Reliability Prediction software is the most effective way to calculate failure rate and MTBF. Other reliability engineering tools also provide valuable ways to assess MTBF. For example, Weibull analysis predicts failure trends based on life data and ALT (Accelerated Life Testing) analysis predicts failure trends based on test data.
To calculate the failure rate, you have todefine the failure mode first. Most lithium ion batteries degrade slowly, having reduced capacity etc. For any item that degrades gradually, you have to define a threshold that renders the item failed. Or you define failure as "battery combusts spontaneously" leaving out all degradation issues.
The methodology used to calculate the total failure rate depends on the techniques defined in Reliability Prediction standard in use. In the simplest case, the total system failure rate is the sum of all the component failure rates. This is the typical case for MIL-HDBK-217 based Reliability Predictions.
Article Remaining energy estimation for lithium-ion batteries via Ga... If you mean failure rate in the sense that a cell fails completely we have tried to get relaible data -there is no. Aging is another issue where failure is defined as less than 80% of the intitial capacity. There is tons of literature, experiments, models.
The failure rate typically decreases slightly over early life, then stabilizes until wear-out which shows an increasing failure rate. This should occur beyond useful life. Measure of failure rate in 109 device hours; e. g. 1 FIT = 1 failure in 109 device hours. The summation of the number of units in operation multiplied by the time of operation.
Assumption: Failure rate follows exponential distribution. Repair rate follows log-normal distribution. λs, system failure rate; λI, subsystem failure rate; n, the number of affected factors; m, the number of subsystems; Wi, the average of weighting factors of each subsystem.