Fichtner, MaximilianMaximilianFichtnerEdström, KristinaKristinaEdströmAyerbe, ElixabeteElixabeteAyerbeBerecibar, MaitaneMaitaneBerecibarBhowmik, A.A.BhowmikCastelli, I.E.I.E.CastelliClark, S.S.ClarkDominko, R.R.DominkoErakca, M.M.ErakcaFranco, A.A.A.A.FrancoGrimaud, A.A.GrimaudHorstmann, B.B.HorstmannLatz, A.A.LatzLorrmann, HenningHenningLorrmannMeeus, M.M.MeeusNarayan, R.R.NarayanPammer, F.F.PammerRuhland, J.J.RuhlandStein, H.H.SteinVegge, T.T.VeggeWeil, M.M.Weil2022-03-062022-03-062022https://publica.fraunhofer.de/handle/publica/27137810.1002/aenm.202102904The development of new batteries has historically been achieved through discovery and development cycles based on the intuition of the researcher, followed by experimental trial and error-often helped along by serendipitous breakthroughs. Meanwhile, it is evident that new strategies are needed to master the ever-growing complexity in the development of battery systems, and to fast-track the transfer of findings from the laboratory into commercially viable products. This review gives an overview over the future needs and the current state-of-the art of five research pillars of the European Large-Scale Research Initiative BATTERY 2030+, namely 1) Battery Interface Genome in combination with a Materials Acceleration Platform (BIG-MAP), progress toward the development of 2) self-healing battery materials, and methods for operando, 3) sensing to monitor battery health. These subjects are complemented by an overview over current and up-coming strategies to optimize 4) manufacturability of batteries and efforts toward development of a circular battery economy through implementation of 5) recyclability aspects in the design of the battery.enbattery 2030battery recyclingmachine learningoperando sensingself-healing batteries666333journal article