A Method of Battery State of Health Prediction based on AR-Particle Filter



SAE 2016 World Congress and Exhibition
Authors Abstract
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.
Meta TagsDetails
Chen, 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.
Additional Details
Apr 5, 2016
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Content Type
Technical Paper