A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

Taichun Qin, Shengkui Zeng, Jianbin Guo and Zakwan Skaf
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Taichun Qin: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Shengkui Zeng: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Jianbin Guo: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Zakwan Skaf: IVHM Centre, Cranfield University, Cranfield MK43 0AL, UK

Energies, 2016, vol. 9, issue 11, 1-18

Abstract: State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

Keywords: lithium-ion batteries; state of health (SOH); rest time; cycle beginning time; support vector machine; hyperplane shift (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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