Comparison of Prognostic Algorithms for Estimating Remaining Useful Life of Batteries

We were interested here in particular in conditions where un-modeled effects are present as manifested by the different degradation curve at 45°C. Although all algorithms were given the same amount of information to the degree practical, there were considerable differences in performance. Specifically, the combined Bayesian regression-estimation approach implemented as a RVM-PF framework has significant advantages over conventional methods of RUL estimation like ARIMA and EKF. ARIMA, being a purely data-driven method, does not incorporate any physics of the process into the computation, and hence ends up with wide uncertainty margins that make it unsuitable for long-term predictions. Additionally, it may not be possible to eliminate all non-stationarity from a dataset even after repeated differencing, thus adding to prediction inaccuracy. EKF, though robust against non-stationarity, suffers from the inability to accommodate un-modeled effects and can diverge quickly as shown. We did not explore other variations of the Kalman Filter that might provide better performance such as the unscented Kalman Filter. The Bayesian statistical approach, on the other hand, appears to be well suited to handle various sources of uncertainties since it defines probability distributions over both parameters and variables and integrates out the nuisance terms. Also, it does not simply provide a mean estimate of the time-to-failure; rather it generates a probability distribution over time that best encapsulates the uncertainties inherent in the system model and measurements and in the core concept of failure prediction.