Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Changhao Piao; Zuncheng Li; Sheng Lu; Zhekui Jin; Chongdu Cho

Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Vol. 16, No. 1, pp. 217-226, Jan. 2016

10.6113/JPE.2016.16.1.217

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Abstract

A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

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Cite this article

[IEEE Style]

C. Piao, Z. Li, S. Lu, Z. Jin, C. Cho, "Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health," Journal of Power Electronics, vol. 16, no. 1, pp. 217-226, 2016. DOI: 10.6113/JPE.2016.16.1.217.

[ACM Style]

Changhao Piao, Zuncheng Li, Sheng Lu, Zhekui Jin, and Chongdu Cho. 2016. Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health. Journal of Power Electronics, 16, 1, (2016), 217-226. DOI: 10.6113/JPE.2016.16.1.217.