Development of a self-adaptive cycle ageing model for Li-ion batteries using Machine Learning methods

International audienc

Efficiency analysis methodology. Deliverable D4.1

This deliverable describes the various elements of a methodology for modelling the energy generated and used by the different parts of an industrial energy system, in REEMAIN. This is necessary to analyse the efficiency of the industrial energy system in order to identify the improvements that can be delivered by the integration of renewable energy systems, the use of technology for waste energy capture and the derivation of more energy efficient manufacturing schedules.Chapter 1 of the deliverable presents the resource networks methodology developed by Fraunhofer IWU, which is a conceptual tool to deal with the complexity of integrating renewable energy systems into manufacturing systems, while respecting the realities of material flows, processing requirements, grid constraints and personnel capabilities. Chapter 2 describes a means by which energy demand profiles may be conceptualised, captured from reality and modelled. This chapter also explains how the different combinations of heating and cooling requirements of processes within the factory give rise to a range of demand profiles whose shape can significantly impact the efficiency of energy systems. This is why the concept of rough-cut demand modelling developed in D3.3 is so important to REEMAIN. The chapter closes with a description of the use of discrete event simulation as a tool for modelling industrial energy demand. Chapter 3 covers the various theories and empirical approaches that underpin the models of the renewable energy technologies selected for inclusion in REEMAIN, including solar PV, solar thermal collectors, solar concentrators, hot water storage, Lithium-ion batteries, solar cooling systems, combined heating, power and cooling and the organic Rankine Cycle. Chapter 4 explains how these discrete models of selected technologies may be integrated to create a model of an energy system that can be used to explore the dynamic efficiency of the complete energy system. Chapter 5 describes and compares a range of commercially available modelling tools that may be used to create dynamic models of the energy supply technologies, the components of new energy systems and the demand from the factory; as well as the means of exchanging data between different tools. Finally chapter 6 consolidates the previous chapters by presenting the results of discussions between the authors in the form of a suggested modelling approach that will be developed and refined through the rest of the tasks within work package 4

Requirements for resource networks compared to the state of the art. Deliverable D4.2

The primary aim of work package 4 is the development of methods supporting the concept of “Energy Efficiency 2.0”. This is a term coined for an approach which goes beyond the current effort to energy efficiency: introduction of technically efficient equipment, reduction of energy waste and mitigation of environmental pollution according to legislative requirements. “Energy Efficiency 2.0” is meant for companies which take a proactive approach in their management towards ecology and sustainability in general. A special concern in the matter is the integration of Renewable Energy Sources (RES) immediately in the production environment of manufacturing companies. For this purpose, a Resource Networks Methodology (RNM) is developed which is aimed to provide an approach which integrates all the different resources (as in requirements for a production operation) into factory planning and control methods. This deliverable details the theoretical background for the RNM and details the exact need for action as well as the requirements for development of the methodology. It discusses the motivation behind the push towards “Energy Efficiency 2.0” from a point of view of the European legislative, European standardisation bodies and the European markets. As RES and energy storages will be a major enabler or even requirement of the RNM, the available technologies and there characteristics are discussed. Furthermore, the state of the art on smart grids and micro grids is presented to give some background on other approaches which are being researched. The deliverable further summarises the state of the art in both science and practice on energy efficiency in production as one of the aspects to be integrated in the RNM. As flexibilities and volatilities are a prime concern of the RNM, a review of these in production systems has been made and is complemented with an overview of other projects considering the issue in relation with the integration of RES. Lastly, the need for action and the requirements for the further developments in Tasks 4.2 and 4.3 of the REEMAIN project are introduced. One example for such requirements is the placement of the RNM in the production system planning process (see Figure 1)