Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics
Abstract
:1. Introduction
2. Lithium-Ion Battery
2.1. Modelling
2.2. Problem Statement
3. Parameter Identification
4. Effects of Vibration and Temperature on Battery State
4.1. Vibration
4.2. Temperature
4.3. SOC Estimation
4.4. SOH Estimation
4.5. State Estimation Based on Double Extended Kalman Filter
5. Experimental Test System
5.1. Experimental Set-Up
5.2. Experimental Procedures
5.3. Results and Discussion
5.4. Future Application of DEKF to Address the Challenges of Battery State Estimation
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- Non-uniform power delivered by the battery as it depends on the current state of the battery;
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- Intensive computational efforts;
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- Difficulty in model parameterization, since parameters requires to be adjusted, and they are difficult to measure them;
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- Poor robustness and relatively low accuracy;
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- The problem of potential over-fitting;
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- The problem of sensitivity of optimization methods in terms of quality and quantity of data, etc.
5.5. Future Research Directions and Discussions
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AKF | Adaptive Kalman filter |
AEKF | Adaptive extended Kalman filter |
BTS | Battery testing system |
BMS | Battery management system |
CCCV | Constant current constant voltage |
DOD | Depth-of-discharge |
DEKF | Dual-extended Kalman filter |
ECM | Equivalent circuit voltage |
EOL | End of life |
EVs | Electric vehicles |
EKF | Extended Kalman filter |
HPPC | Hybrid pulse power characterization |
KF | Kalman filter |
LIB | Lithium-ion battery |
MATLAB | MATrix LABoratory |
OCV | Open-circuit voltage |
RC | Resistance-capacitance |
RLS | Recursive least square |
SOC | State-of-charge |
SOH | State-of-health |
SOE | State-of-energy |
SOF | State-of-function |
SOP | State-of-power |
SOT | State-of-temperature |
SOS | State-of-safety |
SOV | State-of-vibration |
UKF | Unscented Kalman filter |
VEHs | Vibration al energy harvesters |
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Cell Property | Value |
---|---|
Nominal capacity | 2.9 Ah |
Rated capacity | 2.7 Ah |
Nominal voltage | 3.6 V |
Dimensions (D × l) | 18 mm × 65 mm |
Internal resistance | 35 mΩ |
Charging Voltage | 4.20 ± 0.03V |
End of Discharge Voltage | 2.5 V |
Standard charging current | 1.35 A |
Charge/discharge efficiency | 80–90% |
Approximate Weight | 47.0 g |
Series | Cylindrical cell |
Property | Value | Property | Value |
---|---|---|---|
Maximum acceleration | 20 g | Amplitude Range | 0~5 mm |
Frequency range (0.1 Hz) | 1~600 Hz | Vibration waveform | Sine wave (half-wave/full-wave) |
Rated Frequency | 1–600 Hz | Vibration direction | Up and down + left and right + before and after (three axes) |
Table size | 1000 × 1000 mm | Precision | 0.1 Hz |
Max. Test Load | 100 kg | Power | 5 Kw |
Time Rise (s) | Cumulative Time (s) | Relative Current (I) | Relative Voltage (V) |
---|---|---|---|
13 | 13 | 0 | 4.1325 |
11 | 24 | −2.9002 | 3.9186 |
41 | 65 | 0 | 4.1183 |
11 | 76 | 2.1752 | 4.3086 |
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Omariba, Z.B.; Zhang, L.; Kang, H.; Sun, D. Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics. World Electr. Veh. J. 2020, 11, 50. https://doi.org/10.3390/wevj11030050
Omariba ZB, Zhang L, Kang H, Sun D. Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics. World Electric Vehicle Journal. 2020; 11(3):50. https://doi.org/10.3390/wevj11030050
Chicago/Turabian StyleOmariba, Zachary Bosire, Lijun Zhang, Hanwen Kang, and Dongbai Sun. 2020. "Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics" World Electric Vehicle Journal 11, no. 3: 50. https://doi.org/10.3390/wevj11030050