A Markov Model for Subway Composite Energy Prediction

Electric vehicles such as trains must match their electric power supply and demand,such as by using a composite energy storage system composed of lithium batteries and supercapacitors.In this paper,a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train.The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain.Real-time online control of power allocation in the composite energy storage system can be achieved.Using standard train operating conditions for simulation,we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running.Compared with traditional predictive control systems,energy efficiency 10.5%higher.This system provides good stability and robustness,satisfactory speed tracking performance and control comfort,and significant suppression of disturbances,making it feasible for practical applications.

U.S. Department of Energy Research in Intermetallic Alloys and Composites

The program of the Division of Materials Sciences for.intermetallic materials will be surveyed. This program is carried out at Department of Energy National Laboratories and at U.S. universities. Areas of research include theory and material simulation, microalloying, high resolution studies of structure and composition, mechanical properties, point defects and dislocation mechanics, phase transformations, and processing. Finally, general considerations will be discussed for the future program.

Structural energy storage composites based on modified carbon fiber electrode with metal-organic frame enhancing layered double hydroxide

Structural energy storage composites present advantages in simultaneously achieving structural strength and electrochemical properties.Adoption of carbon fiber electrodes and resin structural electrolytes in energy storage composite poses challenges in maintaining good mechanical and electrochemical properties at reasonable cost and effort.Here,we report a simple method to fabricate structural supercapacitor using carbon fiber electrodes(modified by Ni-layered double hydroxide(Ni-LDH)and in-situ growth of Co-metal-organic framework(Co-MOF)in a two-step process denoted as Co-MOF/Ni-LDH@CF)and bicontinuous-phase epoxy resin-based structural electrolyte.Co-MOF/Ni-LDH@CF as electrode material exhibits improved specific capacity(42.45 F·g^(-1))and cycle performance(93.3%capacity retention after 1000 cycles)in a three-electrode system.The bicontinuous-phase epoxy resin-based structural electrolyte exhibits an ionic conductivity of 3.27×10^(-4) S·cm^(-1).The fabricated Co-MOF/Ni-LDH@CF/SPE-50 structural supercapacitor has an energy density of 3.21 Wh·kg^(-1) at a power density of 42.25 W·kg^(-1),whilst maintaining tensile strength and modulus of 334.6 MPa and 25.2 GPa.These results show practical potential of employing modified commercial carbon fiber electrodes and epoxy resin-based structural electrolytes in structural energy storage applications.

Microstructure and Characterization of Capric-stearic Acid/Modified Expanded Vermiculite Thermal Storage Composites

In order to improve the thermal storage capacity of expanded vermiculite（EV） based formstable composite PCM（FS-PCM） via organic modification of EV, first, EV was modified with a sodium stearate（Na St） as surface modifier, and organic EV（OEV） with hydrophobicity and higher adsorption capacity for fatty acid was obtained. A novel capric-stearic acid eutectic（CA-SA）/OEV FS-PCM with high thermal storage capacity was then developed. OEV and CA-SA/OEV were characterized by scanning electron microscopy（SEM）, X-ray diffraction（XRD）, Fourier transform infrared spectroscopy（FTIR）, differential scanning calorimetry（DSC）, thermal gravimetry（TG）, and thermal cycling test. Results showed that OEV has obvious hydrophobicity and a higher adsorption capacity for fatty acid. Its adsorption ratio has increased by 48.71% compared with that of EV. CA-SA/OEV possesses high thermal storage density（112.52 J/g）, suitable melting temperature（20.49 ℃）, good chemical compatibility, excellent thermal stability and reliability, indicating great application potential for building energy efficiency. Moreover, organic modification of inorganic matrix may offer novel options for improving its adsorption capacity for organic PCMs and increasing heat storage capacity of corresponding FS-PCMs.

Observer-based Adaptive Iterative Learning Control for Nonlinear Systems with Time-varying Delays

An observer-based adaptive iterative learning control （AILC） scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays. The linear matrix inequality （LMI） method is employed to design the nonlinear observer. The designed controller contains a proportional-integral-derivative （PID） feedback term in time domain. The learning law of unknown constant parameter is differential-difference-type, and the learning law of unknown time-varying parameter is difference-type. It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized. By constructing a Lyapunov-Krasovskii-like composite energy function （CEF）, we prove the boundedness of all closed-loop signals and the convergence of tracking error. A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.

