Seeking Advice on Updating X-Averaged Particle Concentrations

[kimo707]

Hey,

I'm still working on the code, and I've reached a point where I need to update the initial conditions of the model. Specifically, I'm trying to ensure that each simulation begins from the endpoint of the previous one. To do this, I need to update the X-averaged negative and positive particle concentrations ([mol.m^-3]) for each simulation.

I've already estimated these concentrations based on x_100, x_0, y_0, y_100, and the state of charge, although I'm not entirely sure if my approach for this estimation is correct.

Here's my current challenge: I need guidance on how to convert these estimated X-averaged negative and positive particle concentrations to the required format of [mol.m^-3]. If there's a better or alternative way to achieve this, I'm all ears.

I appreciate any help or suggestions you might have. Below is a snippet of my code where I plan to implement this conversion or update:

    inputs = {"V_min": Vmin, "V_max": Vmax, "Q_n": Q_n, "Q_p": Q_p, "Q_Li": n_Li * parameter_values.evaluate(param.F/3600) }
    esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)
    esoh_sol = esoh_solver.solve(inputs)
    
    
    x_100 = esoh_sol["x_100"]
    y_100 = esoh_sol["y_100"] 
    x_0 = esoh_sol["x_0"]                
    y_0 = esoh_sol["y_0"]
            

    ics = {}
    start = 1

    for var in model.initial_conditions:
        if var.name == "X-averaged negative particle concentration [mol.m-3]":
            ics[var.name] = (((x_100-x_0)*SOC_0+x_0))* np.ones((model.variables[var.name].size, 2)) #here estimated X-averaged #negative particle concentration need to convert to [mol.m-3]
            
        elif var.name == "X-averaged positive particle concentration [mol.m-3]":
            ics[var.name] = (((y_100-y_0)*SOC_0+y_0)) * np.ones((model.variables[var.name].size, 2))#here estimated X-averaged #positive particle concentration need to convert [mol.m-3]
           
        elif var.name == "Discharge capacity [A.h]":
            ics[var.name] = np.zeros(1)
        else:
            if var.name == "Porosity times concentration":
                for child in var.children:
                    # end = start + model.variables[child.name].size
                    # ics[child.name] = y[start:end, np.newaxis]
                    end = start + 1
                    ics[child.name] = y[start] * np.ones((model.variables[var.name].size, 1))
                    start = end
            else:
                # end = start + model.variables[var.name].size
                # ics[var.name] = y[start:end, np.newaxis]
                end = start + 1
                ics[var.name] = y[start] * np.ones((model.variables[var.name].size, 1))
                start = end
              
    model.set_initial_conditions_from(ics)