Pybamm

Protocol and curve contract

The helper accepts model (SPM or DFN), parameter_set (an installed PyBaMM set), initial_soc in (0,1], positive temperature_K, positive sample_period_s, a nonempty steps list, and optional numeric parameter_overrides. Unknown fields are rejected; there is no implicit cycles or repeated-cycle option. Repeat the desired steps explicitly when needed. The example set Chen2020 is tested; other sets require chemistry/geometry compatibility checks.

The root, each step and parameter_overrides must be JSON objects. Each step contains kind and positive duration_s. charge and discharge also require positive c_rate, and may contain until_voltage_V within the set's lower/upper cutoffs. rest has no rate or cutoff. A sample period cannot exceed a step's duration. For example:

{
  "kind": "discharge",
  "c_rate": 0.5,
  "duration_s": 3600,
  "until_voltage_V": 3.0
}

Current is ±C-rate times nominal Ah capacity. All duration and timestamp values are seconds; temperatures are Kelvin, never Celsius. Use temperature_K for the uniform isothermal setting; overrides of initial/ambient temperature are rejected to avoid contradictory settings. The helper is single-cell only: the series-cell count must be one. Upstream voltage step events use Battery voltage [V], while the exported Voltage [V] is cell voltage; these differ in a string of cells. Nominal capacity changes the C-rate-to-current conversion, not the electrode geometry or active material inventory. Reconcile those independently when reparameterizing a cell.

Existing scalar numeric PyBaMM parameters may be overridden; functional OCP and transport laws cannot be replaced through this helper. Current function [A] overrides and initial electrode concentration overrides are rejected because the experiment and initial_soc would replace them. Use the exact installed parameter keys (list(parameters.keys())); current upstream ParameterValues.update() permits new keys, so it is not itself a typo detector. The helper explicitly checks membership. Do not inject functions or executable code through JSON.

initial_soc sets electrode concentrations through PyBaMM's parameterization-dependent initial state solver; it is not a universal concentration multiplier or a guarantee of feasible terminal voltage under load. A discharge cutoff must lie below the loaded starting voltage; a charge cutoff must lie above it. skip_ok=False rejects an already-satisfied step condition. The helper accepts SOC in (0,1] as a deliberately narrower contract than upstream's [0,1].

parameters.json serializes simulation.parameter_values after solve(initial_soc=...), using ParameterValues.to_json(). The original parameter object still has its original concentrations and must not be mistaken for that snapshot. Load the snapshot with pybamm.ParameterValues.from_json(path); retain the protocol because each step overrides the base Current function [A]. It is a parameter snapshot, not a saved solution or a model definition.

Voltage holds (CCCV), power control, thermal dynamics and degradation are available in upstream PyBaMM but are outside this helper's tested protocol. Extend using the upstream step APIs and test against a physically appropriate case rather than translating a voltage hold into constant current.

Numerical comparisons

The baseline uses IDAKLU rtol=1e-6, atol=1e-8; the tightened run uses 1e-8, 1e-10. Both use the same mesh. The third run doubles all x_n,x_s,x_p,r_n,r_p point counts at the tightened tolerances. Only relevant spatial dimensions enter each model (SPM and DFN use different physics).

Voltage is compared at 101 positions over the shared elapsed interval of each corresponding step. This avoids interpolating across a current discontinuity. Event duration differences are reported separately because cutoff shifts change later absolute step times. Always check these alongside the voltage metric, especially near a steep end-of-discharge drop. The output period controls saved samples, not the adaptive solver's internal step size. For a duration not divisible by the period, 26.9.0.0 distributes samples uniformly over the duration using a rounded point count, so the spacing need not equal the requested period exactly. Read the actual time column. The comparisons interpolate saved samples; also reduce the output period when interpolation error could dominate.

net_discharge_capacity_Ah integrates signed current. It decreases during charging and is not cumulative charge throughput, remaining capacity, a cycle count, or measured state of health.

Measured curves

The CSV has exactly:

time_s,voltage_V,current_A
0,3.98,2.5
120,3.93,2.5
300,3.90,2.5

Require finite values and strictly increasing time. These rows are illustrative, not measured data. Convert raw instrument units and polarity explicitly upstream and retain their provenance. The helper interpolates the baseline at measurement times only inside the simulated interval; it never extrapolates. If only an overlapping segment is intended, select and document that segment upstream.

Align experiment start from acquisition evidence; do not optimize a time shift solely to improve fit unless that is an explicit fitting parameter with uncertainty. Samples right at a current switch can represent either side of an instantaneous ohmic change; align acquisition semantics and inspect the current-difference column before comparing those voltages. RMSE gives each supplied sample equal weight, so oversampling a long rest changes its contribution. Resample deliberately if a time-weighted or phase-balanced score is desired, and retain the original curve.

Report residual structure across charge, rest and discharge, not just a single score. Temperature, SOC, capacity calibration and hysteresis can dominate mismatches. Parameter fitting should compare identifiability and uncertainty against independent experiments rather than treating a lower error on the same curve as sufficient validation.

Optional upstream datasets

For a public reference dataset, PyBaMM 26.9.0.0 exposes DataLoader() with no version argument, show_registry() returning a filename list, and get_data(filename) returning a local Path. There is no fetch_data() method. The shipped loader pins the separate data release v1.0.2 and uses Pooch to cache and verify each file against its registered SHA-256 hash.

import hashlib
import pybamm

loader = pybamm.DataLoader()
filename = "Ecker_1C.csv"
assert filename in loader.show_registry()
path = loader.get_data(filename)
print(loader.version, path.name, hashlib.sha256(path.read_bytes()).hexdigest())

This recipe fetched the 1,049-byte public file and verified hash 428dc5113a6430492f430fb9e895f67d3e20f5643dc49a1cc0a922b92a5a8e01 on the review date. The network operation is an unauthenticated HTTPS GET of the release asset at https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.2/Ecker_1C.csv, which may redirect to GitHub's asset storage. It returns file bytes, not a paginated JSON API. An unknown filename raises ValueError before fetching; network or checksum failures are not missing data. Do not edit loader internals to invent a dataset-version constructor option.

Ecker data concerns a Kokam cell, not the Chen2020 LG M50 example. This file has two headerless columns and lacks the helper's required current column. Check the source experiment and units before constructing a comparison CSV; downloading and hashing it does not validate a model. The bundled helper does not fetch datasets automatically.

Release contracts

• Step units, skip behavior and voltage terminations

• Simulation API

• Parameter updates, initial state and JSON serialization

• DataLoader API

• Versioned data registry source