A state of health estimation method of lithium-ion batteries based on DT-IC-V health features extracted from partial charging segment

References

• Allam, A., and S. Onori. 2021. Online capacity estimation for lithium-ion batteries cells via an electrochemical model-based adaptive interconnected observer. IEEE Transactions on Control Systems Technology 29 (4):1636–51. doi:10.1109/tcst.2020.3017566.
   Web of Science ®Google Scholar
• Alrumayh, O., S. Wong, and K. Bhattacharya. 2021. Inclusion of battery SoH estimation in smart distribution planning with energy storage systems. IEEE Transactions on Power Systems 36 (3):2323–33. doi:10.1109/tpwrs.2020.3036448.
   Web of Science ®Google Scholar
• Bermejo, R. 2021. Numerical analysis of a finite element formulation of the P2D model for lithium-ion cells. Numerische Mathematik 149 (3):463–505. doi:10.1007/s00211-021-01235-2.
   Web of Science ®Google Scholar
• Birkl, C. (2017). Diagnosis and prognosis of degradation in lithium-ion batteries [ PhD thesis]. University of Oxford.
   Google Scholar
• Cao, M., T. Zhang, J. Wang, and Y. Liu. 2022.A deep belief network approach to remaining capacity estimation for lithium-ion batteries based on charging process features. Journal of Energy Storage ,48 55: .doi: 10.1016/j.est.2021.103825
   Google Scholar
• Chen, Z., H. Zhao, Y. Zhang, S. Shen, J. Shen, and Y. Liu. 2022. State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network. Journal of Power Sources 521. doi:10.1016/j.jpowsour.2021.230892.
   Web of Science ®Google Scholar
• Deng, L., W. Shen, H. Wang, and S. Wang. 2020. A rest-time-based prognostic model for remaining useful life prediction of lithium-ion battery. Neural Computing & Applications 33 (6):2035–46. doi:10.1007/s00521-020-05105-0.
   Web of Science ®Google Scholar
• Dong, S., P. Wang, and K. Abbas. 2021. A survey on deep learning and its applications. Computer Science Review 40:100379. doi:10.1016/j.cosrev.2021.100379.
   Web of Science ®Google Scholar
• Feng, X., C. Weng, X. He, X. Han, L. Lu, D. Ren, and M. Ouyang. 2019. Online state-of-health estimation for lithium-ion battery using partial charging segment based on support vector machine. IEEE Transactions on Vehicular Technology 68 (9):8583–92. doi:10.1109/tvt.2019.2927120.
   Web of Science ®Google Scholar
• Fu, Y., J. Xu, M. Shi, and X. Mei. 2022. A Fast Impedance Calculation-Based Battery State-of-Health Estimation Method. IEEE Transactions on Industrial Electronics 69 (7):7019–28. doi:10.1109/tie.2021.3097668.
   Web of Science ®Google Scholar
• Goebel, K., B. Saha, A. Saxena, J. Celaya, and J. Christophersen. 2008. Prognostics in Battery Health Management. IEEE Instrumentation & Measurement Magazine 11 4:33–40. 10.1109/mim.2008.4579269
   Web of Science ®Google Scholar
• Gou, B., Y. Xu, and X. Feng. 2021. An ensemble learning-based data-driven method for online state-of-health estimation of lithium-ion batteries. IEEE Transactions on Transportation Electrification 7 (2):422–36. doi:10.1109/tte.2020.3029295.
   Web of Science ®Google Scholar
• Hinton, G. E., S. Osindero, and Y.-W. Teh. 2006. A fast learning algorithm for deep belief nets. Neural Computation 18 (7):1527–54. doi:10.1162/neco.2006.18.7.1527.
   PubMed Web of Science ®Google Scholar
• Jia, J., K. Wang, Y. Shi, J. Wen, X. Pang, and J. Zeng. 2021. A multi-scale state of health prediction framework of lithium-ion battery considering the temperature variation during battery discharge. Journal of Energy Storage 42. doi:10.1016/j.est.2021.103076.
