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Battery parameterization is built around a simple abstraction: pipeline elements that transform input parameters into output parameters, chained together into a pipeline. This design provides flexibility to handle any parameterization workflow—from simple calculations to complex data fitting.

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Pipeline Elements

The basic building block is a pipeline element. Any pipeline element accepts a set of parameter values (possibly empty) and returns another set of parameter values. The full pipeline is built by calling each element in series to yield the complete parameter set. There are four types of pipeline element:
TypeDescription
DirectEntryThe simplest type—ignores input parameters and returns pre-defined values (e.g., from literature or direct measurements)
CalculationComputes new parameters based on provided inputs (e.g., calculating maximum particle concentration from capacity, volume fraction, and thickness)
DataFitEstimates parameters by fitting a model to experimental data
ValidationChecks fitted parameters against held-out data
Each element:
  1. Takes input parameters from the parameter dictionary
  2. Performs computation
  3. Returns output parameters that become available to subsequent elements
To assemble and submit a pipeline programmatically, see Pipelines → Overview in the Documentation tab.

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Naming conventions

Clear Naming

Use descriptive parameter names with units: "Electrode capacity [A.h]" not "cap"

Unit Consistency

Be explicit about unit conversions; use SI units internally

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Built-in Calculations

Geometry & Capacity

Electrode geometry, mass, capacity, cyclable lithium, and microstructure

Thermal Properties

Heat capacity, Arrhenius temperature dependence, and thermal modeling

Piecewise Interpolants

Smooth piecewise functions for SOC and temperature-dependent parameters

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Data Fitting

The pipeline abstraction also powers data fitting—estimating unknown parameters by comparing model predictions to experimental data. A DataFit element wraps a pipeline with an optimization loop:
  • The optimizer proposes parameter values
  • The pipeline runs the model with those values
  • The objective computes how well predictions match data
  • This repeats until the best-fit parameters are found

Data Fitting (theory)

Cost functions, identifiability, regularization, and other theory.

Data Fitting (how-to)

Configure and submit data fits with ionworks-schema + ionworks-api.