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Research Article

Battery state of health prediction based on voltage intervals, BP neural network and genetic algorithm

, , , , &
Pages 1743-1756 | Received 29 May 2023, Accepted 25 Sep 2023, Published online: 04 Oct 2023

References

  • Adaikkappan, M., and N. Sathiyamoorthy. 2022. Modeling, state of charge estimation, and charging of lithium-ion battery in electric vehicle: A review. International Journal of Energy Research 46 (3):2141–65. doi:10.1002/er.7339.
  • Bruen, T., and J. Marco. 2016. Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system. Journal of Power Sources 310:91–101. doi:10.1016/j.jpowsour.2016.01.001.
  • Chen, K., S. Laghrouche, and A. Djerdir. 2019. Fuel cell health prognosis using unscented Kalman filter: Postal fuel cell electric vehicles case study. International Journal of Hydrogen Energy 44:1930–39. doi:10.1016/j.ijhydene.2018.11.100.
  • Cho, S., H. Jeong, C. Han, S. Jin, J. H. Lim, and J. Oh. 2012. State-of-charge estimation for lithium-ion batteries under various operating conditions using an equivalent circuit model. Computers & Chemical Engineering 41:1–9. doi:10.1016/j.compchemeng.2012.02.003.
  • Cui, K., and X. Jing. 2019. Research on prediction model of geotechnical parameters based on BP neural network. Neural Computing and Applications 31 (12):8205–15. doi:10.1007/s00521-018-3902-6.
  • Dai, H., G. Zhao, M. Lin, J. Wu, and G. Zheng. 2019. A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain. IEEE Transactions on Industrial Electronics 66 (10):7706–16. doi:10.1109/TIE.2018.2880703.
  • Di Domenico, D., G. Fiengo, A. Stefanopoulou. 2008. Lithium-ion battery state of charge estimation with a Kalman filter based on a electrochemical model. Control Appl 2008 CCA 2008 IEEE International Conference. 702–07. 10.1109/CCA.2008.4629639.
  • Doyle, M., T. F. Fuller, and J. Newman. 1993. Modeling of Galvanostatic charge and discharge. Journal of the Electrochemical Society 140 (6):1526–33. doi:10.1149/1.2221597.
  • 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.
  • Feng, X., C. Weng, X. He, X. Han, L. Lu, D. Ren, and M. Ouyang. 2019a. Online state-of-health estimation for li-ion battery using partial charging segment based on support vector machine. IEEE Trans Vehicular Technology 68 (9):8583–92. doi:10.1109/TVT.2019.2927120.
  • Feng, X., C. Weng, X. He, X. Han, L. Lu, D. Ren, and M. Ouyang. 2019b. Online state-of-health estimation for li-ion battery using partial charging segment based on support vector machine. IEEE Transactions on Vehicular Technology 68 (9):8583e92. doi:10.1109/TVT.2019.2927120.
  • Goebel, K., B. Saha, A. Saxena, J. R. Celaya, and J. P. Christophersen. 2008. Prognostics in battery health management. IEEE Instrumentation Measurement Magazine 11 (4):33–40. doi:10.1109/MIM.2008.4579269.
  • He, H., F. Sun, Z. Wang, C. Lin, C. Zhang, R. Xiong, J. Deng, X. Zhu, P. Xie, S. Zhang, et al. 2022. China’s battery electric vehicles lead the world: Achievements in technology system architecture and technological breakthroughs. Green Energy and Intelligent Transportation 1 (1):100020. doi:10.1016/j.geits.2022.100020.
  • Hossain Lipu, M. S., M. A. Hannan, A. Hussain, M. H. Saad, A. Ayob, and M. N. Uddin. 2019. Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm. IEEE Transactions on Industry Applications 55 (4):4225–34. doi:10.1109/TIA.2019.2902532.
  • Hu, X., Y. Che, X. Lin, and Z. Deng. 2020. Health prognosis for electric vehicle battery packs: A data-driven approach. IEEE/ASME Transaction on Mechatronics 25:2622–32. doi:10.1109/TMECH.2020.2986364.
  • Hu, X., S. Li, and H. Peng. 2012. A comparative study of equivalent circuit models for li-ion batteries. Journal of Power Sources 198:359–67. doi:10.1016/j.jpowsour.2011.10.013.
  • Hu, X., L. Xu, X. Lin, and M. Pecht. 2020. Battery lifetime prognostics. Joule 4:310–46. doi:10.1016/j.joule.2019.11.018.
  • Hu, Y., and Y. Wang. 2015. Two time-scaled battery model identification with application to battery state estimation. IEEE Transactions on Control Systems Technology 23 (3):1180–88. doi:10.1109/TCST.2014.2358846.