   Web of Science ®Google Scholar
• Li, Y., M. Abdel-Monem, R. Gopalakrishnan, M. Berecibar, E. Nanini-Maury, N. Omar, P. van den Bossche, and J. Van Mierlo. 2018. 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 373:40–53. doi:10.1016/j.jpowsour.2017.10.092.
   Web of Science ®Google Scholar
• Li, J., K. Adewuyi, N. Lotfi, R. G. Landers, and J. Park. 2018. A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation. Applied Energy 212:1178–90. doi:10.1016/j.apenergy.2018.01.011.
   Web of Science ®Google Scholar
• Lin, M., D. Wu, J. Meng, J. Wu, and H. Wu. 2022. A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries. Journal of Power Sources 518. doi:10.1016/j.jpowsour.2021.230774.
   Web of Science ®Google Scholar
• Li, Y., D.-I. Stroe, Y. Cheng, H. Sheng, X. Sui, and R. Teodorescu. 2021. On the feature selection for battery state of health estimation based on charging–discharging profiles. Journal of Energy Storage 33. doi:10.1016/j.est.2020.102122.
   Web of Science ®Google Scholar
• Liu, K., T. R. Ashwin, X. Hu, M. Lucu, and W. D. Widanage. 2020. An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries. Renewable and Sustainable Energy Reviews 131. doi:10.1016/j.rser.2020.110017.
   Web of Science ®Google Scholar
• Liu, K., Z. Wei, C. Zhang, Y. Shang, R. Teodorescu, and Q.-L. Han. 2022. Towards long lifetime battery: ai-based manufacturing and management. IEEE/CAA Journal of Automatica Sinica 9 (7):1139–65. doi:10.1109/jas.2022.105599.
   Google Scholar
• Liu, W., and Y. Xu. 2020. Data-driven online health estimation of lithium-ion batteries using a novel energy-based health indicator. IEEE Transactions on Energy Conversion 35 (3):1715–18. doi:10.1109/TEC.2020.2995112.
   Web of Science ®Google Scholar
• Liu, W., Y. Xu, and X. Feng. 2020. A hierarchical and flexible data-driven method for online state-of-health estimation of lithium-ion battery. IEEE Transactions on Vehicular Technology 69 (12):14739–48. doi:10.1109/tvt.2020.3037088.
   Web of Science ®Google Scholar
• Niu, G., X. Wang, E. Liu, and B. Zhang. 2022. Lebesgue sampling based deep belief network for lithium-ion battery diagnosis and prognosis. IEEE Transactions on Industrial Electronics 69 (8):8481–90. doi:10.1109/tie.2021.3109527.
   Web of Science ®Google Scholar
• Ouyang, Q., Z. Wang, K. Liu, G. Xu, and Y. Li. 2020. Optimal charging control for lithium-ion battery packs: a distributed average tracking approach. IEEE Transactions on Industrial Informatics 16 (5):3430–38. doi:10.1109/tii.2019.2951060.
   Web of Science ®Google Scholar
• Pan, W., X. Luo, M. Zhu, J. Ye, L. Gong, and H. Qu. 2021. A health indicator extraction and optimization for capacity estimation of lithium-ion battery using incremental capacity curves. Journal of Energy Storage 42. doi:10.1016/j.est.2021.103072.
   Web of Science ®Google Scholar
• Ronoud, S., and S. Asadi. 2019. An evolutionary deep belief network extreme learning-based for breast cancer diagnosis. Soft Computing 23 (24):13139–59. doi:10.1007/s00500-019-03856-0.
   Web of Science ®Google Scholar
• Shen, D., L. Wu, G. Kang, Y. Guan, and Z. Peng. 2021. A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current. Energy 218. doi:10.1016/j.energy.2020.119490.
   Google Scholar
• Sui, X., S. He, S. B. Vilsen, J. Meng, R. Teodorescu, and D.-I. Stroe. 2021. A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery. Applied Energy 300. doi:10.1016/j.apenergy.2021.117346.
