Abstract:Regenerative braking is an effective approach for electric vehicles (EVs) to extend their driving range. A fuzzy-logic-based regenerative braking strategy (RBS) integrated with series regenerative braking is developed in this paper to advance the level of energy-savings. From the viewpoint of securing car stability in braking operations, the braking force distribution between the front and rear wheels so as to accord with the ideal distribution curve are considered to prevent vehicles from experiencing wheel lock and slip phenomena during braking. Then, a fuzzy RBS using the driver's braking force command, vehicle speed, battery SOC, battery temperature are designed to determine the distribution between friction braking force and regenerative braking force to improve the energy recuperation efficiency. The experimental results on an "LF620" prototype EV validated the feasibility and effectiveness of regenerative braking and showed that the proposed fuzzy RBS was endowed with good control performance. The maximum driving range of LF620 EV was improved by 25.7% compared with non-RBS conditions.
A variety of microrobots have commonly been used in the fields of biomedical engineering and underwater operations during the last few years. Thanks to their compact structure, low driving power, and simple control systems, microrobots can complete a variety of underwater tasks, even in limited spaces. To accomplish our objectives, we previously designed several bio-inspired underwater microrobots with compact structure, flexibility, and multi-functionality, using ionic polymer metal composite (IPMC) actuators. To implement high-position precision for IPMC legs, in the present research, we proposed an electromechanical model of an IPMC actuator and analysed the deformation and actuating force of an equivalent IPMC cantilever beam, which could be used to design biomimetic legs, fingers, or fins for an underwater microrobot. We then evaluated the tip displacement of an IPMC actuator experimentally. The experimental deflections fit the theoretical values very well when the driving frequency was larger than 1 Hz. To realise the necessary multi-functionality for adapting to complex underwater environments, we introduced a walking biomimetic microrobot with two kinds of motion attitudes: a lying state and a standing state. The microrobot uses eleven IPMC actuators to move and two shape memory alloy (SMA) actuators to change its motion attitude. In the lying state, the microrobot implements stick-insect-inspired walking/rotating motion, fish-like swimming motion, horizontal grasping motion, and floating motion. In the standing state, it implements inchworm-inspired crawling motion in two horizontal directions and grasping motion in the vertical direction. We constructed a prototype of this biomimetic microrobot and evaluated its walking, rotating, and floating speeds experimentally. The experimental results indicated that the robot could attain a maximum walking speed of 3.6 mm/s, a maximum rotational speed of 9°/s, and a maximum floating speed of 7.14 mm/s. Obstacle-avoidance and swimming experiments were also carried out to demonstrate its multi-functionality.
To investigate geometric and electrochemical characteristics of Li ion battery electrode with different packing densities, lithium cobalt oxide (LiCoO 2 ) cathode electrodes were fabricated from a 94:3:3 (wt%) mixture of LiCoO 2 , polymeric binder, and super-P carbon black and calendered to different densities. A synchrotron X-ray nano-computed tomography system with a spatial resolution of 58.2 nm at the Advanced Photon Source of the Argonne National Laboratory was employed to obtain three dimensional morphology data of the electrodes. The morphology data were quantitatively analyzed to characterize their geometric properties, such as porosity, tortuosity, specific surface area, and pore size distribution. The geometric and electrochemical analysis reveal that high packing density electrodes have smaller average pore size and narrower pore size distribution, which improves the electrical contact between carbon-binder matrix and LiCoO 2 particles. The better contact improves the capacity and rate capability by reducing the possibility of electrically isolated LiCoO 2 particles and increasing the electrochemically active area. The results show that increase of packing density results in higher tortuosity, but electrochemically active area is more crucial to cell performance than tortuosity at up to 3.6 g/cm 3 packing density and 4 C rate.
Fluorogenic organic materials have gained tremendous attention due to their unique properties. However, only a few of them are suitable for bioimaging. Their different behaviors in organic and cellular environments hinder their application in bioimaging. Thus understanding the photoluminescent behaviors of organic materials in a cellular context is particularly important for their rational design. Herein, we describe two coumarin-quinazolinone conjugates: CQ and MeCQ. The high structure similarity makes them possess similar physical and photophysical properties, including bright fluorescence ascribed to the monomer forms in organic solvents and aggregation-caused quenching (ACQ) effect due to self-assembly aggregation in aqueous solution. However, they behave quite differently in cellular context: that is, CQ exhibits bright fluorescence in living cells, while the fluorescence of MeCQ is almost undetectable. The different performance between CQ and MeCQ in living cells is attributed to their different scenario in G-quadruplex (G4) DNA interaction. CQ selectively binds with G4 DNA to recover its fluorescence via aggregation–disaggregation switching in living cells, while MeCQ remained in the aggregate form due to its poor interplay with G4 DNA. Furthermore, CQ is applied as a two-photon fluorescent dye, and its photoswitchable fluorescence capability is exploited for super-resolution imaging of the specific mitochondrial structure in living cells via the STORM technique.
A series of bis(catechol) quaternary ammonium derivatives were designed and synthesized. We investigated their ability to cross-link DNA induced by tyrosinase and found that the o-quinone is key intermediate in the process by using the nucleophile 3-methyl-2-benzothiazolinone hydrazone (MBTH) in the tyrosinase assay. Their cytotoxicities to B16F1, Hela, and CHO cells were tested by MTT assays. The specific and potent abilities to kill the tyrosinase-efficient melanoma cells kindled our interest in exploring the relationship between their abilities of cross-linking DNA and their selective cytotoxicities to cells. Through an integrated approach including intracellular imaging for detection of the dihydroxyphenyl groups, alkaline comet assays, and γ-H2AX immunofluorescence assays, the speculation was confirmed. The bis(catechol) quaternary ammonium derivatives showed notable cell selectivity because they displayed cytotoxicities after being oxidized by tyrosinase, and they were able to target the DNA efficiently in the tyrosinase-efficient melanoma cells, forming both alkylated and cross-linked species.
This paper presents a novel upper extremity motor function rehabilitation system and an assessment system. The rehabilitation system is an active rehabilitation that can be manipulated by patients through a haptic device and an inertia sensor to perform a tracking task in virtual environment with coordination training of bilateral upper extremity. The design of system aims to augment patients' force exerted by theirs' upper extremity and the ability of force control, namely, dexterity. The structure of rehabilitation system is compact and the inertia of the haptic device's stylus is very small (only 45 g), which makes the system suitable for home-rehabilitation. Simultaneously, in order to assess the effect of rehabilitation, an assessment system has been developed using a 6-axis force sensor. The proposed rehabilitation system is testified experimentally for the upper limbs' rehabilitation training.
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.
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