Macroscopic evidence of soliton formation in multiterawatt laser-plasma interaction

A novel physical phenomenon has been observed following the interaction of an intense (10(19) W/cm(2)) laser pulse with an underdense plasma. Long-lived, macroscopic bubblelike structures have been detected through the deflection that the associated electric charge separation causes in a proton probe beam. These structures are interpreted as the remnants of a cloud of relativistic solitons generated in the plasma by the ultraintense laser pulse. This interpretation is supported by an analytical study of the soliton cloud evolution, by particle-in-cell simulations, and by a reconstruction of the proton-beam deflection.

Cosmic-ray acceleration and escape from supernova remnants

Galactic cosmic-ray (CR) acceleration to the knee in the spectrum at a few PeV is only possible if the magnetic field ahead of a supernova remnant (SNR) shock is strongly amplified by CRs escaping the SNR. A model formulated in terms of the electric charge carried by escaping CRs predicts the maximum CR energy and the energy spectrum of CRs released into the surrounding medium. We find that historical SNRs such as Cas A, Tycho and Kepler may be expanding too slowly to accelerate CRs to the knee at the present time.

Quantum chemistry and in situ FTir spectroscopy studies on potential-dependent properties of CO adsorbed on Pt electrodes

The electronic and vibrational properties of CO adsorbed on Pt electrodes at different potentials have been studied, by using methods of self-consistent-charge discrete variational Xa (SCC-DV-Xa) cluster calculations and in situ FTir spectroscopy. Two new models have been developed and verified to be successful: (1) using a "metallic state cluster" to imitate a metal (electrode) surface; and (2) charging the cluster and shifting its Fermi level (e{lunate}) to simulate, according to the relation of -d e{lunate}e dE, quantitatively the variation of the electrode potential (E). It is shown that the binding of PtCO is dominated by the electric charge transfer of dp ? 2p, while that of s ? Pt is less important in this binding. The electron occupancy of the 2p orbital of CO weakens the CO bond and decreases the v. Variation of E mainly influences the charge transfer process of dp ? 2p, but hardly influences that of s ? Pt. A linear potential-dependence of v has been shown and the calculated dv/dE = 35.0 cm V. All results of calculations coincide with the ir experimental data. © 1993.

Applied physics: Trawling for complements

A method has been invented for determining nanoscale variations in the distribution of electric charge on surfaces. It has so far been used to examine specific inorganic materials, but could find widespread applications in imaging.

Impulsive electric fields driven by high-intensity interactions

The interaction of high-intensity laser pulses with matter releases instantaneously ultra-large currents of highly energetic electrons, leading to the generation of highly-transient, large-amplitude electric and magnetic fields. We report results of recent experiments in which such charge dynamics have been studied by using proton probing techniques able to provide maps of the electrostatic fields with high spatial and temporal resolution. The dynamics of ponderomotive channeling in underdense plasmas have been studied in this way, as also the processes of Debye sheath formation and MeV ion front expansion at the rear of laser-irradiated thin metallic foils. Laser-driven impulsive fields at the surface of solid targets can be applied for energy-selective ion beam focusing.

Low frequency localized wavepackets in dusty plasmas with opposite charge polarity dust components

The reductive perturbation technique is employed to investigate the modulational instability of dust-acoustic (DA) waves propagating in a four-component dusty plasma. The dusty plasma consists of both positive- and negative-charge dust grains, characterized by a different mass, temperature and density, in addition to a background of Maxwellian electrons and ions. Relying on a multi-fluid plasma model and employing a multiple scales technique, a nonlinear Schrodinger type equation (NLSE) is obtained for the electric potential amplitude perturbation. The occurrence of localized electrostatic wavepackets is shown, in the form of oscillating structures whose modulated envelope is modelled as a soliton (or multi-soliton) solution of the NLSE. The DA wave characteristics, as well as the associated stability thresholds, are studied analytically and numerically. The relevance of these theoretical results with dusty plasmas observed in cosmic and laboratory environments is analysed in detail, by considering realistic multi-component plasma configurations observed in the polar mesosphere, as well as in laboratory experiments.

Dynamics of charge-displacement channeling in intense laser-plasma interactions

The dynamics of transient electric fields generated by the interaction of high intensity laser pulses with underdense plasmas has been studied experimentally with the proton projection imaging technique. The formation of a charged channel, the propagation of its front edge and the late electric field evolution have been characterized with high temporal and spatial resolution. Particle-in-cell simulations and an electrostatic, ponderomotive model reproduce the experimental features and trace them back to the ponderomotive expulsion of electrons and the subsequent ion acceleration.

Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

Ultrafast charge dynamics initiated by high-intensity, ultrashort laser-matter interaction

The interaction of high‐intensity laser pulses with matter releases instantaneously ultra‐large currents of highly energetic electrons, leading to the generation of highly‐transient, large‐amplitude electric and magnetic fields. We report results of recent experiment in which such charge dynamics have been studied by using proton probing techniques able to provide maps of the electrostatic fields with high spatial and temporal resolution. The dynamics of ponderomotive channelling in underdense plasmas have been studied in this way, as also the processes of Debye sheath formation and MeV ion front expansion at the rear of laser‐irradiated thin metallic foils. An application employing laser‐driven impulsive fields for energy‐selective ion beam focusing is also presented.

Investigations of ultrafast charge dynamics in laser-irradiated targets by a self probing technique employing laser driven protons

The divergent and broadband proton beams produced by the target normal sheath acceleration mechanism provide the unique opportunity to probe, in a point-projection imaging scheme, the dynamics of the transient electric and magnetic fields produced during laser-plasma interactions. Commonly such experimental setup entails two intense laser beams, where the interaction produced by one beam is probed with the protons produced by the second. We present here experimental studies of the ultra-fast charge dynamics along a wire connected to laser irradiated target carried out by employing a ‘self’ proton probing arrangement – i.e. by connecting the wire to the target generating the probe protons. The experimental data shows that an electromagnetic pulse carrying a significant amount of charge is launched along the wire, which travels as a unified pulse of 10s of ps duration with a velocity close to speed of light. The experimental capabilities and the analysis procedure of this specific type of proton probing technique are discussed.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.