VRLA Battery Lifetime Fingerprints – Part 2

Inhalt:
Part 1 of this paper was presented at Intelec 2012 (paper 132-2.) This Part 2 continues that paper. With over 19 years of continuous monitoring of stationary batteries, archival of over 1.5 billion points of data and chronology of over two million batteries, we are finding some common history. As batteries age to the point of replacement, individually, and collectively in strings and multiple string systems, we find distinctive fingerprints indicating end-of-life conditions. This is reviewed from Part 1, and described in verbal and graphical format. These individual and collective traits cannot be simulated and seen conclusively in a laboratory setting using accelerated life testing due to the extended time required, quantity of data, plus other constraints of simulation versus actual usage data. These traits can only be reliably observed in real life usage conditions over time. As batteries age, there are changes over time in the their internal ohmic value. As researched and stated in various IEEE battery standards, changes in ohmic value greater than 50% from a baseline indicate aging and loss of capacity. While not definitive, this level of change from a baseline can indicate that capacity has decreased from when the battery was new. These aging changes are caused by time, electrical chemical variances, and mechanical anomalies in the battery. Other measured parameter changes will cause ohmic value changes as a result. Again, as the ohmic value changes, a decrease in capacity occurs. This correlation to capacity indicates aging, and has been proven by lifetime data and other studies referenced by this paper. Continuous monitoring and prognostics based on battery ohmic values, plus other battery measurement parameters are essential to predicting end-of-life conditions. This is true for individual cells and batteries, as well as a complete battery system. Measuring and archiving these values over time, plus prognostic and trend prediction of other measurement parameters that affect these values, allow for predictive analytics. Observation, collection of data, and the use of proven mathematical prognostic techniques and models can be combined for lifetime prediction and forecasts. This process helps maintain battery unit and system state of health (SOH), state of charge (SOC), and the resulting prediction of remaining useful life (RUL). Prognostic methods are used to monitor and track “degradation paths” in one or more battery parameters that are correlated to remaining life. Deviations between the observed and expected parameters are evaluated to determine when a monitored unit is degrading excessively. Remaining life is predicted by classifying the unit’s degradation path in reference to degradation path models calibrated with run-to-failure data for units experiencing a similar mode of failure.