The ECOMAI research consortium has demonstrated that embedding AI control systems directly into electric motors can significantly cut energy use and boost operational reliability through predictive maintenance. Given that electric motors consume approximately 40% of global electricity and contribute 20% of CO₂ emissions, improving their efficiency offers substantial environmental and economic gains. The project, funded by Germany, Austria, and Türkiye under the Penta-Euripides² programmes, tackled this by integrating specialised Edge AI hardware into motor drive systems.

The project united a full value chain of partners, including Infineon Technologies, FEAAM, Moteon, neuroConn, usePAT, Albayrak Makine, Ilmenau University of Technology, Technical University of Munich, and SparxSystems. Key technical outputs include a taped-out Edge AI silicon hardware platform with an AI compiler, a development kit and model-based design environment for easier AI integration, AI-enhanced test environments for analysing loads and faults, and an AI-upgraded ultrasonic transducer for live predictive maintenance signal measurement.

Measured against 13 defined KPIs, ECOMAI delivered two standout results: energy savings of 0.65% to 4% depending on the application, and an increase in system availability from 99.4% to 99.9% in predictive maintenance scenarios. These outcomes were validated across six industrial use cases, spanning predictive maintenance for platform screen doors, ecological drives for automotive compressors and rehabilitation robots, and AI-supported control for energy-efficient drives. The project also successfully lowered the adoption barrier for SMEs seeking to implement AI-driven modelling and control.

The work holds significant market and societal relevance. AI-enhanced motor control is a largely untapped segment, positioned within the rapidly growing AI chip market (projected >30% CAGR) and the steadily expanding motor control sector. By reducing energy demand and downtime, ECOMAI directly contributes to lower CO₂ emissions, greater system reliability, and enhanced competitiveness for European SMEs.

The project has gained notable recognition, including the PENTA Innovation Award 2025, and has generated substantial academic output: 8 master’s theses, 1 doctoral thesis, 1 bachelor thesis, and 11 scientific papers (one receiving a Best Paper Award). The developed technologies are now feeding into follow-up industrial applications, new healthcare services, further research, and university programmes, reinforcing Europe’s position in AI-enabled embedded systems.

The ECOMAI project, funded by Germany, Austria and Türkiye under the Penta-Euripides² programmes, has successfully demonstrated that electric motors enhanced with embedded AI control systems can reduce energy consumption and increase system availability through predictive maintenance. Electric motors account for around 40% of global electricity consumption and 20% of CO₂ emissions. Improving their efficiency and lifetime therefore delivers both environmental and economic benefits. ECOMAI addressed this challenge by integrating specialised Edge AI hardware directly into motor drive systems.

Key Achievements

Involving partners  from Infineon Technologies AG, FEAAM GmbH, Moteon GmbH, neuroConn GmbH, usePAT GmbH, Albayrak Makine Elektronik Sanayi ve Ticaret AŞ, Ilmenau University of Technology, Technical University of Munich, and SparxSystems Software GmbH, the project covered the full value chain from academic chip design models to industrial demonstrators.

Among the main results:

A specialised Edge AI silicon hardware platform with AI compiler, taped out in silicon and compatible with existing boards and firmware.

A development kit and model-based design environment to simplify AI integration into motor systems.

AI-enhanced test environments capable of analysing dynamic load changes and fault scenarios.

An AI-enhanced ultrasonic transducer enabling live signal measurement for predictive maintenance.

Measurable Impact

ECOMAI defined 13 KPIs to quantify improvements. Two headline results stand out:

Energy savings between 0.65% and 4%, depending on application (demonstrated by FEAAM and partners).

Availability increase from 99.4% to 99.9% in predictive maintenance scenarios (Albayrak use case).

Six concrete use cases validated the technology across predictive maintenance (e.g. platform screen doors, ultrasonic monitoring); ecological electric drives (automotive compressor systems, rehabilitation robots); AI-supported control for energy-efficient drives. The project also lowered the entry barrier for SMEs, enabling them to adopt AI-driven modelling and control methodologies more easily.

Market & Societal Relevance

Motor and drive systems form the backbone of modern industry, transport and healthcare. AI-enhanced motor control remains a largely untapped market segment, while AI chip markets are projected to grow at over 30% CAGR, motor control and drive systems markets continue steady expansion.

By reducing energy demand and downtime, ECOMAI contributes directly to lower CO₂ emissions, increased system reliability, strengthened European SME competitiveness.

Recognition & Future Outlook

The project results were showcased at international events, received the PENTA Innovation Award 2025 during the Xecs Matchmaking Event in Riga, and generated 8 master’s theses, 1 doctoral thesis, 1 bachelor thesis, 11 scientific and conference papers (including a Best Paper Award). The technologies developed in ECOMAI will feed into follow-up industrial applications, new healthcare services, further research projects and university training programmes — reinforcing Europe’s leadership in AI-enabled embedded systems.

Discover more in the ECOMAI Project Impact Summary.

Penta and Euripides² are Eureka Clusters, operated by AENEAS.

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