Showing papers in "Energy in 2021"

Abstract: The automotive industry remains one of the most significant contributors to total global emissions worldwide. This growing challenge is primarily attributed to the high dependency on fossil fuel as its primary source of energy. This review highlights the current state of the application of fuel cells in the automotive industry, as well as the technological advances made in comparison to the early years of the automotive sector. Future prospects of these technologies are also thoroughly reviewed. Factors impeding the advancement of these technologies while also impeding their commercialization are presented, with possible solutions to this problem also suggested. In summary, this investigation seeks to explore pragamatic approach that can be adopted to reduce the overall cost of fuel cells and their possible integration in the automotive industry.

Abstract: China is the most polluted country in the world, facing the challenges of increased CO2 emissions and its ecological footprint. In order for China to achieve sustainable growth, it must identify factors that reduce environmental pollution and take essential measures before it is too late. To this end, this study empirically examines the ecological outcomes of income, human capital, globalization, renewable energy consumption, and trade openness for China within the framework of the environmental Kuznets curve (EKC) hypothesis. The paper employs the recently developed augmented ARDL approach in the presence of one structural break to investigate annual time series data during the period 1980–2016. The findings reveal that the EKC hypothesis does not hold for China, and a U-shaped quadratic relationship between environmental pollution and income level has been determined for both CO2 emissions and ecological footprint. The results also suggest that globalization, trade openness, and income drive environmental pollution while increasing human capital reduces the ecological footprint in the long-term. No effects were found for renewable energy consumption. The study highlights that human capital plays a key role in reducing environmental degradation in China, while renewable energy is not sufficient to meet environmental requirements.

Abstract: The production of alternative clean energy vehicles provides a sustainable solution to the transportation sector. An efficient battery cooling system is necessary for safer usage of electric cars during their life cycle. The current work presents a novel modified battery module configuration employing two-layer nanoparticle enhanced phase change materials (nePCM). The design suggests m × n × p arrangement where m denotes the number of Li-ion 18,650 cells, n and p refer to the number of primary containers (filled with nePCM1) and secondary containers (filled with nePCM2). Each Li-ion cell was allowed to discharge at 3C condition for two different configurations: 7 × 7 × 1 and 7 × 1 × 1. The study involves a cooling performance comparison of proposed battery thermal management systems (BTMS) at an ambient temperature ranging from 30 °C to 40 °C with external natural convection conditions. The transient development of heat in batteries and the melting behavior of nePCMs shows better cooling performance for the 7 × 7 × 1 case. BTMS based on 7 × 7 × 1 configuration maintains the cell temperature below 46 °C with combined nePCM and external natural convection cooling even at the hot ambient temperature of 40 °C.

Abstract: This review article critically highlights the latest trends in energy storage applications, both cradle and grave. Several energy storage applications along with their possible future prospects have also been discussed in this article. Comparison between these energy storage mediums, as well as their limitations were also thoroughly discussed. Suggestions and solutions in mitigating some of these challenges in order to improve the overall performance of these energy systems have also been analysed in this investigation. In spite of the accelerated growth in energy storage systems, there is still a grave need for further investigations, in order to reduce their costs. Further research activities will reduce the cost of some of these novel technologies, thereby accelerating their commercialization as well as making them better competitors against traditional energy storage mediums.

