Costs #25
Costs #25
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pybamm_tea/tea.py
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| if pava.get("Initial loss of lithium inventory") is None: | ||
| pava["Initial loss of lithium inventory"] = 0 | ||
| warnings.warn("Warning: 'Initial loss of lithium inventory' is set to 0.") | ||
| if ( |
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by this point neither should be None, based on the above logic
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Thank you for noticing, I'll remove the condition.
pybamm_tea/tea.py
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| # initialize concentrations based on initial loss of lithium inventory | ||
| pava["Initial concentration in negative electrode [mol.m-3]"] = 0 | ||
| pava["Initial concentration in positive electrode [mol.m-3]"] = pava.get("Maximum concentration in positive electrode [mol.m-3]") | ||
| warnings.warn("Warning: 'Initial concentration in negative electrode [mol.m-3]' and 'Initial concentration in positive electrode [mol.m-3]' are set to 0 and maximum concentration in positive electrode, respectively.") |
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In practice the initial concentrations won't be 0 and c_max because you will charge/discharge between voltage limits. I think what you are really doing here is saying that the cyclable lithium is equivalent to starting with a fully lithiated cathode and fully delithiated anode. You could then do something like
param = pybamm.LithiumIonParameters()
Q_n = parameter_values.evaluate(param.n.Q_init)
Q_p = parameter_values.evaluate(param.p.Q_init)
Q_Li = Q_p * f # here 0 < f < 1 gives you initial LLI
inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li}
esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)
sol = esoh_solver.solve(inputs)
c_n_max = parameter_values.evaluate(param.n.prim.c_max)
c_p_max = parameter_values.evaluate(param.p.prim.c_max)
x = sol["x_100"]
y = sol["y_100"]
parameter_values.update(
{
"Initial concentration in negative electrode [mol.m-3]": x * c_n_max,
"Initial concentration in positive electrode [mol.m-3]": y * c_p_max,
},
check_already_exists=False,
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The idea was that if no initial concentrations are supplied, the total lithium inventory is what would be supplied with the maximum concentration in the positive electrode before formation, without considering any losses at this stage. Later the initial concentrations are updated to 100% SoC, so that the TEA parameter-set can directly be used for a discharge simulation. Would you prefer to have the initial lithium inventory set according to 100% SoC (dependent on voltage cut-offs/stoichiometry limits)?
pybamm_tea/tea.py
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| pava["Lithium inventory [mA.h.cm-2]"] = pava.get("Initial lithium inventory [mA.h.cm-2]") - float(pava.get("Initial loss of lithium inventory [mA.h.cm-2]")) | ||
| pava["Initial concentration in negative electrode [mol.m-3]"] = pava.get("Initial concentration in negative electrode [mol.m-3]") * (1 - pava.get("Initial loss of lithium inventory")) | ||
| pava["Initial concentration in positive electrode [mol.m-3]"] = pava.get("Initial concentration in positive electrode [mol.m-3]") * (1 - pava.get("Initial loss of lithium inventory")) | ||
| pava["Initial stoichiometry"] = pava.get("Lithium inventory [mA.h.cm-2]") / ((pava.get("Negative electrode thickness [m]") * pava.get("Negative electrode active material volume fraction") * pava.get("Maximum concentration in negative electrode [mol.m-3]") + pava.get("Positive electrode thickness [m]") * pava.get("Positive electrode active material volume fraction") * pava.get("Maximum concentration in positive electrode [mol.m-3]")) * 96485 / 3.6 / 10000) |
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Is this what people normally mean be "initial stoichiometry"?
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I have no clue, but I don't think so, it is an artefact I delete now. (I don't remember exactly how I wanted to use that value.)
pybamm_tea/tea.py
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| # x0, x100 = self.get_stoichiometries(pava, y0, y100) | ||
| # update cut-off voltages if voltage curve(s) are provided | ||
| # calculate lithium in positive electrode at SOC = 0 | ||
| n_pe_0 = y0 * (pava.get("Maximum concentration in positive electrode [mol.m-3]") |
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Would be cleaner to have already stored the electrode capacities, then you can just get those from the dict instead of repeating the calculation all the time
pybamm_tea/tea.py
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| * 96485 / 3.6 / 10000)) | ||
| if x0 < 0 or x100 < 0 or x0 > 1 or x100 > 1: | ||
| raise ValueError("Error: Stoichiometry calculation for negative electrode failed.") | ||
| if pava.get("Negative electrode OCP [V]") is not None and pava.get("Positive electrode OCP [V]") is not None: |
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These voltage cut-offs are normally defined first for the cell, e.g. 2.5-4.2V, and then the stoichiometry windows are calculated to respect those voltage limits. This way seems backwards?
pybamm_tea/tea.py
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| """ | ||
| Calculate ideal volumetric and gravimetric energy densities on stack level. | ||
| """ | ||
| stack_ed = {} # stack energy densities dict | ||
| pava = None | ||
| pava = self.parameter_values # parameter values | ||
| pava = dict(self.parameter_values) # parameter values | ||
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| # stoichiometries - calculation based on input stoichiometries or cell |
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there is a lot of repeated code here and it is hard to follow. can you explain the use case for being able to independently change sto limits?
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It is useful for capacity balancing in case no OCV curves are supplied. I also generally like it to calculate voltage cut-offs based on it, one could also set target capacities based on it. I'll try to shorten the code section and add more comments so that it becomes easier to follow.
pybamm_tea/tea.py
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| else: | ||
| raise ValueError("Error: Stoichiometry calculation failed.") | ||
| stack_ed["Negative electrode stoichiometry at 0% SoC"] = x0 | ||
| stack_ed["Negative electrode stoichiometry at 100% SoC"] = x100 | ||
| stack_ed["Positive electrode stoichiometry at 100% SoC"] = y100 | ||
| stack_ed["Positive electrode stoichiometry at 0% SoC"] = y0 | ||
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| # update initial concentrations in electrodes to SoC = 1 |
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is this consistent wit previous calculations?
it seems like there are multiple places where the same values get calculated, and it is confusing
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