Optimal power assessment of energy storage devices using nonlinear autoregressive network with extended Kalman filter for electric power farm tiller applications

References

• Atalay, S., M. Sheikh, A. Mariani, Y. Merla, E. Bower, and W. D. Widanage. 2020. Theory of battery ageing in a lithium-ion battery: Capacity fade, nonlinear ageing and lifetime prediction. Journal of Power Sources 478:229026. doi:10.1016/j.jpowsour.2020.229026.
   Web of Science ®Google Scholar
• Bai, Y., X. Wang, X. Jin, Z. Zhao, and B. Zhang. 2020. A neuron-based Kalman filter with nonlinear. Sensors 20 (299):299. doi:10.3390/s20010299.
   PubMedGoogle Scholar
• Barré, A., B. Deguilhem, S. Grolleau, M. Gérard, F. Suard, and D. Riu. 2013. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources 241:680–89. doi:10.1016/j.jpowsour.2013.05.040.
   Web of Science ®Google Scholar
• Braco, E., I. San Martín, A. Berrueta, P. Sanchis, and A. Ursúa. 2020. Experimental assessment of cycling ageing of lithium-ion second-life batteries from electric vehicles. Journal of Energy Storage 32 (June):101695. doi:10.1016/j.est.2020.101695.
   Web of Science ®Google Scholar
• Che, Y., Liu, Y., Cheng, Z., & Zhang, J. (2021). SOC and SOH Identification Method of Li-Ion Battery Based on SWPSO-DRNN. 9(4), 4050–4061.
   Google Scholar
• Chen, C., R. Xiong, R. Yang, W. Shen, and F. Sun. 2019. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter. Journal of Cleaner Production 234:1153–64. doi:10.1016/j.jclepro.2019.06.273.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• De Sutter, L., Y. Firouz, J. De Hoog, N. Omar, and J. Van Mierlo. 2019. Battery aging assessment and parametric study of lithium-ion batteries by means of a fractional differential model. Electrochimica Acta 305:24–36. doi:10.1016/j.electacta.2019.02.104.
   Web of Science ®Google Scholar
• Fridholm, B., T. Wik, H. Kuusisto, and A. Klintberg. 2018. Estimating power capability of aged lithium-ion batteries in presence of communication delays. Journal of Power Sources 383 (February):24–33. doi:10.1016/j.jpowsour.2018.02.018.
   Web of Science ®Google Scholar
• Gao, Y., J. Jiang, C. Zhang, W. Zhang, Z. Ma, and Y. Jiang. 2017. Lithium-ion battery aging mechanisms and life model under different charging stresses. Journal of Power Sources 356:103–14. doi:10.1016/j.jpowsour.2017.04.084.
   Web of Science ®Google Scholar
• Han, X., M. Ouyang, L. Lu, J. Li, Y. Zheng, and Z. Li. 2014. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification. Journal of Power Sources 251:38–54. doi:10.1016/j.jpowsour.2013.11.029.
   Web of Science ®Google Scholar
• Hein, T., A. Ziegler, D. Oeser, and A. Ackva. 2021, October. A capacity-based equalization method for aged lithium-ion batteries in electric vehicles. Electric Power Systems Research 191:106898. ( 2020). doi: 10.1016/j.epsr.2020.106898.
   Web of Science ®Google Scholar
• Hossain Lipu, M. S., M. A. Hannan, A. Hussain, A. Ayob, M. H. M. Saad, T. F. Karim, and D. N. T. How. 2020. Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends. Journal of Cleaner Production 277:124110. doi:10.1016/j.jclepro.2020.124110.
   Web of Science ®Google Scholar
• Jiang, J., Z. Lin, Q. Ju, Z. Ma, C. Zheng, and Z. Wang. 2017. Electrochemical impedance spectra for lithium-ion battery ageing considering the rate of discharge ability. Energy Procedia 105:844–49. doi:10.1016/j.egypro.2017.03.399.
   Google Scholar
• Jiao, M., D. Wang, and J. Qiu. 2020. A GRU-RNN based momentum optimized algorithm for SOC estimation. Journal of Power Sources 459 (February):228051. doi:10.1016/j.jpowsour.2020.228051.