Abstract: The current study aims to evaluate the performance and emission characteristics of a modified common rail direct injection (CRDI) diesel engine fueled by Ricinus communis biodiesel (RCME20), diesel (80%), and their blends with strontium-zinc oxide (Sr@ZnO) nanoparticle additives. The Sr@ZnO nanoparticles were synthesized using aqueous precipitation of zinc acetate dehydrate and strontium nitrate. Several characterization tests were performed to study the morphology and content of synthesized Sr@ZnO nanoparticles. The Sr@ZnO nanoparticles were steadily blended with RCME20-diesel fuel blend in mass fractions of 30, 60 and 90 ppm using a magnetic stirrer and ultrasonication process. For a long term stability of nanoparticles, Cetyl trimethylammonium bromide (CTAB) surfactant was added. The physicochemical properties of the fuel blends were measured using ASTM standards. The CRDI engine was operated at two compression ratios 17.5 and 19.5, 1000 bar injection pressure, 23.5°BTDC injection timing and constant speed. For enhanced swirl and turbulence, and improved spray quality lateral swirl combustion chamber and 6-hole fuel injector were used. The compression ratio of 19.5 and 60 ppm of Sr@ZnO nano-additives showed overall enhancement in engine characteristics compared to RCME20 fuel. The engine characteristics such as BTE, HRR and cylinder pressure increased by 20.83%, 24.35% and 9.55%, and BSFC, ID, CD, smoke, CO, HC and CO2 reduced by 20.07%, 20.64%, 14.5%, 27.90%, 47.63%, 26.81%, and 34.9%, while slight increase in NOx for all nanofuel blends was observed.

Abstract: The electronic equipment developing towards miniaturization and high integration is facing the danger of high heat flux and non-uniform temperature distribution which leads to the reduction of life and reliability of electronic devices. The micro heat sinks have gained significant attention in heat dissipation of electronic devices with a high heat flux due to its large heat transfer surface to volume ratio, compact structure and outstanding thermal performance. In this review, we present the advantages and shortcomings of thermal enhancement technologies in different structural micro heat sinks. Moreover, the non-uniform temperature distribution which includes the temperature rising along the flow direction and hotspots, especially, the random hotspot with high heat flux, has been the serious issues in the thermal management of electronic devices. They are the main challenges for the efficient operation and service life of electronic components. Thus, it is urgent to develop an effective and economic process in automatic adaptive cooling of random hotspots. The purpose of this article is to introduce the existing thermal enhancement technologies in micro heat sinks and the reduction of non-uniform temperature distribution. Finally, the barriers and challenges for the developments of thermal management of electronic devices by micro heat sinks are discussed, and the future directions of the research topic are provided.

Abstract: This article investigates the impact of export product diversification, extensive margin and intensive margin on emerging economies energy demand covering the period from 1971 to 2014. The study contributes to energy economics by unveiling the interaction between export diversification and energy demand of 10 newly industries countries (NICs). Owing to the growth prospect and trade volume of these nations, it is necessary to assess the various facades of export growth on the energy demand. In this pursuit, we have considered the export product diversification index in its aggregate and disaggregated forms (i.e. extensive margin and intensive margin) in this study. The empirical estimation has been carried out based on GMM, FGLS, FMOLS, and DOLS techniques. The empirical results demonstrate that export diversification, extensive margin, and intensive margin help to reduce the overall energy demand in NICs. Further, the empirical outcomes identify that economic growth, urbanization, and natural resources increase energy consumption. The study discusses fruitful policy implications regarding the exports diversification and energy demand nexus for emerging economies.

Abstract: The false hope that economic development would lead to a decrease in fossil sources’ energy consumption can be an obstacle to fighting global warming. Is it realistic to expect that more knowledge will lead public policymakers to take more decisive action to mitigate climate change’s adverse effects? This research attempts to answer both premises using data for developed countries with high human capital levels: 27-member countries of the Organization for Economic Cooperation and Development-OECD during 1980–2015. Access to energy and that it is non-polluting is raised as a goal of the Sustainable Development Goals. We combine linear and non-linear models: we specifically employ threshold regressions and second-generation cointegration techniques, FMOLS, and causality. Our results are disappointing for the first premise: economic development does not reduce energy consumption from fossil sources. However, human capital does decrease the consumption of non-renewable energy. In order to capture current trends in economies, we include the globalization index, the urbanization rate, and services. The results of the cointegration tests suggest the existence of a long-term relationship between the variables. Our results indicate that the human capital index and globalization are the last hope to promote the transition to a more sustainable energy matrix in developed countries.

