In a global context marked by the urgency of the energy transition and growing attention to the sustainability of industrial processes, Restorative is born – an ambitious project that combines AI, numerical simulation, and predictive methodologies to revolutionize the monitoring and efficiency of battery-based energy storage systems.
Coordinated by an international consortium of excellence and supported by Horizon Europe, Restorative aims to develop an integrated digital platform that allows for extending the battery life cycle and reducing its environmental impact through predictive maintenance, operational optimization, and reuse and recycling strategies.
A technological response to sustainability challenges
The increase in demand for batteries for electric vehicles, photovoltaic systems, and industrial storage systems raises the issue of intelligent management of these assets. Current practices, still limited to reactive interventions and linear life cycles, are no longer sufficient. Restorative proposes a radical breakthrough, enabling a circular-by-design approach thanks to the adoption of:
- AI and Machine Learning for predictive diagnosis of battery status and automatic fault classification.
- Digital Twins to simulate system behavior in real conditions and predict performance and degradation.
- Reliability models to support data-driven decisions throughout the entire life cycle, from production to disposal.
Integration with the industrial world
The platform will be designed to be interoperable with existing industrial systems (IoT, SCADA, MES), allowing for scalable adoption in the most energy-intensive sectors: automotive, energy, logistics, and smart grids. Furthermore, thanks to a modular interface, the functionalities will be adaptable to different use contexts, supporting both large utilities and SMEs engaged in energy efficiency pathways.
A distinctive aspect of Restorative is the desire to integrate dependability engineering concepts – namely the analysis of system availability, maintainability, and safety – with AI-driven logic, bridging the gap between traditional approaches and emerging technologies.
Expected impacts: sustainability, reliability, innovation
According to consortium estimates, the application of Restorative solutions can generate significant benefits within the first three years:
- +20% average battery life cycle extension
- -25% unplanned maintenance interventions
- +30% reduction in operational costs at industrial pilot sites
- -15% environmental footprint per kWh stored
These numbers translate into a concrete economic and environmental impact, contributing to the goals of the EU Green Deal and the REPowerEU Plan.
A consortium of European excellence
The project involves research centers, universities, high-tech companies, and top-tier industrial integrators. The role of Datrix, through Aramix, will be central in developing AI infusion modules, creating the predictive platform, and validating models on real data from pilot plants.
Conclusions: AI as an ally of the energy transition
Restorative is an emblematic example of how artificial intelligence can become a key ally for industrial sustainability. Not just automation or optimization, but a systemic vision, predictive capability, and strategic support for the circular management of resources.
The future of sustainability starts here: from the intelligent integration of technological innovation and environmental responsibility.
