Comparison of Prognostic Algorithms for Estimating Remaining Useful Life of Batteries

The estimation of remaining useful life (RUL) of a faulty component is at the center 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 modeling 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). 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 for 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.