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A hybrid kernel extreme learning machine modeling method based on improved dung beetle algorithm optimization for lithium-ion battery state of health estimation

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Abstract

The state of health (SOH) of lithium-ion batteries is an important indicator for evaluating the degradation of battery performance, which is crucial in battery management systems. With the development of science and technology, data-driven models used to predict SOH are widely used, while data-driven models generally suffer from a narrow estimation range, long prediction time, and poor accuracy. Therefore, this paper introduces the extreme learning machine model with a single hidden layer that incorporates hybrid kernel functions, which overcomes the mapping defect problem of single kernel extreme learning machine and extreme learning machine by the introduction of hybrid kernel functions and improves the range and speed of data-driven models for SOH prediction. In addition, to automatically find the optimal parameters of the kernel extreme learning machine to improve the accuracy, the dung beetle algorithm with strong parameter finding ability is used to optimize the model in this paper. Meanwhile, based on the algorithm, we introduced the optimal Latin hypercube idea and weight factor for initializing the population and regulating the position update, respectively, which effectively improved the convergence and parameter-seeking ability of the algorithm to improve the accuracy of the model. The experiments verified the reasonableness and effectiveness of the proposed model, in which the optimal results of root mean square error, average absolute percentage error, and coefficient of determination of the proposed model on the battery dataset were 0.177%, 0.046%, and 99.76%, respectively, which reflected the high-precision prediction ability and strong robustness.

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References

  1. Zhou Y, Wang S, Xie Y, Shen X, Fernandez C (2023) Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm. Energy 285:128761. https://doi.org/10.1016/j.energy.2023.128761

    Article  CAS  Google Scholar 

  2. Zheng L, Zhu J, Lu DD-C, Wang G, He T (2018) Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries. Energy 150:759–769. https://doi.org/10.1016/j.energy.2018.03.023

    Article  Google Scholar 

  3. Takyi-Aninakwa P, Wang S, Zhang H, Yang X, Fernandez C (2023) A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures. Energy 273:127231. https://doi.org/10.1016/j.energy.2023.127231

    Article  Google Scholar 

  4. Yang X, Wang S, Takyi-Aninakwa P, Yang X, Fernandez C (2023) Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference. J Energy Storage 73:108974. https://doi.org/10.1016/j.est.2023.108974

    Article  Google Scholar 

  5. Li X, Lyu M, Li K, Gao X, Liu C, Zhang Z (2023) Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning. Energy 282:128445. https://doi.org/10.1016/j.energy.2023.128445

    Article  Google Scholar 

  6. X. Shu et al. (2021) "State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives".iScience 24 11 103265 https://doi.org/10.1016/j.isci.2021.103265

  7. Rauf H, Khalid M, Arshad N (2022) Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling. Renew Sustain Energy Rev 156:111903. https://doi.org/10.1016/j.rser.2021.111903

    Article  Google Scholar 

  8. Zhang M, Wang S, Xie Y, Yang X, Hao X, Fernandez C (2023) Hybrid gray wolf optimization method in support vector regression framework for highly precise prediction of remaining useful life of lithium-ion batteries. Ionics 29(9):3597–3607. https://doi.org/10.1007/s11581-023-05072-1

    Article  CAS  Google Scholar 

  9. Li Y, Wang S, Chen L, Qi C, Fernandez C (2023) Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries. Energy 282:128776. https://doi.org/10.1016/j.energy.2023.128776

    Article  Google Scholar 

  10. Wang Z, Zhao X, Fu L, Zhen D, Gu F, Ball AD (2023) A review on rapid state of health estimation of lithium-ion batteries in electric vehicles. Sustainable Energy Technol Assess 60:103457. https://doi.org/10.1016/j.seta.2023.103457

    Article  Google Scholar 

  11. Zeng J, Liu S (2023) Research on aging mechanism and state of health prediction in lithium batteries. J Energy Storage 72:108274. https://doi.org/10.1016/j.est.2023.108274

    Article  Google Scholar 

  12. Ge M-F, Liu Y, Jiang X, Liu J (2021) A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries. Measurement 174:109057. https://doi.org/10.1016/j.measurement.2021.109057

    Article  Google Scholar 

  13. Lai X et al (2021) Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter. Energy 216:119233. https://doi.org/10.1016/j.energy.2020.119233

    Article  Google Scholar 

  14. Ouyang T, Xu P, Lu J, Hu X, Liu B, Chen N (2022) Coestimation of State-of-Charge and State-of-Health for Power Batteries Based on Multithread Dynamic Optimization Method. IEEE Trans Industr Electron 69(2):1157–1166. https://doi.org/10.1109/TIE.2021.3062266

