Comparison of different models of future operating condition in Particle-Filter-based Prognostic Algorithms

Abstract

Abstract In literature, a major part of the prognostic studies considers the mission profile as a static parameter when evaluating the system Remaining Useful Life (RUL). However, in practice, the way in which a system operates significantly impacts the future evolution of its degradation. Therefore, this paper aims at evaluating the impact associated with the utilization of three different methods to characterize future operating conditions within the implementation of probability-based prognostic algorithms, namely Long-short term memory (LSTM), Markov Chain and Constant (or time-invariant) usage. These three methods are compared together in terms of both prognostic accuracy and essential update times when investigating the Time-of-Discharge (ToD) of an electric bicycle Lithium-Ion (Li-Ion) battery.