# Amplitude of the Voltage output after cycles is decreased even without aging? **URL:** \ **Category:** PyBaMM Q&A\ **Created:** [27 January 2025 18:44 UTC](https://pybamm.discourse.group/t/amplitude-of-the-voltage-output-after-cycles-is-decreased-even-without-aging/127 "2025-01-27T18:44:30Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![Notslytherin1](https://avatars.discourse-cdn.com/v4/letter/n/54ee81/32.png) [@Notslytherin1](https://pybamm.discourse.group/u/Notslytherin1)\ **Post date:** [27 January 2025 18:44 UTC](https://pybamm.discourse.group/t/amplitude-of-the-voltage-output-after-cycles-is-decreased-even-without-aging/127/1 "2025-01-27T18:44:30Z")
Dear PyBaMM community, I’m trying to apply a current profile on my model, after scaling up the widht to match the actual capacity of the cell. Then i noticed that the voltage output of the model is deviated after cycles and also its amplitude got decreased (As mentionned in the figure attached) even-thought the aging branch is excluded from the model. Could someone please help me understand why this problem is happening? ![image](https://global.discourse-cdn.com/free1/uploads/pybamm/original/1X/9f46c09835d7b2200a650118b7ec0dd626a258de.jpeg) This will be very apperciated. Thank you in advance. ---
**Author:** ![Notslytherin1](https://avatars.discourse-cdn.com/v4/letter/n/54ee81/32.png) [@Notslytherin1](https://pybamm.discourse.group/u/Notslytherin1)\ **Post date:** [28 January 2025 16:16 UTC](https://pybamm.discourse.group/t/amplitude-of-the-voltage-output-after-cycles-is-decreased-even-without-aging/127/2 "2025-01-28T16:16:39Z")
**Code:** import pybamm import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import mean\_squared\_error # Set logging level pybamm.set\_logging\_level(“WARNING”) # Create model model = pybamm.lithium\_ion.DFN( options={ “cell geometry”: “pouch”, } ) # Create parameter set parameter\_values = pybamm.ParameterValues(“OKane2022”) # Calculate total width parameter\_values.update({ # Cell geometry ``` "Nominal cell capacity [A.h]": 65, "Electrode width [m]": 1.58 * 62/5, "Current function [A]": 65.0, ``` }) # Import drive cycle drive\_cycle = pd.read\_csv( r"C:\Users\OO000009\Desktop------.csv", comment=“#”, header=None ).to\_numpy() drive\_cycle[:, 1] = -drive\_cycle[:, 1] # Load experimental data experimental\_data = pd.read\_csv(r"C:\Users\OO000009\Desktop-----.csv") exp\_time\_s = experimental\_data[“Total Time (Seconds)”] exp\_voltage\_mV = experimental\_data[“Voltage (mV)”] # Convert voltage from mV to V exp\_voltage\_V = exp\_voltage\_mV / 1000 # Skip the first 3200 rows and reset time to start from 0 exp\_time\_s = exp\_time\_s.iloc[3200:].reset\_index(drop=True) exp\_voltage\_V = exp\_voltage\_V.iloc[3200:].reset\_index(drop=True) exp\_time\_s = exp\_time\_s - exp\_time\_s.iloc[0] # Reset time to start from 0 # Create drive cycle experiment def create\_experiment(num\_cycles): # Create drive cycle step using the current function drive\_cycle\_step = pybamm.step.current(drive\_cycle) ``` # Create cycling experiment cycle_steps = [ (drive_cycle_step,) # Add rest between cycles ] * num_cycles return pybamm.Experiment(cycle_steps) ``` # Use drive cycle experiment experiment = create\_experiment(num\_cycles=100) # Solver settings solver = pybamm.CasadiSolver( mode=“safe”, atol=1e-6, # Stricter tolerance rtol=1e-6, dt\_max=600, # Smaller timestep ) # Create and run simulation sim = pybamm.Simulation( model, parameter\_values=parameter\_values, experiment=experiment, solver=solver ) print(“Starting simulation…”) solution = sim.solve(initial\_soc=0.3) # Extract data for plotting voltage = solution[“Terminal voltage [V]”].entries time = solution[“Time [s]”].entries # Interpolate experimental data to match simulation time points exp\_voltage\_interp = np.interp(time, exp\_time\_s, exp\_voltage\_V) # Calculate RMSE between simulated and experimental voltage rmse = np.sqrt(mean\_squared\_error(exp\_voltage\_interp, voltage)) print(f"Root Mean Squared Error (RMSE) between simulated and experimental voltage: {rmse:.4f} V") # Plot voltage comparison plt.figure(figsize=(12, 6)) plt.plot(time, voltage, label=“Simulated Voltage”, color=“royalblue”, linewidth=2.5) plt.plot(time, exp\_voltage\_interp, label=“Experimental Voltage”, color=“darkorange”, linewidth=2.5) plt.xlabel(“Time [s]”) plt.ylabel(“Voltage [V]”) plt.title(“Voltage Comparison”) plt.legend() plt.grid(True) plt.show() # Track capacity over cycles cycle\_numbers = capacities = capacity\_losses = # Check if capacity data exists in solution summary variables if “Capacity [A.h]” in solution.summary\_variables: initial\_capacity = solution.summary\_variables[“Capacity [A.h]”][0] capacities = solution.summary\_variables[“Capacity [A.h]”] capacity\_losses = ((initial\_capacity - np.array(capacities)) / initial\_capacity) \* 100 ``` # Print capacity values for each cycle print("Cycle-wise capacities and capacity losses:") for cycle_number, (capacity, capacity_loss) in enumerate(zip(capacities, capacity_losses), start=1): print(f"Cycle {cycle_number}: Capacity = {capacity:.2f} Ah, Capacity Loss = {capacity_loss:.2f} %") # Plot results fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10)) # Capacity plot ax1.plot(range(1, len(capacities) + 1), capacities, 'b-o') ax1.set_xlabel('Cycle Number') ax1.set_ylabel('Capacity [Ah]') ax1.grid(True) ax1.set_title('Capacity vs Cycle Number') # Capacity loss plot ax2.plot(range(1, len(capacities) + 1), capacity_losses, 'r-o') ax2.set_xlabel('Cycle Number') ax2.set_ylabel('Capacity Loss [%]') ax2.grid(True) ax2.set_title('Capacity Loss vs Cycle Number') plt.tight_layout() plt.show() ``` else: print(“Capacity data is not available in the solution’s summary variables.”) ``` # Calculate RMSE between simulated and experimental voltage ``` rmse = np.sqrt(mean\_squared\_error(exp\_voltage\_interp, voltage)) print(f"Root Mean Squared Error (RMSE) between simulated and experimental voltage: {rmse:.4f} V")