    Article  Google Scholar 

  15. Hu X, Che Y, Lin X, Onori S (2021) Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning. IEEE Trans Transp Electrification 7(2):382–398. https://doi.org/10.1109/TTE.2020.3017090

    Article  Google Scholar 

  16. Khaleghi S et al (2021) Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network. Appl Energy 282:116159. https://doi.org/10.1016/j.apenergy.2020.116159

    Article  CAS  Google Scholar 

  17. Liu D, Wang S, Fan Y, Liang Y, Fernandez C, Stroe D-I (2023) State of energy estimation for lithium-ion batteries using adaptive fuzzy control and forgetting factor recursive least squares combined with AEKF considering temperature. J Energy Storage 70:108040. https://doi.org/10.1016/j.est.2023.108040

    Article  Google Scholar 

  18. Reshma P, Manohar VJ (2023) Collaborative evaluation of SoC, SoP and SoH of lithium-ion battery in an electric bus through improved remora optimization algorithm and dual adaptive Kalman filtering algorithm. J Energy Storage 68:107573. https://doi.org/10.1016/j.est.2023.107573

    Article  Google Scholar 

  19. Gao Y, Liu K, Zhu C, Zhang X, Zhang D (2022) Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model. IEEE Trans Industr Electron 69(3):2684–2696. https://doi.org/10.1109/TIE.2021.3066946

    Article  Google Scholar 

  20. Tran M-K et al (2021) A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters. J Energy Storage 43:103252. https://doi.org/10.1016/j.est.2021.103252

    Article  Google Scholar 

  21. Galeotti M, Cinà L, Giammanco C, Cordiner S, Di Carlo A (2015) Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy. Energy 89:678–686. https://doi.org/10.1016/j.energy.2015.05.148

    Article  CAS  Google Scholar 

  22. Li X, Ju L, Geng G, Jiang Q (2023) Data-driven state-of-health estimation for lithium-ion battery based on aging features. Energy 274:127378. https://doi.org/10.1016/j.energy.2023.127378

    Article  CAS  Google Scholar 

  23. Shrivastava P, Naidu PA, Sharma S, Panigrahi BK, Garg A (2023) Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications. J Energy Storage 64:107159. https://doi.org/10.1016/j.est.2023.107159

    Article  Google Scholar 

  24. Li X, Zhao H, Deng W (2024) BFOD: Blockchain-Based Privacy Protection and Security Sharing Scheme of Flight Operation Data. IEEE Internet Things J 11(2):3392–3401. https://doi.org/10.1109/JIOT.2023.3296460

    Article  Google Scholar 

  25. Deng W, Cai X, Wu D, Song Y, Chen H, Ran X, Zhou X, Zhao H  (2024) MOQEA/D: multi-objective QEA with decomposition mechanism and excellent global search and its application. IEEE Transactions Intel Transp Syst 1–11. https://doi.org/10.1109/TITS.2024.3373510

  26. Lakhan A, Mohammed MA, Obaid OI, Chakraborty C, Abdulkareem KH, Kadry S (2022) Efficient deep-reinforcement learning aware resource allocation in SDN-enabled fog paradigm. Autom Softw Eng 29(1):20. https://doi.org/10.1007/s10515-021-00318-6

    Article  Google Scholar 

  27. Jin K, Cai X, Du J, Park H, Tang Z (2022) Toward Energy Efficient and Balanced User Associations and Power Allocations in Multiconnectivity-Enabled mmWave Networks. IEEE Trans Green Commun Netw 6(4):1917–1931. https://doi.org/10.1109/TGCN.2022.3172355

    Article  Google Scholar 

  28. Feng Y, Xu Y, Hu Q, Krishnamoorthy S, Tang Z (2022) Predicting vacant parking space availability zone-wisely: a hybrid deep learning approach. Complex Intell Syst 8(5):4145–4161. https://doi.org/10.1007/s40747-022-00700-1

    Article  Google Scholar 

  29. Chen H et al (2024) M3FuNet: An Unsupervised Multivariate Feature Fusion Network for Hyperspectral Image Classification. IEEE Trans Geosci Remote Sens 62:1–15. https://doi.org/10.1109/TGRS.2024.3380087

    Article  CAS  Google Scholar 

  30. Deng W, Li K, Zhao H (2023) A flight arrival time prediction method based on cluster clustering-based modular with deep neural network. IEEE Trans Intell Transp Syst 1–10. https://doi.org/10.1109/TITS.2023.3338251

  31. X Li, H Zhao, W Deng (2024) "IOFL: Intelligent Optimization-Based Federated Learning for Non-IID Data," IEEE Internet of Things J 1–1, https://doi.org/10.1109/JIOT.2024.3354942

