Dynamic learning of battery capacity estimation for electric vehicle using validity check

Abstract

A battery management system for an electric vehicle is provided. The battery management system may include a battery pack and a battery controller. The battery controller may acquire a first reading of the battery pack in response to first criteria being met, acquire a second reading of the battery pack in response to second criteria being met, and determine an estimated capacity of the battery pack. The battery controller may further validate the estimated capacity in response to third criteria being met. In one example, the capacity of cells is updated in a closed loop manner where both the new learned values and old valid value are engaged. The different learned values of capacity values are adaptively averaged based on the battery conditions such as temperature, accuracy of learned values, time since last update and depth of aging. The proposed methodology updates capacity in a periodic manner where different operation conditions are accommodated to facilitate capacity estimation. It also adopts a conservative method on how to learn new values, and how to merge them with old values so to ensure validity of capacity values.

Dependencies are inferred from the claim text.