Editorial to the Special Issue: How to Reinvent the Ways to Invent the Batteries of the Future – the Battery 2030+ Large‐Scale Research Initiative Roadmap

Abstract

The demand for batteries is accelerating way beyond what was foreseen only a few years ago. This rapid transition towards a more electrified society is a crucial dimension of reaching a carbon-neutral economy as established in the strategies of the European Commission's (EC) green deal, the Fit for 55 package,[1] and the green recovery plans.[2] Batteries are a core technology to transform the energy sector, mainly for large-scale energy storage and the transportation sector, primarily for passenger and commercial EVs. Furthermore, batteries play a vital role in innovation and efficiency in a wide range of other sectors and industries with applications like portable electronics, including electric scooters, power tools, autonomous robotics, surveillance and delivery drones, aviation, medical devices, sensors, etc. However, ultra-high-performance batteries are needed to harvest the full potential of battery-powered innovation in these areas. Equally important is reaching significantly improved battery cost-performance ratios with longer life, enhanced reliability and safety, and more sustainable and scalable approaches using less critical raw materials. These are the batteries that can deliver capacities well above those of the highly successful lithium-ion batteries that are well-performing at a low cost today. To go beyond the state-of-the-art of this battery chemistry (article number 2102904), more sustainable, more scalable, and faster research approaches are required that simultaneously consider the need for novel materials and new concepts for faster exploration and screening and scaling-up of these (both experimentally and theoretically). At the same time, novel research approaches must also integrate the synthesizability, manufacturability, and recyclability of such batteries directly into the discovery and development process to reduce environmental footprint and cost. This is, in essence what this special issue is about. The scientific arguments comprising the Battery 2030+ Roadmap and the different parts of it are described in-depth, highlighting the challenges that need to be overcome for realizing the vision of developing the scientific insights and transformative research infrastructure that is needed to supply the future demand for sustainable, ultra-performant and smart batteries. And this is faster than ever before in scientific and industrial history. For this, new tools and platforms will be realized through the new digital era with artificial intelligence paired with high-throughput simulation, synthesis and characterization techniques are the foundation. The realization of a modular battery Materials Acceleration Platform (MAP) is an essential part of this strategy, as described in the roadmap (article number 2102785) and in this special issue (article number 2102702) (article number 2102638). Globally, there is a range of battery roadmaps and strategic action plans, all more or less focusing on different chemistries and new battery concepts outlining both timelines and expectations in terms of performances. Among the most important ones for Europe is the SET-Plan action 7,[3] where different actions have been described for Europe to become more competitive in this sector. Since this was published in 2018, the ETIP Batteries Europe recently launched six different roadmaps along the full battery value chain,[4] describing the long-term expectations for future battery chemistries, which has a more updated view on the battery landscape compared to the earlier presented SET-Plan. The European battery partnership BEPA – Batt4EU, launched in 2021, has also published a strategic action plan.[5] The Battery 2030+ roadmap takes a different approach[6] and aims at the development of data-assisted transformative tools and methodologies to accelerate the procedure of identifying and discovering novel battery materials, concepts, cell designs and smart functionalities. We are aware that for all these dimensions, we must