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Fixed stoichiometry/stoichiometries spellings #203

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Fixed stoichiometry/stoichiometries spellings #203

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40 changes: 20 additions & 20 deletions libraries/pybamm-developer/01_ode.md
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ x_p(0) &= x_{p0}, \\
\end{align*}
$$

where $x_n$ and $x_p$ are the dimensionless stochiometries of the negative and positive electrodes, $I(t)$ is the current, $Q_n$ and $Q_p$ are the capacities of the negative and positive electrodes, $U_p(x_p)$ and $U_n(x_n)$ are the open circuit potentials of the positive and negative electrodes, and $R$ is the resistance of the battery.
where $x_n$ and $x_p$ are the dimensionless stoichiometries of the negative and positive electrodes, $I(t)$ is the current, $Q_n$ and $Q_p$ are the capacities of the negative and positive electrodes, $U_p(x_p)$ and $U_n(x_n)$ are the open circuit potentials of the positive and negative electrodes, and $R$ is the resistance of the battery.

## PyBaMM variables

Expand All @@ -44,14 +44,14 @@ x = pybamm.Variable("x")

::::challenge{id="ode-variables" title="Define the state variables for the reservoir model"}

Define the state variables for the reservoir model, including the stochiometries
Define the state variables for the reservoir model, including the stoichiometries
$x_n$ and $x_p$.

:::solution

```python
x_n = pybamm.Variable("Negative electrode stochiometry")
x_p = pybamm.Variable("Positive electrode stochiometry")
x_n = pybamm.Variable("Negative electrode stoichiometry")
x_p = pybamm.Variable("Positive electrode stoichiometry")
```

:::
Expand All @@ -70,7 +70,7 @@ a = pybamm.Parameter("a")

The name of the parameter is used to identify it in the model, the name of the Python variable you assign it to is arbitrary.

You can also define a parameter that is defined as a function using the [`pybamm.FunctionParameter`](ihttps://docs.pybamm.org/en/stable/source/api/expression_tree/parameter.html#pybamm.FunctionParameter) class, which is useful for time varying parameters such as the current, or functions like the OCV function parameters $U_p(x_p)$ and $U_n(x_n)$, which are both functions of the stochiometries $x_p$ and $x_n$. For example, to define a parameter that is a function of time, you would write
You can also define a parameter that is defined as a function using the [`pybamm.FunctionParameter`](ihttps://docs.pybamm.org/en/stable/source/api/expression_tree/parameter.html#pybamm.FunctionParameter) class, which is useful for time varying parameters such as the current, or functions like the OCV function parameters $U_p(x_p)$ and $U_n(x_n)$, which are both functions of the stoichiometries $x_p$ and $x_n$. For example, to define a parameter that is a function of time, you would write

```python
P = pybamm.FunctionParameter("Your parameter name here", {"Time [s]": pybamm.t})
Expand All @@ -86,8 +86,8 @@ Define the parameters for the reservoir model, including the current $I(t)$, the

```python
i = pybamm.FunctionParameter("Current function [A]", {"Time [s]": pybamm.t})
x_n_0 = pybamm.Parameter("Initial negative electrode stochiometry")
x_p_0 = pybamm.Parameter("Initial positive electrode stochiometry")
x_n_0 = pybamm.Parameter("Initial negative electrode stoichiometry")
x_p_0 = pybamm.Parameter("Initial positive electrode stoichiometry")
U_p = pybamm.FunctionParameter("Positive electrode OCV", {"x_p": x_p})
U_n = pybamm.FunctionParameter("Negative electrode OCV", {"x_n": x_n})
Q_n = pybamm.Parameter("Negative electrode capacity [A.h]")
Expand Down Expand Up @@ -150,8 +150,8 @@ model.rhs[x_p] = i / Q_p
model.initial_conditions[x_p] = x_p_0

model.variables["Voltage [V]"] = U_p - U_n - i * R
model.variables["Negative electrode stochiometry"] = x_n
model.variables["Positive electrode stochiometry"] = x_p
model.variables["Negative electrode stoichiometry"] = x_n
model.variables["Positive electrode stoichiometry"] = x_p
```

Note that we have defined the dictionaries in a slightly different way to the
Expand Down Expand Up @@ -241,19 +241,19 @@ simulation will stop when any of the events are triggered.
model.events = [stop_at_t_equal_3]
```

::::challenge{id="ode-events" title="Define the stochiometry cut-off events"}
::::challenge{id="ode-events" title="Define the stoichiometry cut-off events"}

Define four events that ensure that the stochiometries $x_n$ and $x_p$ are
Define four events that ensure that the stoichiometries $x_n$ and $x_p$ are
between 0 and 1. The simulation should stop when either reach 0 or 1.

:::solution

```python
model.events = [
pybamm.Event("Minimum negative stochiometry", x_n - 0),
pybamm.Event("Maximum negative stochiometry", 1 - x_n),
pybamm.Event("Minimum positive stochiometry", x_p - 0),
pybamm.Event("Maximum positive stochiometry", 1 - x_p),
pybamm.Event("Minimum negative stoichiometry", x_n - 0),
pybamm.Event("Maximum negative stoichiometry", 1 - x_n),
pybamm.Event("Minimum positive stoichiometry", x_p - 0),
pybamm.Event("Maximum positive stoichiometry", 1 - x_p),
]
```

Expand Down Expand Up @@ -297,8 +297,8 @@ you like for the parameters, but in the solution below we will define the
following values:

- The current is a function of time, $I(t) = 1 + 0.5 \sin(100t)$
- The initial negative electrode stochiometry is 0.9
- The initial positive electrode stochiometry is 0.1
- The initial negative electrode stoichiometry is 0.9
- The initial positive electrode stoichiometry is 0.1
- The negative electrode capacity is 1 Ah
- The positive electrode capacity is 1 Ah
- The electrode resistance is 0.1 Ohm
Expand Down Expand Up @@ -334,8 +334,8 @@ def nmc_LGM50_ocp_Chen2020(sto):
```python
param = pybamm.ParameterValues({
"Current function [A]": lambda t: 1 + 0.5 * pybamm.sin(100*t),
"Initial negative electrode stochiometry": 0.9,
"Initial positive electrode stochiometry": 0.1,
"Initial negative electrode stoichiometry": 0.9,
"Initial positive electrode stoichiometry": 0.1,
"Negative electrode capacity [A.h]": 1,
"Positive electrode capacity [A.h]": 1,
"Electrode resistance [Ohm]": 0.1,
Expand All @@ -354,7 +354,7 @@ Now that we have defined the reservoir model, we can solve it using the PyBaMM s
```python
sim = pybamm.Simulation(model, parameter_values=param)
sol = sim.solve([0, 1])
sol.plot(["Voltage [V]", "Negative electrode stochiometry", "Positive electrode stochiometry"])
sol.plot(["Voltage [V]", "Negative electrode stoichiometry", "Positive electrode stoichiometry"])
```

::::challenge{id="ode-solve" title="Solve the reservoir model"}
Expand Down