Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

大方坯皮下负偏析带形成过程的数值模拟

针对某钢厂大方坯连铸过程中铸坯内部质量问题,通过耦合电磁-流动-热-溶质传输,建立多尺度、多物理场的三维数学模型,研究了高碳钢大方坯皮下负偏析带的形成过程。结果表明,铸坯皮下负偏析带是铸坯凝固前沿钢液流速和凝固速率共同作用的结果。对于四孔水口大方坯,铸坯皮下会经历两次负偏析,第一次负偏析是由水口射流造成的,第二次负偏析是由结晶器电磁搅拌造成的。磁搅参数的改变只会影响电磁搅拌影响区的负偏析程度,而不会影响负偏析带在铸坯中的位置及宽度,也不能改善铸坯中心的正偏析度。     

Three-phase four-wire shunt active power filter based on the SOGI filter and Lyapunov function for DC bus control

The three-phase four-wire shunt active power filter (SAPF) was developed to suppress the harmonic currents generated by nonlinear loads, and for the compensation of unbalanced nonlinear load currents, reactive power, and the harmonic neutral current. In this work, we consider instantaneous reactive power theory (PQ theory) for reference current identification based on the following two algorithms: the classic low-pass filter (LPF) and the second-order generalized integrator (SOGI) filter. Furthermore, since an important process in SAPF control is the regulation of the DC bus voltage at the capacitor, a new controller based on the Lyapunov function is also proposed. A complete simulation of the resultant active filtering system confirms its validity, which uses the SOGI filter to extract the reference currents from the distorted line currents, compared with the traditional PQ theory based on LPF. In addition, the simulation performed also demonstrates the superiority of the proposed approach, for DC bus voltage control based on the Lyapunov function, compared with the traditional proportional-integral (PI) controller. Both novel approaches contribute towards an improvement in the overall performance of the system, which consists of a small rise and settling time, a very low or nonexistent overshoot, and the minimization of the total harmonic distortion (THD).     

Two-dimensional temperature distribution estimation for a cross-flow planar solid oxide fuel cell stack

The uniform temperature distribution of a cross-flow planar solid oxide fuel cell (SOFC) stack plays an essential role in stack thermal safety and electrical property. However, because of the strict requirements in stack sealing struture, it is hard to acquire the temperature inside the stack using thermal detection devices within an acceptable cost. Therefore, accurately estimating the two-dimensional (2-D) temperature distribution of the cross-flow stack is crucial for its thermal management. In this paper, Firstly, a 2-D mechanism model of a cross-flow planar SOFC stack is established. The stack is divided into 5*5 nodes along the gas flow directions, which can reflect the stack states with moderate computational burden. Then, experimental test data is utilized to modify and validate the stack model, guaranteeing the model accuracy as well as the reliability of model-based state estimator design. Finally, easily-measured stack inputs and outputs are selected, and a temperature distribution estimator combined with unscented kalman filter (UFK) approach is developed to achieve accurate and fast temperature distribution estimation of a cross-flow SOFC stack. Simulation results demonstrate that the UKF-based temperature distribution estimator can precisely and quickly achieve the temperature distribution estimation of the cross-flow stack under both static state and dynamic state changes and is applicable to cross-flow stacks with different size or cell number as well, the maximum estimated absolute error is less than 0.15 K with an absolute error rate of 0.015%, which indicates the developed estimator has good estimation performances.     

Honeycomb-like bio-based carbon framework decorated with ternary tantalum-based compounds as efficient and durable electrocatalysts for triiodide reduction reaction

Finding an inexpensive and effective clean energy electrocatalyst is highly important for the new generation of photovoltaic devices. Herein, we report a facile and universal in situ co-precipitation strategy to load three novel tantalum-based compounds (NiTa₂O₆, MnTa₂O₆, and AlTaO₄) on honeycomb-like bio-based carbon (HBC) frameworks. The HBC framework with unique honeycomb-like network structure serves as a support material in nanohybrids that can provide rich surface active sites and rapid electron transport channels for triiodide reduction reaction. The adoption of widely available and abundant biomass-derived carbon into tantalum-based compounds markedly boosts electrocatalytic properties of nanohybrids and exhibits robust corrosion resistance because nanohybrids adequately utilize the synergistic effects of different components. The photovoltaic devices fabricated with NiTa₂O₆/HBC, MnTa₂O₆/HBC, and AlTaO₄/HBC counter electrode catalysts demonstrate the brilliant power conversion efficiency (PCE) of 7.09%, 7.39%, and 7.86%, respectively, outperforming the Pt-based cells (6.80%). This work offers a strategy for the further rational design of efficient, inexpensive, and durable electrocatalysts for advanced energy technology applications.     

Neural network-based learning and estimation of battery state-of-charge: A comparison study between direct and indirect methodology

Faced with the ever-increasing urban environmental pollution, the electric vehicles (EVs) have received increasing attention in the automotive industry. Lithium-ion batteries, serving as electrochemical power storage, have been extensively used in EVs because of the lightweight, no local pollution and high power density. The increasing awareness on the safe operation and reliability of the battery requires an efficient battery management system (BMS), among the parameters monitored by which, state-of-charge (SOC) is critical in preventing overcharge, deep discharge, and irreversible damage. This article investigates the neural network (NN)-based modeling, learning, and estimation of SOC by comparing two different methodologies, that is, direct structure with SOC as network output and indirect structure with voltage as output. Firstly, the nonlinear autoregressive exogenous neural network (NARX-NN) is introduced, in which SOC is directly deemed as an NN output for learning and estimation. Secondly, a radial basis function (RBF)-based NN with unscented Kalman filter (RBFNN-UKF) is proposed, in which the terminal voltage is used as output. Instead, SOC is deemed as an internal state which would be estimated indirectly based on the feedback error of voltage. Experimental results demonstrate that both estimators can achieve accurate SOC estimation for regular cases, in spite of the inaccurate initial conditions. However, the direct NN structure is revealed as not capable of dealing with the cases with sensor bias, which, however, can be well accommodated in the indirect structure by extending the sensor bias as an augmented state. Benefiting from the uncertainty augmentation and feedback compensation, the indirect RBFNN-UKF shows superiority over the direct estimation in the practical experiments, depicting a promising prospect in the future onboard EV-BMS application.     

State of charge estimation method based on the extended Kalman filter algorithm with consideration of time-varying battery parameters

In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended Kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm; the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, time-invariant system; however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with time-varying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of SOC estimated by the proposed algorithm under the New European Driving Cycle (NEDC) is 1.6%, which is 0.2% lower than that of EKF algorithm with fixed battery parameters. Relative to the EKF algorithm with fixed battery parameters, the proposed EFK algorithm with time-varying battery parameters yields higher accuracy.     

State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter

Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.     

DPF主动再生过程颗粒排放特性试验

通过柴油发动机台架,采用后喷助燃的再生方式研究了主动再生过程中柴油机颗粒捕集器(DPF)出口的颗粒排放特性.结果表明:在主动再生期间,DPF出口颗粒浓度可增加2~3个数量级;在升温过程和再生过程,出口颗粒物数量浓度和粒径分布会因为碳载量和再生温度的共同作用而表现出差异;升温过程中,10 nm左右核模态颗粒物的排放主要由来流中颗粒物的穿透引起;再生过程中,10 nm左右核模态颗粒物的排放主要由碳烟颗粒层氧化反应生成的二次颗粒逃逸引起;整个再生期间,100 nm左右的积聚态颗粒物的排放主要由DPF载体内碳烟颗粒的逃逸引起.