Participants:
The aim of the project is to redefine battery performance management for drones by integrating real-time State of Charge (SoC) tracking. Predictive models enhance accuracy, enabling extended flight times, reduced disruptions, and optimized energy usage in inventory automation systems.
Research Partner :
BFH – Energy Storage Research Centre
BFH contributes deep expertise in lithium- ion battery characterization, modeling, and system integration to develop degradation models and optimize operating strategies for battery systems. The Centre has led multiple national and international research projects, including the Innosuisse-funded Smart BMS to Reduce Range Anxiety (46770.1 INNO-ENG), which laid the groundwork for ECM-based SoC estimation. Its recent work in the Circubat project has extended capabilities in real-time State of Health modeling using large operational datasets. The Centre is equipped with state-of- the-art testing facilities, including thermal chambers and high-precision impedance spectroscopy tools, ideal for the development and validation of model-based SoC estimation.
Implementation partner:
Verity is the global leader in autonomous indoor drone systems. In the project, Verity brings operational insight and integration capabilities as the industry partner. With over 150 autonomous drone installations globally, Verity offers the data, platform, and deployment environment needed to validate the system in real-world conditions. The company’s drones operate in high-uptime, warehouse environments where energy management precision directly impacts performance.
