Sustained environmental pollution and energy crisis are constantly promoting the development of hybrid electric vehicles. Among many configurations, the power-split ones based on planetary gear have special advantages of energy saving potential and velocity conversion characteristics. Thus it has attracted considerable attention in related fields. Power-split system based on dual-planetary gear provides a variety of energy flow modes, so it is suitable for constructing a multi-mode hybrid power-system for hybrid electric commercial vehicles. In this paper, a power flow model for the vehicle with such configuration is constructed; the energy flow relationship is analyzed and optimized. The optimal power flow relationship can be used to guide the division of working modes of hybrid electric vehicles and also provided a model basis for further study of related energy management strategies.
In recent decades the oil demand in the transport sector is continuously increasing with rapid increase in the population growth and economic development, resulting in more greenhouse gas (GHG) emissions. Road transport sector in Algeria contributes the most in emissions due to 100% fossil fuel consumption. Government of Algeria launched the National Energy Efficiency Program (PNEE) in 2011 to reduce CO2 burden in the transport sector. The program was revised in 2016 with major objective of saving 15 million tonnes of oil equivalent (Mtoe) of energy consumption by fuel products. The objective of this study is to evaluate the impact of energy efficiency policies of Algeria on road transport sector in context of carbon footprints. In this work two scenarios i.e. Business as Usual (BAU) and Energy Policy scenarios were developed to determine the carbon emission reductions. The BAU scenario was developed based on historical data while the policy energy scenarios were prepared based on government energy policy program which was to promote liquefied petroleum gas (LPG) and natural gas in the road transport. The results described that implementing the government policy targets led to use of less polluting fuels and hence, reduction in CO2 emission to the environment.
This work presents a Macro-Scale electrochemical model of a low temperature Solid Oxide Fuel Cell (SOFC) with a MIEC electrolyte. Low temperature SOFCs potentially merge high efficiency due to relatively high temperature, presence of a solid electrolyte, and lower production costs (among others) compared to really high temperature ones. Therefore, there is great interest on them despite they have not been thoroughly studied yet. The model has been compared with a Micro-Scale one found in the literature which closely reproduces experimental data. Assuming electrochemical reactions occurring just at the electrodes/electrolyte interfaces as usually done in high temperature operation (with “classic” YSZ electrolyte) has been found not to be accurate enough, thus ionic ohmic loss in the electrodes has been accounted. The final polarization curves matching has been quite good, but solving the charge conservation equations within the electrodes would definitely enhance the model accuracy and stability.
Electrification of the transport sector is a crucial goal, which has to be accomplished in order to ensure sustainable development of worldwide economy and human civilization. Currently electrical vehicles (EV) constitute to only a small fraction of the whole automobiles fleet. It is however anticipated that a significant cost reduction of EV, technological progress in energy storage and increasing conventional fuel prices will change this situation dramatically. As with each new product on the market the customers behavior can be modeled only to a certain extent. Bearing in mind the above this paper investigates the potential impact of EVs’ charging station which is a part of an office building infrastructure. Various scenarios are analysed considering different charging strategies. Results indicate need for developing intelligent and forecasts based charging strategy.
Aiming at the problem that multi-energy microgrid involves the coupling of multiple energy sources and the difficulty of quantifying the supply capacity of each energy load, the evaluation methods of reliable energy supply intervals of multi-energy microgrid are proposed. Firstly, taking the multi-energy microgrid as the research object, a typical microgrid system model is constructed based on the energy hub model, and the concept of multi-energy microgrid energy supply capability is proposed. Secondly, considering the constraints of energy supply reliability, a reliable energy supply interval model of integrated energy microgrid is constructed. Thirdly, based on NSGA-2 multi-objective optimization, the solve method of reliable energy supply interval model of multi-energy microgrid is proposed. Finally, the effectiveness and practicability of the proposed model and method are verified by an example.
In the present study, the thermal behavior of the high-temperature Concrete based Thermal Energy Storage (CTES) system is investigated using experimental study and simplified 1D dynamic modeling. The storage module is made up of shell and tube configuration. The shell side is filled with concrete as the energy storage material, and air is circulated in the tube (made of copper) side. The operating temperature range of the storage module is fixed in between 170 and 240 °C. The 1D dynamic modeling is developed using a set of equations, which helps to predict the heat transfer characteristics of the CTES module. In addition, the overall performance of the CTES module is investigated by integrating the developed model with real-time solar collector data and the seed dryer model. It is observed that, during the off-sun hours, the CTES module is capable of generating the hot air for the continuous drying of grape seed.
The main purpose of this study is to obtain clean and efficient thermal energy by producing a direct current electric field to the flame reaction zone and to provide the thermochemical transformation of biomass (wood) particles controlled by the process produced in the field. In this regard, a mathematical model for modeling the thermal decomposition in the presence of an electric field, as well as, an experimental work has been developed. The equations of aerothermochemistry are coupled to balance equations for densities of charged species, and a Poisson equation for electrical potential is solved. The results obtained show that the presence of the electric field significantly improves the stabilization of the flame. The electron injection affects the char combustion process significantly. The field enhancement the combustion characteristics, and the flame reaction zone of field induced ion wind on biomass thermal decomposition was analyzed.
A hierarchical scheduling model of a district heating system which consists of residential energy stations, heat exchange stations and heating consumers was developed in this paper. . Firstly, the optimal scheduling model of the energy station with combined heat and power (CHP) unit and the heat pumps was proposed. The optimal scheduling of the energy station is conducted to decide the output of different heating equipment of the heating system to minimize the operational cost of the energy station while meeting the varying heating loads. Secondly, the heat exchange station model was developed by utilizing the heat balance principle of the heat exchanger. Finally, a model to simulate the thermal characteristic and energy consumption of the buildings was proposed. The resistor-capacitor (RC) network is used to model the thermal characteristics of the building, in which the thermal inertia of the building and the adjustment of the radiator are considered. Consumers can take active controls on their radiators, which can both reduce the heating cost and meet their comfort requirements. Numerical studies demonstrate that the proposed strategy can contribute to the operational cost reduction of energy station and heating consumers, while guarantee the consumers’ temperature comfort level.
Well-to-wheel analysis can be used to quantitatively compare the impact electric vehicles has on global warming with conventional internal combustion vehicles. In this study, we performed a well-to-wheel analysis in Korea in 2030. Furthermore, we evaluated the impact of the WTW standard from the perspective of the government, the manufacturer and the consumer. The government adjusts the penalty rate to present a new GHG standard scheme for the same amount of WTW greenhouse gases, and the vehicle market shares move from PHEVs and BEVs to HEVs and ICEVs depending on the manufacturer’s suggested retail price and consumer choice.
With the development of industrial Internet of Things (IoT) for smart grid, the amount of data in end user side increases sharply. However, the digitizing also brings great possibility for energy theft. Inspired by the good performance of deep learning models and the computation efficiency of the convolutional neural network (CNN), in this work, we present a deep CNN based energy theft detector. By learning the statistical pattern in the customers’ consumption pattern, the detector is supposed to make correct classification. In reality, the whole dataset tends to have a small portion of energy theft data. To overcome such data imbalance, data of malicious consumption behavior is synthesized according to the predicted energy theft patterns. The experiment is conducted on the open source dataset. The proposed method is compared with the support vector classifier (SVC)-based method. The results show that the proposed method is more robust against the changes of non-malicious consumption behavior and can achieve better classification performance. Moreover, accelerated by GPU, the proposed method is more suitable for real time detection.