References
- K. Goebel, et al., "Prognostics in battery health management," IEEE Instrumentation & Measurement Mag., vol. 11, no. 4, Aug. 2008.
- B. Saha, et al., "Prognostics methods for battery health monitoring using a Bayesian framework," IEEE Trans. Instrumentation and Measurement, vol. 58, no. 2, pp. 291-296, 2009. https://doi.org/10.1109/TIM.2008.2005965
- ISO 13381-1, Condition monitoring and diagnostics of machines-prognostics. Part1: General guidelines, International Standard (2004), Retrieved Jan., 30, 2017, from https://www.iso.org/obp/ui#iso:std:iso:13381: -1:ed-1:v1:en
- J. Jun, et al., "Trend on IoT device product and technology," KICS Inf. and Commun. Mag., vol. 31, no. 4, pp. 44-52, Mar. 2014.
- K. Kim, S. Lee and J. Park "Technological trend analysis for configuration of energy storage system using retired electric vehicle battery," KICS Inf. and Commun. Mag., vol. 33, no. 7, pp. 47-52, Jun. 2016.
- Y, Ryu and J. Park, "Open energy storage system based on a profile," KICS Inf. and Commun. Mag., vol. 33, no. 7, pp. 40-46, Jun. 2016.
- Y. Choi and H. Kim, "Electrochemistry modeling based control of battery management system: A tutorial," KIC News, vol. 18, no. 5, pp. 47-60, 2015.
- M. J. Daigle and S. K. Chetan, "Electrochemistry-based battery modeling for prognostics," 2013.
- J. Li, S. Zhou, and Y. Han, Advances in Battery Manufacturing, Services, and Management Systems, John Wiley & Sons, 2016.
- S. K. Rahimian, S. Rayman, and R. E. White, "Extension of physics-based single particle model for higher charge-discharge rates," J. Power Sources, vol. 224, pp. 180-194, Feb. 2013. https://doi.org/10.1016/j.jpowsour.2012.09.084
- S. Dey and B. Ayalew, "A diagnostic scheme for detection, isolation and estimation of electrochemical faults in lithium-ion cells," in Proc. ASME 2015 Dynamic Systems and Control Conf., Columbus, Ohio, Oct. 2015.
- J. Lee, et al., "Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications," Mechanical Syst. and Sign. Process., vol. 42, no. 1, pp. 314-334, Jan. 2014. https://doi.org/10.1016/j.ymssp.2013.06.004
- J. Z. Sikorska, M. Hodkiewicz, and L. Ma, "Prognostic modelling options for remaining useful life estimation by industry," Mechanical Syst. and Sign. Process., vol. 25, no. 5, pp. 1803-1836, Jul. 2011. https://doi.org/10.1016/j.ymssp.2010.11.018
- A. T. Elsayed, C. R. Lashway, and O. A. Mohammed, "Advanced battery management and diagnostic system for smart grid infrastructure," IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 897-905, 2016. https://doi.org/10.1109/TSG.2015.2418677
- L. Liao and F. Kottig, "Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction," IEEE Trans. Reliability, vol. 63, no. 1, pp. 191-207, 2014. https://doi.org/10.1109/TR.2014.2299152
- N.-H. Kim, D. An, and J.-H. Choi, Prognostics and Health Management of Engineering Systems: An Introduction, Springer, 2016.
- H.-J. Zimmermann, "Fuzzy set theory," Wiley Interdisciplinary Rev.: Computational Statistics, vol. 2, no. 3, pp. 317-332, 2010. https://doi.org/10.1002/wics.82
- J. Zhang and J. Lee, "A review on prognostics and health monitoring of Li-ion battery," J. Power Sources, vol. 196, no. 15, pp. 6007-6014, 2011. https://doi.org/10.1016/j.jpowsour.2011.03.101
- A. Malhi, R. Yan, and R. X. Gao, "Prognosis of defect propagation based on recurrent neural networks," IEEE Trans. Instrumentation and Measurement, vol. 60, no. 3, pp. 703-711, 2011. https://doi.org/10.1109/TIM.2010.2078296
- B. Saha, K. Goebel, and J. Christophersen, "Comparison of prognostic algorithms for estimating remaining useful life of batteries," Trans. Inst. Measurement and Control, vol. 31, no. 3-4, pp. 293-308, 2009. https://doi.org/10.1177/0142331208092030
- J. R. Galvan, A. Saxena, and K. Goebel, "Uncertainty representation and interpretation in model-based prognostics algorithms based on kalman filter estimation," Annu. Conf. Prognostics and Health Management Soc. 2012, 2012.
- R. K. Singleton, E. G. Strangas, and S. Aviyente, "Extended kalman filtering for remaining-useful-life estimation of bearings," IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1781-1790, 2015. https://doi.org/10.1109/TIE.2014.2336616
- X. Zhang and P. Pisu, "An unscented kalman filter based approach for the health-monitoring and prognostics of a polymer electrolyte membrane fuel cell," Annu. Conf. Prognostics and Health Management Soc., 2012.
- S. H. Sim, et al., "Remaining useful life prediction of Li-Ion battery based on charge voltage characteristics," Trans. Korean Soc. Mech. Eng. B, vol. 37, no. 4, pp. 313-322, 2013. https://doi.org/10.3795/KSME-B.2013.37.4.313
- Y. Qian and R. Yan, "Remaining useful life prediction of rolling bearings using an enhanced particle filter," IEEE Trans. Instrumentation and Measurement, vol. 64, no. 10, pp. 2696-2707, 2015. https://doi.org/10.1109/TIM.2015.2427891
- M. Bressel, et al., "Remaining useful life prediction and uncertainty quantification of proton exchange membrane fuel cell under variable load," IEEE Trans. Ind. Electron., vol. 63, no. 4, pp. 2569-2577, 2016. https://doi.org/10.1109/TIE.2016.2519328
- J. K. Kimotho, T. Meyer, and W. Sextro, "PEM fuel cell prognostics using particle filter with model parameter adaptation," IEEE Conf. PHM, pp. 1-6, 2014.
- M. R. Palacin, and A. de Guibert, "Why do batteries fail?," Science, vol. 351, no. 6273, 1253292, Feb. 2016. https://doi.org/10.1126/science.1253292
- A. Saxena, et al., "Metrics for offline evaluation of prognostic performance," Int. J. Prognostics and Health Management, vol. 1, no. 1, pp. 4-23, 2010.
- A. Saxena, et al., "Metrics for evaluating performance of prognostic techniques," IEEE Conf. PHM, pp. 1-17, 2008.
Cited by
- 실시간 감시를 통한 교통신호제어기의 열화 감지 vol.18, pp.2, 2017, https://doi.org/10.33162/jar.2018.06.18.2.153
- Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics vol.10, pp.7, 2017, https://doi.org/10.3390/electronics10070846