中国邮电高校学报(英文版) ›› 2020, Vol. 27 ›› Issue (1): 1-9.doi: 10.19682/j.cnki.1005-8885.2020.0004
• Computer Applied Technology • 下一篇
摘要： Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory, high energy density, long cycle life and no pollution to the environment. Accurate prediction for the remaining useful life (RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion. At present, most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles. In this paper, a fusion method based on Wasserstein generative adversarial network (GAN) is proposed. This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network (ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend. The weight of each model is calculated by the discriminator in the Wasserstein GAN model. Four data sets of lithium-ion battery provided by the National Aeronautics and Space Administration (NASA) Ames Research Center are used to prove the feasibility and accuracy of the proposed method.
|1. Zhang Y J, Liu D T, Yu J X, et al. EMA remaining useful life prediction with weighted bagging GPR algorithm. Microelectronics Reliability, 2017, 75: 253-263 2. Doyle M, Newman J, Gozdz A S, et al. Comparison of modeling predictions with experimental data from plastic Lithium ion cells. Journal of the Electrochemical Society, 1996, 143(6): 1890-1903 3. Micea M V, Ungurean L, Carstoiu G N, et al. Online state-of-health assessment for battery management systems. IEEE Transactions on Instrumentation and Measurement, 2011, 60(6): 1997-2006 4. He W, Williard N, Osterman M, et al. Prognostics of Lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 2011, 196(23): 10314-10321 5. Xian W M, Long B, Li M, et al. Prognostics of Lithium-ion batteries based on the Verhulst model, particle swarm optimization and particle filter. IEEE Transactions on Instrumentation and Measurement, 2013, 63(1): 2-17 6. Song Y C, Liu D T, Yang C, et al. Data-driven hybrid remaining useful life estimation approach for spacecraft Lithium-ion battery. Microelectronics Reliability, 2017, 75: 142-153 7. Zheng X J, Wu H Y, Chen Y. Remaining useful life prediction of Lithium-ion battery using a hybrid model-based filtering and data-driven approach. Proceedings of the 11th Asian Control Conference (ASCC’17), Dec 17-20, 2017, Gold Coast, Australia. Piscataway, NJ, USA: IEEE, 2017: 2698-2703 8. Mei X Y, Fang H J. A novel fusion prognostic approach for the prediction of the remaining useful life of a Lithiumion battery. Proceedings of the 37th Chinese Control Conference (CCC’18), Jul 25-27, 2018, Wuhan, China. Piscataway, NJ, USA: IEEE, 2018: 5801-5805 9. Eddahech A, Briat O, Vinassa J M. Lithium-ion battery performance improvement based on capacity recovery exploitation. Electrochimica Acta, 2013, 114: 750-757 10. Zhang Z X, Si X S, Hu C H, et al. A prognostic model for stochastic degrading systems with state recovery: Application to Liion batteries. IEEE Transactions on Reliability, 2017, 66(4): 1293-1308 11. Saha B, Goebel K. Modeling Li-ion battery capacity depletion in a particle filtering framework. Proceedings of the Annual Conference of the Prognostics and Health Management Society. Septe 27-Oct 1, 2009, San Diego, CA USA. Piscataway, NJ, USA: IEEE, 2009: 2909-2924 12. Haykin S S. Neural networks and learning machines. Upper Saddle River, NJ, USA: Pearson Education, 2009 13. Zhou X, Hsieh S J, Peng B, et al. Cycle life estimation of Lithium-ion polymer batteries using artificial neural network and support vector machine with time-resolved thermography. Microelectronics Reliability, 2017, 79: 48-58 14. Bicer Y, Dincer I, Aydin M. Maximizing performance of fuel cell using artificial neural network approach for smart grid applications. Energy, 2016, 116: 1205-1217 15. Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14): Vol 2, Dec 8-13, 2014, Montreal, Canada. Cambridge, MA, USA: MIT Press, 2014: 2672-2680 16. Mirza M, Osindero S. Conditional generative adversarial nets. arXiv e-preprint, arXiv:1411.1784, 2014 17. Arjovsky M, Chintala S, Bottou L. Wasserstein gan. arXiv e-preprint, arXiv:1701.07875, 2017 18. Villani C. Optimal transport: Old and new. Berlin, Germany: Springer-Verlag, 2008 19. Saha B, Goebel K. Battery data set. NASA Ames Prognostics Data Repository. Moffett Field, CA, USA: Ames Research Center, 2007 20. Cai Y S, Yang L, Deng Z W, et al. Prediction of Lithium-ion battery remaining useful life based on hybrid data-driven method with optimized parameter. Proceedings of the 2nd International Conference on Power and Renewable Energy (ICPRE’17), Sept 20-23, 2017, Chengdu, China. Piscataway, NJ, USA: IEEE, 2017: 6p|
|No related articles found!|