DOI: 10.1016/j.eswa.2011.03.063. Intelligent prognostics for battery health monitoring based on sample entropy

Intelligent prognostics for battery health monitoring based on sample entropy

10.1016/j.eswa.2011.03.063

Crossref journal-article

Elsevier BV

Expert Systems with Applications (78)

Bibliography

Widodo, A., Shim, M.-C., Caesarendra, W., & Yang, B.-S. (2011). Intelligent prognostics for battery health monitoring based on sample entropy. Expert Systems with Applications, 38(9), 11763â11769.

Authors 4

Achmad Widodo (first)
Min-Chan Shim (additional)
Wahyu Caesarendra (additional)
Bo-Suk Yang (additional)

References 25 Referenced 345

10.1016/j.enconman.2004.10.020 / Energy Conversion and Management / Application of EoEP principle with variable heat transfer coefficient in minimizing entropy production in heat exchangers by Balkan (2005)
Goebel, K., Saha, B., Saxena, A., Celaya, J. R., & Christophersen, J. (2008). Prognostics in battery health management. In IEEE instrumentation & measurement magazine (pp. 33–40). (10.1109/MIM.2008.4579269)
10.1016/0167-2789(83)90298-1 / Physica D / Measuring the strangeness of strange attractors by Grasberger (1983)
{'year': '1999', 'series-title': 'Neural network', 'author': 'Haykin', 'key': '10.1016/j.eswa.2011.03.063_b0020'} / Neural network by Haykin (1999)
IEEE Std. 1188-2005 (2006). IEEE recommended practices for maintenance, testing and replacement of valve regulated lead acid (VRLA) batteries in stationary applications.
10.1016/j.enconman.2006.03.006 / Energy Conversion and Management / Thermodynamics analysis of optional mass flow rate for fully developed force convection in a helical coil tube based on minimal entropy generation principle by Ko (2006)
10.1016/j.jpowsour.2005.11.008 / Journal of Power Sources / Battery condition monitoring (BCM) technologies about lead-acid batteries by Okoshi (2006)
10.1073/pnas.88.6.2297 / National Academy of Sciences / Approximate entropy as a measure of system complexity by Pincus (1991)
{'issue': '15–16', 'key': '10.1016/j.eswa.2011.03.063_b0045', 'first-page': '2577', 'article-title': 'The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy: A case study', 'volume': '47', 'author': 'Ramirez', 'year': '2006', 'journal-title': 'Energy Conversion and Management'} / Energy Conversion and Management / The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy: A case study by Ramirez (2006)
10.1152/ajpheart.2000.278.6.H2039 / The American Journal of Physiology – Heart and Circulatory Physiology / Physiological time series analysis using approximate entropy and sample entropy by Richman (2000)
Saha, B., & Goebel, K. (2007). Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. In Proceedings of the IEEE aerospace conference, Big Sky, MT (pp. 1–8). (10.1109/AERO.2008.4526631)
Saha, B., & Goebel, K. (2007). “Battery data set”. NASA ames prognostics data repository. See also: <http://ti.arc.nasa.gov/project/prognostic-data-repository>. Last accessed 25.1.2010.
10.1109/TIM.2008.2005965 / IEEE Transaction on Instrumentation and Measurement / Prognostics methods for battery health monitoring using Bayesian framework by Saha (2009)
Saha, B., & Goebel, K. (2009). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society (pp. 1–10).
10.1016/S0378-7753(99)00079-8 / Journal of Power Source / Determination of state-of-health of batteries by fuzzy logic methodology by Salkind (1999)
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009). On applying the prognostic performance metrics. In Proceedings of the annual conference of the prognostics and health management society (pp. 1–16).
{'year': '2002', 'series-title': 'Learning with kernels: Support vector machines, regularization, optimization, and beyond', 'author': 'Schölkopf', 'key': '10.1016/j.eswa.2011.03.063_b0085'} / Learning with kernels: Support vector machines, regularization, optimization, and beyond by Schölkopf (2002)
Sun, Y. H., Jou, H. L., & Wu, J. C. (2008). Intelligent aging estimation method for lead acid battery. In Proceedings of the 8th international conference on intelligent system design and applications (pp. 251–256). (10.1109/ISDA.2008.17)
10.1016/j.enconman.2009.05.001 / Energy Conversion and Management / Auxiliary diagnosis method for lead-acid battery health based on sample entropy by Sun (2009)
{'key': '10.1016/j.eswa.2011.03.063_b0100', 'first-page': '287', 'article-title': 'The relevance vector machine', 'volume': 'Vol. 12', 'author': 'Tipping', 'year': '2000'} / The relevance vector machine by Tipping (2000)
{'key': '10.1016/j.eswa.2011.03.063_b0105', 'first-page': '211', 'article-title': 'Sparse Bayesian learning and the relevance vector machine', 'volume': '1', 'author': 'Tipping', 'year': '2001', 'journal-title': 'Journal of Machine Learning Research'} / Journal of Machine Learning Research / Sparse Bayesian learning and the relevance vector machine by Tipping (2001)
Tuzku, V., & Selman, N. (2005). Sample entropy analysis of heart rhythm following cardiac transplantation. In IEEE proceedings of the international conference on systems, man and cybernetics, Waikoloa (pp. 198–202). (10.1109/ICSMC.2005.1571145)
{'year': '1995', 'series-title': 'The nature of statistical learning theory', 'author': 'Vapnik', 'key': '10.1016/j.eswa.2011.03.063_b0115'} / The nature of statistical learning theory by Vapnik (1995)
10.1016/j.ymssp.2006.12.007 / Mechanical Systems and Signal Processing / Support vector machine in machine condition monitoring and fault diagnosis by Widodo (2007)
10.1016/j.eswa.2006.04.020 / Expert System with Application / Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors by Widodo (2007)

Dates

Type	When
Created	14 years, 5 months ago (March 16, 2011, 4:29 p.m.)
Deposited	3 years, 1 month ago (July 4, 2022, 9:59 p.m.)
Indexed	5 days, 6 hours ago (Aug. 19, 2025, 6:51 a.m.)
Issued	13 years, 11 months ago (Sept. 1, 2011)
Published	13 years, 11 months ago (Sept. 1, 2011)
Published Print	13 years, 11 months ago (Sept. 1, 2011)

Funders 3

Ministry of Education, Science and Technology 10.13039/501100004085
Region: Asia
gov (National government)
Labels2
1. Korean Ministry of Education, Science and Technology
2. MEST
Ministry of Education 10.13039/501100002701
Region: Asia
gov (National government)
Labels3
1. Ministry of Education of the Republic of Korea
2. 교육부
3. MOE
National Research Foundation of Korea 10.13039/501100003725
Region: Asia
pri (Trusts, charities, foundations (both public and private))
Labels3
1. 한국연구재단이 창의적 연구와
2. National Research Foundation (South Korea)
3. NRF

BibTeX

@article{Widodo_2011, title={Intelligent prognostics for battery health monitoring based on sample entropy}, volume={38}, ISSN={0957-4174}, url={http://dx.doi.org/10.1016/j.eswa.2011.03.063}, DOI={10.1016/j.eswa.2011.03.063}, number={9}, journal={Expert Systems with Applications}, publisher={Elsevier BV}, author={Widodo, Achmad and Shim, Min-Chan and Caesarendra, Wahyu and Yang, Bo-Suk}, year={2011}, month=sep, pages={11763–11769} }

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