   Web of Science ®Google Scholar
• Juang, L. W., P. J. Kollmeyer, A. E. Anders, T. M. Jahns, R. D. Lorenz, and D. Gao. 2017. Investigation of the influence of superimposed AC current on lithium-ion battery aging using statistical design of experiments. Journal of Energy Storage 11:93–103. doi:10.1016/j.est.2017.02.005.
   Web of Science ®Google Scholar
• Khaleghi, S., D. Karimi, S. H. Beheshti, M. S. Hosen, H. Behi, M. Berecibar, and J. Van Mierlo. 2021. Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network. Applied Energy 282 PA: 116159. doi:10.1016/j.apenergy.2020.116159.
   Web of Science ®Google Scholar
• Leng, F., Z. Wei, C. M. Tan, and R. Yazami. 2017. Hierarchical degradation processes in lithium-ion batteries during ageing. Electrochimica Acta 256:52–62. doi:10.1016/j.electacta.2017.10.007.
   Web of Science ®Google Scholar
• Li, W., Y. Fan, F. Ringbeck, D. Jöst, X. Han, M. Ouyang, and D. U. Sauer. 2020. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter. Journal of Power Sources 476 (June):228534. doi:10.1016/j.jpowsour.2020.228534.
   Web of Science ®Google Scholar
• Li, Z., Huang, J., Liaw, B. Y., & Zhang, J. (2017). On state-of-charge determination for lithium-ion batteries. Journal of Power Sources, 348, 281–301. https://doi.org/10.1016/j.jpowsour.2017.03.001
   Web of Science ®Google Scholar
• Liu, J., Q. Duan, M. Ma, C. Zhao, J. Sun, and Q. Wang. 2020, October. Aging mechanisms and thermal stability of aged commercial 18650 lithium ion battery induced by slight overcharging cycling. Journal of Power Sources 445:227263. doi:10.1016/j.jpowsour.2019.227263. 2019.
   Web of Science ®Google Scholar
• Lu, X., H. Li, and N. Chen. 2019. An indicator for the electrode aging of lithium-ion batteries using a fractional variable order model. Electrochimica Acta 299:378–87. doi:10.1016/j.electacta.2018.12.097.
   Web of Science ®Google Scholar
• Maures, M., Y. Zhang, C. Martin, J. Y. Delétage, J. M. Vinassa, and O. Briat. 2019. Impact of temperature on calendar ageing of Lithium-ion battery using incremental capacity analysis. Microelectronics Reliability 100–101 (May):113364. doi:10.1016/j.microrel.2019.06.056.
   Web of Science ®Google Scholar
• Müller, V., R. G. Scurtu, M. Memm, M. A. Danzer, and M. Wohlfahrt-Mehrens. 2019. Study of the influence of mechanical pressure on the performance and aging of Lithium-ion battery cells. Journal of Power Sources 440 (May):227148. doi:10.1016/j.jpowsour.2019.227148.
   Web of Science ®Google Scholar
• Oeser, D., A. Ziegler, and A. Ackva. 2018. Single cell analysis of lithium-ion e-bike batteries aged under various conditions. Journal of Power Sources 397 (May):25–31. doi:10.1016/j.jpowsour.2018.06.101.
   Web of Science ®Google Scholar
• Pan, B., D. Dong, J. Wang, J. Nie, S. Liu, Y. Cao, and Y. Jiang. 2020. Aging mechanism diagnosis of lithium ion battery by open circuit voltage analysis. Electrochimica Acta 362:137101. doi:10.1016/j.electacta.2020.137101.
   Web of Science ®Google Scholar
• Qin J, Zhao S, Liu X and Liu Y. (2020). Simulation study on thermal runaway suppression of 18650 lithium battery. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–13. 10.1080/15567036.2020.1817189
   Web of Science ®Google Scholar
• Redondo-Iglesias, E., P. Venet, and S. Pelissier. 2017. Eyring acceleration model for predicting calendar ageing of lithium-ion batteries. Journal of Energy Storage 13:176–83. doi:10.1016/j.est.2017.06.009.
   Web of Science ®Google Scholar
• Ren, D., H. Hsu, R. Li, X. Feng, D. Guo, X. Han, L. Lu, X. He, S. Gao, J. Hou, et al. 2019. A comparative investigation of aging effects on thermal runaway behavior of lithium-ion batteries. ETransportation 2:100034. doi:10.1016/j.etran.2019.100034.
