This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Review Study of Methods for Lithium-ion Battery Health Monitoring and Remaining Life Estimation in Hybrid Electric Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 16, 2012 by SAE International in United States
Annotation ability available
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.
CitationSamadani, 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.
- December 2009 Dashboard: Year-End Tally. Available: http://www.hvbridcars.com/hybrid-sales-dashboard/december-2009-dashboard.html
- Jardine, A. K. S., Lin, D. and Banjevic, D., “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, pp. 1483-1510, 2006.
- Kozlowski, J. D., “Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques,” in Aerospace Conference, 2003. Proceedings. 2003 IEEE, 2003, pp. 3257-3270.
- Shim, J., Kostecki, R., Richardson, T. J. and Song, X., “Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature,” Journal of Power Sources, vol. 112, pp. 222-230, 2002.
- Zhang, X., Ross, P. N., Kostecki, R., Kong, F., Sloop, S., Kerr, J. B., Striebel, K., Cairns, E. J. and Mclarnon, F., “Diagnostic Characterization of High Power Lithium-Ion Batteries for Use in Hybrid Electric Vehicles,” Journal of The Electrochemical Society, vol. 148, pp. A463-A470, 2001.
- Gomadam, P. M., Weidner, J. W., Dougal, R. A. and White, R. E., “Mathematical modeling of lithium-ion and nickel battery systems,” Journal of Power Sources, vol. 110, pp. 267-284, 2002.
- Pop, V., Bergveld, H. J., Notten, P. H. L. and Regtien, P. P. L., “State-of-the-art of battery state-of-charge determination,” Measurement Science and Technology, 2005.
- Available: http://www.learnartificialneuralnetworks.com/
- (2002). Inaccuracies of Estimating Remaining Cell Capacity with Voltage Measurements Alone. Available: 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. and Govar, J., “A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery,” in Power Electronics (IICPE), 2010 India International Conference on, 2011, pp. 1-8.
- Bergveld, H.J., Kruijt, W. S. and Notten, P.H.L., “Battery Management Systems: Design by Modelling”, Springer, 2002.
- Chenghui, C., Dong, D., Zhiyu, L. and Jingtian, G., “State-of-charge (SOC) estimation of high power Ni-MH rechargeable battery with artificial neural network,” in Neural Information Processing, 2002. ICONIP ′02. Proceedings of the 9th International Conference on, 2002, pp. 824-828 vol.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. and Farrokhi, M., “State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,” Industrial Electronics, IEEE Transactions on, vol. 57, pp. 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, vol. 134, pp. 262-276, 2004.
- Salkind, A. J., Fennie, C., Singh, P., Atwater, T. and Reisner, D. E., “Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology,” Journal of Power Sources, vol. 80, pp. 293-300, 1999.
- Singh, P., Fennie, C. Jr and Reisner, D., “Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries,” Journal of Power Sources, vol. 136, pp. 322-333, 2004.
- Mendel, J. M., Lessons in Estimation Theory for Signal Processing, Communications, and Control Prentice Hall; 2 edition 1995.
- Plett, G. L., “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background,” Journal of Power Sources, vol. 134, pp. 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, vol. 134, pp. 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, vol. 161, pp. 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, vol. 36, pp. 1401-1425, 2000.
- Il-Song, K., “The novel state of charge estimation method for lithium battery using sliding mode observer,” Journal of Power Sources, vol. 163, pp. 584-590, 2006.
- Zhang, F., Liu, G. and fang, L., “A battery state of charge estimation method using sliding mode observer,” in Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2008, pp. 989-994.
- Hansen, T. and Wang, C.-J., “Support vector based battery state of charge estimator,” Journal of Power Sources, vol. 141, pp. 351-358, 2005.
- Bhangu, B. S., Stone, P. and 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, vol. 54, pp. 783-794, 2005.
- Chan, C. C., Lo, E. W. C. and Weixiang, Shen, “The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles,” Journal of Power Sources, vol. 87, pp. 201-204, 2000.
- Rufus, F., Seungkoo, L. and Thakker, A., “Health monitoring algorithms for space application batteries,” in Prognostics and Health Management, 2008. PHM 2008. International Conference on, 2008, pp. 1-8.
- Saha, B., Goebel, K., Poll, S. and Christophersen, J. “An integrated approach to battery health monitoring using bayesian regression and state estimation,” in Autotestcon, 2007 IEEE, 2007, pp. 646-653.
- Tipping, M., “Sparse Bayesian Learning and the Relevance Vector Machine,” Journal of Machine Learning Research, vol. 1, pp. 211-244, 2001.
- Tipping, M., “Bayesian Inference: An Introduction to Principles and Practice in Machine Learning Advanced Lectures on Machine Learning.” vol. 3176, Bousquet, O., et al., Eds., ed: Springer Berlin / Heidelberg, 2004, pp. 41-62.
- Saha, B. and Goebel, K., “Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework,” presented at the 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