some questions about the simulation.step()

[fireyan8]

Hallo everybody

i want to use pybamm with reinforcment learning
To build the pybamm in Gym, the step() function in very useful.

params = pybamm.ParameterValues("Ecker2015") solver = pybamm.CasadiSolver(mode="safe") model = pybamm.lithium_ion.DFN()
sim = pybamm.Simulation(model=model, solver=solver, parameter_values=params)
sims =[]
for i in range(5):
singlestep = sim.step(dt=0.01, inputs={"Initial temperature [K]": 310})
sims.append(singlestep)
sims[3]["Cell temperature [K]"].entries

but the values for Cell Temperature doesnt chang at all.

Where am I doing it wrong?

By the way do you have any ideas about how to put pybamm into Gym?

thank you very much

[brosaplanella]

From your example I can't tell which model you are using, but my guess would be you are not using a thermal model. You can read more on how to use thermal models here: https://docs.pybamm.org/en/latest/source/examples/notebooks/models/thermal-models.html

Sorry, never used Gym before, so can't help you here.

[fireyan8]

Hi,
I am using the default DFN model. I tried with "Current Function [A]" as well. But it showed no difference on Current either.

Thank you

[brosaplanella]

Yes, but unless you enable a thermal model on top of the DFN (see link above) temperature will remain constant no matter what the current is.

[fireyan8]

thank you

[JayYDG]

The following code works with current.

experiment = pybamm.Experiment(
    [
        "Discharge at 1C for 0.1 hours",
        "Charge at C/5 for 0.1 hours",
        "Discharge at 1C for 0.1 hours",
    ]
)

options = {"thermal": "isothermal"}
model = pybamm.lithium_ion.DFN(options)

sim = pybamm.Simulation(model, experiment=experiment, parameter_values=pybamm.ParameterValues("Chen2020").copy())
sim.solve()
sim.plot()

[JayYDG]

Hope below code address your problem.

options = {"thermal": "isothermal"}
model = pybamm.lithium_ion.DFN(options)

param = pybamm.ParameterValues("Chen2020").copy()
geometry = model.default_geometry
submesh_types = model.default_submesh_types
var_pts = model.default_var_pts
spatial_methods = model.default_spatial_methods

param.process_geometry(geometry)
mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
disc = pybamm.Discretisation(mesh, spatial_methods)

solutions = []

def make_new_model(model, param, disc, update_input):
  model1 = model.new_copy()
  param1 = param.copy()
  param1.update(update_input)
  model1 = param1.process_model(model1, inplace=False)
  built_model = disc.process_model(model1, inplace=True, check_model=True)
  return built_model

update_input = {
        "Current function [A]": 0.1,
        "Ambient temperature [K]": 310,
        "Initial temperature [K]": 310,
    }
model1 = make_new_model(model, param, disc, update_input)

solver = pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-3)

step_solution = solver.step(pybamm.EmptySolution(),
                            model1,
                            10,
                            npts=100,
                            save=False,)

solutions += [step_solution]

update_input = {
        "Current function [A]": 0.1,
        "Ambient temperature [K]": 311,
        "Initial temperature [K]": 311,
    }
model2 = make_new_model(model, param, disc, update_input)

t_eval = np.linspace(0, 10, 100)
solver = pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-3)
step_solution = solver.step(solution.last_state,
                            model2,
                            10,
                            npts=100,
                            save=False,)

solutions += [step_solution]

output_variables = [
    "Voltage [V]",
    "Current [A]",
    "Cell temperature [K]",
]
plot = pybamm.QuickPlot(solutions, output_variables, labels=["step1", "step2"])
plot.dynamic_plot()

[fireyan8]

hello, your code helps me a lot.

Here is my code to do update the values for every step.
Is it making sense doing so?

thanks

options = {"thermal": "lumped"}
model = pybamm.lithium_ion.DFN(options)

params = pybamm.ParameterValues("Ecker2015").copy()

geometry = model.default_geometry
submesh_types = model.default_submesh_types
var_pts = model.default_var_pts
spatial_methods = model.default_spatial_methods

params.process_geometry(geometry)
mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
disc = pybamm.Discretisation(mesh, spatial_methods)

solutions = []

def make_new_model(model, param, disc, update_input):
  model1 = model.new_copy()
  param1 = param.copy()
  param1.update(update_input)
  model1 = param1.process_model(model1, inplace=False)
  built_model = disc.process_model(model1, inplace=True, check_model=True)
  return built_model

def update_model_step(input, model, param, disc, solutions):
    model = make_new_model(model, param, disc, input)
    solver = pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-3)
    if len(solutions) == 0:
        step_solution = solver.step(pybamm.EmptySolution(),
                                    model,
                                    1,
                                    npts=100,
                                    save=False,)
    else:
        step_solution = solver.step(solutions[-1].last_state,
                                    model,
                                    1,
                                    npts=100,
                                    save=False,)
    return step_solution

new_input = {
        "Current function [A]": 0.1,
        "Ambient temperature [K]": 310,
        "Initial temperature [K]": 310,
    }

solutions += [update_model_step(new_input, model, params, disc, solutions)]
def update_input(current, temp):
    update_input = {
        "Current function [A]": current,
        "Ambient temperature [K]": temp,
        "Initial temperature [K]": temp,
    }
    return update_input

new_input = update_input(0.12, 310)


solutions += [update_model_step(new_input, model, params, disc, solutions)]
inputs = []
for i in range(10):
    inputs += [update_input(random.random(), random.random()*10+300)]
    print(len(inputs))



for input in inputs:
    solutions += [update_model_step(input, model, params, disc, solutions)]

output_variables = [
    "Voltage [V]",
    "Current [A]",
    "Cell temperature [K]",
]


labels = ["step" + str(i+1) for i in range(12)]
print(len(labels))```

[brosaplanella]

Why do you need to use step instead of solve?

[fireyan8]

I want to use it as a Env for Reinforcement Learning. It needs steps to
get action and rewards

[JayYDG]

Looks good to me. Should be good to go.