Comparison of prognostic algorithms for estimating remaining useful life of batteries

TLDR

Remaining useful life estimation is central to prognostics, but classical methods suffer from modelling, noise, and sensor errors, while Bayesian approaches can mitigate these issues, especially for batteries whose performance is affected by environment. The study compares RVM‑based and PF‑based RUL estimation approaches on experimental Li‑ion battery data. The authors develop a Bayesian relevance vector machine model integrated into a particle filter to produce a probability density function of RUL, and evaluate it against other methods on Li‑ion battery data.

Abstract

The estimation of remaining useful life (RUL) of a faulty component is at the centre of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. RUL prediction needs to contend with multiple sources of errors, like modelling inconsistencies, system noise and degraded sensor fidelity, which leads to unsatisfactory performance from classical techniques like autoregressive integrated moving average (ARIMA) and extended Kalman filtering (EKF). The Bayesian theory of uncertainty management provides a way to contain these problems. The relevance vector machine (RVM), the Bayesian treatment of the well known support vector machine (SVM), a kernel-based regression/classification technique, is used for model development. This model is incorporated into a particle filter (PF) framework, where statistical estimates of noise and anticipated operational conditions are used to provide estimates of RUL in the form of a probability density function (pdf). We present here a comparative study of the above-mentioned approaches on experimental data collected from Li-ion batteries. Batteries were chosen as an example of a complex system whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions.