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Measure, analyze, and optimize IoT power use across all operating states
The Keysight IoT battery life solution is a comprehensive platform for accurately measuring and optimizing the power consumption of IoT devices. It combines precision hardware with enhanced analysis software to capture dynamic current profiles across all operating states — active, idle, and sleep. With seamless current measurement, long-duration data logging, and automatic correlation of power usage with RF and system events, you can quickly identify power inefficiencies, validate design changes, and confidently estimate battery life under real-world conditions. Accelerate development and improve device reliability — request a quote for a Keysight IoT battery life solution today. Want to learn more about this solution? Explore the resources below.
Current drain analysis
Accurately measures current drain from nanoamperes to amperes with high resolution, enabling detailed insights across all operational states.
RF / DC event correlation
Links power consumption to specific system or RF events, helping you isolate and optimize high-energy usage components.
Multiday data logging
Enables up to eight days of continuous current monitoring, supporting long-term behavioral analysis and energy profiling.
Battery life estimation
Automatically calculates battery life from usage patterns, removing guesswork and expediting design validation.
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Number of outputs
4
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Maximum power
600 W
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Maximum voltage per output
60 V
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Maximum current per output
40 A
Typical configuration
IoT Battery Life Optimization Solution
Services and support
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Ensure your test system performs to specification and meets local and global standards.
Make measurements quickly with in-house, instructor-led training, and eLearning.
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Frequently asked questions
IoT battery life optimization testing involves measuring and analyzing the power consumption of connected devices to accurately estimate and extend their operational lifetime. By profiling current draw across various device states, such as active communication, idle, and sleep, engineers can identify energy-heavy processes and optimize firmware or hardware accordingly.
This testing helps validate battery life claims, improve device reliability, and support longer field deployment. Effective testing ensures IoT products like sensors, wearables, or trackers deliver consistent performance and lower maintenance costs by maximizing battery usage under real-world operating conditions.
Event-based power profiling is a precise method that links specific device actions, like RF transmission, sensor wake-up, or processor activity, to moment-by-moment current consumption. By correlating these events with power usage data, developers can pinpoint subsystems or behaviors that drain battery life. This insight enables targeted design improvements, firmware updates, and optimization of duty cycles.
Event-based profiling is especially useful for battery-powered IoT devices that spend most of their time sleeping or intermittently transmitting data, as it uncovers power inefficiencies at the subsystem level that traditional static measurements often miss.
Effective IoT power measurement often employs tools like source measure units (SMUs), power profiler kits, oscilloscopes with current probes, and battery emulators. SMUs combine precise voltage sourcing with current measurement capabilities, offering high dynamic range and fast sampling. Power profiler kits provide real-time graphs and long-term logging suited for embedded platforms. Oscilloscopes with current probes capture transient current spikes during RF activity or compute bursts. Battery emulators replicate different discharge conditions, aiding in stress testing and lifespan estimation. These tools help engineers gain an accurate understanding of power consumption patterns and optimize designs for extended battery performance.
Accurate battery life estimation combines detailed current profiling with knowledge of the battery’s capacity and device duty cycle. You can start by measuring average current draw across different modes (active, idle, and sleep) using high-resolution tools. You can then calculate the proportion of time the device spends in each state and multiply by the average current.
Summing up gives an estimated average current draw, which is divided into the battery’s rated capacity to predict runtime. This method helps validate battery life claims, optimize communication intervals, and refine firmware, ensuring devices meet deployment expectations without premature battery depletion.