A Battery SOC Estimation Method Based on AFFRLS-EKF
<p>Thevenin equivalent circuit model.</p> "> Figure 2
<p>Fitted Map of VOC–SOC.</p> "> Figure 3
<p>Adaptive forgetting factor regression least-quares–extended Kalman filter (AFFRLS–EKF) algorithm flow chart.</p> "> Figure 4
<p>Current curve.</p> "> Figure 5
<p>Terminal voltage curve.</p> "> Figure 6
<p>Regression least-squares–extended Kalman filter (RLS–EKF) algorithm. (<b>a</b>) Estimated terminal voltage and measured terminal voltage; (<b>b</b>) estimated terminal voltage error; (<b>c</b>) estimated state of charge (SOC)and measured SOC; (<b>d</b>) estimated SOC error.</p> "> Figure 7
<p>AFFRLS—EKF algorithm. (<b>a</b>) Estimated terminal voltage and measured terminal voltage; (<b>b</b>) estimated terminal voltage error; (<b>c</b>) estimated SOC and measured SOC; (<b>d</b>) estimated terminal voltage error.</p> ">
Abstract
:1. Introduction
- The paper proposed an AFFRLS–EKF SOC estimation strategy based on parameter identification modeling aiming at the uncertainty of battery model parameters under the condition of abrupt change of battery charge and discharge.
- The second-order Thevenin equivalent circuit model (2-order ECM) of the battery was established, and the SOC–OCV relationship was obtained. According to the charging and discharging conditions, a segment-adaptive recursive least square algorithm was designed to identify the parameters to improve the model accuracy.
- The proposed estimation strategy is applied to numerical simulation experiments, and RLS–EKF and AFFRLS–EKF are compared. The latter one has better performance in accuracy and robustness.
2. Equivalent Circuit Model
3. Adaptive Forgetting Factor Regression Least Squares
4. Simulation Results and Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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SOC | VOC | SOC | VOC | SOC | VOC |
---|---|---|---|---|---|
0% | 2.6936 | 35% | 3.6504 | 70% | 3.8270 |
5% | 3.2567 | 40% | 3.6776 | 75% | 3.8627 |
10% | 3.4552 | 45% | 3.6982 | 80% | 3.9073 |
15% | 3.5220 | 50% | 3.7184 | 85% | 3.9628 |
20% | 3.5408 | 55% | 3.7414 | 90% | 4.0056 |
25% | 3.5750 | 60% | 3.7670 | 95% | 4.0510 |
30% | 3.6144 | 65% | 3.7954 | 100% | 4.1000 |
1 Parameter initialization: , |
2 State Prediction: |
3 Kalman filter gain: |
4 Measure values updated: |
5 Posteriori estimates: |
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Li, M.; Zhang, Y.; Hu, Z.; Zhang, Y.; Zhang, J. A Battery SOC Estimation Method Based on AFFRLS-EKF. Sensors 2021, 21, 5698. https://doi.org/10.3390/s21175698
Li M, Zhang Y, Hu Z, Zhang Y, Zhang J. A Battery SOC Estimation Method Based on AFFRLS-EKF. Sensors. 2021; 21(17):5698. https://doi.org/10.3390/s21175698
Chicago/Turabian StyleLi, Ming, Yingjie Zhang, Zuolei Hu, Ying Zhang, and Jing Zhang. 2021. "A Battery SOC Estimation Method Based on AFFRLS-EKF" Sensors 21, no. 17: 5698. https://doi.org/10.3390/s21175698
APA StyleLi, M., Zhang, Y., Hu, Z., Zhang, Y., & Zhang, J. (2021). A Battery SOC Estimation Method Based on AFFRLS-EKF. Sensors, 21(17), 5698. https://doi.org/10.3390/s21175698