   Google Scholar
• Schott, B., A. Püttner, and M. Müller. 2015. Advances in battery technologies for electric vehicles. Advances in Battery Technologies for Electric Vehicles. http://www.sciencedirect.com/science/article/pii/B9781782423775000030.
   Google Scholar
• Šeruga, D., A. Gosar, C. A. Sweeney, J. Jaguemont, J. Van Mierlo, and M. Nagode. 2021. Continuous modelling of cyclic ageing for lithium-ion batteries. Energy 215:119079. doi:10.1016/j.energy.2020.119079.
   Web of Science ®Google Scholar
• Shafiei Sabet, P., A. J. Warnecke, F. Meier, H. Witzenhausen, E. Martinez-Laserna, and D. U. Sauer. 2020. Non-invasive yet separate investigation of anode/cathode degradation of lithium-ion batteries (nickel–cobalt–manganese vs. graphite) due to accelerated aging. Journal of Power Sources 449 (October):227369. doi:10.1016/j.jpowsour.2019.227369.
   Web of Science ®Google Scholar
• Tang, X., C. Zou, K. Yao, J. Lu, Y. Xia, and F. Gao. 2019b. Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method. Applied Energy 254 (May):113591. doi:10.1016/j.apenergy.2019.113591.
   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 261:120813. doi:10.1016/j.jclepro.2020.120813.
   Web of Science ®Google Scholar
• Xie, S., L. Ren, X. Yang, H. Wang, Q. Sun, X. Chen, and Y. He. 2020. Influence of cycling aging and ambient pressure on the thermal safety features of lithium-ion battery. Journal of Power Sources 448 (October):227425. doi:10.1016/j.jpowsour.2019.227425.
   Web of Science ®Google Scholar
• Xiong, R., Y. Pan, W. Shen, H. Li, and F. Sun. 2020. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives. Renewable and Sustainable Energy Reviews 131 (5):110048. doi:10.1016/j.rser.2020.110048.
   Google Scholar
• Xue, Z., Y. Zhang, C. Cheng, and G. Ma. 2020. Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing 376:95–102. doi:10.1016/j.neucom.2019.09.074.
   Web of Science ®Google Scholar
• Yang, C., X. Wang, Q. Fang, H. Dai, Y. Cao, and X. Wei. 2020. An online SOC and capacity estimation method for aged lithium-ion battery pack considering cell inconsistency. Journal of Energy Storage 29 (January):101250. doi:10.1016/j.est.2020.101250.
   Web of Science ®Google Scholar
• Zhang, X., Y. Gao, B. Guo, C. Zhu, X. Zhou, L. Wang, and J. Cao. 2020. A novel quantitative electrochemical aging model considering side reactions for lithium-ion batteries. Electrochimica Acta 343:136070. doi:10.1016/j.electacta.2020.136070.
   Web of Science ®Google Scholar
• Zhang, Y. Z., R. Xiong, H. W. He, X. Qu, and M. Pecht. 2019. Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries. ETransportation 100004. doi:10.1016/j.etran.2019.100004.
   Google Scholar
• Zheng, Y., C. Qin, X. Lai, X. Han, and Y. Xie. 2019. A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model. Applied Energy 251 (February):113327. doi:10.1016/j.apenergy.2019.113327.
   Web of Science ®Google Scholar
• Zhou, X., J. Huang, Z. Pan, and M. Ouyang. 2019, December. Impedance characterization of lithium-ion batteries aging under high-temperature cycling: Importance of electrolyte-phase diffusion. Journal of Power Sources 426:216–22. doi:10.1016/j.jpowsour.2019.04.040. 2018.
   Web of Science ®Google Scholar
• Zhou, Z., B. Duan, Y. Kang, Y. Shang, N. Cui, L. Chang, and C. Zhang. 2020. An efficient screening method for retired lithium-ion batteries based on support vector machine. Journal of Cleaner Production 267:121882. doi:10.1016/j.jclepro.2020.121882.
   Web of Science ®Google Scholar
• Zilberman, I., S. Ludwig, M. Schiller, and A. Jossen. 2020, November. Online aging determination in lithium-ion battery module with forced temperature gradient. Journal of Energy Storage 28:101170. doi:10.1016/j.est.2019.101170. 2019.
   Web of Science ®Google Scholar