Abstract：Most of the conventional batteries model for lithium-ion batteries dependent on theoretical and simplified mechanism model. Actually for lithium-ion batteries, because of unable to measure the process of the internal complex electrochemical reaction and vulnerable to the impact of external environment, the error is exist by theoretical model, and can not accurately reflect the dynamic characteristics of lithium-ion batteries. To solve this problem, according to ideal of data-driven, this paper uses a stochastic dynamic modeling method based on the EM algorithm and a lithium-ion battery discharge time series of stochastic dynamic model is proposed. Experimental results show that the use of this model to establish the proposed algorithm can effectively fit the experimental data, with good stability and robustness.
刘晓程,王建明,王武. 基于数据驱动的锂电池随机动态系统建模[J]. 电气技术, 2015, 16(05): 17-21.
Liu Xiaocheng,Wang Jianming,Wang Wu. Modeling of Lithium-Ion Battery Stochastic Dynamic System based on Data-Driven. Electrical Engineering, 2015, 16(05): 17-21.
[1] Saha B, Goebel K. Modeling Li-ion battery capacity depletion in a particle filtering framework[C] Proceedings of the annual conference of the prognostics and health management society. 2009: 1-10. [2] Rao R, Vrudhula S, Rakhmatov D N. Battery modeling for energy aware system design[J]. Computer, 2003, 36(12): 77-87. [3] Budde-Meiwes H, Kowal J, Sauer D U, et al. Influence of measurement procedure on quality of impedance spectra on lead-acid batteries[J]. Journal of Power Sources, 2011, 196(23): 10415-10423. [4] Li J, Mazzola M, Gafford J, et al. A new parameter estimation algorithm for an electrical analogue battery model[C] Applied Power Electronics Conference and Exposition (APEC), 2012 Twenty-Seventh Annual IEEE. IEEE, 2012: 427-433. [5] Hu Y, Wang Y Y. Two Time-Scaled Battery Model Identification With Application to Battery State Estimation[J] [6] Kozlowski J D. Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques[C]. Aerospace Conference, 2003. Proceedings. 2003 IEEE. IEEE, 2003, 7: 3257-3270. [7] Goebel K, Saha B, Saxena A, et al. Prognostics in battery health management[J]. IEEE instrumentation & measurement magazine, 2008, 11(4): 33. [8] Shumway R H, Stoffer D S. An approach to time series smoothing and forecasting using the EM algorithm[J]. Journal of time series analysis, 1982, 3(4): 253-264. [9] Weinstein E, Oppenheim A V, Feder M, et al. Iterative and sequential algorithms for multisensor signal enhancement[J]. Signal Processing, IEEE Transactions on, 1994, 42(4): 846-859. [10] Ziskind I, Hertz D. Maximum-likelihood localization of narrow-band autoregressive sources via the EM algorithm[J]. Signal Processing, IEEE Transactions on, 1993, 41(8): 2719-2724. [11] Wang Z, Yang F, Ho D W C, et al. Stochastic dynamic modeling of short gene expression time-series data[J]. NanoBioscience, IEEE Transactions on, 2008, 7(1): 44-55. [12] Ghahramani Z, Hinton G E. Parameter estimation for linear dynamical systems[R]. Technical Report CRG- TR-96-2, University of Totronto, Dept. of Computer Science, 1996. [13] Saha B, Goebel K, Battery Data Set. http://ti.arc.nasa.gov/ project/prognostic-data-repository.
2015, Vol. 16 
