State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework

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dc.contributor.authorQin, Taichun
dc.contributor.authorZeng, Shengkui
dc.contributor.authorGuo, Jianbin
dc.contributor.authorSkaf, Zakwan
dc.date.accessioned2017-01-06T14:23:09Z
dc.date.available2017-01-06T14:23:09Z
dc.date.issued2016-12-24
dc.description.abstractState of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-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 frameworken_UK
dc.identifier.citationQin, T.; Zeng, S.; Guo, J.; Skaf, Z. State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework. Symmetry, 2017, Volume 9, Issue 1, Article number 4en_UK
dc.identifier.issn2073-8994
dc.identifier.urihttp://dx.doi.org/10.3390/sym9010004
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11225
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLi-ion batteriesen_UK
dc.subjectstate of healthen_UK
dc.subjectregeneration phenomenaen_UK
dc.subjectparticle swarm optimizationen_UK
dc.subjectGaussian processen_UK
dc.titleState of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic frameworken_UK
dc.typeArticleen_UK

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