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Research Article

Impact study on continuous overcharging of precycled lithium batteries and control algorithm development using machine learning approach

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Pages 3946-3963 | Received 18 Dec 2023, Accepted 26 Feb 2024, Published online: 13 Mar 2024

References

  • Aghaabbasi, M., M. Ali, M. Jasiński, Z. Leonowicz, and T. Novák. 2023. On hyperparameter optimization of machine learning methods using a bayesian optimization algorithm to predict work travel mode choice. IEEE Access 11:19762–74. doi:10.1109/ACCESS.2023.3247448.
  • Alibrahim, H., and S. A. Ludwig (2021). Hyperparameter optimization: Comparing genetic algorithm against grid search and bayesian optimization. 2021 IEEE Congress on Evolutionary Computation (CEC), 1551–9. 10.1109/CEC45853.2021.9504761
  • Almasoud, M., and T. E. Ward. 2019. Detection of chronic kidney disease using machine learning algorithms with least number of predictors. International Journal of Advanced Computer Science and Applications (IJACSA) 10 (8): Article 8. doi:10.14569/IJACSA.2019.0100813.
  • Eftekhari, A. 2019. Lithium batteries for electric vehicles: From economy to research strategy. ACS Sustainable Chemistry & Engineering 7 (6):5602–13. doi:10.1021/acssuschemeng.8b01494.
  • Feng, X., D. Ren, X. He, and M. Ouyang. 2020. Mitigating thermal runaway of lithium-ion batteries. Joule 4 (4):743–70. doi:10.1016/j.joule.2020.02.010.
  • Gao, S., L. Lu, M. Ouyang, Y. Duan, X. Zhu, C. Xu, B. Ng, N. Kamyab, R. E. White, and P. T. Coman. 2019. Experimental study on module-to-module thermal runaway-propagation in a battery pack. Journal of the Electrochemical Society 166 (10):A2065. doi:10.1149/2.1011910jes.
  • Hu, X., K. Zhang, K. Liu, X. Lin, S. Dey, and S. Onori. 2020. Advanced fault diagnosis for lithium-ion battery systems: A review of fault mechanisms, fault features, and diagnosis procedures. IEEE Industrial Electronics Magazine 14 (3):65–91. doi:10.1109/MIE.2020.2964814.
  • Kaboli, S., H. Demers, A. Paolella, A. Darwiche, M. Dontigny, D. Clément, A. Guerfi, M. L. Trudeau, J. B. Goodenough, and K. Zaghib. 2020. Behavior of solid electrolyte in Li-Polymer battery with NMC Cathode via in-situ scanning electron microscopy. Nano Letters 20 (3):1607–13. doi:10.1021/acs.nanolett.9b04452.
  • Li, L.-L., Z.-F. Liu, M.-L. Tseng, and A. S. F. Chiu. 2019. Enhancing the Lithium-ion battery life predictability using a hybrid method. Applied Soft Computing 74:110–21. doi:10.1016/j.asoc.2018.10.014.
  • Li, X., and Z. Wang. 2018. A novel fault diagnosis method for lithium-ion battery packs of electric vehicles. Measurement 116:402–11. doi:10.1016/j.measurement.2017.11.034.
  • Locorotondo, E., V. Cultrera, L. Pugi, L. Berzi, M. Pasquali, N. Andrenacci, G. Lutzemberger, and M. Pierini (2020). Impedance spectroscopy characterization of lithium batteries with different ages in second life application. 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 1–6. 10.1109/EEEIC/ICPSEurope49358.2020.9160616
  • Ma, S., M. Jiang, P. Tao, C. Song, J. Wu, J. Wang, T. Deng, and W. Shang. 2018. Temperature effect and thermal impact in lithium-ion batteries: A review. Progress in Natural Science: Materials International 28 (6):653–66. doi:10.1016/j.pnsc.2018.11.002.
  • Manoharan, A., K. M. Begam, V. R. Aparow, and D. Sooriamoorthy. 2022. Artificial neural networks, gradient boosting and support vector machines for electric vehicle battery state estimation: A review. Journal of Energy Storage 55:105384. doi:10.1016/j.est.2022.105384.
