A computer-implemented method, comprising: determining whether a predictive model of an adaptive battery life extension unit is predictive of future battery usage of a battery of a mobile device, then determining, by the adaptive battery life extension unit, a first battery number representing a battery condition of the battery of the mobile device using the predictive model, wherein determining whether the predictive model is predictive of future battery usage includes performing a correlation analysis of predicted battery data amongst at least two periods of time in the past by: performing a coefficient of determination analysis between a first set of predicted battery usage data and actual measured battery usage data for a first period of time in the past; and performing a correlation analysis of a second set of predicted battery usage data for a second period in the past to the first set of predicted battery usage data; determining a second battery number representing the battery condition using a drain model, wherein the drain model is configured to predict a future battery discharge rate based on a past battery discharge rate; determining a third battery number representing the battery condition based on a current battery level corresponding to a remaining life of the battery at a point in time; and performing, by power management logic, a power management action based on the battery condition derived from at least one of the first battery number, the second battery number and the third battery number. determining whether a predictive model of an adaptive battery life extension unit is predictive of future battery usage of a battery of a mobile device, then determining, by the adaptive battery life extension unit, a first battery number representing a battery condition of the battery of the mobile device using the predictive model, wherein determining whether the predictive model is predictive of future battery usage includes performing a correlation analysis of predicted battery data amongst at least two periods of time in the past by: performing a coefficient of determination analysis between a first set of predicted battery usage data and actual measured battery usage data for a first period of time in the past; and performing a correlation analysis of a second set of predicted battery usage data for a second period in the past to the first set of predicted battery usage data; performing a coefficient of determination analysis between a first set of predicted battery usage data and actual measured battery usage data for a first period of time in the past; and performing a correlation analysis of a second set of predicted battery usage data for a second period in the past to the first set of predicted battery usage data; determining a second battery number representing the battery condition using a drain model, wherein the drain model is configured to predict a future battery discharge rate based on a past battery discharge rate; determining a third battery number representing the battery condition based on a current battery level corresponding to a remaining life of the battery at a point in time; and performing, by power management logic, a power management action based on the battery condition derived from at least one of the first battery number, the second battery number and the third battery number.