The Predictive Sensor Battery Runtime Calculator estimates battery life for wireless condition-monitoring sensors from battery energy, usable-capacity allowance, sleep power, active power, and active duty cycle. Unlike a single average-power input, this model explicitly blends sleep and active states, which is useful for vibration, temperature, acoustic, or multi-sensor nodes that wake periodically to measure, process, and transmit condition data.
The main result is estimated runtime in years. Supporting values show the calculated average power, daily energy use, and usable battery energy. The estimate assumes the entered duty cycle adequately represents all active periods and that sleep and active power values are stable averages for those states. Battery chemistry, temperature, self-discharge, pulse-current capability, radio retries, firmware changes, and aging can materially affect field life, so the result is best used for design comparison and maintenance planning rather than as a guaranteed service interval.
Calculator inputs
Wh
%
mW
mW
%
Result
—
Estimated battery runtime
Average power—
Daily energy use—
Usable battery energy—
1. Enter battery energy Use the total battery-pack capacity in watt-hours.
2. Set usable capacity Reserve part of the nameplate energy for aging, voltage limits, temperature, and design margin.
3. Enter sleep and active power Use representative power levels for the low-power state and for measurement, processing, and communication activity.
4. Set active duty cycle Enter the percentage of total time the sensor spends in the active state.
5. Review runtime and average power The calculator blends the two states, then converts usable energy into estimated years of operation.
Average power = Active power × Duty cycle + Sleep power × (1 − Duty cycle)
Usable energy = Battery capacity × Usable capacity
Daily energy use = Average power ÷ 1,000 × 24
Runtime years = Usable energy ÷ Daily energy use ÷ 365.25
Duty cycle and usable capacity are entered as percentages and converted to decimals.
What the result means
The result estimates battery life using a two-state duty-cycle model that blends sleep and active power over time.
Actual service life can differ due to self-discharge, temperature, battery aging, high-current pulses, radio retries, changing sampling schedules, and firmware behavior.
Given
36 Wh battery
80% usable capacity
0.15 mW sleep power
120 mW active power
0.5% active duty cycle
Calculation Average power = 120 × 0.005 + 0.15 × 0.995 = 0.749 mW. Usable energy = 36 × 0.80 = 28.8 Wh. Daily use = 0.000749 × 24 = 0.01798 Wh/day. Runtime = 28.8 ÷ 0.01798 ÷ 365.25 = 4.39 years.
Result 4.39 years
Under the modeled duty cycle, short active bursts dominate average power even though the sensor sleeps 99.5% of the time.
Why separate sleep and active power?
Low-power predictive sensors often spend most of their time asleep and consume much more power while measuring or transmitting. A duty-cycle model captures that contrast more directly than entering one guessed average value.
How do I convert a sampling schedule into duty cycle?
Estimate total active time during a representative period and divide it by the total period. For example, 7.2 minutes active per day is 0.5% of 24 hours.
Should radio retries be included in active power or duty cycle?
Include their expected energy impact in whichever assumption best represents your measurements. Frequent retries often increase active time and can also change the average active-state power.
Does usable capacity account for self-discharge?
It can provide a rough allowance, but self-discharge is time-dependent and may not scale perfectly as a one-time percentage reduction. Long-life designs should use battery-specific data.
Why might a field sensor miss the estimated runtime?
Temperature, aging, pulse-current limits, network conditions, firmware changes, and actual duty cycle can all differ from design assumptions. Validate the estimate against logged power data whenever possible.