   Web of Science ®Google Scholar
• Sun, T., S. Jiang, X. Li, Y. Cui, X. Lai, X. Wang, Y. Ma, and Y. Zheng. 2021. A Novel capacity estimation approach for lithium-ion batteries combining three-parameter capacity fade model with constant current charging curves. IEEE Transactions on Energy Conversion 36 (3):2574–84. doi:10.1109/tec.2021.3058531.
   Web of Science ®Google Scholar
• Tang, X., K. Liu, J. Lu, B. Liu, X. Wang, and F. Gao. 2020. Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter. Applied Energy 280. doi:10.1016/j.apenergy.2020.115895.
   Web of Science ®Google Scholar
• Tang, X., Y. Wang, Q. Liu, and F. Gao. 2021. Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries. iScience 24 (10):103103. doi:10.1016/j.isci.2021.103103.
   PubMed Web of Science ®Google Scholar
• Tang, X., C. Zou, K. Yao, G. Chen, B. Liu, Z. He, and F. Gao. 2018. A fast estimation algorithm for lithium-ion battery state of health. Journal of Power Sources 396:453–58. doi:10.1016/j.jpowsour.2018.06.036.
   Web of Science ®Google Scholar
• Tian, H., P. Qin, K. Li, and Z. Zhao. 2020. A review of the state of health for lithium-ion batteries: Research status and suggestions. Journal of Cleaner Production 261. doi:10.1016/j.jclepro.2020.120813.
   Web of Science ®Google Scholar
• Tian, J., R. Xiong, and W. Shen. 2020. State-of-health estimation based on differential temperature for lithium ion batteries. IEEE Transactions on Power Electronics 35 (10):10363–73. doi:10.1109/tpel.2020.2978493.
   Web of Science ®Google Scholar
• Tran, M.-K., M. Mathew, S. Janhunen, S. Panchal, K. Raahemifar, R. Fraser, and M. Fowler. 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. Journal of Energy Storage 43. doi:10.1016/j.est.2021.103252.
   Web of Science ®Google Scholar
• Wang, Z., G. Feng, D. Zhen, F. Gu, and A. Ball. 2021. A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles. Energy Reports 7:5141–61. doi:10.1016/j.egyr.2021.08.113.
   Web of Science ®Google Scholar
• Wei, Z., H. Ruan, Y. Li, J. Li, C. Zhang, and H. He. 2022. Multistage state of health estimation of lithium-ion battery with high tolerance to heavily partial charging. IEEE Transactions on Power Electronics 37 (6):7432–42. doi:10.1109/tpel.2022.3144504.
   Web of Science ®Google Scholar
• Xue, J., and B. Shen. 2020. A novel swarm intelligence optimization approach: Sparrow search algorithm. Systems Science & Control Engineering 8 (1):22–34. doi:10.1080/21642583.2019.1708830.
   Web of Science ®Google Scholar
• Zhang, Y., and J. Ji. 2021. Intelligent fault diagnosis of a reciprocating compressor using mode isolation convolutional deep belief networks. IEEE/ASME Transactions on Mechatronics 26 (3):1668–77. doi:10.1109/tmech.2020.3027912.
   Web of Science ®Google Scholar
• Zhang, Y., and Y.-F. Li. 2022. Prognostics and health management of lithium-ion battery using deep learning methods: A review. Renewable and Sustainable Energy Reviews 161. doi:10.1016/j.rser.2022.1122821.
   Web of Science ®Google Scholar
• Zhang, Y., Y. Liu, J. Wang, and T. Zhang. 2022. State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression. Energy 239. doi:10.1016/j.energy.2021.121986.
   Google Scholar
• Zhou, D., W. Zheng, S. Chen, P. Fu, H. Zhu, B. Song, X. Qu, and T. Wang. 2021. Research on state of health prediction model for lithium batteries based on actual diverse data. Energy 230: 230.doi: 10.1016/j.energy.2021.120851
   Web of Science ®Google Scholar