A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter
Abstract
:1. Introduction
2. Battery Model and Parameters Identification
2.1. Battery Model
2.2. Parameters Identification
Parameters | Ro | R1 | R2 | C1 | C2 |
---|---|---|---|---|---|
Value | 0.0377 Ω | 0.0242 Ω | 0.00300 Ω | 1673.3 F | 17,823 F |
3. SOC Estimation Based on the Strong Tracking Cubature Kalman Filter
3.1. Cubature Kalman Filter Algorithm (CKF)
- (a)
- Initialization:
- (b)
- Time update
- (1)
- Calculate the cubature points:
- (2)
- Calculate the propagated cubature points:
- (3)
- Calculate the predicted state and covariance:
- (c)
- Measurement update
- (1)
- Calculate the cubature points:
- (2)
- Calculate the propagated cubature points:
- (3)
- Calculate the predicted measurement and covariance:
- (d)
- Estimate the Kalman gain, updated state and error covariance:
3.2. Strong Tracking Cubature Kalman Filter (STCKF)
- (1)
- Poor robustness against model uncertainties.
- (2)
- Loss of tracking ability for sudden changes of the state when it has reached steady state.
- (3)
- Cannot be used to estimate time-varying parameters.
Initialization | |
(1) Time update | |
(a) The cubature points | |
(b) Propagated cubature points | |
(c) State and covariance time update | |
(2) Measurement update | |
(a) The cubature points | |
(b) Propagated cubature points | |
(c) Measurement and error covariance | |
(3) The fading factor | |
(4) Update after add fading factor | |
(a) The cubature points | |
(b) Propagated cubature points | |
(c) Measurement and error covariance | |
(d) Estimate the Kalman gain, updated state and error covariance | |
4. Experimental Configurations
5. Verification Results and Analysis
5.1. Estimation Results under Dynamic Stress Test (DST) Cycle
5.2. Estimation Results under New European Driving Cycle (NEDC) Cycle with Voltage Noise
Estimation Method | EKF | CKF | STCKF |
---|---|---|---|
RMSE | 0.0233 | 0.0157 | 0.0133 |
Maximum error | 6.13% | 5.28% | 4.17% |
Execution time | 0.89 s | 1.55 s | 2.58 s |
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Xia, B.; Wang, H.; Wang, M.; Sun, W.; Xu, Z.; Lai, Y. A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter. Energies 2015, 8, 13458-13472. https://doi.org/10.3390/en81212378
Xia B, Wang H, Wang M, Sun W, Xu Z, Lai Y. A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter. Energies. 2015; 8(12):13458-13472. https://doi.org/10.3390/en81212378
Chicago/Turabian StyleXia, Bizhong, Haiqing Wang, Mingwang Wang, Wei Sun, Zhihui Xu, and Yongzhi Lai. 2015. "A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter" Energies 8, no. 12: 13458-13472. https://doi.org/10.3390/en81212378