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His research interests include multiphase induction motor and grid-connected inverter.

Constructing biomimetic structure and immobilizing antithrombus factors are two effective methods to ensure rapid endothelialization and long-term anticoagulation for small-diameter vascular grafts. However, few literatures are available regarding simultaneous implementation of these two strategies. Herein, a nano-micro-fibrous biomimetic graft with a heparin coating was prepared via a step-by-step in situ biosynthesis method to improve potential endothelialization and anticoagulation. The 4-mm-diameter tubular graft consists of electrospun cellulose acetate (CA) microfibers and entangled bacterial nanocellulose (BNC) nanofibers with heparin coating on dual fibers. The hybridized and heparinized graft possesses suitable pore structure that facilitates endothelia cells adhesion and proliferation but prevents infiltration of fibrous tissue and blood leakage. In addition, it shows higher mechanical properties than those of bare CA and hybridized CA/BNC grafts, which match well with native blood vessels. Moreover, this dually modified graft exhibits improved blood compatibility and endothelialization over the counterparts without hybridization or heparinization according to the testing results of platelet adhesion, cell morphology, and protein expression of von Willebrand Factor. This novel graft with dual modifications shows promising as a new small-diameter vascular graft. This study provides a guidance for promoting endothelialization and blood compatibility by dual modifications of biomimetic structure and immobilized bioactive molecules.

It is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of batteries. In order to analyze the effect of voltage dataset selection on the accuracy of entropy-based estimation methods, six voltage datasets were collected, considering the current direction (i.e., charging or discharging) and the state of charge level. Furthermore, three kinds of entropies (approximate entropy, sample entropy, and multiscale entropy) were introduced, and the relationship between the entropies and the battery capacity was established by using first-order polynomial fitting. Finally, the interaction between the test conditions, entropy features, and estimation accuracy was analyzed. Moreover, the results can be used to select the correct voltage dataset and improve the estimation accuracy.

To improve and validate the performance of humanoid robot, the research on a table-tennis game by both humanoid robots is supported by 863, the national hi-tech program. Due to the limitation of the visual feedback system and the motion ability of the humanoid robot, a precise model of the table-tennis game is necessary to predict the ball's trajectory. This paper details the dynamic model of the ball's flight trajectory as well as its parameters calibration by Photron Fastcam, a high speed camera with 2kfps to capture the trajectory of ball. With the model's characteristics, ball's flight trajectory prediction based on a few observation points is employed as well as its noise sources are discussed. According to the requirement of table-tennis game by humanoid robot, a novel nonlinear observer is developed to improve the prediction accuracy. Finally, Experiments results with binocular vision show that the model and trajectory prediction proposed by the paper outperform the requirement of the game.

Accurate estimation of the state of health (SOH) of batteries is essential for maximizing the lifetime of the battery and improving the safety and economy of any energy storage system. Data-driven methods can use measurement data to effectively estimate the SOH, but the estimation performance depends on the relevance between the selected feature and SOH. In this paper, fuzzy entropy (FE) of battery voltage, is proposed as a new feature for SOH estimation and validated on Li-ion batteries. Compared with the traditional sample entropy, the FE can capture the variation of voltage during the battery degradation more efficiently in terms of the parameter selection, data noise, data size and test condition. Moreover, the aging temperature variation is involved in the established SOH estimator as the temperature is a disturbance variable in the real applications. The FE-SOH is used as the input-output data pair of the support vector machine, and a single-temperature model, a full-temperature model, and a partial-temperature model are established. As a result, the FEbased method has better estimation accuracy under aging temperature variation. The FE-based method also decreases the dependence on the size of the required training data. Finally, the effectiveness of the proposed method is verified by experimental results.

Battery technology is a major technical bottleneck with electric vehicles (EVs). It is necessary to perform state of charge (SoC) estimation in order to ensure battery safe usage and reduce its average lifecycle cost. Sliding mode observer (SMO) has been used widely in battery SoC estimation owing to its simplicity and robustness to both parameter variations and external disturbances. The SMO uses a switching function of the model error as feedback to drive estimated states to a hypersurface where there is no difference between measured and estimated output exactly. In this paper, three kinds of SMOs based on equivalent circuit model (ECM) for SoC estimation in the existing literatures are reviewed. Their difference in the structures and principles are discussed in the hope of providing some inspirations to the design of efficient SMO based SoC estimation methods.