battery-cell-extraction - SKILL.md Agent Skill

name: battery-cell-extraction description: Extract and normalize Li-ion battery cell specifications from supplier PDFs to a standardized format. Handles heterogeneous supplier data formats including cylindrical (18650, 21700, 4680), pouch, and prismatic cells.

Battery Cell Datasheet Extraction Skill

Overview

This skill extracts and standardizes battery cell specifications from supplier datasheets (PDFs). It handles variations in terminology, units, and table formats across different manufacturers.

Section A: Data Identification

1. Locate Specification Tables

Primary locations: "Electrical Characteristics", "Physical Specifications", "General Specifications", "Nominal Specification" tables.

Secondary sources: Header/footer areas, performance graphs, notes sections.

2. Table Layout Patterns

Pattern	Identifier	Handling
A: Key-Value	2-column (param \| value)	Direct mapping
B: Multi-Condition	3+ columns with merged cells	Inherit parent from previous row for empty cells
C: Visual Grouping	Indentation-based, no borders	Use text position for hierarchy
D: Multi-Product	Multiple model columns	Extract each column as separate cell object

3. Common Field Variations

Standard Field	Variations
Nominal Capacity	Rated Capacity, Typical Capacity, Cell Capacity
Nominal Voltage	Cell Voltage, Operating Voltage
DCIR	DC Internal Resistance, AC Impedance, Internal Resistance
Max Discharge	Maximum Continuous Discharge, Discharge Current

Section B: Extraction Procedures

1. Basic Cell Information

Extract: Manufacturer, Model Number, Chemistry (NMC/LFP/NCA/NCMA), Cell Format (cylindrical/pouch/prismatic), Format Code (18650/21700/4680).

2. Electrical Properties

Property	Notes	Conversion
Capacity	Look for Nominal/Rated/Typical; may have Typ/Min values	mAh → Ah (÷1000)
Voltage	Nominal, Max (charge cutoff), Min (discharge cutoff)	—
Impedance	ACIR at 1kHz most common; record test conditions	Ω → mΩ (×1000)
Current	Continuous discharge, charge, pulse (with duration)	C-rate → A: C × Capacity(Ah)

3. Temperature Properties

Parse formats: "0~45°C", "0°C to 45°C", "-20 to 60 deg C" → {min_c, max_c}

Extract separately for: charge, discharge, storage ranges.

4. Physical Properties

Format	Dimensions
Cylindrical	Diameter (mm), Height (mm)
Pouch	Length × Width × Thickness (mm)
Prismatic	Length × Width × Height (mm)

Weight: grams (convert kg → g if needed).

5. Cycle Life

Parse: "500 cycles at 80% DOD" → {cycles: 500, dod: 80}

Record: temperature, C-rates, EOL SOH threshold.

Section C: Normalization Rules

1. Standard Field Names

Use canonical names: capacity.nominal_ah, voltage.nominal_v, impedance.acir.value_mohm, current.max_continuous_discharge_a, temperature.operating.charge.min_c.

2. Unit Conversions

From	To	Operation
mAh	Ah	÷ 1000
Ω	mΩ	× 1000
kg	g	× 1000
C-rate	A	C × Capacity(Ah)

3. Missing/Ambiguous Data

Not found: set null, add to fields_missing
Ambiguous: extract best estimate, flag "medium" confidence
Multiple values: extract all (typ/min/max)
Store unit_original field

4. Reference Files

normalization_rules.yaml: Supplier-specific patterns
validation_schema.json: JSON schema for output validation
scripts/extractor.py: Deterministic parsing functions

Section D: Validation

1. Required Fields

Manufacturer, Model Number, Nominal Capacity, Nominal Voltage, Cell Format.

2. Value Ranges

Property	Valid Range
Capacity	0.5 - 200 Ah
Nominal Voltage	2.5 - 4.5 V
Max Voltage	≤ 5.0 V
DCIR	1 - 200 mΩ
Weight	10 - 5000 g

3. Confidence Scoring

High: All fields found, clear values, known format
Medium: Some inferred, ambiguous conditions
Low: Many missing, unfamiliar format, OCR issues

Section E: Output Format

1. JSON Structure

Output follows validation_schema.json. Top-level objects:

cell_info: manufacturer, model, chemistry, format, confidence
mechanical: dimensions, weight, volume
electrical: capacity, voltage, impedance, current
temperature: operating ranges (charge/discharge/storage)
lifetime: cycle life specifications
derived: calculated properties
extraction_metadata: fields_extracted, fields_missing, warnings

2. Derived Properties (calculate if not provided)

Property	Calculation
Energy (Wh)	Voltage × Capacity
Max Power (W)	Voltage × Max Discharge Current
Gravimetric Energy (Wh/kg)	Energy ÷ Weight
Volumetric Energy (Wh/L)	Energy ÷ Volume

3. Export to Sheets

python sheets_exporter.py output.json --sheet "Battery Cell Database"

Worked Example: Samsung INR21700-50E

Input (Pattern B - multi-condition):

2.1 Capacity: Typical 5000mAh, Minimum 4850mAh
2.2 Voltage: Nominal 3.6V, Charge 4.2V, Cutoff 2.5V
2.3 Impedance: AC 1kHz ≤18mΩ (50% SOC, 25°C)

Extraction:

Pattern B identified → parse nested structure
Capacity: typical=5.0Ah, minimum=4.85Ah
Voltage: nominal=3.6V, max=4.2V, min=2.5V
Impedance: ACIR=18mΩ at 1kHz, 50% SOC, 25°C

Output (abbreviated):

{
  "cell_info": {"manufacturer": "Samsung", "model_number": "INR21700-50E", "chemistry": "NMC", "cell_format": "cylindrical"},
  "electrical": {
    "capacity": {"nominal_ah": 5.0, "minimum_ah": 4.85},
    "voltage": {"nominal_v": 3.6, "max_v": 4.2, "min_v": 2.5},
    "impedance": {"acir": {"value_mohm": 18.0, "frequency_hz": 1000, "temperature_c": 25, "soc_percent": 50}}
  },
  "derived": {"energy_wh": {"value": 18.0, "calculation": "3.6V × 5.0Ah"}}
}

Quick Tips

Start with manufacturer ID → predicts format patterns
Check notes/footnotes → contain critical conditions
Cross-reference values → verify energy = V×Ah
When uncertain → flag with warnings, don't guess