Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter
Abstract
:1. Introduction
2. Establish the Equivalent Circuit Model of the Battery
3. EKF Algorithm
4. Model Parameter Identification
4.1. Online Identification Model Based on EKF
4.2. Optimizing Noise Matrix Based on IPSO
4.3. Identification Based on IPSO–EKF
- 1.
- EKF
- 2.
- IPSO
5. Battery SOC State Estimation
5.1. Comprehensive Battery Model
5.2. SOC Estimation Based on IPSO–DEKF
5.3. Test and Simulation Analysis
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Wang, H.; Zheng, Y.; Yu, Y. Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter. Processes 2021, 9, 1412. https://doi.org/10.3390/pr9081412
Wang H, Zheng Y, Yu Y. Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter. Processes. 2021; 9(8):1412. https://doi.org/10.3390/pr9081412
Chicago/Turabian StyleWang, Hao, Yanping Zheng, and Yang Yu. 2021. "Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter" Processes 9, no. 8: 1412. https://doi.org/10.3390/pr9081412