> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/pybamm-team/PyBaMM/llms.txt
> Use this file to discover all available pages before exploring further.
# Using Parameter Sets
> Learn how to load built-in parameter sets, modify individual parameters, define custom parameter functions, and load parameters from BPX files.
`pybamm.ParameterValues` holds all the numerical values that define a specific battery cell — geometry, material properties, transport coefficients, kinetic parameters, and more. Every parameter in PyBaMM carries SI units in its name.
## Loading a built-in parameter set
PyBaMM ships with a collection of published parameter sets. Pass the name as a string:
```python theme={null}
import pybamm
param = pybamm.ParameterValues("Chen2020")
```
You can also load from a dict or copy from another `ParameterValues` object:
```python theme={null}
# From a dict
param = pybamm.ParameterValues({"Current function [A]": 1.0, ...})
# Copy from existing
param2 = pybamm.ParameterValues(param)
```
## Available parameter sets
### Lithium-ion
LG M50 NMC/graphite cell. A widely used general-purpose parameter set.
```python theme={null}
param = pybamm.ParameterValues("Chen2020")
```
NMC/graphite cell from the DFN model derivation paper. Used in many PyBaMM examples.
```python theme={null}
param = pybamm.ParameterValues("Marquis2019")
```
Graphite/NMC cell with degradation parameters (SEI, lithium plating).
```python theme={null}
param = pybamm.ParameterValues("OKane2022")
```
NMC/graphite cell with thermal and mechanical parameters.
```python theme={null}
param = pybamm.ParameterValues("ORegan2022")
```
Kokam NMC/graphite cell with full porous-electrode parameters.
```python theme={null}
param = pybamm.ParameterValues("Ecker2015")
```
NMC/graphite cell with mechanical parameters for stress-driven LAM.
```python theme={null}
param = pybamm.ParameterValues("Ai2020")
```
NMC/graphite with aging parameters.
```python theme={null}
param = pybamm.ParameterValues("Mohtat2020")
```
NCA/graphite cell.
```python theme={null}
param = pybamm.ParameterValues("NCA_Kim2011")
```
LFP/graphite A123 cell.
```python theme={null}
param = pybamm.ParameterValues("Prada2013")
```
Sony US18650 cell, one of the earliest complete DFN parameter sets.
```python theme={null}
param = pybamm.ParameterValues("Ramadass2004")
```
NMC/graphite pouch cell.
```python theme={null}
param = pybamm.ParameterValues("Xu2019")
```
Composite graphite-SiOx/NMC cell (multi-phase negative electrode).
```python theme={null}
param = pybamm.ParameterValues("Chen2020_composite")
```
### Half-cell parameter sets
```python theme={null}
param = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
param = pybamm.ParameterValues("OKane2022_graphite_SiOx_halfcell")
```
### Other chemistries
```python theme={null}
# Lead-acid
param = pybamm.ParameterValues("Sulzer2019")
# Equivalent circuit model
param = pybamm.ParameterValues("ECM_Example")
# MSMR model
param = pybamm.ParameterValues("MSMR_Example")
# Sodium-ion
param = pybamm.ParameterValues("Chayambuka2022")
```
## Exploring parameters
```python theme={null}
param = pybamm.ParameterValues("Chen2020")
# Access a single parameter
print(param["Nominal cell capacity [A.h]"])
# List all keys
print(list(param.keys()))
# Get parameter metadata (units, category, etc.)
info = param.get_info("Reference temperature [K]")
print(info.units) # 'K'
# List parameters by category
neg_electrode_params = param.list_by_category("negative electrode")
```
## Updating parameters
Update individual values with dict-style assignment:
```python theme={null}
param = pybamm.ParameterValues("Chen2020")
# Set constant current
param["Current function [A]"] = 5.0
# Change cell geometry
param["Electrode height [m]"] = 0.065
param["Electrode width [m]"] = 1.58
# Update multiple values at once
param.update({
"Negative electrode thickness [m]": 75e-6,
"Positive electrode thickness [m]": 70e-6,
"Separator thickness [m]": 25e-6,
})
```
## Custom parameter functions
Many parameters are functions (e.g. diffusivity as a function of concentration and temperature). Define them as Python callables:
```python theme={null}
import pybamm
# Diffusivity as a function of concentration and temperature
def my_diffusivity(c_e, T):
return 1.0e-10 * pybamm.exp(-500 / T)
param = pybamm.ParameterValues("Chen2020")
param["Electrolyte diffusivity [m2.s-1]"] = my_diffusivity
```
The function arguments must use `pybamm` symbolic expressions so they can be differentiated and compiled.
### Drive cycle (time-varying current)
Use an `Interpolant` to define a current from data:
```python theme={null}
import numpy as np
import pybamm
# Time [s] and current [A] arrays
times = np.array([0, 100, 200, 300, 400, 500])
currents = np.array([1.0, 2.0, 1.5, 0.5, -1.0, -2.0])
current_interpolant = pybamm.Interpolant(times, currents, pybamm.t)
param = pybamm.ParameterValues("Chen2020")
param["Current function [A]"] = current_interpolant
sim = pybamm.Simulation(model, parameter_values=param)
sol = sim.solve() # t_eval is inferred from the data
```
## BPX format
PyBaMM supports the [Battery Parameter eXchange (BPX)](https://bpxstandard.com/) format, a standardised JSON schema for battery parameters.
BPX support requires the optional `bpx` dependency: `pip install pybamm[bpx]`
```python title="Loading from a BPX file" theme={null}
param = pybamm.ParameterValues.create_from_bpx("my_cell.json")
# Load at a specific target SOC
param = pybamm.ParameterValues.create_from_bpx("my_cell.json", target_soc=0.8)
```
```python title="Loading from a BPX dict object" theme={null}
bpx_dict = {
"Header": {...},
"Cell": {...},
"Parameterisation": {...},
}
param = pybamm.ParameterValues.create_from_bpx_obj(bpx_dict)
```
## Setting initial state of charge
You can set the initial lithium distribution to correspond to a target SOC:
```python theme={null}
param = pybamm.ParameterValues("Chen2020")
model = pybamm.lithium_ion.SPM()
sim = pybamm.Simulation(model, parameter_values=param)
# Solve starting at 50% SOC
sol = sim.solve([0, 3600], initial_soc=0.5)
```
Alternatively, set it before solving:
```python theme={null}
sim.build()
sim.set_initial_state(0.5)
sol = sim.solve([0, 3600])
```
## Input parameters
For parameter sweeps or sensitivity analysis, mark a parameter as an `"[input]"` to pass it at solve time without rebuilding the model:
```python theme={null}
param = pybamm.ParameterValues("Chen2020")
param["Current function [A]"] = "[input]"
model = pybamm.lithium_ion.SPM()
sim = pybamm.Simulation(model, parameter_values=param)
sim.build()
# Solve with different currents without rebuilding
for current in [1.0, 2.0, 5.0]:
sol = sim.solve([0, 3600], inputs={"Current function [A]": current})
print(current, sol["Battery voltage [V]"].entries[-1])
```