System and method for forecasting battery state with imperfect data

A system for forecasting a state-of-health for a battery, comprising:

• one or more processors;
• a memory communicably coupled to the one or more processors and storing:
• a behavioral forecast system including instructions that when executed by the one or more processors cause the one or more processors to, in response to, to generate trajectory to failure of the battery by:
• obtaining a first dataset;
• selecting an arbitrary time window from a subset of the first dataset;
• selecting at least one feature from the arbitrary time-window;
• structuring the subset for feature extraction by a convolutional filter
• capturing an evolution of the at least one feature using the convolutional filter,
• generating a state-of-health model;
• training the state-of-health model by inputting the at least one feature, the evolution of the at least one feature, and a future state-of-health as variables;
• predicting a trajectory to failure for the battery based on a trained state-of-health-model.

A battery monitoring system of a vehicle comprising:

• one or more processors;
• a memory communicably coupled to the one or more processors and storing:
• a behavioral forecast system including instructions that when executed by the one or more processors cause the one or more processors to, in response to, to generate trajectory to failure of the battery by:
• obtaining a first dataset;
• selecting an arbitrary time window from a subset of the first dataset;
• selecting at least one feature from the arbitrary time-window, the at least one feature comprising at least one of current data, voltage data, capacity data, temperature data or an engineered metric;
• structuring the subset for feature extraction by a convolutional filter
• capturing an evolution of the at least one feature using the convolutional filter,
• generating a state-of-health model;
• training the state-of-health model by inputting the at least one feature, the evolution of the at least one feature, and a future state-of-health as variables;
• predicting a trajectory to failure for the battery based on a trained state-of-health-model.