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Comparison of prognostic algorithms for estimating remaining useful life of batteries

Mission Critical Technologies, Inc. (NASA ARC), 2041 Rosecrans Ave., Ste. 354, El Segundo, CA 90245, USA, bhaskar.saha_1{at}nasa.gov

Kai Goebel

NASA Ames Research Center, Moffett Field, CA 94035, USA

Jon Christophersen

Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415, USA

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.

Key Words: autoregressive integrated moving average • battery prognostics • extended Kalman filtering • particle filter • relevance vector machine • remaining useful life • uncertainty management.

This version was published on June 1, 2009

Transactions of the Institute of Measurement and Control, Vol. 31, No. 3-4, 293-308 (2009)
DOI: 10.1177/0142331208092030


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