Review on the State of Charge Estimation Methods for Electric Vehicle Battery
Abstract
:1. Introduction
2. The Definition of SOC
3. SOC Estimation Methods
3.1. Traditional Methods Based on Experiments
3.1.1. Open Circuit Voltage
3.1.2. Ampere-Hour Integral Method
3.1.3. Internal Resistance Method
3.1.4. Discharge Test Method
3.2. Modern Methods Based on Control Theory
3.2.1. Neural Network Method
3.2.2. Kalman Filter Method
3.2.3. Linear Model Method
3.2.4. Particle Filter Algorithm
3.3. Other Methods Based on the Innovative Ideas
4. Conclusions
5. Current and Future Developments
- A rich database should be established to make the SOC estimation more reliable, which depends on a large number of experiments.
- It is important to improve the hardware technology, improve the accuracy of voltage and current parameters, and strive to ensure the accuracy of SOC.
- A more accurate battery with good dynamic characteristics and versatility model should be built to accurately describe the dynamic characteristics of the battery in use.
- We must carry on the effective synthesis of each kind of method, strives for the biggest degree to display respective superiority, promotes the strong point and avoids the weak point.
- Make full use of interdisciplinary advantages and transfer theoretical knowledge from other disciplines to the remaining electricity estimates.
- Establish theoretical methods with better dynamic adaptability and precision, and improve the processing methods and theoretical basis of nonlinear systems.
- Increase efforts to study more stable batteries, such as battery internal resistance and polarization problems.
Author Contributions
Funding
Conflicts of Interest
References
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Estimation | Advantages | Disadvantages |
---|---|---|
Traditional methods based on experiments | The simple and stable algorithm is simple and stable Mature technology Easy to implement | High requirements on hardware The effect is usually better in a certain period of battery estimation A large amount of experimental investment is required Obvious cumulative effect of errors |
Modern methods based on control theory | Better eliminate error accumulation effect Correct the noise well High convergence speed and accuracy | Higher requirements for battery model The algorithm is too complex |
other methods | Strong pertinence Interdisciplinary and interdisciplinary applications | The practical applicability needs to be further verified Complex algorithm |
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Zhang, M.; Fan, X. Review on the State of Charge Estimation Methods for Electric Vehicle Battery. World Electr. Veh. J. 2020, 11, 23. https://doi.org/10.3390/wevj11010023
Zhang M, Fan X. Review on the State of Charge Estimation Methods for Electric Vehicle Battery. World Electric Vehicle Journal. 2020; 11(1):23. https://doi.org/10.3390/wevj11010023
Chicago/Turabian StyleZhang, Mingyue, and Xiaobin Fan. 2020. "Review on the State of Charge Estimation Methods for Electric Vehicle Battery" World Electric Vehicle Journal 11, no. 1: 23. https://doi.org/10.3390/wevj11010023