An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems

This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control(AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance.To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control(ILC), a new boundary layer function is proposed by employing MittagLeffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function(CEF)containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

Carbonate-salt-based composite materials for medium- and high-temperature thermal energy storage

This paper discusses composite materials based on inorganic salts for medium- and high-temperature thermal energy storage application. The composites consist of a phase change material （PCM）, a ceramic material, and a high thermal conductivity material. The ceramic material forms a microstructural skeleton for encapsulation of the PCM and structural stability of the composites; the high thermal conductivity material enhances the overall thermal conductivity of the composites. Using a eutectic salt of lithium and sodium carbonates as the PCM, magnesium oxide as the ceramic skeleton, and either graphite flakes or carbon nanotubes as the thermal conductivity enhancer, we produced composites with good physical and chemical stability and high thermal conductivity. We found that the wettability of the molten salt on the ceramic and carbon materials significantly affects the microstructure of the composites.

Consensus control for heterogeneous uncertain multi-agent systems with hybrid nonlinear dynamics via iterative learning algorithm

In this study,We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion,where the leader's dynamics are unknown,and the controlled system's parameters are uncertain.The multiagent systems are considered a kind of hybrid order nonlinear systems,which relaxes the strict requirement that all agents are of the same order in some existing work.For theoretical analyses,we design a composite energy function with virtual gain parameters to reduce the restriction that the controller gain depends on global information.Considering the stability of the controller,we introduce a smooth continuous function to improve the piecewise controller to avoid possible chattering.Theoretical analyses prove the convergence of the presented algorithm,and simulation experiments verify the effectiveness of the algorithm.

Adaptive Iterative Learning Control for Nonlinearly Parameterized Systems with Unknown Time-varying Delay and Unknown Control Direction

This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separation technique and signal replacement mechanism,the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems.By incorporating a Nussbaum-type function,the proposed approach can deal with the unknown control direction of the nonlinear systems.Based on a Lyapunov-Krasovskii-like composite energy function,the convergence of tracking error sequence is achieved in the iteration domain.Finally,two simulation examples are provided to illustrate the feasibility of the proposed control method.

Distributed Algorithms for Event Reporting in Mobile-Sink WSNs for Internet of Things

Wireless Sensor Networks（WSNs） have many applications, such as climate monitoring systems, fire detection, smart homes, and smart cities. It is expected that WSNs will be integrated into the Internet of Things（IoT）and participate in various tasks. WSNs play an important role monitoring and reporting environment information and collecting surrounding context. In this paper we consider a WSN deployed for an application such as environment monitoring, and a mobile sink which acts as the gateway between the Internet and the WSN. Data gathering is a challenging problem in WSNs and in the IoT because the information has to be available quickly and effectively without delays and redundancies. In this paper we propose several distributed algorithms for composite event detection and reporting to a mobile sink. Once data is collected by the sink, it can be shared using the IoT infrastructure. We analyze the performance of our algorithms using WSNet simulator, which is specially designed for event-based WSNs. We measure various metrics such as average residual energy, percentage of composite events processed successfully at the sink, and the average number of hops to reach the sink.

Origin of hardening in spectra of cosmic ray nuclei at a few hundred GeV using AMS-02 data

Many experiments have confirmed spectral hardening at a few hundred GeV in the spectra of cosmic ray(CR)nuclei.Three different origins have been proposed:primary source acceleration,propagation,and the superposition of different kinds of sources.In this work,a broken power law has been employed to fit each of the spectra of cosmic ray nuclei from AMS-02 directly,for rigidities greater than 45 GeV.The fitting results of the break rigidity and the spectral index differences less than and greater than the break rigidity show complicated relationships among different nuclear species,which cannot be reproduced naturally by a simple primary source scenario or a propagation scenario.However,with a natural and simple assumption,the superposition of different kinds of sources could have the potential to explain the fitting results successfully.Spectra of CR nuclei from a single future experiment,such as DAMPE,will provide us the opportunity to do cross checks and reveal the properties of the different kinds of sources.