Nordic Semiconductor Announces High-Precision, Adaptive Battery Health Monitoring to Enable Smarter, Longer-Lasting IoT Devices

Nordic Semiconductor, a global leader in low-power wireless connectivity solutions, announced "Nordic Fuel Gauge v2.0", a major update to its high-precision software-based battery health management solution for the award-winning nPM1300 and nPM1304 power management ICs, at Embedded World 2026.

This update extends advanced battery management to a wide range of power-constrained IoT products by adding advanced State-of-Health (SoH) estimation, adaptive battery models, and long-term fleet analytics capabilities.

Nordic Fuel Gauge v2.0 helps manufacturers develop more reliable, sustainable, and longer-lasting products, while also enabling compliance with newly strengthened battery replacement regulations. This includes the EU Battery Regulation (2023/1542), which mandates that batteries must be readily removable and replaceable by the end-user at any time during the product's lifetime. With this announcement, manufacturers will be able to properly determine when a battery needs replacing, supporting the "Right to Repair" while simultaneously improving product reliability and reducing warranty costs.

Geir Kjosavik, PMIC Product Director at Nordic Semiconductor, stated: "Battery behavior in the field rarely matches laboratory measurements. With Fuel Gauge v2.0, we are bringing adaptive, real-world intelligence—previously limited to high-end consumer devices—to the IoT sector. This will be a game-changer for billions of battery-powered devices."

**Fleet-wide Battery Intelligence via nRF Cloud Integrated with Memfault** Fuel Gauge v2.0 seamlessly integrates with Nordic's cloud lifecycle service, "nRF Cloud powered by Memfault". Devices can automatically report State-of-Health (SoH), State-of-Charge (SoC), and various performance metrics without requiring a proprietary cloud infrastructure. This enables engineering and operations teams to monitor battery health across the entire fleet, detect anomalies, optimize charging parameters, and