A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles
Abstract
:1. Introduction
1.1. Contribution of the Paper
1.2. Organization of the Paper
2. Battery Modeling and Real-Time Parameter Identification
3. Central Difference Kalman Filter (CDKF)-Based State of Charge (SoC) Estimator
3.1. State of Charge Definition
3.2. State-Space Modeling
3.3. SoC Estimation Using the Central Difference Kalman Filter Algorithm
- Data measurement. The sensors collect the real-time data on current, voltage and temperature at each sampling time, and then the collected data are applied to identify the model parameters and estimate the SoC real-timely.
- Model parameter identification. The RLS method is used to realize real-time model parameter identification based on the collected data of current and voltage. Then the identified model parameters are transferred to the CDKF-based SoC estimator and the estimated OCV value is transferred back in turn. Herein, a stable and accurate RLS-based model parameter identification process can ensure, and at the same time is based on the good stability and high accuracy of the CDKF estimator.
- CDKF-based SoC estimator. The CDKF algorithm is used to estimate the SoC based on the identified model parameters. In this process, if model parameters are not identified correctly, the CDKF estimator will not work normally, thus leading to the wrong returned OCV value. However, the RLS and CDKF automatically correct the wrong estimates based on the big observer errors and gain matrices simultaneously, then both estimates of them will converge to the true values quickly, which realizes the close-loop SoC estimation process. Herein the proposed estimator in this paper is able to estimate the SoC accurately against different operating environment disturbances.
4. Data Set of Lithium-Ion Cell for Verification
4.1. Experiment Setup
4.2. Battery Test
Lithium-Ion Battery Cell | LiFePO4 | LiMn2O4 |
---|---|---|
Nominal capacity (Ah) | 2.3 | 35 |
Maximum available capacity (Ah) | 2.2 | 31.81 |
Nominal voltage (V) | 3.3 | 3.7 |
Upper cut-off voltage (V) | 3.8 | 4.2 |
Lower cut-off voltage (V) | 1.6 | 3.0 |
5. Verification and Discussion
5.1. Model Selection
n | Maximum (mV) | Mean (mV) | Standard Deviation (mV) | AIC | Duration (s) |
---|---|---|---|---|---|
0 | 67.02 | −0.30 | 6.02 | 7.18 | 4.64 |
1 | 7.68 | 7.9 × 10−3 | 0.18 | −4.80 | 4.75 |
2 | 8.82 | 9.3× 10−3 | 0.15 | −3.67 | 4.93 |
3 | 8.81 | 7.2 × 10−3 | 0.14 | −1.93 | 5.28 |
4 | 8.77 | 5.7 × 10−3 | 0.13 | −0.04 | 5.54 |
5 | 8.74 | 4.4 × 10−3 | 0.13 | 1.88 | 5.90 |
5.2. SoC Estimation
Initial SoC (%) | Maximum (mV) | Mean (mV) | Standard Deviation (mV) |
---|---|---|---|
90% | 4.71 | −8.42 × 10−2 | 3.56 × 10−2 |
80% | 4.72 | –8.43 × 10−2 | 3.56 × 10−2 |
Index | Maximum | Mean | Standard Deviation | Duration (s) |
---|---|---|---|---|
SoC (%) | 0.04 | −0.01 | 0.01 | 48.585 |
Voltage (mV) | 74.63 | −14.30 | 11.32 |
Index | Maximum | Mean | Standard Deviation | Duration (s) |
---|---|---|---|---|
SoC (%) | 0.05 | −0.02 | 0.01 | 49.115 |
Voltage (mV) | 74.67 | −14.31 | 11.32 |
Initial SoC (%) | Maximum (mV) | Mean (mV) | Standard (mV) |
---|---|---|---|
90% | 6.95 | −8.16 × 10−2 | 0.74 |
80% | 6.85 | −8.58 × 10−2 | 0.74 |
Initial SOC (%) | Voltage Prediction Error (mV) | SOC Estimation Error (%) | Duration (s) | ||||
---|---|---|---|---|---|---|---|
Maximum | Mean | Standard Deviation | Maximum | Mean | Standard Deviation | ||
80 | 73.98 | −4.83 | 9.29 | 0.77 | −0.28 | 0.23 | 63.017 |
90 | 72.93 | −4.89 | 9.36 | 0.65 | −0.40 | 0.21 | 62.900 |
6. Conclusions
Author Contributions
Conflicts of Interest
Abbreviations
SoC | State of charge |
AIC | Akaike information criterion |
PNGV | Partnership for New Generation Of Vehicles |
RC | Resistance-capacitance |
OCV | Open circuit voltage |
CDKF | Central difference Kalman filter |
HPPC | Hybrid pulse power characteristic |
DST | Dynamic Stress Test |
BJDC | Beijing Driving cycles |
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Gao, J.; Zhang, Y.; He, H. A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles. Energies 2015, 8, 8594-8612. https://doi.org/10.3390/en8088594
Gao J, Zhang Y, He H. A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles. Energies. 2015; 8(8):8594-8612. https://doi.org/10.3390/en8088594
Chicago/Turabian StyleGao, Jianping, Yongzhi Zhang, and Hongwen He. 2015. "A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles" Energies 8, no. 8: 8594-8612. https://doi.org/10.3390/en8088594