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A Method of Battery State of Health Prediction based on AR-Particle Filter
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 05, 2016 by SAE International in United States
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Lithium-ion battery plays a key role in electric vehicles, which is critical to the system availability. One of the most important aspects in battery managements systems(BMS) in electric vehicles is the stage of health(SOH) estimation. The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. The classical approach of current integration(coulomb counting) can't get the accurate values because of accumulative error. In order to provide timely maintenance and replacements of electric vehicles, several estimation approaches have been proposed to develop a reliable and accurate battery state of health estimation. A common drawback of previous algorithm is that the computation quantity is huge and not quite accurate, that is updated partially in this study. In this paper, a training strategy is diagrammed and models based on autoregressiveparticle filter (AR-PF) are developed with novel parameters to provide better prediction of electric vehicle state of health. The estimations are compared to measurements using autoregressive algorithm and particle filter respectively. The proposed training strategy achieves a better prediction precision than other strategies used in this paper. Battery experimental data and AR-PF predicted value are presented in X-Y axis figure as well as other two algorithms in this paper that we can observe them directly. Experiments based on NASA battery data set show that the proposed method yields a good performance in SOH estimation of Lithium-ion battery. The results showed that the health condition parameters accurately reflected the real time SOH of the battery and ensured that an accurate capacity estimation can be calculated.
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CitationChen, Y. and Huang, M., "A Method of Battery State of Health Prediction based on AR-Particle Filter," SAE Technical Paper 2016-01-1212, 2016, https://doi.org/10.4271/2016-01-1212.
- P.J. F.J de Cos , J.C A et al. Battery state-of-charge estimator using the SVM technique Applied Mathematical Modeling 37 2013 6244 6253
- Verena K , Marten B,G. Et al. A support vector machine-based state-of-health estimation method for lithium-ion bateries under electric vehicle operation Journal of Power Sources 270 2014 262 272
- Matteo G , Corrado , L. Synthetic methods for evalution of the SOH of nickel-metal hybrid batteries Energy Conversion and Management 92 2015 1 9
- Hancheng Dong , Xiaoning Jin etal. Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter Journal of Power Sources 271 2014 114 123
- Haifeng Dai , Letao Zhu , Jiangonog Zhu Adaptive Kalman filtering based internal temperature estimation with an equivalent electrical network thermal model for hard_cased batteries Journal of Power Sources 293 2015 351 365