Battery Feature Extraction

Battery Feature Extraction

When to Use

• Any task involving battery test data feature extraction: cycling degradation trending, SOH estimation, RUL prediction, capacity fade analysis
• Differential curve analysis (IC dQ/dV, DV dV/dQ, DT dT/dV) for electrode degradation diagnosis
• Single-segment measurement statistics from partial or full charge/discharge data
• Batch processing of multiple battery cycling test files

When NOT to Use

• The task has no battery test data context (no cycling or differential-curve data)
• The primary goal is battery simulation, equivalent circuit modeling, or Simulink battery plant models
• The task is general signal processing, machine learning model training, or visualization without feature extraction
• The data is not from electrochemical battery tests (e.g., fuel cells, supercapacitors, or generic sensor data)

This skill covers the 5 released PMT battery feature extraction functions. These functions work natively with MATLAB tables and vectors, handle segmentation and peak detection internally, and are performance-optimized. Prefer these PMT functions over manual feature computation (e.g., hand-coded cumtrapz loops, manual peak finding). Override only if the user explicitly requests otherwise.

API Overview

The Predictive Maintenance Toolbox provides 5 public functions for battery feature extraction:

                        Battery Cycling Test Data
                                  │
                    ┌─────────────┴─────────────┐
                    │                           │
              Full Pipeline               Individual Functions
                    │                           │
             batteryTest-                       ├── batteryMeasurementFeatures
             DataParser                         ├── batteryDifferentialCurves
                 +                              └── batteryDifferentialCurveFeatures
             batteryTest-
             Feature-
             Extractor
                 │
                 ▼