  32. Deng W, Chen X, Li X, Zhao H (2024) Adaptive Federated Learning With Negative Inner Product Aggregation. IEEE Internet Things J 11(4):6570–6581. https://doi.org/10.1109/JIOT.2023.3312059

    Article  Google Scholar 

  33. Zhao H, Wu Y, Deng W (2023) An Interpretable Dynamic Inference System Based on Fuzzy Broad Learning. IEEE Trans Instrum Meas 72:1–12. https://doi.org/10.1109/TIM.2023.3316213

    Article  Google Scholar 

  34. Wang Y, Zhang X, Li K, Zhao G, Chen Z (2023) Perspectives and challenges for future lithium-ion battery control and management. eTransportation 18:100260. https://doi.org/10.1016/j.etran.2023.100260

    Article  Google Scholar 

  35. Patil MA et al (2015) A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation. Appl Energy 159:285–297. https://doi.org/10.1016/j.apenergy.2015.08.119

    Article  CAS  Google Scholar 

  36. Ma Y, Yao M, Liu H, Tang Z (2022) State of Health estimation and Remaining Useful Life prediction for lithium-ion batteries by Improved Particle Swarm Optimization-Back Propagation Neural Network. J Energy Storage 52:104750. https://doi.org/10.1016/j.est.2022.104750

    Article  Google Scholar 

  37. Bai X, Ma Z, Chen W, Wang S, Fu Y (2023) Fault diagnosis research of laser gyroscope based on optimized-kernel extreme learning machine. Comput Electr Eng 111:108956. https://doi.org/10.1016/j.compeleceng.2023.108956

    Article  Google Scholar 

  38. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme Learning Machine for Regression and Multiclass Classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513–529. https://doi.org/10.1109/TSMCB.2011.2168604

    Article  Google Scholar 

  39. Lv L, Wang W, Zhang Z, Liu X (2020) A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowledge-Based Systems 195:105648. https://doi.org/10.1016/j.knosys.2020.105648

    Article  Google Scholar 

  40. Wang S, Liu Z, Jia Z, Li Z (2023) Incipient fault diagnosis of analog circuit with ensemble HKELM based on fused multi-channel and multi-scale features. Eng Appl Artif Intel 117:105633. https://doi.org/10.1016/j.engappai.2022.105633

    Article  Google Scholar 

  41. Yang M, Guo Y, Huang Y (2023) Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process. Energy 282:128947. https://doi.org/10.1016/j.energy.2023.128947

    Article  Google Scholar 

  42. Wang Z, Chen H, Wang M, Zhang X, Dou Y (2022) Solid particle erosion prediction in elbows based on machine learning and swarm intelligence algorithm. J Petrol Sci Eng 218:111042. https://doi.org/10.1016/j.petrol.2022.111042

    Article  CAS  Google Scholar 

  43. J Jia, S Yuan, Y Shi, J Wen, X Pang, J Zeng (2022) "Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction," iScience 25 4 103988 https://doi.org/10.1016/j.isci.2022.103988

  44. Zhao J, Xuebin L, Daiwei Y, Jun Z, Wenjin Z (2023) Lithium-ion battery state of health estimation using meta-heuristic optimization and Gaussian process regression. J Energy Storage 58:106319. https://doi.org/10.1016/j.est.2022.106319

    Article  Google Scholar 

  45. Yi J, Li X, Xiao M, Xu J, Zhang L (2017) Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm. Eng Optim 49:161–180

    Article  Google Scholar 

  46. Duan W, Song S, Xiao F, Chen Y, Peng S, Song C (2023) Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter. J Energy Storage 65:107322. https://doi.org/10.1016/j.est.2023.107322

    Article  Google Scholar 

  47. Xue J, Shen B (2023) Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J Supercomput 79(7):7305–7336. https://doi.org/10.1007/s11227-022-04959-6

    Article  Google Scholar 

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (Nos. 62173281), and the Natural Science Foundation of Sichuan Province (2023NSFSC1436).

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Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Daijiang Mo, Shunli Wang, Mengyun Zhang, Yongcun Fan, Yangtao Wang & Jiawei Zeng

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Daijiang Mo wrote the body of the manuscript, Shunli Wang and Yongcun Fan supervised the writing process, and Mengyun Zhang, Jiawei Zeng, and Yangtao Wang provided guidance and revisions. All authors reviewed the manuscript.

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Correspondence to Shunli Wang.

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Mo, D., Wang, S., Zhang, M. et al. A hybrid kernel extreme learning machine modeling method based on improved dung beetle algorithm optimization for lithium-ion battery state of health estimation. Ionics (2024). https://doi.org/10.1007/s11581-024-05573-7

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