IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224037563.html
   My bibliography  Save this article

Evolutionary hybrid deep learning based on feature engineering and deep projection encoded echo-state network for lithium batteries state of health estimation

Author

Listed:
  • Tang, Zhongyi
  • Zhang, Zhirong
  • Shen, Xianxian
  • Zhong, Anjie
  • Nazir, Muhammad Shahzad
  • Peng, Tian
  • Zhang, Chu

Abstract

The state of health estimation for lithium batteries is crucial for optimizing their performance, extending their lifetime, ensuring safety, and reducing their maintenance cost. This study proposes a hybrid deep learning model for SOH estimation in lithium batteries. The model utilizes Random Forest (RF) and Variational Mode Decomposition (VMD) for feature processing, and then utilizes a deep projection-encoded echo-state network (DeePESN) for health state estimation. To improve the estimation accuracy, a logistic initialization method was used to optimize the Newton-Raphson-based optimizer (NRBO) algorithm. In this study, the LoNRBO algorithm is used to determine the hyperparameters of the model. To improve the generalization ability of the model, a Kernel Extreme Learning Machine Autoencoder (KELMAE) was used to reduce the dimensionality of the input data within the DeePESN model. After experimental verification, the hybrid deep learning model proposed in this paper exhibits excellent estimation performance under different working conditions: at a temperature of 24°C and a discharge current of 2A, at a temperature of 24°C and a discharge current of 4A, and at a temperature of 4 °C and a discharge current of 1A. The root-mean-square errors (RMSE) of SOH were 0.0768, 0.0998, 0.0734and 0.0979, respectively. In summary, the proposed hybrid deep learning method for the SOH estimation of lithium-ion batteries is feasible and can better fit lithium-ion battery data under different working conditions.