  • Huo, D., J. Chen, H. Zhang, Y. Shi, and T. Wang. 2023. Intelligent prediction for digging load of hydraulic excavators based on RBF neural network. Measurement 206. doi:10.1016/j.measurement.2022.112210.
  • Kumar, R., E. Joanni, R. K. Singh, D. P. Singh, and S. A. Moshkalev. 2018. Recent advances in the synthesis and modification of carbon-based 2D. Progress in Energy and Combustion Science 67:115e57. doi:10.1016/j.pecs.2018.03.001.
  • Li, J., M. Ye, W. Meng, X. Xu, and S. Jiao. 2020. A novel state of charge approach of lithium ion battery using Least Squares support vector machine. IEEE Access 8:195398–410. doi:10.1109/ACCESS.2020.3033451.
  • Li, Q., D. Li, K. Zhao, L. Wang, and K. Wang. 2022. State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression. Journal of Energy Storage 50. doi:10.1016/j.est.2022.104215.
  • Li, X., C. Yuan, X. Li, and Z. Wang. 2020. State of health estimation for li-ion battery using incremental capacity analysis and Gaussian process regression. Energy 190:116467. doi:10.1016/j.energy.2019.116467.
  • Li, X., C. Yuan, and Z. Wang. 2020. State of health estimation for li-ion battery via partial incremental capacity analysis based on support vector regression. Energy 203. doi:10.1016/j.energy.2020.117852.
  • Li, Y., K. Liu, A. M. Foley, A. Zülke, M. Berecibar, E. N. Maury, J. Van Mierlo, and H. E. Hoster. 2019. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews 113:109254. doi:10.1016/j.rser.2019.109254.
  • 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.
  • Liu, C., Q. Li, and K. Wang. 2021. State-of-charge estimation and remaining useful life prediction of supercapacitors. Renewable and Sustainable Energy Reviews 150:111408. doi:10.1016/j.rser.2021.111408.
  • Liu, K., T. Lin, T. Zhong, X. Ge, F. Jiang, X. Zhang New methods based on a genetic algorithm back propagation (GABP) neural network and general regression neural network (GRNN) for predicting the occurrence of trihalomethanes in tap water. Science of The Total Environment 870, 2023, 161976. 10.1016/j.scitotenv.2023.161976.
  • Liu, K., Q. Peng, Y. Che, Y. Zheng, K. Li, R. Teodorescu, D. Widanage, and A. Barai. 2023. Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects. Advances in Applied Energy 9:100117. doi:10.1016/j.adapen.2022.100117.
  • Liu, K., Z. Wei, C. Zhang, Y. Shang, R. Teodorescu, and Q. 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.
  • Ma, Y., M. Yao, H. Liu, and Z. Tang. 2022. State of health estimation and remaining useful life prediction for lithium-ion batteries by improved particle Swarm optimization-back propagation neural network. Journal of Energy Storage 52. doi:10.1016/j.est.2022.104750.
  • Qiu, X., W. Wu, and S. Wang. 2020. Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method. Journal of Power Sources 450:1–13. doi:10.1016/j.jpowsour.2020.227700.
  • Rahman, M. A., S. Anwar, and A. Izadian. 2016. Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources 307:86–97. doi:10.1016/j.jpowsour.2015.12.083.
  • Rao, Z., S. Wang, and G. Zhang. 2011. Simulation and experiment of thermal energy management with phase change material for ageing LiFePO4 power battery. Energy Conversion and Management 52:3408–14. doi:10.1016/j.enconman.2011.07.009.
  • Richardson, R. R., M. A. Osborne, and D. A. Howey. 2017. Gaussian process regression for forecasting battery state of health. Journal of Power Sources 357:209e19. doi:10.1016/j.jpowsour.2017.05.004.
  • Saw, L. H., Y. Ye, and A. A. O. Tay. 2016. Integration issues of lithium-ion battery into electric vehicles battery pack. Journal of Cleaner Production 113:1032–45. doi:10.1016/j.jclepro.2015.11.011.
  • She, C., Z. Wang, F. Sun, P. Liu, and L. Zhang. 2020. Battery aging assessment for real-world electric buses based on incremental capacity analysis and radial basis function neural network. IEEE Transactions on Industrial Informatics 16 (5). doi:10.1109/TII.2019.2951843.
  • Shi, E., F. Xia, D. Peng, L. Li, X. Wang, and B. Yu. 2019. State-of-health estimation for lithium battery in electric vehicles based on improved unscented particle filter. Journal of Renewable Sustainable Energy 11:24101. doi:10.1063/1.5065477.
