Battery Life Estimation
Technical Considerations, Contributing Factors, and Best Practice Guidance
Written by Daan de Waard
Updated at November 17th, 2025
Table of Contents
Battery Life Estimation
Overview
Battery life estimation in Device Manager is influenced by many dynamic factors, including device configuration, environmental conditions, network quality, SIM choice, and battery manufacturing variances. This article provides a comprehensive explanation of how Device Manager generates battery life estimates, the limitations and assumptions behind these calculations, and the operational realities that affect real-world performance.
Understanding these variables and their impact is essential for setting accurate expectations, improving troubleshooting outcomes, and making sound deployment decisions for battery powered devices.
Device Manager bases its calculation on:
- Device Statistics (usage counters).
- Assumptions about typical current draw.
- Discharge curves for common battery chemistries.
- Device configuration and reporting logic.
Understanding these inputs, along with their limitations, helps with accurate expectations, troubleshooting, and planning.
Important
Battery life estimation is an approximation, not a guarantee. It relies on idealised models and assumptions that often differ from real-world conditions.
How Device Statistics Influence Battery Estimates
Device Statistics record real-world behaviour, including:
- GPS fix attempts (success / failure).
- Upload attempts (success / failure).
- Network registration attempts.
- Total run-time.
- Last statistics reset timestamp.
These counters help determine average current consumption over time. Resetting statistics clears this historical data, temporarily improving battery estimates until new statistics accumulate.
Recommendation
Only reset device statistics when necessary and expect temporarily inaccurate readings afterward.
Factors Affecting Real-World Battery Life
Battery performance is influenced by a range of conditions that may differ significantly from model assumptions.
1. Cellular Network Quality
- Poor coverage increases modem transmit power.
- More retries and reconnections.
- Longer active (non-sleeping) time.
2. GPS (GNSS) Performance
Long ‘Time to First Fix’ (TTFF) greatly increases power consumption. TTFF depends on:
- Sky visibility.
- Device (antenna) placement.
- Satellite availability.
- Ephemeris/almanac freshness.
- Temperature, RF noise, and environment.
Longer TTFF → more GNSS on-time → higher consumption.
3. Sleep Current Variability
Sleep current changes with temperature and firmware/configuration. Even minor increases in sleep current compound significantly over long deployments.
4. Battery Chemistry & Manufacturing Variance
The estimation model assumes batteries follow a standard, predictable discharge curve typical of non-rechargeable primary chemistries used in DM devices such as Li-FeS₂, Li-SOCl₂, Alkaline or similar curves. Battery performance can vary by:
- Quality.
- Manufacturing tolerances.
- Internal resistance.
- Ageing and storage conditions.
- Differences between brands and chemistry batches.
If a battery with a significantly different discharge profile is used:
- Reported voltage may not align with expected curves.
- The estimator may overstate or understate remaining life.
- Temperature sensitivity may differ.
5. Temperature Effects
Temperature affects available battery capacity, discharge characteristics, voltage stability, and peak current capability.
-
Cold (< 0°C): reduced capacity, voltage sag, higher shutdown risk.
- Hot (> 40°C): accelerated degradation and reduced long-term health.
Device Manager does not dynamically adjust for temperature.
6. Higher Current Events
Battery discharge is non-linear, yet estimates rely on simplified curves. Real device operation includes bursts of activity:
- GNSS fixes.
- LTE/LPWAN transmissions.
- Accelerometer / Movement wake events.
- BLE/Wi-Fi scans.
These are difficult to model precisely and increase real consumption.
7. Configuration Impact on Battery Life
Battery draw is heavily influenced by settings such as:
- Heartbeat/upload intervals.
- Tracking profiles (periodic, movement-based, recovery).
- GNSS fix frequency.
- BLE/Wi-Fi scan rates.
- Accelerometer sensitivity.
Even small configuration changes can significantly shorten or extend life.
Situations That Distort Battery Estimates
Battery estimates may become unreliable when:
-
Device Counter Statistics have been reset.
- Cellular coverage is poor or inconsistent.
- Retry counts are high.
- TTFF is long.
- Battery chemistry is atypical.
- Temperature is extreme.
Estimates stabilise once new statistics accumulate.
Battery Flags: The Most Reliable Indicators
Partners should treat Battery Good and Battery Critical flags as the authoritative signal of battery health. These reflect real voltage behaviour under load and serve as reliable triggers for alerts.
Recommended usage:
- Trigger alerts using Battery Bad or Critical.
- Treat estimates as advisory.
- Investigate unexpected changes.
Where to View Battery Estimates
Estimates appear in Device Manager in two locations:
1. Device Details
- Current battery voltage.
- Battery Good / Critical flags.
- Estimated percentage (if supported).
- Battery Level History.
- Device Statistics summary.
2. Device Manager Grid
Add battery columns using the Column Chooser and save layouts using Saved Views for fleet-level monitoring.
Available grid fields include:
- Battery Voltage.
- Battery Status.
- Battery Estimate.
- Battery Last Estimated.
To set this up:
-
Open the Device Manager Grid
-
Click on the Column Chooser (Icon to the far right, above the grid)
-
Drag the desired battery fields and arrange these in the grid to your prefernce.
-
User the Saved Views feature to save the grid layout if desired.
- This allows quick comparison of estimated battery life across large deployments.
API Access
Battery estimates and battery status information can be retrieved programmatically using the Device Manager API.
GET
Device Statistics counters used for estimate modelling, meaning partners integrating with the API should treat the estimate as advisory only.
Best Practices for Reliable Battery Operation
- Use conservative tracking / logging intervals.
- Test devices in actual deployment conditions.
- Avoid unnecessary statistics resets.
- Review statistics for retries and TTFF.
- Ensure a good installation and device placement.
- Account for temperature extremes.
- Use high-quality batteries.
Digital Matter Energy Tool
Still concerned or curious? Speak to your Digital Matter representative about the DM Energy Tool for improved modelling and deeper insights for battery-powered deployments.
Conclusion
Battery life estimation in Device Manager provides a useful guideline but is inherently limited by assumptions about network conditions, GPS performance, temperature, battery chemistry, and device configuration. Accurate interpretation requires monitoring Device Statistics, avoiding unnecessary resets, and considering environmental factors. Following best practices leads to longer deployments and more reliable outcomes.
