|
[1] Cox, D.R., and Isham, V. (1980), Point Processes, London: Chapman and Hall. [2] He, W., Williard, N., Osterman, M., and Pecht, M. (2011). Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 196 (23), pp. 10314-10321. [3] Kozlowski, James D. (2003). Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques. Aerospace Conference, 2003. Proceedings. 2003 IEEE (Vol. 7, 3257-3270). IEEE. [4] Lindqvist, B. H., G. Elvebakk, and Knut Heggland. (2003). The trend-renewal process for statistical analysis of repairable systems. Technometrics, 45 (1), pp. 31-44. [5] Lindqvist, B. H. (2006). On the statistical modeling and analysis of repairable systems. Statistical science, pp. 532-551. [6] Long, B., Xian, W., Jiang, L., and Liu, Z. (2013). An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries. Microelectronics Reliability, 53 (6), pp. 821-831. [7] Meeker, W. Q. and Escobar, L. A. (1998). Statistical Methods for Reliability Data, John Wiley and Sons, New York. [8] Micea, M. V., Ungurean, L., Carstoiu, G. N., and Groza, V. (2011). Online state-of-health assessment for battery management systems. IEEE Transactions on Instrumentation and Measurement, 60(6), pp. 1997-2006. [9] Nelson, W. (1990), Accelerated Testing: Statistical Models, Test Plans, and Data Analyses, New York: John Wiley and Sons. [10] Ross, S. M. (2014). Introduction to probability models. 10th Edition, Academic Press, New York. [11] Saha, B., Goebel, K., and Christophersen, J. (2009). Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Control, 31, pp. 293-308. [12] Xing, Y., Ma, E. W., Tsui, K. L., and Pecht, M. (2013). An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability, 53(6), pp. 811-820.
|