Abstract: Climate change threats and the necessity to achieve global Sustainable Development Goals demand unprecedented economic and social shifts around the world, including a fundamental transformation of the global energy system. An energy transition is underway in most regions, predominantly in the power sector. This research highlights the technical feasibility and economic viability of 100% renewable energy systems including the power, heat, transport and desalination sectors. It presents a technology-rich, multi-sectoral, multi-regional and cost-optimal global energy transition pathway for 145 regional energy systems sectionalised into nine major regions of the world. This 1.5 °C target compatible scenario with rapid direct and indirect electrification via Power-to-X processes and massive defossilisation indicates substantial benefits: 50% energy savings, universal access to fresh water and low-cost energy supply. It also provides an energy transition pathway that could lead from the current fossil-based system to an affordable, efficient, sustainable and secure energy future for the world.

Abstract: Wind speed forecasting is gaining importance as the share of wind energy in electricity systems increases. Numerous forecasting approaches have been used to predict wind speeds. However, considering the differences in wind speed time-series, there is no universal approach that has proven to be accurate under all circumstances. In our study, a combined prediction system is proposed, which consists of four parts: optimal sub-model selection, point prediction based on a modified multi-objective optimization algorithm, interval forecasting based on distribution fitting, and forecasting system evaluation. The developed combined system integrates the merits of the sub-models and provides accurate point and interval forecasting performance. The experimental results reveal that the proposed combined forecasting system can provide effective wind speed point and interval forecasts. The absolute percentage error values of the proposed system for point forecasting are 2.9220%, 3.1696%, and 4.8358% at Site 1 and 2.2719%, 2.5882%, and 3.4799% at Site 2 for one-, two-, and three-step forecasts, respectively. Therefore, the proposed system is deemed more useful for the scheduling and management of electric power systems than other benchmark models.

Abstract: Internationally, the importance of clean energy is greatly appreciated in the context of sustainable development and to protect the atmosphere. Therefore, the objective of this article is to determine the effect of natural gas, renewable energy and nuclear energy consumption on economic growth and carbon dioxide emissions in the ten highest CO2 emitting countries within a multivariate context for the duration of 1990–2014. The panel co-integration test, panel fully modified ordinary least squares and panel heterogonous Dumitrescu and Hurlin causality assessment are used to analyze the estimation of long-run elasticity along with the course of causality among the variables. The panel co-integration test confirms the existence of a long-run equilibrium correlation among the variables. The findings of the long run elasticity and causality test reveal that natural gas does not contribute to economic growth and CO2 reduction like nuclear energy and renewable energy. However, except for natural gas, the expansion and improvement of renewable energy and nuclear energy are vital to avoid global warming and climate change as well as to promote economic growth.

Abstract: Stochastic energy management of smart microgrids (MGs) is an important subject due to the high integration of intermittent resources, including wind turbine (WT) and photovoltaic (PV) units. The complexity of the multi MGs management algorithm increases, considering their participation in an electricity market. In this paper, we proposed a stochastic energy management algorithm to address the participation of smart MGs in the electricity market, which minimizes the total cost and finds the optimal size of different components, including WT, PV unit, fuel cell, Electrolyzer, battery, and microturbine. The intermittencies in the PV output power, WT output power, and electric vehicle (EV) are modeled and integrated into the management algorithm using the Copula method. The market clearing price (MCP) is found using a game theory (GT) model and Cournot equilibrium. To verify the efficiency of the proposed method, it is tested on a sample three-MG, where the optimal size of various components is obtained. The obtained results verify that the total cost of MG decreases and the better performance can be obtained after participation in the electricity market. A sensitivity analysis is also done to evaluate the effects of various parameter changes (e.g., capital cost, replacement cost, and operation and maintenance cost) in various scenarios, where the obtained results verify that the cost reduction is obtained over different scenarios.

