Simulate lithium-ion battery charging, discharging, and rest with PyBaMM. Track voltage and capacity, record parameter sources, check how numerical settings affect predictions, and compare simulated curves with measured cycling data.
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Simulates lithium-ion battery charge, discharge and rest experiments with PyBaMM, records parameter-set provenance, checks mesh and solver sensitivity, and compares predicted voltage curves with measured cycling data. Use for SPM or DFN electrochemical battery modeling, C-rate protocols, voltage cutoffs, parameter studies and numerical validation of battery simulations.
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Use this skill to model single-cell constant-current charge/discharge and rest, examine voltage and charge trajectories, or compare SPM/DFN predictions to cycling measurements. The helper runs real PyBaMM experiments and three numerical resolutions; it does not control a battery cycler or establish an operating envelope for hardware.
uv venv --python 3.12 battery-env
uv pip install --python battery-env/bin/python pybamm==26.9.0.0 pybammsolvers==0.10.0
This release requires pybammsolvers>=0.10.0, NumPy 2 or newer, and CasADi 3.8.1.
The tested environment used Python 3.12.10, NumPy 2.5.3 and SciPy 1.18.1. IDAKLU is the
recommended solver; CasadiSolver and ScipySolver are deprecated in this release. Refer to
the 26.9.0.0 manual below, since latest can describe unreleased APIs.
The included assets/chen2020-protocol.json is a synthetic isothermal 298.15-K SPM case: 80% initial SOC, discharge at 0.5C for 600 s, rest for 120 s, charge at 0.5C for 600 s. Chen2020 supplies an LG M50 parameterization with 5-Ah nominal capacity; here 0.5C means 2.5 A. This is an executable reference example, not a claim that an arbitrary user's cell has those parameters. The helper disables PyBaMM usage telemetry unless the caller has already explicitly configured that variable.
From the skill directory, point battery-env/bin/python at the environment created above:
battery-env/bin/python scripts/simulate_battery.py assets/chen2020-protocol.json \
--output battery-reference
# measured.csv is user data with time_s,voltage_V,current_A columns.
battery-env/bin/python scripts/simulate_battery.py protocol.json \
--measured measured.csv --mesh-points 30 --output battery-comparison
The first command was executed as written with an external output location. The second uses illustrative user filenames; the measurement path was exercised against a frozen synthetic reference curve in the tests. Output directories must be new.
| Artifact | Interpretation |
|---|---|
curve.csv | Baseline time, step, voltage, current and net discharge capacity |
tight-tolerance.csv | Same mesh, tighter solver |
refined-mesh.csv | Doubled mesh with tighter solver |
parameters.json | Base parameters after SOC initialization; step currents remain in the protocol |
report.json | Protocol/checksum, package versions, parameter source, numerical comparisons and terminations |
measurement-residuals.csv | Prediction minus measurement and current mismatch, when measurements were supplied |
The reference case conserved integrated charge: 600 s at 2.5 A yielded 0.4166667 Ah, then equal charge returned net discharge capacity to zero. Voltage stayed within the Chen2020 limits in this case. Tightening tolerances changed voltage by about 1 microvolt; doubling mesh from 20 to 40 points changed it by about 2.17 mV, so claiming sub-millivolt mesh accuracy would be unjustified. A separate real DFN test stopped at the requested 3.9-V event and verified its charge integral. Native tests also exercise charge cutoff, infeasible discharge, capacity-to-current conversion, and replay of the exported SOC-adjusted parameters without reinitializing SOC. The frozen reference is numerical regression evidence, not measured-cell validation.
The linked release manuals, bundled helper, parameter serialization and optional DataLoader recipe in the reference were verified against PyBaMM 26.9.0.0.
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Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.
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