  • Stetzel, K. D., L. L. Aldrich, M. S. Trimboli, and G. L. Plett. 2015. Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter. Journal of Power Sources 278:490–505. doi:10.1016/j.jpowsour.2014.11.135.
  • Su, M., J. Liu, M. K. Kim, and X. Wu. 2022. Predicting moisture condensation risk on the radiant cooling floor of an office using integration of a genetic algorithm-back-propagation neural network with sensitivity analysis. Energy and Built Environment. doi:10.1016/j.enbenv.2022.08.004.
  • 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.
  • Voronov, S., E. Frisk, and M. Krysander. 2018. Data-driven battery lifetime prediction and confidence estimation for heavy-duty trucks. IEEE Transactions on Reliability 67:623e39. doi:10.1109/TR.2018.2803798.
  • Wang, J., Z. Deng, T. Yu, A. Yoshida, L. Xu, G. Guan, and A. Abudula. 2022. State of health estimation based on modified Gaussian process regression for lithium-ion batteries. Journal of Energy Storage 51. doi:10.1016/j.est.2022.104512.
  • Wang, Q., Z. Wang, L. Zhang, P. Liu, and L. Zhou. A battery capacity estimation framework combining hybrid deep neural network and regional capacity calculation based on real-world operating data. IEEE Transactions on Industrial Electronics 70 (8):8499–508. doi:10.1109/TIE.2022.3229350.
  • Wang, Y., D. Yang, X. Zhang, and Z. Chen. 2016. Probability based remaining capacity estimation using data-driven and neural network model. Journal of Power Sources 315:199e208. doi:10.1016/j.jpowsour.2016.03.054.
  • Wen, J., X. Chen, X. Li, and Y. Li. 2022. SOH prediction of lithium battery based on IC curve feature and BP neural network. Energy 261. doi:10.1016/j.energy.2022.125234.
  • Widodo, A., M. C. Shim, W. Caesarendra, and B. S. Yang. 2011. Intelligent prognostics for battery health monitoring based on sample entropy. Expert Systems with Applications 38 (9):11763e9. doi:10.1016/j.eswa.2011.03.063.
  • Wu, J., Y. Wang, X. Zhang, and Z. Chen. 2016. A novel state of health estimation method of li-ion battery using group method of data handling. Journal of Power Sources 327:457–64. doi:10.1016/j.jpowsour.2016.07.065.
  • Yan, H., J. Zhang, N. Zhou, and M. Li. 2020. Application of hybrid artificial intelligence model to predict coal strength alteration during CO2 geological sequestration in coal seams. Science of the Total Environment 711:135029. doi:10.1016/j.scitotenv.2019.135029.
  • Yang, D., X. Zhang, R. Pan, Y. Wang, and Z. Chen. 2018. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. Journal of Power Sources 384:387–95. doi:10.1016/j.jpowsour.2018.03.015.
  • Yin, G., X. Chen, H. Zhu, Z. Chen, C. Su, Z. He, J. Qiu, and T. Wang. 2022. A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model. Science of the Total Environment 825:153948. doi:10.1016/j.scitotenv.2022.153948.
  • Yun, H., D. Koo, and G. Na. 2020. Collapse moment estimation for wall-thinned pipe bends and elbows using deep fuzzy neural networks. Nuclear Engineering and Technology 52 (11). doi:10.1016/j.net.2020.05.006.
  • Zhang, F., X. Teng, W. Shi, Y. Song, J. Zhang, X. Wang, H. Li, Q. Li, S. Li, and H. Hu. 2020. SnO2nanoflowerarrays on an amorphous buffer layer as binder-free electrodes for flexible lithium-ion batteries. Applied Surface Science 527:146910. doi:10.1016/j.apsusc.2020.146910.
  • Zhang, L., L. Huang, Z. Zhang, Z. Wang, and D. Dorrell. 2022. Degradation characteristics investigation for lithium-ion cells with NCA cathode during overcharging. Applied Energy 327. doi:10.1016/j.apenergy.2022.120026.
  • Zhang, L., M. Zheng, D. Du, Y. Li, M. Fei, Y. Guo, K. Li, and J. Na. 2020. State-of-charge estimation of lithium-ion battery pack based on improved RBF neural networks. Hindawi 2020:1–10. doi:10.1155/2020/8840240.
  • Zhang, M., K. Wang, and Y. T. Zhou. 2020. Online state of charge estimation of lithium-ion cells using particle filter-based hybrid filtering approach. Complexity 2020. doi:10.1155/2020/8231243.
  • Zhou, Y., Y. Wang, K. Wang, L. Kang, F. Peng, L. Wang, and J. Pang. 2020. Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors. Applied Energy 260:114169. doi:10.1016/j.apenergy.2019.114169.
  • 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). doi:10.1016/j.geits.2022.100008.

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