Abstract: The present work is dedicated to the experimental analysis on the influence of fuel borne additives on ternary fuel blend operated in a single cylinder DI diesel engine. Alumina (Al2O3) nanoparticles were chosen as fuel additives at dosing levels of 10 ppm, 20 ppm and 30 ppm respectively and the ternary fuel (TF) is prepared by blending 70% diesel, 20%Jatropha biodiesel and 10% ethanol. Performance characteristics like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), emission characteristics like HC, CO, NOx and smoke along with combustion characteristics like cylinder pressure, HRR (Heat release rate) and CHRR (Cumulative heat release rate) were considered for analysis. Based on experimentation, it is observed that, TF blended with 20 ppm alumina nano additive (TF20) resulted in higher BTE and lowered BSEC by 7.8% and 4.93%, lowered HC, CO, NOx and smoke emissions by 5.69%, 11.24%, 9.39% and 6.48% in comparison with TF. Moreover, TF20 resulted in higher cylinder pressure, HRR and CHRR of about 72.67 bar, 76.22 J/oCA and 1171.1 J respectively which are higher than diesel and TF. Hence, it is concluded that addition of 20 ppm alumina nano additive in TF can enhance the engine performance and combustion as well as lower the exhaust pollutants simultaneously.

Abstract: This paper proposes an effective computing framework for Short-Term Load Forecasting (STLF). The proposed technique copes with the stochastic variations of the load demand using a stacked generalization approach. This approach combines three models, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Multi-Layer Perceptron (MLP). The inner mechanism of Stacked XGB-LGBM-MLP model consists of generating a meta-data from XGB and LGBM models to compute the final predictions using MLP network. The performance of the proposed Stacked XGB-LGBM-MLP model is validated using two datasets from different locations: Malaysia and New England. The main contributions of this paper are: 1) A novel stacking ensemble-based algorithm is proposed; 2) An effective STLF technique is introduced; 3) A critical multi-study analysis for hyperparameter optimization with five techniques is comprehensively performed; 4) A performance comparative study using two datasets and reference models is conducted. Several case studies have been carried out to prove the performance superiority of the proposed model compared to both existing benchmark techniques and hybrid models.

Abstract: Wind speed fluctuation at wind farms leads to intermittent and unstable power generation with diverse amplitudes and frequencies. Compressed air energy storage (CAES) is an energy storage technology which not only copes with the stochastic power output of wind farms, but it also assists in peak shaving and provision of other ancillary grid services. In this paper, a CAES facility is proposed for two adjacent wind farms, Abhar and Kahak sites in Iran, with a total nominal power of 162.5 MW. To assess site peak profiles and storage potential, annual thermodynamic and wind assessments are carried out predominantly for the three critical months of the year requiring mitigation of electricity scarcity (July, August, and September). Results indicate that the sustained wind speed in July at both the Abhar and Kahak sites is higher than the other two critical months. Around 93, 74 and 60 MW stored power in the CAES facility are added to the grid during 5 h of peak demand in July, August and September months with round trip efficiencies of 52, 47, and 43%, respectively.

Abstract: Since the role of fiscal decentralization cannot be overlooked in tracking sustainable development goals targets of a clean environment and climate mitigation, it is inevitable to understand the comprehensive picture of its link with environmental quality. Unlike past studies, this study investigates the combined influence of energy prices and non-linear fiscal decentralization on carbon emissions in the presence of institutional quality and gross domestic product in the model. It employed advanced econometric panel techniques on data from 1990 to 2018 for the top seven fiscally decentralized Organisation for Economic Co-operation and Development (OECD) nations, including Spain, Belgium, Austria, Switzerland, Germany, Australia, and Canada. The main outcomes are as follows: first, a cointegrating equilibrium link is existent among the study variables. Second, the linear term of fiscal decentralization promotes carbon emissions, while the non-linear term mitigates it. It verified the inverted U-shaped curve between fiscal decentralization and carbon emissions. Third, increasing energy prices for non-renewable energy decrease carbon emission due to a substitution effect. Among other explanatory variables, improvement in the quality of institutions decreases carbon emissions, while the gross domestic product increases it. These findings suggest strengthening fiscal decentralization, lowering non-renewable energy prices, and improving institutional quality to check the deteriorating environmental quality in the study sample and other worldwide regions.

