A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health
Abstract
:1. Introduction
2. Battery Modeling and Parameter Identification
2.1. Equivalent Circuit Model of Lithium Battery
2.2. Online Parameter Identification of Lithium Battery
3. Joint State Estimation of Battery Power
3.1. UKF Algorithm Principle
3.2. UKPF Algorithm Principle
- (1)
- PF algorithm principle
- (2)
- UKPF algorithm principle
3.3. Multi-Time Scale Joint Estimation of Battery power State
- (1)
- Input the battery voltage and current data into the battery model for online parameter identification;
- (2)
- Determine whether the time scale transformation is met, if so, step (3) is carried out, otherwise, step (4) is carried out;
- (3)
- UKF estimates SOH and uses estimated results to update system parameters;
- (4)
- UKPF cycle estimation SOC;
- (5)
- Output the SOC and SOH estimation results.
4. Test Results and Analysis
4.1. Test Platform Building and Test Data Collection
4.2. Parameter Identification Results
4.3. Multi-Time Scale Joint Estimation Results
- (1)
- UKPF algorithm estimation of SOC
- (2)
- UKF algorithm estimation of SOH
- (3)
- Multi-time scale joint estimation results
5. Conclusions
- (1)
- The battery parameters can be identified online. The error of the parameter identification results is less than 5%, which verifies the validity and accuracy of the model. Therefore, this model can accurately represent the working process of a lithium battery and lays a foundation for the subsequent estimation of its battery state.
- (2)
- Compared with the UKF and the PF algorithm, the UKPF algorithm has higher robust accuracy and stability, and its estimation error of a lithium battery’s state of charge is less than 3.4%. The SOH error of the UKF algorithm is less than 2.5%, which can accurately and effectively estimate the SOH of the battery.
- (3)
- The multi-time scale joint estimation error is within 2.2%, which significantly improves the estimation accuracy of a battery’s SOC and ensures the long-term estimation performance of a battery.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Battery Parameters | Nominal Capacity (mAh) | Charge Cut-Off Voltage (V) | Discharge Cut-Off Voltage (V) | Nominal Voltage (V) |
---|---|---|---|---|
INR18650-30Q | 3000 | 4.2 | 2.5 | 3.6 |
SOC Estimated Method | SOC Estimated Mean Error | SOC Estimated MAXIMUM error |
---|---|---|
Joint estimation algorithm | 0.74% | 2.11% |
UKPF algorithm | 1.19% | 3.37% |
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Yang, Q.; Ma, K.; Xu, L.; Song, L.; Li, X.; Li, Y. A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health. Coatings 2022, 12, 1047. https://doi.org/10.3390/coatings12081047
Yang Q, Ma K, Xu L, Song L, Li X, Li Y. A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health. Coatings. 2022; 12(8):1047. https://doi.org/10.3390/coatings12081047
Chicago/Turabian StyleYang, Qingxia, Ke Ma, Liyou Xu, Lintao Song, Xiuqing Li, and Yefei Li. 2022. "A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health" Coatings 12, no. 8: 1047. https://doi.org/10.3390/coatings12081047
APA StyleYang, Q., Ma, K., Xu, L., Song, L., Li, X., & Li, Y. (2022). A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health. Coatings, 12(8), 1047. https://doi.org/10.3390/coatings12081047