Systems and methods for predicting remaining useful life in batteries and assets

Abstract

In one aspect, a method comprises receiving first data pertaining to a battery pack of a vehicle, wherein the first data is received from sensors associated with the vehicle, and the first data pertains to a battery pack current, a cell voltage, a cell current, a cell temperature, or some combination thereof; receiving second data pertaining to simulation of the battery pack; receiving third data from a manufacturer; receiving historical data on a fleet of vehicles that use the battery pack; predicting a remaining useful life of the battery pack of the vehicle by using a hybrid model comprising a physics-based model that receives the based on the first, second, third, and historical data, and generates properties pertaining to the battery pack; a machine learning model that uses the properties to predict the remaining useful life of each cell of the battery pack; and transmitting the remaining useful life.