Electrolyte direct entries

Direct entries in iwp.direct_entries that set the four electrolyte transport properties (conductivity, diffusivity, thermodynamic factor, transference number) for a DFN model from literature or as fit unknowns

A DirectEntry populates parameter values without doing any calculation or fitting — useful for dropping a coherent literature parameter set into a pipeline. ionworkspipeline provides four direct entries in iwp.direct_entries for the four electrolyte transport properties needed by a binary-electrolyte DFN model.

Available entries

constant_electrolyte

Just sets the initial salt concentration; everything else is left to defaults or other entries.

nyman_electrolyte

Full set for LiPF$_6$ in EC:EMC from Nyman et al. 2008. Concentration-dependent $\kappa$ and $D_e$, constant $\chi=1$ and $t_+^0 = 0.2594$. Isothermal.

landesfeind_electrolyte

Full set with concentration and temperature dependence for three solvent systems (EC:DMC (1:1), EC:EMC (3:7), EMC:FEC (19:1)) from Landesfeind & Gasteiger 2019.

arrhenius_electrolyte_*

arrhenius_electrolyte_diffusivity and arrhenius_electrolyte_conductivity wrap a reference $D_e(c_e)$ or $\kappa(c_e)$ in an Arrhenius temperature factor. Useful when you have isothermal data and need to bolt on $T$-dependence.

A typical use of landesfeind_electrolyte looks like:

import ionworkspipeline as iwp

electrolyte = iwp.direct_entries.landesfeind_electrolyte(
    c_e=1000,                # initial concentration [mol/m^3]
    system="EC:EMC (3:7)",
)

pipeline = iwp.Pipeline({"electrolyte": electrolyte, ...})
parameter_values = pipeline.run()

The returned DirectEntry populates the initial concentration plus all four transport-property functions and the coefficients those functions reference. Plugging it into a pipeline is enough to fully specify the electrolyte block of a DFN model.

Fitting your own coefficients

The six conductivity coefficients (and the diffusivity, thermodynamic-factor and transference-number coefficients) are stored as pybamm.Parameters inside landesfeind_electrolyte precisely so they can be replaced with fit unknowns. To fit them, override the relevant parameter names with iwp.Parameter in the data-fit parameters dict and run a pipeline that includes the direct entry — the published values act as the base and the optimizer searches over the overridden ones.

The Landesfeind electrolyte fit notebook walks through this for the conductivity coefficients on a single isotherm: it loads digitized paper data alongside the published equation (no fit needed — the values come straight from the direct entry) and re-fits the identifiable subset of coefficients to a synthetic noisy dataset.

For the physics behind the four transport properties and how they are measured experimentally, see Electrolyte transport properties.