E-Mobility -- Advancements and Challenges

Mobile platforms cover a broad range of applications from small portable electric devices, drones, and robots to electric transportation, which influence the quality of modern life. The end-to-end energy systems of these platforms are moving toward more electrification. Despite their wide range of power ratings and diverse applications, the electrification of these systems shares several technical requirements. Electrified mobile energy systems have minimal or no access to the power grid, and thus, to achieve long operating time, ultrafast charging or charging during motion as well as advanced battery technologies are needed. Mobile platforms are space-, shape-, and weight-constrained, and therefore, their onboard energy technologies such as the power electronic converters and magnetic components must be compact and lightweight. These systems should also demonstrate improved efficiency and cost-effectiveness compared to traditional designs. This paper discusses some technical challenges that the industry currently faces moving toward more electrification of energy conversion systems in mobile platforms, herein referred to as E-Mobility, and reviews the recent advancements reported in literature

Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands

14 páginas.- 4 figuras.- 67 referencias.- The online version contains supplementary material available at https://doi.org/10.1038/s41477-024-01670-7Perennial plants create productive and biodiverse hotspots, known as fertile islands, beneath their canopies. These hotspots largely determine the structure and functioning of drylands worldwide. Despite their ubiquity, the factors controlling fertile islands under conditions of contrasting grazing by livestock, the most prevalent land use in drylands, remain virtually unknown. Here we evaluated the relative importance of grazing pressure and herbivore type, climate and plant functional traits on 24 soil physical and chemical attributes that represent proxies of key ecosystem services related to decomposition, soil fertility, and soil and water conservation. To do this, we conducted a standardized global survey of 288 plots at 88 sites in 25 countries worldwide. We show that aridity and plant traits are the major factors associated with the magnitude of plant effects on fertile islands in grazed drylands worldwide. Grazing pressure had little influence on the capacity of plants to support fertile islands. Taller and wider shrubs and grasses supported stronger island effects. Stable and functional soils tended to be linked to species-rich sites with taller plants. Together, our findings dispel the notion that grazing pressure or herbivore type are linked to the formation or intensification of fertile islands in drylands. Rather, our study suggests that changes in aridity, and processes that alter island identity and therefore plant traits, will have marked effects on how perennial plants support and maintain the functioning of drylands in a more arid and grazed world.This research was supported by the European Research Council (ERC grant 647038 (BIODESERT) awarded to F.T.M.) and Generalitat Valenciana (CIDEGENT/2018/041). D.J.E. was supported by the Hermon Slade Foundation (HSF21040). J. Ding was supported by the National Natural Science Foundation of China Project (41991232) and the Fundamental Research Funds for the Central Universities of China. M.D.-B. acknowledges support from TED2021-130908B-C41/AEI/10.13039/501100011033/Unión Europea Next Generation EU/PRTR and the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. O.S. was supported by US National Science Foundation (Grants DEB 1754106, 20-25166), and Y.L.B.-P. by a Marie Sklodowska-Curie Actions Individual Fellowship (MSCA-1018 IF) within the European Program Horizon 2020 (DRYFUN Project 656035). K.G. and N.B. acknowledge support from the German Federal Ministry of Education and Research (BMBF) SPACES projects OPTIMASS (FKZ: 01LL1302A) and ORYCS (FKZ: FKZ01LL1804A). B.B. was supported by the Taylor Family-Asia Foundation Endowed Chair in Ecology and Conservation Biology, and M. Bowker by funding from the School of Forestry, Northern Arizona University. C.B. acknowledges funding from the National Natural Science Foundation of China (41971131). D.B. acknowledges support from the Hungarian Research, Development and Innovation Office (NKFI KKP 144096), and A. Fajardo support from ANID PIA/BASAL FB 210006 and the Millennium Science Initiative Program NCN2021-050. M.F. and H.E. received funding from Ferdowsi University of Mashhad (grant 39843). A.N. and M.K. acknowledge support from FCT (CEECIND/02453/2018/CP1534/CT0001, SFRH/BD/130274/2017, PTDC/ASP-SIL/7743/2020, UIDB/00329/2020), EEA (10/CALL#5), AdaptForGrazing (PRR-C05-i03-I-000035) and LTsER Montado platform (LTER_EU_PT_001) grants. O.V. acknowledges support from the Hungarian Research, Development and Innovation Office (NKFI KKP 144096). L.W. was supported by the US National Science Foundation (EAR 1554894). Y.Z. and X.Z. were supported by the National Natural Science Foundation of China (U2003214). H.S. is supported by a María Zambrano fellowship funded by the Ministry of Universities and European Union-Next Generation plan. The use of any trade, firm or product names does not imply endorsement by any agency, institution or government. Finally, we thank the many people who assisted with field work and the landowners, corporations and national bodies that allowed us access to their land.Peer reviewe

Parameter Identification of Chaotic Dynamic Systems through an Improved Particle Swarm Optimization

This paper is concerned with the parameter identification problem for chaotic dynamic systems. An improved particle swarm optimization (IPSO), which is a novel evolutionary computation technique, is proposed to solve this problem. The feasibility of this approach is demonstrated through identifying the parameters of Lorenz chaotic system. The performance of the proposed IPSO is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and computational time. It is illustrated in simulations that the proposed IPSO is more successful than the SPSO and GA. IPSO is also improved to detect and determine the variation of parameters. In this case, a sentry particle is introduced to detect any changes in system parameters and if any change is detected, IPSO runs to find new optimal parameters. Hence, the proposed algorithm is a promising particle swarm optimization algorithm for system identification