A novel state of health estimation model for lithium-ion batteries incorporating signal processing and optimized machine learning methods

Abstract

An accurate assessment of the state of health (SOH) is the cornerstone for guaranteeing the long-term stable operation of electrical equipment. However, the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model. To this end, this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies. The model employs a whale optimization algorithm (WOA) to seek the optimal parameter combination (K, α) for the variational modal decomposition (VMD) method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries. Then, the excellent local feature extraction capability of the convolutional neural network (CNN) was utilized to obtain the critical features of each modal of SOH. Finally, the support vector machine (SVM) was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets. The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures, discharge rates, and discharge depths. The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation. The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation. Compared with traditional techniques, the fused algorithm achieves significant results in solving the interference of data noise, improving the accuracy of SOH estimation, and enhancing the generalization ability.

Graphical abstract

Keywords

state of health (SOH) estimation / optimized machine learning / signal processing / whale optimization algorithm-variational modal decomposition (WOA-VMD) / convolutional neural network-support vector machine (CNN-SVM)

References

[1] Wang D, Liu N, Chen F. . Progress and prospects of energy storage technology research: Based on multidimensional comparison. Journal of Energy Storage, 2024, 75: 109710

[2] Ramasubramanian B, Prasada R R, Dalapati G K. . Sustainable materials and decarbonization prospects in battery technologies. ACS Applied Energy Materials, 2024, 7(8): 3018–3020

[3] Chombo P, Laoonual Y. A review of safety strategies of a Li-ion battery. Journal of Power Sources, 2020, 478: 228649

[4] Zhao Z, Hu H, He Z. . Power electronics-based safety enhancement technologies for lithium-ion batteries: An overview from battery management perspective. IEEE Transactions on Power Electronics, 2023, 38(7): 8922–8955

[5] Feng J, Cai F, Zhan X. . A novel state-of-health prediction and assessment strategies for high-capacity mining lithium-ion batteries based on multi-indicator. Journal of the Electrochemical Society, 2024, 171(5): 050514

[6] Tian J, Ma L, Zhang T. . Exploiting domain knowledge to reduce data requirements for battery health monitoring. Energy Storage Materials, 2024, 67: 103270

[7] Sarwar D, Wang C, Amietszajew T. . Capacity fade detection in lithium-ion batteries using non-linear frequency response analysis (NFRA) under multiple open-circuit voltages (OCVs). Electrochemistry Communications, 2022, 140: 107338

[8] Lin M, You Y, Meng J. . Lithium-ion batteries SOH estimation with multimodal multilinear feature fusion. IEEE Transactions on Energy Conversion, 2023, 38(4): 2959–2968

[9] Li Y, Abdel-Monem M, Gopalakrishnanet R. . A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter. Journal of Power Sources, 2018, 373: 40–53

[10] Fonso R, Teodorescu R, Cecati C. . A battery digital twin from laboratory data using wavelet analysis and neural networks. IEEE Transactions on Industrial Informatics, 2024, 20(4): 6889–6899

[11] Kim J, Cho B. An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform. Journal of Power Sources, 2014, 260: 115–130

[12] Li X, Wang Z, Yan J. Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression. Journal of Power Sources, 2019, 421: 56–67

[13] Ali M, Zafar A, Masood H. . A hybrid data-driven approach for multistep ahead prediction of state of health and remaining useful life of lithium-ion batteries. Computational Intelligence and Neuroscience, 2022, 2022: 1575303

[14] Xia F, Tang C, Chen J. Online two-dimensional filter for anti-interference aging features extraction to accurately predict the battery health. Measurement, 2024, 234: 114758

[15] He Y, Bai W, Wang L. . SOH estimation for lithium-ion batteries: An improved GPR optimization method based on the developed feature extraction. Journal of Energy Storage, 2024, 83: 110678

[16] Marri I, Petkovski E, Cristaldi L. . Comparing machine learning strategies for SoH estimation of lithium-ion batteries using a feature-based approach. Energies, 2023, 16(11): 4423

[17] Akbar K, Zou Y, Awais Q. . A machine learning-based robust state of health (SOH) prediction model for electric vehicle batteries. Electronics, 2022, 11(8): 1216

[18] Li X, Ju L, Geng G. . Data-driven state-of-health estimation for lithium-ion battery based on aging features. Energy, 2023, 274: 127378

[19] Obregon J, Han Y, Ho C. . Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy. Journal of Energy Storage, 2023, 60: 106680

[20] Nozarijouybari Z, Fathy H. Machine learning for battery systems applications: Progress, challenges, and opportunities. Journal of Power Sources, 2024, 601: 234272

[21] Naresh V, Rao G, Prabhakar D. Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions. WIREs Data Mining and Knowledge Discovery, 2024, 14(5): e1539

[22] Qi N, Yan K, Yu Y. . Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery. Frontiers in Energy, 2024, 18(2): 223–240

[23] Tian H, Qin P, Li K. . A review of the state of health for lithium-ion batteries: Research status and suggestions. Journal of Cleaner Production, 2020, 261: 120813

[24] He Y, Deng Z, Chen J. . State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks. Journal of Energy Chemistry, 2024, 98: 1–11

[25] Ma L, Tian J, Zhang T. . Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning. Journal of Energy Chemistry, 2024, 91: 512–521

[26] Deng Z, Xu L, Liu H. . Rapid health estimation of in-service battery packs based on limited labels and domain adaptation. Journal of Energy Chemistry, 2024, 89: 345–354

[27] Shen S, Liu B, Zhang K. . Toward fast and accurate SOH prediction for lithium-ion batterie. IEEE Transactions on Energy Conversion, 2021, 36(3): 2036–2046

[28] Dong M, Li X, Yang Z. . State of health (SOH) assessment for LIBs based on characteristic electrochemical impedance. Journal of Power Sources, 2024, 603: 234386

[29] Feng J, Cai F, Wu L. . State of charge estimation for lithium-ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm. Energy Science & Engineering, 2024, 12(3): 896–912

[30] Hong J, Li K, Liang F. . A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks. Energy, 2024, 289: 129918

[31] Feng J, Cai F, Zhao Y. . A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries. Energy, 2024, 299: 131474

[32] Ma G, Xu S, Jiang B. . Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning. Energy & Environmental Science, 2022, 15(10): 4083–4094

[33] Chen M, Ma G, Liu W. . An overview of data-driven battery health estimation technology for battery management system. Neurocomputing, 2023, 532: 152–169

[34] Fan Y, Xiao F, Li C. . A novel deep learning framework for state of health estimation of lithium-ion battery. Journal of Power Sources, 2020, 32: 101741

[35] Wang S, Ma H, Zhang Y. . Remaining useful life prediction method of lithium-ion batteries is based on variational modal decomposition and deep learning integrated approach. Energy, 2023, 282: 128984

[36] Chen Y, Duan W, He Y. . A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction. Green Energy and Intelligent Transportation, 2024, 3(2): 100160

[37] Xiong B, Meng X, Xiong G. . Multi-branch wind power prediction based on optimized variational mode decomposition. Energy Reports, 2022, 8: 11181–11191

[38] Peng D, Li H, Ou J. . Milling chatter identification by optimized variational mode decomposition and fuzzy entropy. International Journal of Advanced Manufacturing Technology, 2022, 121(9-10): 6111–6124

[39] Mi X, Liu H, Li Y. Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine. Energy Conversion and Management, 2019, 180: 196–205

RIGHTS & PERMISSIONS

Higher Education Press 2024