Battery Life Calculator

This tool estimates total battery runtime (in hours or days) for low-power electronic devices by modeling duty-cycled operation—accounting for active current draw during processing/transmission, ultra-low sleep current, and DC-DC conversion efficiency. It uses weighted average current consumption based on duty cycle and applies a cutoff voltage factor to reflect usable capacity.

It’s ideal for designing battery-powered IoT sensors (e.g., environmental monitors), wearables, smart meters, and remote telemetry devices where sleep/active cycles dominate power consumption. Engineers use it during early-stage power budgeting, battery selection, and firmware optimization to meet target deployment lifetimes (e.g., 5+ years on a single coin cell).

Duty cycle is the percentage of time the device spends in active (high-current) mode per full operating cycle (e.g., 1% means 1 second active per 100 seconds total). The calculator uses it to compute the effective average current: I_avg = I_act × (duty/100) + I_sleep × (1 − duty/100), then adjusts for efficiency and cutoff.

Sleep current is typically orders of magnitude smaller than active current (e.g., 0.1–50 μA vs. 1–100 mA), so using μA avoids decimal clutter and improves precision. The calculator automatically converts sleep current to mA internally before averaging—ensuring consistent units in the final calculation.

Battery Capacity: 100–10,000 mAh (coin cells to LiPo packs); Active Current: 1–500 mA (MCU + radio active); Sleep Current: 0.05–50 μA (modern ultra-low-power MCUs); Duty Cycle: 0.01–20% (most IoT devices < 5%); Efficiency: 75–95% (typical DC-DC converters); Cutoff Factor: 0.8–0.95 (accounts for voltage sag and end-of-discharge threshold).

It reflects the fraction of nominal battery capacity that remains usable before the system resets or fails due to undervoltage. A value of 0.9 means only 90% of rated capacity is practically available—accounting for battery chemistry, load regulation, and minimum operating voltage of downstream components.

Verify sleep current (a common error is entering 10 mA instead of 10 μA), confirm duty cycle aligns with real-world wake intervals (e.g., 1% ≠ once per minute if cycle period is misjudged), and ensure DC-DC efficiency isn’t overestimated—especially at light loads. Also check whether your battery’s capacity rating is at the appropriate discharge rate (C-rate).

Yes—but apply conservative derating: reduce nominal capacity by 10–20% for aging and temperature effects, and ensure the cutoff factor accounts for the battery’s discharge curve (e.g., Li-ion drops sharply below 3.0 V/cell). For precise lifetime modeling, pair this tool with datasheet discharge profiles and temperature derating curves.

Since most low-voltage systems use switching regulators to maintain stable supply rails, inefficiency directly increases average current draw from the battery. An 85% efficient converter draws ~18% more current than ideal—this loss is factored into the effective average current, making runtime estimates significantly more realistic than assuming ideal conversion.

Yes—rearrange the core formula: Capacity (mAh) ≈ Runtime (h) × I_avg / Efficiency × (1 / Cutoff Factor). Use the calculator iteratively: plug in your target runtime, adjust capacity until the result matches, or use spreadsheet goal-seek. Always add 15–25% margin for aging, temperature, and measurement uncertainty.