Suggested Citation

  • Tang, Zhongyi & Zhang, Zhirong & Shen, Xianxian & Zhong, Anjie & Nazir, Muhammad Shahzad & Peng, Tian & Zhang, Chu, 2024. "Evolutionary hybrid deep learning based on feature engineering and deep projection encoded echo-state network for lithium batteries state of health estimation," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224037563
    DOI: 10.1016/j.energy.2024.133978
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224037563
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133978?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    2. Zhang, Chu & Hu, Haowen & Ji, Jie & Liu, Kang & Xia, Xin & Nazir, Muhammad Shahzad & Peng, Tian, 2023. "An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC," Applied Energy, Elsevier, vol. 330(PA).
    3. Peng, Simin & Miao, Yifan & Xiong, Rui & Bai, Jiawei & Cheng, Mengzeng & Pecht, Michael, 2024. "State of charge estimation for a parallel battery pack jointly by fuzzy-PI model regulator and adaptive unscented Kalman filter," Applied Energy, Elsevier, vol. 360(C).
    4. Yu, Quanqing & Nie, Yuwei & Peng, Simin & Miao, Yifan & Zhai, Chengzhi & Zhang, Runfeng & Han, Jinsong & Zhao, Shuo & Pecht, Michael, 2023. "Evaluation of the safety standards system of power batteries for electric vehicles in China," Applied Energy, Elsevier, vol. 349(C).
    5. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    6. Wang, Yuhan & Zhang, Chu & Fu, Yongyan & Suo, Leiming & Song, Shihao & Peng, Tian & Shahzad Nazir, Muhammad, 2023. "Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm," Energy, Elsevier, vol. 280(C).
    7. Suo, Leiming & Peng, Tian & Song, Shihao & Zhang, Chu & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad, 2023. "Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm," Energy, Elsevier, vol. 276(C).
    8. Yin, Wanjun & Jia, Leilei & Ji, Jianbo, 2024. "Energy optimal scheduling strategy considering V2G characteristics of electric vehicle," Energy, Elsevier, vol. 294(C).
    9. Zhang, Chu & Li, Zhengbo & Ge, Yida & Liu, Qianlong & Suo, Leiming & Song, Shihao & Peng, Tian, 2024. "Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD," Energy, Elsevier, vol. 296(C).
    10. Peng, Simin & Sun, Yunxiang & Liu, Dandan & Yu, Quanqing & Kan, Jiarong & Pecht, Michael, 2023. "State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network," Energy, Elsevier, vol. 282(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Chu & Tao, Zihan & Xiong, Jinlin & Qian, Shijie & Fu, Yongyan & Ji, Jie & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Research and application of a novel weight-based evolutionary ensemble model using principal component analysis for wind power prediction," Renewable Energy, Elsevier, vol. 232(C).
    2. Zhang, Chu & Zhang, Yue & Li, Zhengbo & Zhang, Zhao & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction," Applied Energy, Elsevier, vol. 359(C).
    3. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Yuan, Caichenran & Cang, Junjie & Zhang, Kai & Pecht, Michael, 2024. "Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model," Energy, Elsevier, vol. 298(C).
    4. Zhang, Chu & Qiao, Xiujie & Zhang, Zhao & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator," Energy, Elsevier, vol. 295(C).
    5. Peng, Simin & Chen, Shengdong & Liu, Yong & Yu, Quanqing & Kan, Jiarong & Li, Rui, 2025. "State of power prediction joint fisher optimal segmentation and PO-BP neural network for a parallel battery pack considering cell inconsistency," Applied Energy, Elsevier, vol. 381(C).
    6. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).
    7. Peng, Tian & Song, Shihao & Suo, Leiming & Wang, Yuhan & Nazir, Muhammad Shahzad & Zhang, Chu, 2024. "Research and application of a novel graph convolutional RVFL and evolutionary equilibrium optimizer algorithm considering spatial factors in ultra-short-term solar power prediction," Energy, Elsevier, vol. 308(C).
    8. Chen, Yuan & Duan, Wenxian & Huang, Xiaohe & Wang, Shunli, 2024. "Multi-output fusion SOC and SOE estimation algorithm based on deep network migration," Energy, Elsevier, vol. 308(C).
    9. Zhang, Yue & Wang, Yeqin & Zhang, Chu & Qiao, Xiujie & Ge, Yida & Li, Xi & Peng, Tian & Nazir, Muhammad Shahzad, 2024. "State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural netw," Applied Energy, Elsevier, vol. 356(C).
    10. Peng, Simin & Zhang, Daohan & Dai, Guohong & Wang, Lin & Jiang, Yuxia & Zhou, Feng, 2025. "State of charge estimation for LiFePO4 batteries joint by PID observer and improved EKF in various OCV ranges," Applied Energy, Elsevier, vol. 377(PA).
    11. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Tang, Aihua & Kan, Jiarong & Pecht, Michael, 2024. "SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion," Energy, Elsevier, vol. 308(C).
    12. Wang, Zheng & Peng, Tian & Zhang, Xuedong & Chen, Jialei & Qian, Shijie & Zhang, Chu, 2025. "Enhancing multi-step short-term solar radiation forecasting based on optimized generalized regularized extreme learning machine and multi-scale Gaussian data augmentation technique," Applied Energy, Elsevier, vol. 377(PD).
    13. Dongran Song & Xiao Tan & Qian Huang & Li Wang & Mi Dong & Jian Yang & Solomin Evgeny, 2024. "Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023," Energies, MDPI, vol. 17(6), pages 1-22, March.
    14. Ruan, Guanqiang & Liu, Zixi & Cheng, Jinrun & Hu, Xing & Chen, Song & Liu, Shiwen & Guo, Yong & Yang, Kuo, 2024. "A deep learning model for predicting the state of energy in lithium-ion batteries based on magnetic field effects," Energy, Elsevier, vol. 304(C).
    15. Zhong, Mingwei & Xu, Cancheng & Xian, Zikang & He, Guanglin & Zhai, Yanpeng & Zhou, Yongwang & Fan, Jingmin, 2024. "DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting," Energy, Elsevier, vol. 286(C).
    16. Tang, Aihua & Wu, Xinyu & Xu, Tingting & Hu, Yuanzhi & Long, Shengwen & Yu, Quanqing, 2024. "State of health estimation based on inconsistent evolution for lithium-ion battery module," Energy, Elsevier, vol. 286(C).
    17. Zhang, Chu & Li, Zhengbo & Ge, Yida & Liu, Qianlong & Suo, Leiming & Song, Shihao & Peng, Tian, 2024. "Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD," Energy, Elsevier, vol. 296(C).
    18. Yang, Mao & Guo, Yunfeng & Fan, Fulin & Huang, Tao, 2024. "Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering," Energy, Elsevier, vol. 302(C).
    19. Chen, Jie & Peng, Tian & Qian, Shijie & Ge, Yida & Wang, Zheng & Nazir, Muhammad Shahzad & Zhang, Chu, 2025. "An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction," Applied Energy, Elsevier, vol. 377(PD).
    20. Mu, Guixiang & Wei, Qingguo & Xu, Yonghong & Li, Jian & Zhang, Hongguang & Yang, Fubin & Zhang, Jian & Li, Qi, 2025. "State of health estimation of lithium-ion batteries based on feature optimization and data-driven models," Energy, Elsevier, vol. 316(C).

    More about this item

    Keywords

    RF; VMD; DeePESN; NRBO; State-of-health;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224037563. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.