System and method for controlling a battery management system

A method for managing a battery management system comprising the steps of:

• receiving, by one or more processors, a mode selection from a user via an input device, the mode selection includes a selection between extending a driving range of the vehicle and increasing cycle life of the battery;
• predicting, by the one or more processors, a characteristic of a battery based on the mode selection by utilizing an active machine learning model, the battery comprising a plurality of cells, wherein the characteristic of the battery is a cycle life of the battery;
• and
• outputting, by the one or more processors, an estimated cost to an output device based on the characteristic of the battery determined by the active machine learning model, the estimated cost being a prediction how the selection of mode selection impacts the driving range of the vehicle and the cycle life of the battery.

A non-transitory computer-readable medium for controlling a battery management system, the non-transitory computer-readable medium comprising instructions that when executed by one or more processors cause the one or more processors to:

• receive a mode selection from a user via an input device, the mode selection includes a selection between extending a driving range of the vehicle and increasing cycle life of the battery;
• predict a characteristic of a battery based on the mode selection by utilizing an active machine learning model, the battery comprising a plurality of cells, wherein the characteristic of the battery is a cycle life of the battery;
• and
• output an estimated cost to an output device based on the characteristic of the battery determined by the active machine learning model, the estimated cost being a prediction how the selection of mode selection impacts the driving range of the vehicle and the cycle life of the battery.