Abstract:
This application relates to methods and apparatus for predicting power and energy availability of a battery. The prediction is made based on a given amount of time, which represents a period in which the battery may be required to operate. Additionally, a learning cycle is incorporated to update a battery model of the battery with certain parameters. The battery model is updated by introducing a time-varying current to the battery and analyzing the voltage response of the battery. A model-based predictive algorithm is used in combination with the battery model to predict battery output parameters based on variables derived from the learning cycle and additional inputs supplied to the model-based predictive algorithm. After one or more iterations, or using a simplified model-based equation, the model-based predictive algorithm can provide an accurate prediction for the maximum current that the battery can supply for a predetermined period of time.
Abstract:
An electronic device that displays a battery status is described. In particular, based on the occurrence or presence of an environmental condition (such as an extrinsic environmental factor and/or a current electronic-device usage factor), the electronic device may determine an inaccessible-charge condition of a battery in the electronic device. For example, the environmental condition may include: a temperature of the battery less than the temperature threshold value; and/or a discharge rate of the battery greater than the discharge threshold value. In response to the inaccessible-charge condition, the electronic device may display indications of two or more battery-charge parameters, including: an accessible battery charge, an inaccessible battery charge that is currently unavailable for use because of the environmental condition, and/or a total battery charge.