Control Strategy and Simulation of the Regenerative Braking of an Electric Vehicle Based on an Electromechanical Brake

The electromechanical brake (EMB) has very broad prospects for application in the automotive industry, especially in small- and medium-sized vehicles. To extend the endurance range of pure electric vehicles, a regenerative braking control strategy combined with an electromechanical brake model is designed that divides the braking modes according to the braking intensity and controls the regenerative braking force based on fuzzy theory. Considering a front-wheel-drive pure electric vehicle equipped with a floating clamp disc electromechanical brake as the research object, a structural form of electromechanical brake is proposed and a mathematical model of the electromechanical brake is built. Combined with the relevant influencing factors, the regenerative braking force is limited to a certain extent, and the simulation models of the electromechanical brake and the regenerative braking force distribution control strategy are built in MATLAB/Simulink. Co-simulation in MATLAB and AVL CRUISE software is conducted. The simulation results demonstrate that the braking energy recovery rate of the whole vehicle with the fuzzy control strategy put forward in this paper is 28.9% under mild braking and 34.11% under moderate braking. The control method substantially increases the energy utilization rate

Preparation, structural and magnetic characterization of trinuclear and one-dimensional cyanide-bridged Co(III)-Cu(II) complexes

341-345By employing two trans-dicyanocobolt(III) building blocks K[Co(bpb)(CN)2] (bpb2- = 1,2-bis(pyridine-2-carboxamido)benzenate), K[Co(bpmb)(CN)2] (bpmb2- = 1,2-bis(pyridine-2-carboxamido)-4-methyl-benzenate) and one 14-membered macrocycle Cu(II) compound as assembling segment, two cyanide-bridged CoIII-CuII complexes {{[Cu(cyclam)][Co(bpb)(CN)2]}ClO4}n·nCH3OH·nH2O (1) and {[Cu(cyclam)][Co(bpmb)(CN)2]2}·4H2O (2) (cyclam = 1,4,8,11-tetraazacyclotetradecane) have been successfully prepared and characterized by elemental analysis, IR spectroscopy and X-ray structure determination. Single X-ray diffraction analysis shows that complex 1 can be structurally characterized as one-dimensional cationic single chain consisting of alternating units of [Cu(cyclam)]2+ and [Co(bpb)(CN)2]- with free ClO4- as balanced anion, while complex 2 presents cyanide-bridged neutral trinuclear bimetallic structure containing Co2Cu core, giving clear information that the substitute group on the cyanide precursor has obvious influence on the structure type of the target compound. Investigation over magnetic properties of complex 1 reveals the weak antiferromagnetic coupling between the neighboring Cu(II) ions through the diamagnetic cyanide building block

H2RBox-v2: Boosting HBox-supervised Oriented Object Detection via Symmetric Learning

With the increasing demand for oriented object detection e.g. in autonomous driving and remote sensing, the oriented annotation has become a labor-intensive work. To make full use of existing horizontally annotated datasets and reduce the annotation cost, a weakly-supervised detector H2RBox for learning the rotated box (RBox) from the horizontal box (HBox) has been proposed and received great attention. This paper presents a new version, H2RBox-v2, to further bridge the gap between HBox-supervised and RBox-supervised oriented object detection. While exploiting axisymmetry via flipping and rotating consistencies is available through our theoretical analysis, H2RBox-v2, using a weakly-supervised branch similar to H2RBox, is embedded with a novel self-supervised branch that learns orientations from the symmetry inherent in the image of objects. Complemented by modules to cope with peripheral issues, e.g. angular periodicity, a stable and effective solution is achieved. To our knowledge, H2RBox-v2 is the first symmetry-supervised paradigm for oriented object detection. Compared to H2RBox, our method is less susceptible to low annotation quality and insufficient training data, which in such cases is expected to give a competitive performance much closer to fully-supervised oriented object detectors. Specifically, the performance comparison between H2RBox-v2 and Rotated FCOS on DOTA-v1.0/1.5/2.0 is 72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%, 89.66% vs. 88.99% on HRSC, and 42.27% vs. 41.25% on FAIR1M.Comment: 13 pages, 4 figures, 7 tables, the source code is available at https://github.com/open-mmlab/mmrotat