Ecological Motor Control and
Predictive Maintenance with AI

Electric motors are everywhere from laptop fans and dishwashers to industrial machinery, robots, public transport and more. A modern car can alone contain about 40 motors for various functions. But these valuable uses come at a cost. It has been calculated that electric motors account for 40% of worldwide power consumption and 20% of CO2 emissions*.


To address these issues, the ECOMAI project is developing technologies

  • to enhance electric motor drive systems with an embedded AI system running on a specialised AI hardware platform

  • to optimise the efficiency and lifetime of electric motors, thereby reducing energy consumption and enabling development of more ‘ecological’ systems

  • to lead to market opportunities for applications in numerous sectors including automotive, medical and transportation.


Technical Goals

This platform will provide both cost-efficient AI functionality and explore advanced accelerator and approximate computing principles. Furthermore, ECOMAI will deliver an innovative Model-based Design and Automation Framework: a full development toolkit that combines model-based design and an AI compiler for the specialised hardware platform along with a full system modelling and simulation environment. This will make ECOMAI’s technologies easily accessible, particularly for SMEs.

Develop a Edge AI Solution to be integrated into Motor drive systems consisting of:

  • Specialized AI hardware platform
  • Development kit with AI compiler, model-based design and simulation environment
  • AI Applications for Predictive Maintenance and Energy-efficient Control to execute on the hardware.

Expected impact

The ECOMAI project offers a basis for Europe to establish a leading role in AI-enhanced electrical motor drive technology – from hardware to applications – through solutions that support the green and digital transitions.

  • Contribute to the objectives of the European Green Deal by enabling more ‘ecological’ solutions now and paving the way to further energy saving options in the future
  • Respond to a rapidly growing market for AI chips, motor control chips and motors in general (growth rate globally of 8% by 2025 for Electric Motors)

Key Application Areas

Digital Industry


Quality, Reliability, Safety and Cybersecurity

Essential Capabilities

Architecture and Design: Methods and Tools

Artificial Intelligence, Edge Computing and Advanced Control

Components, Modules and Systems Integration

Technology Value Chain and Use Cases

The ECOMAI technologies will also be tested in a range of use cases in applications in the transportation, power supply, medical and automotive domains.

‘Ecological’ use cases:

  • An automotive compressor system demonstrating the potential of AI to deliver better control performance.
  • An electrical bike testbench for intelligent sensorless electric bike traction applications, incorporating an AI-based torque control algorithm.
  • A test environment for a robotic rehabilitation system for mobility impaired people, exploring, amongst other functionality, capabilities for neuro-feedback.

Condition monitoring and predictive maintenance:

  • A railway Platform Screen Door (PSD) system, including AI enhanced PdM algorithms and the TinyML edge device developed by ECOMAI to prolong operational availability.
  • An ultrasonic traducer, using AI to understand which changes of the driving signal will be the result of certain alterations in a structure or volume when excited by the ultrasound: a motor, a battery or another complex medium for ultrasonic-based condition monitoring.