Interactive Simulation

Run Python code client-side to analyze battery data.

Battery Degradation Model

Simulate capacity fade over time using a semi-empirical model.

Loading Python...

import numpy as np
import matplotlib.pyplot as plt
import io, base64

# Simulation Parameters
cycles = np.linspace(0, 2000, 100)
temp_c = 25  # Temperature in Celsius

# Arrhenius equation for temperature dependence
R = 8.314
T = temp_c + 273.15
k = np.exp(-30000 / (R * T))

# Degradation model (simplified SEI growth)
capacity_retention = 100 * (1 - k * np.sqrt(cycles))

# Plotting
plt.figure(figsize=(10, 6), facecolor='#0A0A0A')
ax = plt.gca()
ax.set_facecolor('#0A0A0A')

plt.plot(cycles, capacity_retention, color='#00D4FF', linewidth=2, label=f'Temp: {temp_c}°C')
plt.axhline(y=80, color='#39FF14', linestyle='--', alpha=0.5, label='EOL (80%)')

plt.title('Battery Capacity Fade Prediction', color='white', pad=20)
plt.xlabel('Cycles', color='gray')
plt.ylabel('Capacity Retention (%)', color='gray')
plt.grid(True, color='#333333', linestyle='--')
plt.legend(facecolor='#1A1A1A', labelcolor='white')

# Style ticks
ax.tick_params(colors='gray')
for spine in ax.spines.values():
    spine.set_color('#333333')

# Save plot
buf = io.BytesIO()
plt.savefig(buf, format='png', facecolor='#0A0A0A', edgecolor='none', bbox_inches='tight')
buf.seek(0)
img_str = base64.b64encode(buf.read()).decode('utf-8')
print(f"PLOT_BASE64:{img_str}")

print(f"Simulation complete at {temp_c}°C")
print(f"Final capacity at 2000 cycles: {capacity_retention[-1]:.1f}%")

Output will appear here...

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