The Battery Life Calculator determines how long a battery powers a device from capacity, current draw, depth of discharge, and efficiency. Accounts for DoD limits and efficiency losses reducing actual runtime below nameplate — key for IoT sensors, electronics, and backup power applications.
3,600
mAh
14.4
h
864
min
0.6
days
0.93
W
3,600
mAh
14.4
h
864
min
0.6
days
0.93
W
A remote sensor powered by a 10,000 mAh lithium pack drawing 15 mA average current sounds straightforward — but the actual runtime depends critically on the depth of discharge limit, self-discharge rate, and temperature derating that together can reduce actual operating time to half the theoretical maximum. The calculator for battery life computes realistic runtime by incorporating all these factors rather than the oversimplified "capacity divided by current" estimate that consistently disappoints product developers and field engineers alike.
Theoretical battery life with key correction factors:
Runtime (hours) = (Capacity_mAh × DoD × Efficiency) / Current_mA
For a 5,000 mAh LiFePO4 pack at 80% DoD, 95% efficiency, powering a 25 mA average load: Runtime = (5,000 × 0.80 × 0.95) / 25 = 3,800 / 25 = 152 hours = 6.3 days. The reserve factor (holding back 5–15% as a safety buffer) further reduces useful runtime. Key insight: the efficiency factor accounts for the fact that battery capacity ratings are specified at a low "reference" discharge rate (C/20 or C/10), but actual loads often draw current faster — reducing effective capacity below the nameplate value. Use this online calculator for any battery and load combination. The battery capacity calculator handles the inverse problem: sizing a battery for a target runtime.
Battery capacity is not constant across discharge rates — it decreases as current increases. This is the Peukert effect, described by: C_actual = C_rated × (C_rated / I)^(n-1), where n is the Peukert exponent (1.05–1.15 for LiFePO4; 1.15–1.30 for lead-acid; 1.02–1.08 for NMC lithium). A 100 Ah lead-acid battery rated at the 20-hour rate delivers only about 77 Ah when discharged in 5 hours, and 64 Ah when discharged in 2 hours. LiFePO4 is far less sensitive to this effect — one of the key advantages over lead-acid for high-rate applications like power tools and electric vehicles.
Battery capacity and internal resistance change significantly with temperature:
For outdoor deployments, always derate battery capacity for the minimum expected operating temperature — not the average. A sensor deployed in northern Canada must be sized for −30°C winter performance, not the annual average.
Self-discharge reduces battery capacity even when no load is connected. Monthly self-discharge rates at room temperature: NMC lithium 1–2%/month; LiFePO4 1–3%/month; primary lithium (non-rechargeable, e.g., AA lithium) 0.5–1%/year; lead-acid 3–5%/month. For IoT sensors deployed for 1–5 years, self-discharge can represent 20–50% of total capacity consumption over the deployment lifetime — particularly when the load current is very low (microamps) and self-discharge represents a significant fraction of total drain. Use this energy consumption calculator and power calculators for complete power budget analysis.
Usable capacity = nominal capacity × DoD% × efficiency%. Runtime = usable capacity (mAh) / current draw (mA). This gives hours. Multiply by 60 for minutes. The DoD factor ensures the battery isn't damaged by deep discharge; the efficiency factor accounts for internal losses and the Peukert effect at the given discharge rate.
Runtime above 24 hours is suitable for overnight operation without charging. For critical applications, target 2× calculated runtime (use 50% DoD instead of full DoD). If runtime is insufficient, options are: larger battery capacity, lower device power consumption (sleep modes, reduced transmission power), or more frequent charging/recharging.
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4500 mAh phone with 300 mA average draw gives ~11 hours real-world use. Heavy users (gaming, navigation) may draw 600+ mA, halving this.
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A 0.5 mA average IoT device runs for ~165 days on a single AA battery. Deep sleep modes are critical to achieving this low average current.
mAh (milliampere-hours) measures charge. Wh (watt-hours) measures energy. Conversion: Wh = mAh × V / 1000. A 5000 mAh battery at 3.7V (Li-ion nominal) stores 18.5 Wh. Wh is more useful for comparing batteries at different voltages.
Manufacturers typically rate capacity under ideal conditions (room temperature, moderate discharge rate, new battery). Real-world factors that reduce runtime: operating temperature (cold reduces capacity), battery age (capacity fades with cycles), high discharge rate (Peukert effect), and usage patterns (peak current spikes).
For maximum cycle life, limit DoD to 80% (discharge to 20% remaining). For maximum energy use with acceptable cycle life, 90% DoD is acceptable in modern Li-ion. Discharging to 0% is harmful. Many consumer devices have protection circuits that cut off at 2.5-3.0V to protect the cell.
At 0°C, Li-ion capacity drops to ~80% of room temperature capacity. At -20°C, it drops to 50% or less. High temperatures (>45°C) accelerate degradation, reducing long-term capacity. For outdoor/industrial IoT, always derate battery life by 20-30% to account for cold temperature operation.
Peukert's law states that a battery's effective capacity decreases at higher discharge rates: C_eff = C_rated × (I_rated/I_actual)^(k-1), where k is the Peukert exponent (1.1-1.3 for lead-acid, ~1.05 for Li-ion). Higher k means more capacity loss at high rates. Li-ion has minimal Peukert effect, lead-acid is significantly affected.
Use a multimeter in series (ammeter mode) between battery and device. For microcontrollers with variable duty cycles, use an oscilloscope with current probe, or a power profiler tool (Nordic PPK2, Otii Arc) that logs current vs. time. Calculate the time-average current for runtime predictions.
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