State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm
Abstract
:1. Introduction
2. The Battery Model
- Selecting the battery model (A novel composite electrochemical model).
- Determining the different parameters to be identified based on the selected model.
- Performing a series of characteristics tests on the battery.
- Linear fitting of the experimental data (offline identification).
- Building the model in MATLAB.
- Obtaining the equation of state.
- Developing the online estimation algorithm (iEKF).
- Inputting the current, temperature and SoC (simulations in static and dynamic conditions).
The Composite Battery Model
3. The Experiments and Offline Parameter Identification
3.1. Charge and Discharge Rate Test
3.2. Temperature Characteristics Test
3.3. Hybrid Pulse Power Characterization (HPPC) Test
3.4. OCV-SoC Test
3.5. Offline Parameter Identification
4. The SoC Estimation Based on an Improved EKF Algorithm
4.1. Analysis of the KF and EKF Algorithm
- Initial value of and :
- Predictive estimate of the and :
- KF gain Lk (weighting coefficient matrix)
- Optimal estimate of the and :
4.2. The SoC Estimation Model Corrected by EKF Based on Composite Model
- Model establishment: Use Equations (6) and (7).
- Determination of system parameters:
- Initialization of the state variable and the covariance.
- Iterative calculation of the EKF.
5. The Simulation Validation of the Improved EKF Algorithm
5.1. The Validation of the Improved EKF Algorithm
5.2. The Simulation Results
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Charge Rate Test | 0.2 C | 0.5 C | 1.0 C | 2.0 C | 3.0 C |
---|---|---|---|---|---|
Capacity/Ah | 211.45 | 208.95 | 206.88 | 204.07 | 194.76 |
Capacity retention rate/% | 102.21 | 101.00 | 100.00 | 98.64 | 94.14 |
Energy/Wh | 707.76 | 706.36 | 708.04 | 712.64 | 692.15 |
Energy retention rate/% | 99.96 | 99.76 | 100.00 | 100.65 | 97.76 |
Charge Rate Test | 0.2 C | 0.5 C | 1.0 C | 2.0 C | 3.0 C |
---|---|---|---|---|---|
Capacity/Ah | 214.31 | 212.94 | 211.84 | 211.28 | 211.08 |
Capacity retention rate/% | 101.16 | 100.52 | 100.00 | 99.74 | 99.64 |
Energy/Wh | 687.83 | 672.22 | 657.33 | 641.41 | 628.45 |
Energy retention rate/% | 104.64 | 102.26 | 100.00 | 97.58 | 95.61 |
Temperature Characteristic Test | −20 °C | −10 °C | 0 °C | 25 °C | 45 °C |
---|---|---|---|---|---|
Capacity/Ah | 194.18 | 201.14 | 205.49 | 211.27 | 211.35 |
Capacity retention rate/% | 91.91 | 95.20 | 97.26 | 100.00 | 100.00 |
Energy/Wh | 523.18 | 566.18 | 600.52 | 657.80 | 668.46 |
Energy retention rate/% | 79.54 | 86.07 | 91.29 | 100.00 | 101.62 |
SoC | 206Ah 4C Discharge/3C Charge | |||||
---|---|---|---|---|---|---|
Discharge | Charge | |||||
Vbefore (mV) | Vafter (mV) | DCR (mΩ) | Vbefore (mV) | Vafter (mV) | DCR (mΩ) | |
90% | 3328.30 | 2910.70 | 0.45 | 3277.20 | 3613.20 | 0.49 |
80% | 3327.40 | 2893.70 | 0.47 | 3267.20 | 3612.90 | 0.50 |
70% | 3327.40 | 2881.60 | 0.48 | 3258.90 | 3610.10 | 0.51 |
60% | 3296.70 | 2857.40 | 0.48 | 3244.90 | 3586.90 | 0.50 |
50% | 3289.00 | 2829.80 | 0.50 | 3230.40 | 3580.40 | 0.51 |
40% | 3288.00 | 2805.90 | 0.52 | 3217.30 | 3577.30 | 0.52 |
30% | 3284.30 | 2775.60 | 0.55 | 3201.50 | 3570.10 | 0.53 |
20% | 3254.50 | 2707.40 | 0.59 | 3169.90 | 3539.40 | 0.53 |
10% | 3209.60 | 2422.10 | 0.86 | 3110.40 | 3497.00 | 0.56 |
Operation Condition | Maximum Error (%) | Average Error (%) | Relative Error (%) |
---|---|---|---|
Static | 2.39 | 1.43 | 1.20 |
Dynamic | 6.76 | 3.94 | 2.15 |
Estimation Error | Initial SoC of 20% | Initial SoC of 50% | Initial SoC of 70% | ||||
---|---|---|---|---|---|---|---|
Operation | Ah Counting | iEKF | Ah Counting | iEKF | Ah Counting | iEKF | |
Static | 14.9 | 0.8 | 15.3 | 1.0 | 15.0 | 1.2 | |
Dynamic | 17.7 | 1.7 | 18.5 | 2.0 | 19.7 | 2.1 |
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Ding, N.; Prasad, K.; Lie, T.T.; Cui, J. State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm. Inventions 2019, 4, 66. https://doi.org/10.3390/inventions4040066
Ding N, Prasad K, Lie TT, Cui J. State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm. Inventions. 2019; 4(4):66. https://doi.org/10.3390/inventions4040066
Chicago/Turabian StyleDing, Ning, Krishnamachar Prasad, Tek Tjing Lie, and Jinhui Cui. 2019. "State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm" Inventions 4, no. 4: 66. https://doi.org/10.3390/inventions4040066
APA StyleDing, N., Prasad, K., Lie, T. T., & Cui, J. (2019). State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm. Inventions, 4(4), 66. https://doi.org/10.3390/inventions4040066