The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics
Abstract
Maximum releasable capacity and internal resistance are often used as the health indicators (HIs) of a lithium-ion battery for degradation modeling and estimation of remaining useful life (RUL). However, the maximum releasable capacity is usually difficult to estimate in online applications due to complex operating conditions in the field. Moreover, measuring the internal resistance is too expensive to be implemented on-line. In this paper, an HI extraction and optimization framework requiring only the operating parameters of lithium-ion batteries is proposed for battery degradation modeling and RUL estimation. The framework carries out raw HI extraction, transformation, correlation analysis, and verification and evaluation to achieve HI enhancement. In particular, the Box-Cox transformation is adopted to improve the correlation between the extracted HI and the battery's actual degradation state. To estimate the battery's RUL using the enhanced HI, an optimized relevance vector-machine algorithm is utilized, which can be performed in a flexible and agile way. Experimental studies using two different industrial testing data sets illustrate the high efficiency and adaptability of the proposed framework in lithium-ion battery degradation modeling and RUL estimation.
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Cites
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
Prognostics in Battery Health Management
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594
Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.03.010 · Cited by 443
Self-citationModeling Lithium Ion battery degradation in electric vehicles
Millner, A. (2010) · 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply · doi:10.1109/citres.2010.5619782 · Cited by 397
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2010.v2i1.1896 · Cited by 274
Cited by
Zhang, Z., Si, X., Hu, C., & Lei, Y. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.02.033 · Cited by 696
A brief review on key technologies in the battery management system of electric vehicles
Liu, K., Li, K., Peng, Q., & Zhang, C. (2019) · Frontiers of Mechanical Engineering · doi:10.1007/s11465-018-0516-8 · Cited by 635
Wu, J., Zhang, C., & Chen, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.057 · Cited by 528
LSTM-Based battery remaining useful life prediction with multi-channel charging profiles
Park, K., Choi, Y., Choi, W. J., Ryu, H.-Y., & Kim, H. (2020) · IEEE Access · doi:10.1109/access.2020.2968939 · Cited by 397
A review on prognostics and health management (PHM) methods of lithium-ion batteries
Meng, H., & Li, Y.-F. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109405 · Cited by 389
Metadata
- Authors
- Liu, D., Zhou, J., Liao, H., Peng, Y., & Peng, X.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2015-06
- Volume
- 45
- Issue
- 6
- Pages
- 915-928
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2047152377
- Cited by
- 313
- References
- 6
- FWCI
- 14.68
- Type
- journal article
- Language
- en
- Open access
- closed
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