Lund, Sweden — Qoitech today released Otii Software 3.7, introducing the Battery Life Estimator: a measurement-based battery runtime prediction tool integrated into the Otii power measurement workflow. The release addresses a problem that has shaped how IoT and embedded products are specified for over a decade — that most battery life claims on product datasheets are derived from oversimplified spreadsheet calculations rather than measured device behavior.
The Battery Life Estimator combines a captured device load profile with a measured battery discharge curve specific to the application’s usage pattern — either created by the engineer using Otii instruments or supplied by the battery manufacturer. This replaces the generic battery assumptions that underpin most average-current calculations with real discharge data captured under the conditions the battery will actually experience, accounting for chemistry behavior, temperature, voltage cutoff, and self-discharge.
The Battery Life Estimator is part of the Otii Battery Toolbox license, which runs on Otii Software. It is available now in Otii Software 3.7, with a free trial for engineers who want to evaluate it on their own devices before purchasing.
Closing the gap between simplified math and specialist labs
“Battery knowledge inside the IoT and embedded engineering community is scarcer than it should be,” said Vanja Samuelsson, CEO of Qoitech. “And the tools available today have forced an impossible choice. On one end, you have a spreadsheet that divides battery capacity by average current — a calculation that ignores almost every variable that actually determines battery life in the field. On the other end, you have laboratory-grade battery testing racks that cost tens of thousands of euros and require dedicated specialists to operate. There has been almost nothing in between. The Battery Life Estimator is our answer to that gap. We want any embedded engineer — not just the battery experts — to be able to find a battery that genuinely matches the diverse, unpredictable usage their IoT product will face in the field. That means the tool has to be approachable, the workflow has to fit a normal development cycle, and the prediction has to be grounded in real measured discharge curves from the actual use case — not theoretical models or generic datasheet assumptions. This release is one step in a longer effort to make battery life something every engineering team can do well, not just a handful of specialists. And it is not just an embedded engineering problem. Battery manufacturers today often have to make recommendations without a clear picture of how the customer’s device actually behaves — what its load profile looks like, what conditions it operates in, what end-of-life means for that specific product. When electronics developers can characterize their device with real measurements and share that with the battery manufacturer, both sides have something concrete to talk about. That conversation gets faster, more accurate, and better for everyone — including the battery manufacturer’s sales funnel.”
How the Battery Life Estimator works
The workflow is built around three steps that fit existing Otii usage patterns:
- Capture a real load profile. The engineer records actual current consumption from their device under realistic operating conditions, using the Otii Arc Pro or Otii Ace Pro power analyzer.
- Select a measured discharge curve for the use case. Instead of a generic battery model, the Estimator works from real discharge data captured under representative conditions. Engineers can record their own discharge curves directly using Otii Battery Toolbox — running the candidate battery against a load profile that matches the deployment’s real usage pattern and temperature — or they can use discharge curves supplied by the battery manufacturer. This anchors the prediction in measured battery behavior under the same kind of load the device will actually see.
- Generate a runtime prediction. The Estimator combines the load profile with the discharge curve to produce a battery life prediction, accounting for pulse current behavior, voltage cutoff, and chemistry-specific discharge characteristics.
Because the prediction is grounded in measured load data and measured battery behavior rather than averaged assumptions, engineers can also use the Estimator comparatively — testing how firmware changes, duty-cycle adjustments, or alternative batteries affect predicted runtime, with the answer visible in minutes rather than weeks of bench testing.
Bridging electronics developers and battery manufacturers
The Battery Life Estimator, and the broader Otii Product Suite, addresses a long-standing communication gap in the battery-powered product industry. Electronics developers know how their device behaves but often lack the tools to characterize it in terms a battery specialist can act on. Battery manufacturers know their cells in detail but are frequently asked to recommend a battery for a device whose real-world load profile they have never seen.
The result is a slow, imprecise back-and-forth where recommendations are based on average-current estimates and rule-of-thumb assumptions, often leading to over-specified batteries, under-performing products, or both.
By giving electronics developers a way to capture and share measured device behavior — and giving battery manufacturers a way to align their recommendations with that data — Otii instruments turn the conversation from guesswork into engineering. Battery manufacturers get a clearer picture of the application earlier in the cycle. Electronics teams get recommendations they can trust. And the path from prototype to production-ready battery decision becomes shorter for both sides.
Pricing and availability
Otii Software 3.7 is available now. The Battery Life Estimator is included in the Otii Battery Toolbox license, with a free trial available at qoitech.com.
