Extreme Learning Machine Based Prognostics of Battery Life

Roozbeh Razavi-Far (1) , Shiladitya Chakrabarti (1) , Mehrdad Saif (1) , Enrico Zio (2, 3) , Vasile Palade (4)

1 Department of Electrical and Computer Engineering [Windsor]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
3 LGI - Laboratoire Génie Industriel - EA 2606
4 Coventry University

This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions , using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques.

Mots clés: multi-steps pre-diction; Lithium-ion batteries; Prognostics; extreme learning machines; one-step prediction

Domaines: Automatique

Roozbeh Razavi-Far, Shiladitya Chakrabarti, Mehrdad Saif, Enrico Zio, Vasile Palade. Extreme Learning Machine Based Prognostics of Battery Life. International Journal on Artificial Intelligence Tools, 2018, 27 (08), pp.1850036. ⟨10.1142/s0218213018500367⟩. ⟨hal-01988909⟩