Comparison of prognostic algorithms for estimating remaining useful life of batteries

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.

Multidimensional prognostics for rotating machinery: A review

“…In regression problems, instead of searching for a maximum separation classifier, the SVM seeks to find a minimum margin fit for the input data points. 55 Similar to the classification SVM, when the regression SVM is applied to nonlinear regressable data points, a kernel function is often used to map nonlinear inputs into a higher dimensional feature space, after which a linear minimum margin fit can be constructed in that space to perform function estimation. SVMs have many different configurations based on the different kernel functions used to perform feature space transformation.…”

Modelo para vida útil restante de ativos em terminais aeroportuários

“…Pode-se citar trabalhos na indústria aeroespacial (X. WANG et al, 2009;CHEN et al, 2012), na indústria elétrica (DATLA; PANDEY, 2006), relacionados a baterias (SAHA et al, 2009;LEE, 2011), em máquinas e equipamentos (PENG;DONG, 2011;CAMCI, 2015), em estruturas (KARANDIKAR et al, 2012) Scarf (1997) possivelmente foi o primeiro a consolidar modelos matemáticos utilizados na manutenção e defendeu a criação e aplicação deles para resolver problemas reais, aumentando a cooperação entre ciência e indústria, ou teoria e prática.…”

Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

“…Techniques developed for SOC and SOH estimation include: fuzzy logic, electrochemical impedance spectroscopy (EIS), extended Kalman filter (EKF), and neuro-fuzzy [11,[16][17][18][19][20][21][22][23][24]. Bole et al [25][26][27] stated that the unscented Kalman filter (UKF) generally has better accuracy than the EKF [28] and derived a physics-based model for Li-ion batteries, in which an electrochemical model was constructed and a state age-dependent parameter was identified over randomized discharge profiles data via a UKF algorithm.…”