Abstract

Recent research progress has witnessed the emergence of intelligent batteries integrating cell-level sensors and controllers for distributed and enhanced management. However, the highly integrated structure rises up multifold concerns over the rigorous thermal condition, and the trade-off between sensing quality and cost. Motivated by this, this paper pursuits the possibility of designing an intelligent battery with the function of state of charge (SOC) self-monitoring without using the current sensor. The essence is to propose a method converting the co-estimation of SOC and input current into a state-space model-based optimization problem, which is virtually solved in a moving horizon framework. Results suggest that the proposed method can realize accurate estimation of the SOC even if freeing the need of integrating a current sensor. The encouraging results are insightful for reducing the structural complexity and lowering down the cost of intelligence batteries.

Abstract

This paper presents a comparative study of two-stage, segmented and conventional thermoelectric generators. A detailed numerical study is performed using COMSOL Multiphysics and finite element method. The effects of thermoelectric leg height ratio, hot and cold side temperatures on the power output of the two-stage thermoelectric generator (TTEG), segmented thermoelectric generator (STEG) and conventional thermoelectric generator (TEG) are studied using three-dimensional numerical models. Results show that the TTEG and STEG provide an increased power output of 14.90% and 21.04% respectively compared to the skutterudite TEG at hot side temperature of 425 ℃. Furthermore, it is found that the STEG is the best performing system studied and it provides an increased power output of 5.34% compared to that of the TTEG at 425 ℃ hot side temperature. In addition, results show that the power output of the TTEG and STEG increased respectively by 19.09% and 20.61% as the leg height ratio is increased from 1 to 4 for hot side temperature of 425 ℃. Therefore, thermoelectric leg height ratio optimization is essential for performance enhancement.

Abstract

The route of buses is relatively fixed compared with private vehicles. This characteristic provides a suitable scenario for path following control. The purpose of path following control is to control the steering system of the vehicle to track the desired trajectory curve while satisfying performance such as safety and energy consumption. Model predictive control (MPC) method is used in this paper for vehicle lateral control. Preview is used to reduce the track error. The vehicle kinematic model is established as predictive model. The path following problem is described as a quadratic programming (QP) problem that can be solved in real-time situations. Simulations on Matlab/Simulink

Abstract

The researches on Integrated Energy System continue to thrive nowadays, since it can promote the use of renewable energy and increase energy consumption efficiency. In this paper, a novel modelling method of District Heating Network (DHN) considering the change of the thermodynamic states of the working fluid is proposed, based on the basic components in DHN, including the pipelines, pressure sources (PSs) and junctions. And a corresponding DHN model constructed of these components is established. By applying this method, DHNs with diverse topologies, even with different supply and return networks, can be analyzed. In order to solve the equations efficiently, Newton’s method with its partial derivatives obtained by a numerical method is applied, and an appropriate set of initial values is also discussed. Finally, case studies of a cogeneration system are performed to demonstrate the validity of the proposed models as well as the analyzing methods.

Abstract

The scenario generation of wind power profiles is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a novel generative network is proposed to model wind power profiles based on implicit maximum likelihood estimation (IMLE). Firstly, the fake sample closest to each real sample is found to calculate the loss function used for updating weights. After training the model, the new wind power profiles are generated by feeding some Gaussian noises to the generator of the IMLE model. Compared with explicit density models, the IMLE model does not need to artificially assume the probability distribution of wind power profiles. The simulation results show that the proposed approach not only fits the probability distribution of wind power profiles well, but also accurately captures the shape, temporal correlation, and fluctuation of wind power profiles.

Abstract

Energy is vital for creating resilient and zero emission carbon cities. Over 70% of global carbon emission originate from urban activities. Urban carbon mapping is an effective approach for planning, managing, and monitoring energy consumption and carbon emission. However, this approach has not been exploited for evaluating the impact of the coronavirus on a city due to limited data. In this study, the carbon emission from buildings during the coronavirus pandemic is estimated and visualized using the three-dimensional fine-scale spatiotemporal urban carbon mapping approach through big data. We studied the 23 wards of Tokyo, Japan from January to June 2020, representing the COVID-19 first wave period in the area. The results reveal that the carbon emitted from commercial buildings decreased by approximately 40% compared to the January value. Conversely, residential buildings emission increased by at least 10% due to the increased stay at home lifestyle including working from home.

Abstract

Reduction in energy use and carbon emissions is essential for achieving resilient, green, and smart cities. Understanding the various patterns of energy changes in cities can serve as a foundation for planning the strategies related to mitigation of carbon emissions. It has been reported that carbon emissions were reduced during the global COVID-19 pandemic. However, this reduction pattern in cities has not been investigated using spatially-temporally fine-resolutions due to data unavailability. Therefore, this study aims to estimate reduction rates of carbon emissions, especially those that are related to transport activity, which is a key factor for building resilient, green, and smart cities. This was accomplished by using the urban carbon mapping approach with big data. Our target city was Tokyo 23 wards, Japan and the study was conducted for a time frame of January to June 2020. The results show that carbon emissions and transport activities in May decreased by approximately 40% when compared to those in January.

Abstract

The wet scrubber can be used for reducing particles and recover waste heat from flue gas, but its particle removal performance is relatively poor. To improve its performance, an effective algorithm combining genetic aggregation response surface method with multi-objective genetic algorithm was adopted to study the effects of geometrical parameters. Based on particle scavenging model, a gas-liquid-solid three phase numerical method was adopted to simulate the process of wet scrubbing. The particle removal efficiency, heat transfer efficiency and pressure drop were considered as objective functions. After response surface analysis, the local sensitivity and a set of Pareto-optimal points were obtained. The results show that the response surface results have a good agreement with simulation data and the particle removal efficiency and temperature drop of optimized structure can be increased by 4.66% and 17.42%, respectively, which provides guidelines for engineering applications.

Abstract

In this study, a 3D (three-dimensional) multiphase CFD (computational fluid dynamics) model of AB-PEMFC (air-breathing proton exchange membrane fuel cell) is developed, in which the influence of GDL compression is considered. The anisotropic electric and thermal conductivities in the in-plane and through-plane directions of GDLs and the contact resistance between GDL and BP (bipolar plate) are also included in this model. It was found that the optimal open area ratio for the maximum performance of AB-PEMFC is different under different GDL compression rates, and generally the larger the compression rate is, the larger the optimal open area ratio.

Abstract

The liquid sulfur precipitated from high-sulfur gas reservoir will change the situation of only single-phase gas permeability. Moreover, the continuous accumulation of precipitated liquid sulfur will bring certain damages to the reservoir, reduce the productivity and affect the development of gas well. In order to define the law of gas-liquid sulfur phase permeability, this paper sets up a flow resistance model considering the liquid sulfur boundary layer at the pore scale including the capillary force, gas phase viscosity force and liquid sulfur phase viscosity force. Then in combination with the fractal theory of porous media, this paper establishes the prediction model of gas-liquid sulfur relative permeability and compares the prediction curve calculated by the model with the result of real core gas-liquid sulfur phase permeability experiment in non-steady state. According to the comparison result, the mathematical model of gas-liquid sulfur phase permeability can reflect the features of gas-liquid sulfur phase permeability, but there is a certain error with the experimental results, for instance, the sulfur saturation of isotonic point is 5% less than the experimental result while the relative permeability of isotonic point is 10% higher than the experimental result. This is because the mathematical model of phase permeability is deduced based on the steady state theory while in the experiment the non-steady state method is adopted. The two methods are based on different assumptions, which naturally results in a certain error.