Method and Apparatus for Operating a System for Providing Predicted States of Health of Electrical Energy Stores for a Device Using Machine Learning Methods

An apparatus for predicting a modeled state of health of an electrical energy store having at least one electrochemical battery cell, the apparatus being configured to:

• provide a data-based state of health model that is trained to assign a modeled state of health to the electrical energy store based on characteristics of operating variables of the electrical energy store;
• provide time characteristics of the operating variables that characterize operation of the electrical energy store;
• determine a modeled state of health based on the time characteristics of the operating variables using the data-based state of health model, the modeled state of health being one of present state of health and a predicted state of health;
• and
• complete data gaps in the time characteristics of the operating variables owing to a phase of inactivity based on a characteristic of a temperature of the electrical energy store that is derived from at least one provided ambient condition.

A non-transitory machine-readable storage medium for predicting a modeled state of health of an electrical energy store having at least one electrochemical battery cell, the non-transitory machine-readable storage medium storing instructions of a computer program that, when executed by at least one data processing device, cause the at least one data processing device to:

• provide a data-based state of health model that is trained to assign a modeled state of health to the electrical energy store based on characteristics of operating variables of the electrical energy store;
• provide time characteristics of the operating variables that characterize operation of the electrical energy store;
• determine a modeled state of health based on the time characteristics of the operating variables using the data-based state of health model, the modeled state of health being one of present state of health and a predicted state of health;
• and
• complete data gaps in the time characteristics of the operating variables owing to a phase of inactivity based on a characteristic of a temperature of the electrical energy store that is derived from at least one provided ambient condition.