Digital tools for energy transition
SII Research invests in digital tools to make the energy transition concrete, measurable, and industrializable. Our research focuses specifically on the following objectives:
Accelerating the energy transition in transportation and infrastructure through multiphysics modeling and simulation.
Improving energy management: batteries, smart grids, and energy communities.
Developing digital tools to manage the environmental impact of industrial activities and infrastructure.
OUR RESEARCH TOPICS
DECO (Decentralized Energy Communities)
The transition from centralized energy systems, which have long dominated the energy supply infrastructure, to more sustainable and resilient alternatives has become a priority in Europe. These decentralized energy communities (DECOs) are local energy networks, capable of operating autonomously or in connection with larger networks, whose members are both energy producers and consumers (prosumers).
This R&D project has three main scientific contributions:
The development of an AI-based tool to predict the consumption of different types of households, taking into account uncertainties.
The development of an AI tool for rapid and accurate forecasting of photovoltaic energy production, based on historical and meteorological data.
The optimization of energy exchange within and between energy communities, with buy/sell recommendations using blockchain for users.
M4ECOCO
Today, decisions on changes to systems engineering processes are made based on two criteria: financial and performance.
Environmental impact analysis, when it is carried out, is done after the fact and is not taken into account in the decision-making cycle.
The aim of the project is to propose a solution that allows the impacts of industrial process changes to be compared and to provide the information needed for informed decision-making.
Our approach combines LCA (Life Cycle Assessment) methodology and process modeling to offer a solution that can be adapted to all industrial contexts.
THOR (Tracking battery Health for Optimized Recharge)
The rise of electric vehicles—84 million expected in Europe by 2030—and the massive rollout of residential and public charging stations are making battery aging management much more complex. The rise of bidirectional charging exacerbates these challenges by increasing the number of charge/discharge cycles and introducing interoperability issues between infrastructures.
The project aims to model this aging more accurately based on realistic usage profiles, to use empirical and machine learning approaches to predict their health status, and to integrate these models into control systems capable of adapting charging and discharging strategies according to the actual degradation of the batteries.