A battery state of charge online estimation method, device, equipment and medium
By acquiring the current basic variable information of the battery, optimizing the identification parameters and correcting the OCV-SOC relationship curve, and combining the Kalman filter algorithm, the problems of model complexity and noise disturbance in battery state of charge estimation are solved, achieving high-precision and stable state of charge estimation.
CN115932586BActive Publication Date: 2026-07-24SUNGROW POWER SUPPLY (NANJING) CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUNGROW POWER SUPPLY (NANJING) CO LTD
- Filing Date
- 2022-11-16
- Publication Date
- 2026-07-24
AI Technical Summary
Technical Problem
Existing technologies suffer from uncertainties in battery state of charge estimation due to model complexity, parameter imbalance, and noise disturbances, which affect estimation accuracy and robustness.
Method used
By acquiring the current basic variable information of the battery, optimizing the identification parameters, correcting the OCV-SOC relationship curve, using the Kalman filter algorithm for state of charge estimation, and adaptively updating the OCV-SOC curve, the error is reduced.
Benefits of technology
It improves the accuracy and robustness of state of charge estimation, ensures that the estimation results do not diverge, and enhances the ability to identify and correct long-period cumulative errors.
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Figure CN115932586B_ABST
Abstract
The application discloses a battery state of charge online estimation method, device, equipment and medium. The method comprises the following steps: determining current basic variable information of a battery to be estimated, and obtaining an OCV-SOC relationship curve of a previous open circuit voltage of the battery to be estimated; determining current identification parameter information of the battery to be estimated according to the current basic variable information; correcting the previous OCV-SOC relationship curve according to the current identification parameter information to obtain a current OCV-SOC relationship curve; and determining a state of charge estimation value of the battery to be estimated according to the current identification parameter information and the current OCV-SOC relationship curve. By obtaining the current basic variable information, the identification target is optimized, the identification correction capability of long-period cumulative error is improved, the robustness and precision of the state of charge estimation are improved, and the estimation result is ensured not to diverge. The OCV-SOC curve can be adaptively updated, and the error caused by the OCV-SOC curve offset is reduced. The state of charge estimation value is online estimated, and the estimation accuracy is improved.
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