In recent years, we have witnessed an explosion of artificial intelligence (AI) applications which will continue to grow over the next decade. An intelligent and digitized society will be ubiquitous, enabled by increased advances in nanoelectronics. Key drivers will be sensors interfacing with the physical world and taking appropriate action in a timely manner while operating with energy efficiency and flexibility to adapt. The vast majority of sensors receive analog inputs from the real world and generate analog signals to be processed. However, digitizing these signals not only creates enormous amount of raw data but also require a lot of memory and high-power consumption. As the number of sensor-based IoTs grows, bandwidth limitations make it difficult to send everything back to a cloud rapidly enough for real-time processing and decision-making, especially for delay-sensitive applications such as driverless vehicles, robotics, or industrial manufacturing. In this context, PHASTRAC proposes to develop a novel analog-to-information neuromorphic computing paradigm based on oscillatory neural networks (ONNs). We propose a first-of-its-kind and novel analog ONN computing architecture to seamlessly interface with sensors and process their analog data without any analog-to-digital conversion. ONNs are biologically inspired neuromorphic computing architecture, where neuron oscillatory behavior will be developed by innovative phase change VO2 material coupled with synapses to be developed by bilayer Mo/HfO2 RRAM devices. PHASTRAC will address key issues 1) novel devices for implementing ONN architecture, 2) novel ONN architecture to allow analog sensor data processing, and 3) processing the data efficiently to take appropriate action. This “sensing-to-action” computing approach based on ONN technology will allow energy efficiency improvement 100x-1000x and establish a novel analog computing paradigm for improved future human-machine interactions.

The principal aim of the project is to develop an EU-centric supply base for key automotive PEM fuel cell components that achieve high power density and with volume production capability along with embedded quality control as a key focus - to enable the establishment of a mature Automotive PEM fuel cell manufacturing capability in Europe. It will exploit existing EU value adding competencies and skill sets to enhance EU employment opportunities and competitiveness while supporting CO2 reduction and emissions reduction targets across the Transport sector with increased security of fuel supply (by utilising locally produced Hydrogen).

CRESCENDO will develop highly active and long-term stable electrocatalysts of non-platinum group metal (non-PGM) catalysts for the PEMFC cathode using a range of complementary and convergent approaches, and will re-design the cathode catalyst layer so as to reach the project target power density and durability requirements of 0.42 W/cm2 at 0.7 V, and 1000 h with less than 30% performance loss at 1.5 A/cm2 after 1000 h under the FC-DLC, initially in small and ultimately full-size single cells tested in an industrial environment on an industrially scaled-up catalyst. The proposal includes the goal of developing non-PGM or ultra-low PGM anode catalysts with greater tolerance to impurities than current low Pt-loaded anodes. It will develop and apply advanced diagnostics methods and tests, and characterisation tools for determination of active site density and to better understand performance degradation and mass transport losses. The proposal builds on previous achievements in non-PGM catalyst development within all of the university and research organisation project partners. It benefits from the unrivalled know-how in catalyst layer development at JMFC and the overarching expertise at BMW in cell and stack testing, and in guiding the materials development to align with systems requirements.

ID-FAST aims at supporting and promoting the deployment of Proton Exchange Membrane Fuel Cell (PEMFC) technologies for automotive applications through the development of Accelerated Stress Tests (AST) together with a methodology allowing durability prediction, thus accelerating the introduction of innovative materials in next generation designs. The project is founded and focused on two main points: degradation mechanisms understanding and durability prediction improvement via the development and validation of specific ASTs and associated transfer functions. Degradation investigations will be based on consolidated data (objects with known history and ageing data) from both real systems tested in cars and ID-FAST test program to ensure relevant analysis of failure modes and performance losses together with a mean to validate the developed methodology. Investigation of stressors impact on components degradation and performance losses will give access to the accelerating factor for each single mechanism AST. Thanks to the expertise of partners, understanding will be ensured by advanced ex-situ and in-situ characterisations to identify and quantify components degradation phenomena, and by modelling and multi-scale simulation tools to investigate the impact of various stressors and to relate causes to performance losses. Combined AST protocols will be developed and validated with regard to their capability to actually reduce testing time and their relevance assessed by correlation to real world ageing. The methodology developed will allow prediction of stack lifetime and thus will be valuable for the whole automotive fuel cell community. To achieve its objectives, ID-FAST will benefit from the strong expertise of 8 partners (4 research centres, 1 university, 1 SME and 2 large companies) all along the value chain, and from an Advisory Group gathering industrial companies from components manufacturers to end-users, as well as recognised laboratories from USA and Japan.