Abstract: The role of renewable energy in protecting the environment is well established. This study explores the dynamic links among energy transitions, energy consumption, and sustainable economic growth in thirty-eight International Energy Agency (IEA) countries. We apply advanced econometric methodologies for empirical analysis from 1995 to 2015 and find long-run relationships among the variables. However, the effect of energy transitions on economic growth is significant only in the long run, and economic sustainability influences economic growth in both the short run and the long run. Moreover, the energy transition is negatively associated with host countries’ economic growth, while economic sustainability, renewable energy consumption, non-renewable energy consumption, labor, and capital are positively related to that growth. Policymakers in the IEA countries are encouraged to settle carbon costs and taxation, provide continuous support to research and development, commercialize low–CO2–emission technologies, reduce subsidies on non-renewable energy, offer cooperative programs for technology transfers, and generate a green trade policy to procure sustainable development. Study limitations and directions for future research in the area are presented.

Abstract: Pakistan has been confronting economic challenges for two decades due to many factors such as the electricity crisis, among others. It is therefore essential to identify such factors that may play a constructive role in economic growth. In doing so, this study investigates the determinants of economic growth in Pakistan from 1972 to 2018. The dynamic autoregressive distributed lag (ARDL) simulations approach is applied to analyze positive and negative changes in energy consumption, industrial growth, urbanization, and carbon emissions on economic growth in Pakistan. The frequency-domain causality (FDC) test is utilized to check long-, medium-, and short-run relationships. Our empirical evidence reveals that electricity consumption and industrial value-added have a short- and long-run impact on economic growth. However, carbon emissions and urbanization have positive effects on economic growth in the short run. Consequently, we conclude that energy consumption, industrial growth, urbanization, and CO2 emissions positively impact economic growth in Pakistan. The FDC also confirms the long-, medium-, and short-run causality hypothesis. The study suggests a requirement to integrate better electricity generation and management with the planning of economic policies. The government is advised to invest more in renewable energy to protect the environment from degradation, ban the import of low-efficiency electrical appliances, and evaluate the refugee reception policy.

Abstract: Wind energy is one of the sources which is still in development in Brazil. However, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, predicting wind energy with high accuracy is challenging. In this context, this paper proposes a novel decomposition-ensemble learning approach that combines Complete Ensemble Empirical Mode Decomposition (CEEMD) and Stacking-ensemble learning (STACK) based on Machine Learning algorithms to forecast the wind energy of a turbine in a wind farm at Parazinho city, Brazil, using multi-step-ahead forecasting strategy. The approached forecasting models were k-Nearest Neighbors, Partial Least Squares Regression, Ridge Regression, Support Vector Regression, and Cubist Regression. Additionally, Box-Cox transformation, correlation matrix, and principal component analysis were used to pre-process the data. The performance of the proposed forecasting models was evaluated by using three performance metrics: mean absolute error, mean absolute percentage error, and root mean square error, and the Diebold-Mariano statistical test to evaluate the forecasting error signals. The proposed models outperform the CEEMD, STACK, and single models in all forecasting horizons, with a performance improvement that ranges 0.06%–97.53%. Indeed, the decomposition-ensemble learning model is an efficient and accurate model for wind energy forecasting.

