is proposed to realize an accurate SOC estimation of batteries in the context of different disturbances. Then, the proposed method is applied to cases of error interference, such as Gaussian noise, voltage sensor drift, an unknown initial state, and inaccurate model parameters. The simulation and experimental results show that compared with the standard UKF algorithm, the proposed estimation algorithm can effectively suppress the influence of various errors and disturbances and achieve higher accuracy and robustness. |
| first_indexed | 2024-03-09T22:03:25Z |
| format | Article |
| id | doaj.art-4ef052052c5044dc82ac1b31cbaff2df |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T22:03:25Z |
| publishDate | 2022-02-01 |
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| record_format | Article |
| series | Energies |
| spelling | doaj.art-4ef052052c5044dc82ac1b31cbaff2df2023-11-23T19:45:41ZengMDPI AGEnergies1996-10732022-02-01154153710.3390/en15041537A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor BiasYang Guo0Ziguang Lu1School of Electrical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Electrical Engineering, Guangxi University, Nanning 530004, ChinaAccurate estimation of the state of charge (SOC) of zinc–nickel single-flow batteries (ZNBs) is an important problem in battery management systems (BMSs). A nonideal electromagnetic environment will usually cause the measured signal to contain nonnegligible noise and bias. At the same time, due to the influence of battery ageing, environmental temperature changes, and a complex reaction mechanism, it is difficult to establish a very accurate system model that can be applied to various complex working conditions. The unscented Kalman filter (UKF) is a widely used SOC estimation algorithm, but the UKF will reduce the estimation accuracy and divergence under the influence of inaccurate model and sensor errors. To improve the performance of the UKF, a robust desensitized unscented Kalman filter (RDUKF) is proposed to realize an accurate SOC estimation of batteries in the context of different disturbances. Then, the proposed method is applied to cases of error interference, such as Gaussian noise, voltage sensor drift, an unknown initial state, and inaccurate model parameters. The simulation and experimental results show that compared with the standard UKF algorithm, the proposed estimation algorithm can effectively suppress the influence of various errors and disturbances and achieve higher accuracy and robustness.https://www.mdpi.com/1996-1073/15/4/1537real-time estimationrobust desensitized unscented Kalman filterstate of chargezinc–nickel single-flow batteries |
| spellingShingle | Yang Guo Ziguang Lu A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias Energies real-time estimation robust desensitized unscented Kalman filter state of charge zinc–nickel single-flow batteries |
| title | A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias |
| title_full | A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias |
| title_fullStr | A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias |
| title_full_unstemmed | A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias |
| title_short | A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias |
| title_sort | robust algorithm for state of charge estimation under model uncertainty and voltage sensor bias |
| topic | real-time estimation robust desensitized unscented Kalman filter state of charge zinc–nickel single-flow batteries |
| url | https://www.mdpi.com/1996-1073/15/4/1537 |
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