Post-Processing

Most sources of battery data provide the voltage and current over time, but the other properties which are derived from them may be missing. The battery data toolkit provides “post-processing” classes which add compute these derived data sources.

All post-processing tools are based on the BaseFeatureComputer class and, as a result, provide a compute_features function that adds new information to a battery dataset. Use them by first creating the tool and invoking that method with a BatteryDataset:

computer = FeatureComputer()
new_columns = computer.compute_features(data)

New columns will be added to a part of the dataset (e.g., the cycle-level statistics) and those new columns will be returned from the function.

The feature computers fall into two categories:

• RawDataEnhancer, which add information to the raw data as a function of time

• CycleSummarizer, which summarize the raw data and add new columns to the cycle_stats

Integral Quantities

Functions which add columns associated with the accumulated values of data in other columns.

• Cell Capacity

Time

Compute columns which are derived fields associated with the relative time or timespans of data.

• Cycle Times