Abstract: CO2 mineral sequestration is a promising strategy to combat global warming. Indirect CO2 mineral sequestration was proposed in our previous study by using blast furnace slag as feedstock. As the continuity of this research, the continuous experiment process was carried out in a self-designed rotary kiln, and the results were compared with those of the batch experiment. The results showed that several problematic situations, such as the formation of kiln rings, insufficient mass, and heat transfer, occurred in the continuous experiment. A “returning charge” operation by adding an inert component into the feedstock was suggested for the prevention of ring formation and achieved good results. The reaction conditions for the continuous experiment were harsher than those of the batch experiment due to the scale-up effects. Preliminary analysis of CO2 net-emission reduction was conducted based on the continuous experimental results. It was shown that the net reduction of CO2 emissions amounted to 36 kg for 1000 kg of blast furnace slag processed. The results demonstrated in this study can act as guidance for pilot- or industrial-scale applications of indirect CO2 mineral sequestration technologies, especially for process parameter optimization and equipment design.

Abstract: The geothermal resource in depleted high-temperature gas fields is abundant, and CO2 is more suitable to exploit geothermal energy from these gas fields due to its high mobility and thermal physical properties. However, all the related mechanisms, operation processes, and economic analyses have not been comprehensively analyzed yet. To assess the technical and economic feasibility of this method of geothermal exploitation, a 120 °C depleted gas reservoir was selected to build geological and numerical models for analyzing its gas composition, temperature, and pressure during the whole process, including enhanced gas recovery, pressure build-up, and pure geothermal exploitation, based on existing wells. The results reveal that the CO2 injection during EGR and pressure build-up can affect the reservoir temperature, and the optimization analyses indicate the heat mining rate can be maintained about 10 MWth for 30 years. The thermodynamic cycle analyses show that a power of 132.7 kW can be obtained if the organic Rankine cycle system with R134a is adopted, and the cost of geothermal power generation is about 0.45 $/(kW∙h) when the CO2 price is 12 $/t. However, if the produced CO2 directly drives the turbine, the power can increase to 718.5 kW and the cost reduces to 0.1$/(kW∙h).

Abstract: Covalently functionalized carbon nanoplatelets and non-covalent functionalized metal oxides nanoparticles (surfactant-treated) have been used to synthesize water-based nanofluids in this paper. To prove nanofluid stability, ultraviolet–visible (UV–vis) spectroscopy is used, and the results show that nanofluid is stable for sixty days for carbon and thirty days for metal oxides. The thermophysical properties are evaluated experimentally and validated with theoretical models. Thermal conductivities of f-GNPs, SiO2, and ZnO nanofluids are enhanced by 25.68%, 11.49%, and 15.42%, respectively. Lu-Li and Bruggeman’s thermal conductivity models are correctly matched with the experimental data. Similarly, the viscosity, density, and specific heat capacity of nanofluids are measured and compared with theoretical models. The enhancement in density, specific heat and viscosity of f-GNPs, ZnO, and SiO2 nanofluids are 0.12%, 0.22%, and 0.12%; 1.54%, 0.96%, and 0.73%; 12%, 9.41%, and 24.05% respectively in comparison of distilled water. A flat-plate solar collector is installed, and its thermal performance is evaluated by using carbon and metal oxides based nanofluids, following the ASHRAE standard 93–2003, at different heat flux intensities (597, 775, and 988 W/m2), mass flow rates (0.8, 1.2 and 1.6 kg/min), inlet fluid temperatures (30–50 °C) and the weight concentrations (0.025–0.2%). The thermal efficiency of the flat-plate solar collector is measured for distilled water and compared with the weight concentration (0.025–0.2%) of functionalized carbon and metal oxide-based nanofluids. A comparison of 0.1 wt% water-based nanofluids can be sequenced f-GNPs > ZnO > SiO2 because of a percentage improvement of thermal efficiency of the flat-plate solar collector obtained at a mass flow rate of 1.6 kg/min with values of 17.45% > 13.05% > 12.36%, respectively in comparison to water.

