Robust SVM optimization using PSO and ACO for accurate lithium-ion battery health monitoring
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Mar 9, 2025
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Abstract
The increasing demand for reliable lithium-ion battery in various applications is focused on the need for accurate State of Health (SOH) predictions to prevent performance degradation and potential safety risks. Therefore, this research aimed to improve the accuracy of SOH prediction by integrating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) with Support Vector Machine (SVM) to overcome the overfitting problem in traditional machine learning models. The dataset used consisted of data from 1000 cycles of lithium-ion battery, collected under laboratory conditions. Data from lithium-ion battery cycles were analyzed using optimized PSO-SVM and ACO-SVM models. These models were evaluated using Mean Square Error (MSE) and Root Mean Square Error (RMSE) metrics, showing significant improvements in prediction accuracy and model generalization. The results showed that although both optimized models were superior to the baseline SVM, PSO-SVM had higher generalization performance during testing. The higher performance was due to the effective balance between exploring the search space and exploiting optimal solutions, making it more suitable for real-world applications. In comparison, ACO-SVM showed superior performance in training data accuracy but was more prone to overfitting, suggesting the potential for scenarios prioritizing high training accuracy. These results could be applied to extend the lifespan of lithium-ion battery, contributing to enhanced reliability and cost-effectiveness in applications.
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References
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[38] H. H. Goh, Z. Lan, D. Zhang, W. Dai, T. A. Kurniawan, and K. C. Goh, “Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction,” Journal of Energy Storage, vol. 50, p. 104646, Jun. 2022, doi: 10.1016/j.est.2022.104646.
[39] Z. Bao, J. Nie, H. Lin, J. Jiang, Z. He, and M. Gao, “A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery,” Energy, vol. 282, p. 128306, Nov. 2023, doi: 10.1016/j.energy.2023.128306.
[40] Y. Yang, S. Chen, T. Chen, and L. Huang, “State of Health Assessment of Lithium-ion Batteries Based on Deep Gaussian Process Regression Considering Heterogeneous Features,” Journal of Energy Storage, vol. 61, p. 106797, May 2023, doi: 10.1016/j.est.2023.106797.
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[2] F. Jiang et al., “A comprehensive review of energy storage technology development and application for pure electric vehicles,” Journal of Energy Storage, vol. 86, p. 111159, May 2024, doi: 10.1016/j.est.2024.111159.
[3] H. Zuo, B. Zhang, Z. Huang, K. Wei, H. Zhu, and J. Tan, “Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation,” Energy, vol. 238, p. 121854, Jan. 2022, doi: 10.1016/j.energy.2021.121854.
[4] Y. Deng et al., “Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries,” Energy, vol. 176, pp. 91–102, Jun. 2019, doi: 10.1016/j.energy.2019.03.177.
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[6] Z. Li, S. Shen, Y. Ye, Z. Cai, and A. Zhen, “An interpretable online prediction method for remaining useful life of lithium-ion batteries,” Scientific Reports, vol. 14, no. 1, p. 12541, May 2024, doi: 10.1038/s41598-024-63160-2.
[7] J. Nan, B. Deng, W. Cao, and Z. Tan, “Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window,” World Electric Vehicle Journal, vol. 13, no. 2, p. 25, Jan. 2022, doi: 10.3390/wevj13020025.
[8] J. Zhu, T. Tan, L. Wu, and H. Yuan, “RUL Prediction of Lithium-Ion Battery Based on Improved DGWO-ELM Method in a Random Discharge Rates Environment,” IEEE Access, vol. 7, pp. 125176–125187, 2019, doi: 10.1109/ACCESS.2019.2936822.
[9] S. Wen, N. Lin, S. Huang, X. Li, Z. Wang, and Z. Zhang, “Lithium battery state of health estimation using real-world vehicle data and an interpretable hybrid framework,” Journal of Energy Storage, vol. 96, p. 112623, Aug. 2024, doi: 10.1016/j.est.2024.112623.
