Online capacity estimation of lithium-ion batteries based on convolutional self-attention

References

• Ali, J. B., and L. Saidi. 2018. A new suitable feature selection and regression procedure for lithium-ion battery prognostics. International Journal of Computer Applications in Technology 58 (2):102. doi:10.1504/IJCAT.2018.094573.
   Google Scholar
• Bai, J., J. Huang, K. Luo, F. Yang, and Y. Xian. 2023. A feature reuse based multi-model fusion method for state of health estimation of lithium-ion batteries. Journal of Energy Storage 70:107965. doi:10.1016/j.est.2023.107965.
   Web of Science ®Google Scholar
• Bian, X., Z. G. Wei, W. Li, J. Pou, D. U. Sauer, and L. Liu. 2022. State-of-health estimation of lithium-ion batteries by fusing an open circuit voltage model and incremental capacity analysis. IEEE Transactions on Power Electronics 37 (2):2226–36. doi:10.1109/TPEL.2021.3104723.
   Web of Science ®Google Scholar
• Chen, D., W. Hong, and X. Zhou. 2022. Transformer network for remaining useful life prediction of lithium-ion batteries. IEEE Access 10:19621–28. doi:10.1109/ACCESS.2022.3151975.
   Web of Science ®Google Scholar
• Chen, S., Z. Liang, H. Yuan, L. Yang, F. Xu, and Y. Fan. 2023. A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network. Energy 283:129103. doi:10.1016/j.energy.2023.129103.
   Web of Science ®Google Scholar
• Chu, A., A. Allam, A. C. Arenas, G. Rizzoni, and S. Onori. 2020. Stochastic capacity loss and remaining useful life models for lithium-ion batteries in plug-in hybrid electric vehicles. Journal of Power Sources 478:228991. doi:10.1016/j.jpowsour.2020.228991.
   Web of Science ®Google Scholar
• Cui, S., and I. Joe. 2021. A dynamic spatial-temporal attention-based gru model with healthy features for state-of-health estimation of lithium-ion batteries. IEEE Access 9:27374–88. doi:10.1109/ACCESS.2021.3058018.
   Web of Science ®Google Scholar
• Fan, Y., F. Xiao, C. Li, G. Yang, and X. Tang. 2020. A novel deep learning framework for state of health estimation of lithium-ion battery. Journal of Energy Storage 32:101741. doi:10.1016/j.est.2020.101741.
   Web of Science ®Google Scholar
• Ge, D., Z. Zhang, X. Kong, and Z. Wan. 2022. Extreme learning machine using bat optimization algorithm for estimating state of health of lithium-ion batteries. Applied Sciences 12 (3):1398. doi:10.3390/app12031398.
   Google Scholar
• Gong, Y., X. Zhang, D. Gao, H. Li, L. Yan, J. Peng, and Z. Huang. 2022. State-of-health estimation of lithium-ion batteries based on improved long short-term memory algorithm. Journal of Energy Storage 53:105046. doi:10.1016/j.est.2022.105046.
   Web of Science ®Google Scholar
• Guha, A., and A. Patra. 2018. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models. IEEE Transactions on Transportation Electrification 4 (1):135–46. doi:10.1109/TTE.2017.2776558.
   Web of Science ®Google Scholar
• Hu, X., L. Xu, X. Lin, and M. Pecht. 2020. Battery lifetime prognostics. Joule 4 (2):310–46. doi:10.1016/j.joule.2019.11.018.
   Web of Science ®Google Scholar
• Jia, J., J. Liang, Y. Shi, J. Wen, X. Pang, and J. Zeng. 2020. SOH and rul prediction of lithium-ion batteries based on gaussian process regression with indirect health indicators. Energies 13 (2):375. doi:10.3390/en13020375.
   Web of Science ®Google Scholar
• Khaleghi, S., M. S. Hosen, D. Karimi, H. Behi, S. H. Beheshti, J. V. Mierlo, and M. Berecibar. 2022. Developing an online data-driven approach for prognostics and health management of lithium-ion batteries. Applied Energy 308:118348. doi:10.1016/j.apenergy.2021.118348.
   Web of Science ®Google Scholar
• Khumprom, P., and N. Yodo. 2019. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies 12 (4):660. doi:10.3390/en12040660.
   Web of Science ®Google Scholar
• Li, L., S. You, C. Yang, B. Yan, J. Song, and Z. Chen. 2016. Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses. Applied Energy 162:868–79. doi:10.1016/j.apenergy.2015.10.152.
   Web of Science ®Google Scholar
• Li, S., X. Jin, Y. Xuan, X. Zhou, W. Chen, Y. Wang, and X. Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in Neural Information Processing Systems 32. https://www.webofscience.com/wos/alldb/summary/1edae2a6-d56f-4fad-8b10-d24db66377e9-a5fe270c/relevance/1.
   Google Scholar
• Ling, L., and Y. Wei. 2021. State-of-charge and state-of-health estimation for lithium-ion batteries based on dual fractional-order extended Kalman filter and online parameter identification. IEEE Access 9:47588–602. doi:10.1109/ACCESS.2021.3068813.