Abstract: As a type of clean energy, wind energy has been effectively used in power systems. However, due to the influence of the atmospheric boundary layer, wind speed exhibits strong nonlinearity and nonstationarity. Therefore, the accurate and stable prediction of wind speed is highly important for the security of the power grid. To improve the forecasting accuracy, a novel hybrid forecasting system is proposed in this paper that includes effective data decomposition techniques, recurrent neural network prediction algorithms and error decomposition correction methods. In this system, a novel decomposition approach is used to first decompose the original wind speed series into a set of subseries, then it predicts the wind speed by recurrent neural network, and finally, it decomposes the error to correct the previously predicted wind speed. The effectiveness of the proposed model is verified using data from four different wind farms in China. The results show that the proposed hybrid system is superior to other single models and traditional models and realizes highly accurate prediction of wind speed. The proposed system may be a useful tool for smart grid operation and management.

Abstract: This paper proposes a real-time predictive energy management strategy (PEMS) of plug-in hybrid electric vehicles for coordination control of fuel economy and battery lifetime, including velocity predictor, state-of-charge (SOC) reference generator, and online optimization. In velocity predictor, the radial basis function neural network algorithm is adopted to accurately estimate the future drive velocity. Based on predictive velocity and current driven distance, the SOC reference in predictive horizon can be determined online by reference generator. To coordinate fuel consumption and battery degradation, a model predictive control problem of cost minimization including fuel consumption cost, electricity cost of battery charging/discharging, and equivalent cost of battery degradation, is formulated. To mitigate the huge calculation burden in optimization, the continuation/generalized minimal residual (C/GMRES) algorithm is delegated to find the expected engine power command in real time. Since original C/GMRES algorithm cannot directly handle inequality constraints, the external penalty method is employed to meet physical inequality limits of powertrain. Numerical simulations are carried out and yield the desirable performance of the proposed PEMS in fuel consumption minimization and battery aging restriction. More importantly, the proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms.

Abstract: The Paris Agreement invited Parties to develop low-emission development strategies. This study presents national low-emission scenarios to inform such strategies for Australia, Brazil, Canada, China, EU-28, India, Indonesia, Japan, Republic of Korea, Russia and the USA. We use country-level technology-rich energy-economy and integrated assessment models that include detailed representations of the energy, transport and land systems and provide insights on emissions, energy system and economic implications of low-emission pathways until 2050. We show that the low-emission pathways of most economies studied here are consistent with pathways limiting global temperature increase to well-below 2 °C, while emission reductions are achieved through uptake of renewable energy, energy efficiency improvements and electrification of energy services. The role of mitigation options like nuclear, carbon capture and storage (CCS) and advanced biofuels is differentiates across countries, depending on national priorities, specificities and resource endowments. The energy system transformation requires a pronounced reallocation of investments towards low-carbon technologies, but without raising significant affordability issues in most countries. National pathways improve the consistency between country policy plans with global temperature goals and capture structural heterogeneities and broad socio-economic considerations.

Abstract: For the safe operation of the electric vehicle, it is critical to quickly detect the safety state and accurately identify the fault degree in battery packs. This article proposes a novel intelligent fault diagnosis method for Lithium-ion batteries based on the support vector machine, which can identify the fault state and degree timely and efficiently. Due to the noise signal’s existence, firstly, the discrete cosine filtering method is adopted, and the truncated frequency is optimized based on the characteristic of white noise to achieve reasonable denoising. Secondly, since the covariance matrix (CM) of filtered data is sensitive to the current fluctuation, a modified covariance matrix (MCM) is proposed to reduce the influence of current variation on the condition indicators. Thirdly, to ensure the accuracy and robustness of Support Vector Machine (SVM), the grid search method is proposed to optimize the kernel function parameter and penalty factor. Finally, the MCM and CM are respectively introduced into the model as the condition indicators, and the results show that the former has high accuracy and timeliness. In summary, the proposed intelligent fault diagnosis method is feasible. It provides the theoretical basis for future fault hierarchical management strategy of the battery system.