[10] E. Ezemobi, M. Silvagni, A. Mozaffari, A. Tonoli, and A. Khajepour, “State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions,” Energies, vol. 15, no. 3, p. 1234, Feb. 2022, doi: 10.3390/en15031234.
[11] M. Bao et al., “Interpretable machine learning prediction for li-ion battery’s state of health based on electrochemical impedance spectroscopy and temporal features,” Electrochimica Acta, vol. 494, p. 144449, Aug. 2024, doi: 10.1016/j.electacta.2024.144449.
[12] Y. Zhao and S. Behdad, “State of Health Estimation of Electric Vehicle Batteries Using Transformer-Based Neural Network,” in Volume 5: 28th Design for Manufacturing and the Life Cycle Conference (DFMLC), American Society of Mechanical Engineers, Aug. 2023. doi: 10.1115/DETC2023-116426.
[13] T. Zhu, S. Wang, Y. Fan, N. Hai, Q. Huang, and C. Fernandez, “An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition,” Energy, vol. 306, p. 132464, Oct. 2024, doi: 10.1016/j.energy.2024.132464.
[14] Y. Sun, F. Liu, W. Qin, J. Li, X. Cheng, and J. Zeng, “Dynamic internal resistance modeling and thermal characteristics of lithium-ion batteries for electric vehicles by considering state of health,” Journal of Power Sources, vol. 612, p. 234806, Aug. 2024, doi: 10.1016/j.jpowsour.2024.234806.
[15] B. Chen, Y. Liu, and B. Xiao, “A novel hybrid neural network-based SOH and RUL estimation method for lithium-ion batteries,” Journal of Energy Storage, vol. 98, p. 113074, Sep. 2024, doi: 10.1016/j.est.2024.113074.
[16] N. Cai, X. Que, X. Zhang, W. Feng, and Y. Zhou, “A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images,” Energy, vol. 302, p. 131700, Sep. 2024, doi: 10.1016/j.energy.2024.131700.
[17] H. Sun et al., “A novel multiple kernel extreme learning machine model for remaining useful life prediction of lithium-ion batteries,” Journal of Power Sources, vol. 613, p. 234912, Sep. 2024, doi: 10.1016/j.jpowsour.2024.234912.
[18] W. Liu, S. Gao, and W. Yan, “Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-Ion Battery,” Journal of Electrochemical Energy Conversion and Storage, vol. 21, no. 4, Nov. 2024, doi: 10.1115/1.4064656.
[19] C. F. G. dos Santos and J. P. Papa, “Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks,” Computer Vision and Pattern Recognition, vol. arXiv:2201, 2022, doi: 10.48550/arXiv.2201.03299v.
[20] M. M. Hassan, A. Gumaei, A. Alsanad, M. Alrubaian, and G. Fortino, “A hybrid deep learning model for efficient intrusion detection in big data environment,” Information Sciences, vol. 513, pp. 386–396, Mar. 2020, doi: 10.1016/j.ins.2019.10.069.
[21] S. Ghosh, A. Dasgupta, and A. Swetapadma, “A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification,” in 2019 International Conference on Intelligent Sustainable Systems (ICISS), IEEE, Feb. 2019, pp. 24–28. doi: 10.1109/ISS1.2019.8908018.
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[24] A. Althnian et al., “Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain,” Applied Sciences, vol. 11, no. 2, p. 796, Jan. 2021, doi: 10.3390/app11020796.
[25] M. Abu Gunmi, F. Hu, D. Abu-Ghunmi, and L. Abu-Ghunmi, “A smart home energy management system methodology for techno-economic optimal sizing of standalone renewable-storage power systems under uncertainties,” Journal of Energy Storage, vol. 85, p. 111072, Apr. 2024, doi: 10.1016/j.est.2024.111072.
[26] M. Alsehli et al., “Insights into water-lubricated transport of heavy and extra-heavy oils: Application of CFD, RSM, and metaheuristic optimized machine learning models,” Fuel, vol. 374, p. 132431, Oct. 2024, doi: 10.1016/j.fuel.2024.132431.