   Web of Science ®Google Scholar
• Liu, R., Y. Zhang, and W. Wen. 2010. Study on the design and analysis methods of orthogonal experiment. Experimental Technology & Management 27 (9):4. doi:10.16791/j.cnki.sjg.2010.09.016.
   Google Scholar
• Liu, Y., J. Sun, Y. Shang, X. Zhang, S. Ren, and D. Wang. 2023. A novel remaining useful life prediction method for lithium-ion battery based on long short-term memory network optimized by improved sparrow search algorithm. Journal of Energy Storage 61:106645. doi:10.1016/j.est.2023.106645.
   Web of Science ®Google Scholar
• Nuhic, A., T. Terzimehic, T. Soczka-Guth, M. Buchholz, and K. Dietmayer. 2013. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. Journal of Power Sources 239:680–88. doi:10.1016/j.jpowsour.2012.11.146.
   Web of Science ®Google Scholar
• Richardson, R. R., M. A. Osborne, and D. A. Howey. 2017. Gaussian process regression for forecasting battery state of health. Journal of Power Sources 357:209–19. doi:10.1016/j.jpowsour.2017.05.004.
   Web of Science ®Google Scholar
• Severson, K. A., P. M. Attia, N. Jin, N. Perkins, B. Jiang, Z. Yang, M. H. Chen, M. Aykol, P. K. Herring, D. Fraggedakis, et al. 2019. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy 4(5):383–91. doi:10.1038/s41560-019-0356-8.
   Web of Science ®Google Scholar
• Sun, T., S. Wang, S. Jiang, B. Xu, X. Han, X. Lai, and Y. Zheng. 2022. A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning. Energy 239:122185. doi:10.1016/j.energy.2021.122185.
   Web of Science ®Google Scholar
• Tian, J., R. Xiong, W. Shen, J. Lu, and X. Yang. 2021. Deep neural network battery charging curve prediction using 30 points collected in 10 min. Joule 5 (6):1521–34. doi:10.1016/j.joule.2021.05.012.
   Web of Science ®Google Scholar
• Tian, J., R. Xiong, W. Shen, J. Wang, and R. Yang. 2020. Online simultaneous identification of parameters and order of a fractional order battery model. Journal of Cleaner Production 247:119147. doi:10.1016/j.jclepro.2019.119147.
   Web of Science ®Google Scholar
• Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30. https://www.webofscience.com/wos/alldb/summary/30d5cbe7-ffdf-4bfb-af66-dd347ea0eaf1-a5fe39e4/relevance/1.
   Google Scholar
• Wang, Z., N. Liu, C. Chen, and Y. Guo. 2023. Adaptive self-attention lstm for rul prediction of lithium-ion batteries. Information Sciences 635:398–413. doi:10.1016/j.ins.2023.01.100.
   Web of Science ®Google Scholar
• Widodo, A., M. Shim, W. Caesarendra, and B. Yang. 2011. Intelligent prognostics for battery health monitoring based on sample entropy. Expert Systems with Applications 38 (9):11763–69. doi:10.1016/j.eswa.2011.03.063.
   Web of Science ®Google Scholar
• Wu, J., Z. Liu, Y. Zhang, D. Lei, B. Zhang, and W. Cao. 2023. Data-driven state of health estimation for lithium-ion battery based on voltage variation curves. Journal of Energy Storage 73:109191. doi:10.1016/j.est.2023.109191.
   Google Scholar
• Xu, H., L. Wu, S. Xiong, W. Li, A. Garg, and L. Gao. 2023. An improved cnn-lstm model-based state-of-health estimation approach for lithium-ion batteries. Energy 276:127585. doi:10.1016/j.energy.2023.127585.
   Web of Science ®Google Scholar
• Zhang, Y., R. Xiong, H. He, and M. G. Pecht. 2018. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology 67 (7):5695–705. doi:10.1109/TVT.2018.2805189.
   Web of Science ®Google Scholar
• Zhou, D., X. Song, W. Lu, and P. Fu. 2019. Research on real-time evaluation method of lithium battery health status based on daily fragment charging data. Journal of Chinese Institute of Electrical Engineering 39 (1):105–11+325. doi:10.13334/j.0258-8013.pcsee.181026.
   Google Scholar
• Zhou, Y., M. Huang, Y. Chen, and Y. Tao. 2016. A novel health indicator for on-line lithium-ion batteries remaining useful life prediction. Journal of Power Sources 321:1–10. doi:10.1016/j.jpowsour.2016.04.119.
   Web of Science ®Google Scholar
• Zhu, J., Y. Wang, Y. Huang, R. B. Gopaluni, Y. Cao, M. Heere, M. J. Muhlbauer, L. Mereacre, H. Dai, X. Liu, et al. 2022. Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation. Nature Communications 13(1):2261. doi:10.1038/s41467-022-29837-w.
   PubMed Web of Science ®Google Scholar
• Zubi, G., R. Dufo-Lopez, M. Carvalho, and G. Pasaoglu. 2018. The lithium-ion battery: State of the art and future perspectives. Renewable & Sustainable Energy Reviews 89:292–308. doi:10.1016/j.rser.2018.03.002.
   Web of Science ®Google Scholar