Abstract: Estimating the capacity of lithium-ion cells employed in electric vehicles is challenging because of the complex vehicle conditions and inconsistent cell decay. This paper proposes a novel capacity estimation method realized by combining model-based and data-driven methods based on a sequential extended Kalman filter (SEKF), to improve the accuracy, and reliability of capacity estimation. First, cycle aging tests are conducted on four cells under different aging stress. Second, the state-of-charge and capacity of the cells are co-estimated using a third-order extended Kalman filter (EKF) driven by dynamic data obtained under different aging stages. The advantages and disadvantages of this data-driven method are investigated. Third, a discrete Arrhenius aging model (DAAM) is developed to estimate the capacity, and its parameter mismatch problem is addressed. Finally, an SEKF estimator is proposed to integrate the capacities obtained using these methods. The SEKF comprises two EKFs connected in series: one to update the model parameters of the DAAM via the feedback provided by the third-order EKF and the other to combine the capacities from the third-order EKF and DAAM. The experimental results show that the proposed SEKF is suitable for capacity estimation with excellent accuracy and stability under different aging stress and dynamic conditions.

Abstract: Based on the design concept of a fourth-generation smart pipe network system, this paper innovatively proposes a new TOTS (Two-supply/One-return, triple pipe structure) arrangement method for district heating systems. Moreover, to accurately predict the heat loss due to the pipeline operation of the multi-pipe system, based on the multipole calculation method, a new heat loss theoretical analytical model for the TOTS was created; additionally, a corresponding three-dimensional numerical simulation model was established, which was analyzed and numerically solved. The results showed that in comparison with thermal loss data measured by Danfoss et al., the above analytical and numerical models have a high accuracy, and the deviation is within 2%. Additionally, through calculations, it was found that the distance between the heating pipes is an important factor that affects the total heat loss from the new multi-control heating system and the actual heat exchange between pipes.

Abstract: Building energy use is becoming increasingly important in China. Despite a rapid growth in recent years, energy use intensity in China is still relatively low compared to other advanced economies; thus, there is still substantial room for it to increase as living standards and industrial services are improved. It is therefore important to focus on the future development of building energy use by considering new trends in the building and energy sectors, as well as the role of occupant behavior. This study uses the China Building Energy Model (CBEM) to model China’s building energy consumption and carbon emissions up to 2050 for different scenarios based on these considerations. The results indicate that building energy use will be 80% higher than the current situation if the strategies of the 13th Five-Year Plan are maintained and approximately 10% higher if stronger strategies toward energy efficiency are employed. Carbon emissions are predicted to peak around 2020 to 2035. The contributions of key strategies in different subsectors are also discussed. This research suggests that, through the use of suitable strategies and policies, energy use and carbon emissions in China’s building sector can achieve the combined goals of energy revolution and climate change mitigation.

Abstract: The acetone–butanol–ethanol (ABE) is the intermediate product during the bio-butanol fermentation process, and is widely considered as one of the promising alternative fuels in the diesel engine for its advantages of oxygenated fuels, better air–fuel mixing, lower NOx and soot emissions, and lower production cost. In this paper, different ABE ratios in the diesel fuel and injection timings were tested in the AVL diesel engine, and obtained the combustion and emission characteristics parameters for the late model calibration. Then, the CFD KIVA-3V code coupled with the CANTERA code was built and validated against the experimental data. Subsequently, different injection timings and exhaust gas recirculation (EGR) ratios were investigated on the diesel engine fuelled with the ABE/diesel blended fuel to unveil their effects on the combustion and emissions behaviors. The results indicated that the injection timing and EGR strongly affected the combustion process of the diesel engine fuelled with the ABE/diesel fuel. In addition, the oxidation and formation process of the intermediate species, soot precursors, final soot, NOx, CO and HC emissions in the diesel engine fuelled with the ABE/diesel fuel were also significantly impacted by the injection timing and EGR strategies.