The use of nonlinear future reduction techniques as a trend parameter for state of health estimation of lithium-ion batteries

Remaining Useful Life (RUL) prediction accurately is an imperative industrial challenge. In this sense, the monitoring of lithium-ion battery is very significant for planning repair work and minimizing unexpected electricity outage. As the RUL estimation is essentially a problem of pattern recognition, the most valuable feature extraction techniques and more accurate classifier are needed to obtain higher prognostic effectiveness. Consequently, this paper discusses the importance of non linear feature reduction techniques for more adequate prognosis feature data base. For more convenience, the isometric feature mapping technique (ISOMAP) is used to reduce some features extracted from lithium-ion batteries, with different health states, in both modes of charge and discharge. Experimental results show that non linear feature reduction techniques are very promising to provide some trend parameters for industrial prognostic.