State of Health Estimation for Lithium-Ion Batteries Using IAO–SVR
Abstract
:1. Introduction
2. IAO–SVR
2.1. Aquila Optimizer
2.1.1. Expanded Exploration
2.1.2. Narrow Exploration
2.1.3. Expanded Exploitation
2.1.4. Narrow Exploitation
2.2. Improved Aquila Optimizer
2.2.1. Population Initialization Method by Logistic-Sin–Cos Chaotic Mapping
2.2.2. Adaptive t-Distribution Variation
2.2.3. Support Vector Regression
2.3. IAO–SVR Model
- Data pre-processing: Sample data is normalized, which allows the pre-processed data to be limited to a certain range, eliminates the influence of abnormal feature vectors, and places each data in the same order of magnitude; then, we can divide the data into training and test samples. The expression is shown in Equation (24) as follows:
- SVR parameter settings: The SVR parameter settings include the dimension of the individuals , the maximum number of iterations , the upper bound for individual values and lower bound for individual values , and the number of populations .
- Define fitness function: We define the two parameters as the individuals of the IAO algorithm; during the training process, each individual will be verified in the SVR. We propose the root mean square error (RMSE) as the fitness function, and the optimal individual is selected by the principle of minimum mean square error as follows:
3. Experiment and Analysis of Health Features
3.1. Definition of SOH
3.2. Battery Experiment
3.3. Selection of Health Features
3.4. Correlation Analysis
- (1)
- Set the reference sequence and the comparison sequence ;
- (2)
- Pre-process the variables to simplify calculations by reducing the range of variables;
- (3)
- Calculate the correlation coefficient between the comparison series and the reference series, where it is a constant value and is taken as 0.5. The specific formula is expressed as follows:
4. The SOH Estimation and Result Analysis
4.1. Experimental Environment
4.2. Analysis of Estimation Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Parameters | Specifications |
---|---|
Shape | Prismatic |
Capacity | 1100 mAH |
Weight | 21.1 g |
Dimensions | 5.4 × 33.6 × 50.6 mm |
Battery | Method | MAE | RMSE | |
---|---|---|---|---|
CS2-35 | IAO–SVR | 0.9976 | 0.0052 | 0.0081 |
AO–SVR | 0.9926 | 0.0111 | 0.0141 | |
SSA–SVR | 0.9918 | 0.0109 | 0.0148 | |
SVR | 0.9781 | 0.0226 | 0.0334 | |
CS2-36 | IAO–SVR | 0.9960 | 0.0103 | 0.0124 |
AO–SVR | 0.9915 | 0.0133 | 0.0180 | |
SSA–SVR | 0.9877 | 0.0169 | 0.0217 | |
SVR | 0.9502 | 0.0316 | 0.0437 | |
CS2-37 | IAO–SVR | 0.9987 | 0.0042 | 0.0071 |
AO–SVR | 0.9822 | 0.0192 | 0.0267 | |
SSA–SVR | 0.9943 | 0.0138 | 0.0150 | |
SVR | 0.9764 | 0.0211 | 0.0307 | |
CS2-38 | IAO–SVR | 0.9965 | 0.0088 | 0.0102 |
AO–SVR | 0.9917 | 0.0126 | 0.0156 | |
SSA–SVR | 0.9810 | 0.0200 | 0.0238 | |
SVR | 0.9691 | 0.0215 | 0.0304 |
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Xing, L.; Liu, X.; Luo, W.; Wu, L. State of Health Estimation for Lithium-Ion Batteries Using IAO–SVR. World Electr. Veh. J. 2023, 14, 122. https://doi.org/10.3390/wevj14050122
Xing L, Liu X, Luo W, Wu L. State of Health Estimation for Lithium-Ion Batteries Using IAO–SVR. World Electric Vehicle Journal. 2023; 14(5):122. https://doi.org/10.3390/wevj14050122
Chicago/Turabian StyleXing, Likun, Xiao Liu, Wenfei Luo, and Long Wu. 2023. "State of Health Estimation for Lithium-Ion Batteries Using IAO–SVR" World Electric Vehicle Journal 14, no. 5: 122. https://doi.org/10.3390/wevj14050122