[27] J. Wang, Z. Wang, P. Guo, X. Hu, J. Zhu, and T. Yu, “Multi-objective optimization of phase change cooling battery module based on optimal support vector machineoptimal support Vector Machine,” Applied Thermal Engineering, vol. 236, p. 121386, Jan. 2024, doi: 10.1016/j.applthermaleng.2023.121386.
[28] A. Kukkar, Y. Kumar, A. Sharma, and J. Kaur Sandhu, “Bug severity classification in software using ant colony optimization based feature weighting technique,” Expert Systems with Applications, vol. 230, p. 120573, Nov. 2023, doi: 10.1016/j.eswa.2023.120573.
[29] J. Chen, Y. Wei, and X. Ma, “Forecasting Slope Displacement of the Agricultural Mountainous Area Based on the ACO-SVM Model,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–10, Sep. 2022, doi: 10.1155/2022/2519035.
[30] M. Oszczypała, J. Konwerski, J. Ziółkowski, and J. Małachowski, “A genetic algorithm and particle swarm optimization for redundancy allocation problem in systems with limited number of non-cooperating repairmen,” Expert Systems with Applications, vol. 256, p. 124841, Dec. 2024, doi: 10.1016/j.eswa.2024.124841.
[31] M. Xie, D. Pi, Y. Xu, Y. Chen, and B. Li, “Path optimization algorithm for mobile sink in wireless sensor network,” Expert Systems with Applications, vol. 255, p. 124801, Dec. 2024, doi: 10.1016/j.eswa.2024.124801.
[32] B. F. Azevedo, A. M. A. C. Rocha, and A. I. Pereira, “Hybrid approaches to optimization and machine learning methods: a systematic literature review,” Machine Learning, vol. 113, no. 7, pp. 4055–4097, Jul. 2024, doi: 10.1007/s10994-023-06467-x.
[33] W. Deng, H. Zhao, L. Zou, G. Li, X. Yang, and D. Wu, “A novel collaborative optimization algorithm in solving complex optimization problems,” Soft Computing, vol. 21, no. 15, pp. 4387–4398, Aug. 2017, doi: 10.1007/s00500-016-2071-8.
[34] Z. Fouad, M. Alfonse, M. Roushdy, and A.-B. M. Salem, “Hyper-parameter optimization of convolutional neural network based on particle swarm optimization algorithm,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 6, pp. 3377–3384, Dec. 2021, doi: 10.11591/eei.v10i6.3257.
[35] R. Pan, T. Liu, W. Huang, Y. Wang, D. Yang, and J. Chen, “State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree,” Energy, vol. 285, p. 129460, Dec. 2023, doi: 10.1016/j.energy.2023.129460.
[36] L. Chen et al., “State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method,” Journal of Energy Storage, vol. 73, p. 109195, Dec. 2023, doi: 10.1016/j.est.2023.109195.
[37] P. Yang et al., “Joint evaluation and prediction of SOH and RUL for lithium batteries based on a GBLS booster multi-task model,” Journal of Energy Storage, vol. 75, p. 109741, Jan. 2024, doi: 10.1016/j.est.2023.109741.
[38] H. H. Goh, Z. Lan, D. Zhang, W. Dai, T. A. Kurniawan, and K. C. Goh, “Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction,” Journal of Energy Storage, vol. 50, p. 104646, Jun. 2022, doi: 10.1016/j.est.2022.104646.
[39] Z. Bao, J. Nie, H. Lin, J. Jiang, Z. He, and M. Gao, “A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery,” Energy, vol. 282, p. 128306, Nov. 2023, doi: 10.1016/j.energy.2023.128306.
[40] Y. Yang, S. Chen, T. Chen, and L. Huang, “State of Health Assessment of Lithium-ion Batteries Based on Deep Gaussian Process Regression Considering Heterogeneous Features,” Journal of Energy Storage, vol. 61, p. 106797, May 2023, doi: 10.1016/j.est.2023.106797.
[41] Y. Zhang, Y. Liu, J. Wang, and T. Zhang, “State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression,” Energy, vol. 239, p. 121986, Jan. 2022, doi: 10.1016/j.energy.2021.121986.