AI for Energy Harvesting Optimization
Module: Sustainable Tech | Difficulty: Premium
Piezoelectric Power
Thermoelectric Generator
Comparison
| Technology | Power Density | Efficiency | Operating Conditions |
|---|---|---|---|
| Piezoelectric | 1-100 uW/cm3 | 5-30% | Vibration |
| Thermoelectric | 10-100 uW/cm2 | 5-15% | Temperature gradient |
| RF harvesting | 0.1-1 uW/cm2 | 1-5% | EM radiation |
| Solar (indoor) | 10-100 uW/cm2 | 10-20% | Ambient light |
Python Implementation
import numpy as np
from scipy.optimize import minimize_scalar
class EnergyHarvestingOptimizer:
def piezoelectric_power(self, mass, freq, disp, zeta=0.02):
omega = 2 * np.pi * freq
d31 = 275e-12
return mass * omega**3 * disp**2 * d31**2 / (2 * zeta)
def thermoelectric_power(self, seebeck, delta_T, resistance):
return (seebeck * delta_T)**2 / (4 * resistance)
def optimal_impedance(self, source_Z, load_range):
result = minimize_scalar(lambda R: -source_Z * R / (source_Z + R)**2,
bounds=load_range, method='bounded')
return result.x, -result.fun
Research Insight: Hybrid energy harvesting achieves 99.9% uptime for IoT sensors with AI-optimized power management.