AirGradient Go Battery Life Simulator

Estimated Battery Life (2000 mAh Battery) 15.2 daysSmart Daily Use

How The Daily Model Works

The model assumes AirGradient Go shifts between three operating states: mobile use, daytime stationary use, and nighttime stationary use. Each state has its own sampling pattern so you can model lower overnight measurement frequency without relying on a single stationary cadence all day.

Mobile

GPS on, TVOC on, sensors every 1 minute, PM every 5 minutes, display every 60 seconds.

28.9 mA

Daytime Stationary

GPS off, TVOC on, deep sleep between 5 minute reads, PM every 60 minutes, display every 300 seconds.

4.6 mA

Nighttime Stationary

GPS off, TVOC off, deep sleep between 15 minute reads, PM every 120 minutes, display every 900 seconds.

929 µA

Daily Pattern

2 hours mobile, 14 hours daytime stationary, and 8 hours nighttime stationary.

Mobile hours2 h

Daytime stationary hours14 h

Nighttime stationary hours8 h

Auto-filled so the daily pattern always totals 24 hours.

0 hTotal 24 h

Top Daily Energy Budget

SGP41 (VOC/NOx)37.0%

GPS (TAU1113)24.7%

SPS30 (PM2.5)13.8%

Regulator losses10.0%

Wi-Fi (bursts)6.2%

Others8.4%

Total129.7 mAh

Quick Compare

Smart daily use

Commuter	15.2 days
Desk Worker	21.1 days
24/7 Monitor	24.2 days

Manual configurations

All On	12.9 hrs
All On, No GPS	14.5 hrs
Stationary	15.0 days
Mobile	2.9 days
Ultra Low	268.9 days

Power Assumptions And Notes

This simulator is a directional estimate based on modeled component behavior, representative sleep states, sensor cadence, and connectivity assumptions from the current AirGradient Go design work.

Component	Assumed Power Consumption	Model Notes
ESP32-C5	25 mA modeled awake average, 0.25 mA light sleep typ, 12 µA deep sleep typ at chip level	Averaged based on wake time and configured sensor read interval.
SPS30 (PM2.5)	95 mA active equivalent at battery side, <50 µA in sleep / ~330 µA in idle	Derived from 60 mA at 5 V through the modeled boost converter.
E-Paper Display	30 mA during refresh for 0.3 seconds, 5 µA between refreshes	Averaged from the selected refresh interval in seconds.
SGP41 (VOC/NOx)	3 mA continuous when enabled	Modeled as a simple on/off load with no separate conditioning behavior.
Senseair S12 CO₂	34 µA average	Treated as a steady average load in the model.
SHT43 temperature and humidity	10 µA average	Modeled as a steady background sensor load.
DPS368 barometric pressure	5 µA average	Modeled as a steady background sensor load.
TAU1113 GPS	16 mA tracking, 15 µA standby assumption	Averaged across the selected GPS active hours per day.
LIS2DH12 accelerometer	6 µA average	Assumed to stay on for motion-based wake detection.
BQ27427 fuel gauge	9 µA average	Modeled in auto mode.
Always-on miscellaneous load	60 µA average	Covers small background loads not modeled separately.
Wi-Fi uploads	120 mA bursts for about 5 seconds per upload	Averaged using the selected upload interval.
Regulator losses	10% buck-boost overhead with 16 µA minimum floor	Applied on top of the modeled load total.

The simulator uses these values to estimate both the manual configurations and the smart daily-use states. Intermittent components are averaged over time using the selected read interval, upload interval, and active hours.

Battery self-discharge is also included in the runtime estimate as a hidden background loss using a Li-ion assumption of about 2.5% per month. This mainly affects very low-power configurations where cell aging can dominate the final runtime.

It should help compare tradeoffs and expected usage patterns, but it is not a final promised runtime figure. Real-world battery life can change with firmware tuning, environmental conditions, hardware validation, and final product behavior.

Explore AirGradient Go

Use the simulator alongside the AirGradient Go product page and UI simulator to understand how the device fits into daily carry, desk use, and route-based monitoring.

View Go Product Page Try The Go UI Simulator