  • Naylor Marlow, M., J. Chen, and B. Wu. 2024. Degradation in parallel-connected lithium-ion battery packs under thermal gradients. Communications Engineering 3(1): Article 1. doi:10.1038/s44172-023-00153-5.
  • Ojo, O., H. Lang, Y. Kim, X. Hu, B. Mu, and X. Lin. 2021. A neural network based method for thermal fault detection in lithium-ion batteries. IEEE Transactions on Industrial Electronics 68 (5):4068–78. doi:10.1109/TIE.2020.2984980.
  • Ouyang, D., J. Liu, M. Chen, and J. Wang. 2017. Investigation into the fire hazards of lithium-ion batteries under overcharging. Applied Sciences 7 (12):1314. doi:10.3390/app7121314. Article 12.
  • Palacín, M. R., and A. de Guibert. 2016. Why do batteries fail? Science 351 (6273):1253292. doi:10.1126/science.1253292.
  • Praveenkumar, T., M. Saimurugan, and K. I. Ramachandran. 2017. Comparison of vibration, sound and motor current signature analysis for detection of gear box faults. International Journal of Prognostics and Health Management 8 (2):Article 2. doi:10.36001/ijphm.2017.v8i2.2642.
  • Shen, D., D. Yang, C. Lyu, G. Hinds, L. Wang, and M. Bai. 2023. Detection and quantitative diagnosis of micro-short-circuit faults in lithium-ion battery packs considering cell inconsistency. Green Energy and Intelligent Transportation 2 (5):100109. doi:10.1016/j.geits.2023.100109.
  • Spingler, F. B., S. Kücher, R. Phillips, E. Moyassari, and A. Jossen. 2021. Electrochemically stable in situ dilatometry of NMC, NCA and graphite electrodes for lithium-ion cells compared to XRD measurements. Journal of the Electrochemical Society 168 (4):040515. doi:10.1149/1945-7111/abf262.
  • Wang, F.-K., and T. Mamo. 2020. Gradient boosted regression model for the degradation analysis of prismatic cells. Computers & Industrial Engineering 144:106494. doi:10.1016/j.cie.2020.106494.
  • Wang, X., S. Li, L. Wang, Y. Sun, and Z. Wang. 2020. Degradation and dependence analysis of a lithium-ion battery pack in the unbalanced state. Energies 13 (22):5934. doi:10.3390/en13225934. Article 22.
  • Wu, J., X.-Y. Chen, H. Zhang, L.-D. Xiong, H. Lei, and S.-H. Deng. 2019. Hyperparameter optimization for machine learning models based on bayesian optimizationb. Journal of Electronic Science and Technology 17 (1):26–40. doi:10.11989/JEST.1674-862X.80904120.
  • Xu, P., J. Li, N. Lei, F. Zhou, and C. Sun. 2021. An experimental study on the mechanical characteristics of Li-ion battery during overcharge-induced thermal runaway. International Journal of Energy Research 45 (14):19985–20000. doi:10.1002/er.7072.
  • Yao, L., Z. Fang, Y. Xiao, J. Hou, and Z. Fu. 2021. An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine. Energy 214:118866. doi:10.1016/j.energy.2020.118866.
  • Zhang, J., Y. Wang, B. Jiang, H. He, S. Huang, C. Wang, Y. Zhang, X. Han, D. Guo, G. He, et al. 2023. Realistic fault detection of li-ion battery via dynamical deep learning. Nature Communications 14 (1):Article 1. doi:10.1038/s41467-023-41226-5.
  • Zhang, K., X. Hu, Y. Liu, X. Lin, and W. Liu. 2022. Multi-fault detection and isolation for lithium-ion battery systems. IEEE Transactions on Power Electronics 37 (1):971–89. doi:10.1109/TPEL.2021.3098445.
  • 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 161:112282. doi:10.1016/j.rser.2022.112282.
  • Zhou, Z., Y. Liu, M. You, R. Xiong, and X. Zhou. 2022. Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction. Green Energy and Intelligent Transportation 1 (1):100008. doi:10.1016/j.geits.2022.100008.

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