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A Review Study of Methods for Lithium-ion Battery Health Monitoring and Remaining Life Estimation in Hybrid Electric Vehicles
Technical Paper
2012-01-0125
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Due to the high power and energy density and also relative safety, lithium ion batteries are receiving increasing acceptability in industrial applications especially in transportation systems with electric traction such as electric vehicles and hybrid electric vehicles. In this regard, to ensure performance reliability, accurate modeling of calendar life of such batteries is a necessity. In fact, potential failure of Li-ion battery packs remains a barrier to commercialization. Battery pack life is a critical feature to warranty and maintenance planning for hybrid vehicles, and will require adaptive control systems to account for the loss in vehicle range, and loss in battery charge and discharge efficiency. Failure not only results in large replacement costs, but also potential safety concerns such as overheating or short circuiting which may lead to fires. That's why health monitoring, fault detection and end of life prediction capability in battery-equipped systems are of great importance. This paper reviews recent research and achievements in the field of Li-ion battery health monitoring and prognostics. The different models, algorithms and techniques being applied to estimate state of charge (SoC) and capacity, and prediction of the remaining useful life (RUL), are presented along with an analysis of the pros and cons of each model or method. It is hoped that these review and discussions prepare a wider perspective on progresses and challenges of Li-ion battery health monitoring and prognostics.
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Samadani, S., Fraser, R., and Fowler, M., "A Review Study of Methods for Lithium-ion Battery Health Monitoring and Remaining Life Estimation in Hybrid Electric Vehicles," SAE Technical Paper 2012-01-0125, 2012, https://doi.org/10.4271/2012-01-0125.Also In
References
- December 2009 Dashboard: Year-End Tally http://www.hvbridcars.com/hybrid-sales-dashboard/december-2009-dashboard.html
- Jardine, A. K. S. Lin, D. Banjevic, D. “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing 20 1483 1510 2006
- Kozlowski, J. D. “Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques,” Aerospace Conference, 2003. Proceedings. 2003 IEEE 2003 3257 3270
- Shim, J. Kostecki, R. Richardson, T. J. Song, X. “Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature,” Journal of Power Sources 112 222 230 2002
- Zhang, X. Ross, P. N. Kostecki, R. Kong, F. Sloop, S. Kerr, J. B. Striebel, K. Cairns, E. J. Mclarnon, F. “Diagnostic Characterization of High Power Lithium-Ion Batteries for Use in Hybrid Electric Vehicles,” Journal of The Electrochemical Society 148 A463 A470 2001
- Gomadam, P. M. Weidner, J. W. Dougal, R. A. White, R. E. “Mathematical modeling of lithium-ion and nickel battery systems,” Journal of Power Sources 110 267 284 2002
- Pop, V. Bergveld, H. J. Notten, P. H. L. Regtien, P. P. L. “State-of-the-art of battery state-of-charge determination,” Measurement Science and Technology 2005
- http://www.learnartificialneuralnetworks.com/
- 2002 Inaccuracies of Estimating Remaining Cell Capacity with Voltage Measurements Alone http://www.maxim-ic.com/app-notes/index.mvp/id/121
- Prajapati, V. Hess, H. Wiliam, E. J. Gupta, V. Huff, M. Manic, M. Rufus, F. Thakker, A. Govar, J. “A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery,” Power Electronics (IICPE), 2010 India International Conference on 2011 1 8
- Bergveld, H.J. Kruijt, W. S. Notten, P.H.L. “Battery Management Systems: Design by Modelling” Springer 2002
- Chenghui, C. Dong, D. Zhiyu, L. Jingtian, G. “State-of-charge (SOC) estimation of high power Ni-MH rechargeable battery with artificial neural network,” Neural Information Processing, 2002. ICONIP ′02. Proceedings of the 9th International Conference on 2002 824 828 2
- Patillon, E. A. “System for monitoring charging/discharging cycles of a rechargeable battery, and host device including a smart battery,” New York NY Patent 1998
- Charkhgard, M. Farrokhi, M. “State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,” Industrial Electronics, IEEE Transactions on 57 4178 4187 2010
- Plett, G. L. “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification,” Journal of Power Sources 134 262 276 2004
- Salkind, A. J. Fennie, C. Singh, P. Atwater, T. Reisner, D. E. “Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology,” Journal of Power Sources 80 293 300 1999
- Singh, P. Fennie, C. Jr Reisner, D. “Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries,” Journal of Power Sources 136 322 333 2004
- Mendel, J. M. Lessons in Estimation Theory for Signal Processing, Communications, and Control Prentice Hall 2 1995
- Plett, G. L. “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background,” Journal of Power Sources 134 252 261 2004
- Plett, G. L. “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation,” Journal of Power Sources 134 277 292 2004
- Plett, G. L. “Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimation,” Journal of Power Sources 161 1369 1384 2006
- Hu, Y. “Identification and State Estimation for Linear Parameter Varying Systems with Application to Battery Management System Design,” PhD Electrical and Computer Engineering, The Ohio State University 2010
- Wu, F. “Control of linear parameter varying systems,” PhD Thesis UC Berkely 1995
- Shamma, W. R. a. J. “Research on gain scheduling,” Automatica 36 1401 1425 2000
- Il-Song, K. “The novel state of charge estimation method for lithium battery using sliding mode observer,” Journal of Power Sources 163 584 590 2006
- Zhang, F. Liu, G. fang, L. “A battery state of charge estimation method using sliding mode observer,” Proceedings of the World Congress on Intelligent Control and Automation (WCICA) 2008 989 994
- Hansen, T. Wang, C.-J. “Support vector based battery state of charge estimator,” Journal of Power Sources 141 351 358 2005
- Bhangu, B. S. Stone, P. Bingham, D. A. “Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles,” Vehicular Technology, IEEE Transactions on 54 783 794 2005
- Chan, C. C. Lo, E. W. C. Weixiang, Shen “The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles,” Journal of Power Sources 87 201 204 2000
- Rufus, F. Seungkoo, L. Thakker, A. “Health monitoring algorithms for space application batteries,” Prognostics and Health Management, 2008. PHM 2008. International Conference 2008 1 8
- Saha, B. Goebel, K. Poll, S. Christophersen, J. “An integrated approach to battery health monitoring using bayesian regression and state estimation,” Autotestcon, 2007 IEEE 2007 646 653
- Tipping, M. “Sparse Bayesian Learning and the Relevance Vector Machine,” Journal of Machine Learning Research 1 211 244 2001
- Tipping, M. “Bayesian Inference: An Introduction to Principles and Practice in Machine Learning Advanced Lectures on Machine Learning.” 3176 Bousquet, O. et al. Springer Berlin / Heidelberg 2004 41 62
- Saha, B. Goebel, K. “Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework,” Annual Conference of the Prognostics and Health Management Society 2009 2009
- Anonymous Toyota, EDF and Strasbourg Launch Large-Scale, 3-Year Plug-in Hybrid Demonstration Project, 2011(03/29) 2010
- Anonymous Better Place and Renault Launch Fluence Z.E., the First “unlimited Mileage” Electric Car Together with Innovative eMobility Packages, in Europe's First Better Place Center, 2011(03/29) 2011