Energy Conversion and Management-X最新文献

The urgency to decarbonize the transportation sector covers all kinds of vehicles, here included high-performance competition vehicles. Among the technologies able to guarantee zero emissions during the use phase, fuel cells (FCs) and energy storage systems (ESS), e.g. batteries, offer a great and still largely underexplored potential for complementary and synergic use in hybrid powertrains. Vehicles based on such technologies are cells-battery hybrid electric vehicles (FCHEV), and a niche of these are electric supercars (FCHES). In this context, the degrees of freedom of hybrid powertrains design and the different requirements of FCs and batteries frame the highly complex task of defining a clear and objective methodology to identify an optimal ratio among FC-battery power sources, whose lack jeopardizes a rigorous decision process as well as a general consensus and leads to the acceptance of sub-optimal solutions.

In this study an energy/power-based methodology is developed in MATLAB environment considering the longitudinal vehicle dynamics of a typical high-performance parallel FCHES, using telemetry data from a real racetrack as common target for all the evaluated powertrain candidates and using realistic mass values. Under the constraint of equal performance (i.e., equal lap time), several FC-battery parallel hybrid powertrains are numerically evaluated with varying relative energy, power, weight, and under different regenerative braking levels. The set of obtained results allows to draw an objective rightsizing on the FC-battery power share and on the required energy capacity for a parallel FCHEV, as well as mass, hydrogen consumption, etc. The presented methodology offers a general use workflow applicable to any category of vehicles, supporting the engineering of hybrid FC-battery high-performance propulsion systems. The developed code will be made available upon request under the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines.

One essential energy vector for building a sustainable bioeconomy is hydrogen, which may be obtained from renewable biomass sources. This study discusses many biological routes used in the conversion of biomass to hydrogen, as well as a variety of thermochemical routes such as pyrolysis and gasification. Thermochemical routes include fast pyrolysis, steam and supercritical water gasification, and related processes; biological routes include photo, dark, and mixed fermentation techniques in addition to bio-photolysis processes. Notwithstanding its promise, improving the reliability and selectivity of hydrogen processing is necessary for economically viable industrial uses in the hydrogen economy. The importance of operating conditions, process parameters, variables influencing hydrogen production, parameters of storage methods, hydrogen transportation, separation, and difficulties in producing hydrogen through thermochemical and biological routes are all covered in this paper. It looks at the problems that come with these procedures, highlighting important knowledge gaps that need for more investigation. Combining biological processes with thermochemical pathways can ensure economic sustainability. Both thermochemical and biological routes can help fulfilling future demand for a hydrogen based society.

The performance of electric bikes has increased with their increased use commercially and it has led to a corresponding increase in the charge and discharge rates for the batteries, and associated battery temperatures. Unfortunately, the cooling mechanisms for these electric bikes have not been able to keep up, with most organizations not implementing any cooling aside from passive ambient air cooling. This has led to a general decrease in battery life and charge capacity for these electric bikes. The proposed research has focused on the development of a novel electric bike cooling system under the practical industrial and environmental framework. It is cheap, effective, and simple to manufacture. Contemporary papers related to the topic have been studied, and the most feasible have been shortlisted to 4 distinct cooling plate designs, 3 Radiator Designs and 5 motor cooling jacket designs, which have been modeled in CAD software and then analyzed through use of CFD software. For the cooling plate design 1 had the lowest cooling capability, design 2 showed a 53.3% increase in total heat transfer from plate to coolant, design 3 showed a 107.52% increase, and design 4 showed a 183.03% increase relative to design 1. For the radiator, design number 2 has been recommended due to optimal cooling and temperature drop of coolant, within the dimensional and space constraints on the electric bike. For the motor cooling jacket, design number 5 was deemed to be the most feasible due to the high heat extraction from motor and good temperature uniformity of contact surface. Their respective advantages and disadvantages are discussed, and the most effective one of them all has been proposed for use. Further potential improvements to its design have also been recommended along with Thermoelectric Generator (TEG) integration for harvesting waste heat to produce energy.

One of the main limitations to the economic sustainability of biodiesel production remains the high feedstock cost. Modeling and optimization are crucial steps to determine if processes (esterification and transesterification) involved in biodiesel production are economically viable. Phenomenological or mechanistic models can simulate the processes. These methods have been used to simulate and manage the processes, but their broad use has been constrained by computational complexity and numerical difficulties. Therefore, it is necessary to use quick, effective, accurate, and resilient modeling methodologies to simulate and regulate such complex systems. Data-driven computational and machine-learning (ML) techniques offer a potential replacement for conventional modeling methodologies to deal with the nonlinear, unpredictable, complex, and multivariate nature of biodiesel systems. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are the most often utilized ML tools in biodiesel research. To effectively attain maximum biodiesel yield, suitable optimization techniques based on nature-inspired optimization algorithms need to be integrated with these tools to obtain the best possible combination of various operating variables. Future research should focus on utilizing ML approaches for monitoring and managing biodiesel production systems to increase their effectiveness and promote commercial feasibility. Thus, the review discusses the various ML techniques used in modeling and optimizing biodiesel production systems.

Photovoltaic (PV) systems are popular for their reliability and zero fuel costs. However, only around 20 % of solar energy is converted into electricity, while the remainder is dissipated as waste heat. Excessive waste heat affects the lifespan of PV systems, leading to abnormal operating temperatures. In this notion, Photovoltaic-thermal (PV/T) systems are introduced to extract waste heat through various cooling techniques to harness electrical and thermal energies, demonstrating their capabilities through experimental and modeling techniques. Researchers have sought to develop optimized modeling techniques based on empirical, semi-empirical, and AI-based modeling for efficient execution of PV/T systems. This study reviews the current optimization developments in the PV/T systems, focusing on multiple numerical and experimental designs. Various cooling methods, including air, water, and phase change materials (PCM) with nanofluids, are examined for their promising contributions to electrical and thermal efficiency enhancement. Additionally, optimization methods have been investigated by incorporating automated processes into PV/T systems employing self-automation techniques. These processes aim to reduce the overall cost and establish a self-sustaining performance. Finally, the challenges and recommendations for future research for PV/T enhancement are highlighted.

Francis turbines are now widely used to support the integration of renewable and intermittent energy sources such as solar and wind power. Consequently, these turbines often operate away from their best efficiency point (BEP). Such operations cause detrimental pressure fluctuations in the runner and draft tube, leading to early fatigue failures. To address these harmful flow conditions and extend the operating range of Francis turbines, a mitigation system was developed and tested on a large-scale, high-head 200 MW Francis turbine. The system consists of four circular rods placed in the draft tube with variable radial protrusion lengths, adjustable using linear actuators. Pressure, accelerometer, and vibration sensors installed on the turbine allowed quantification of the rod system performance. The results demonstrate the system’s capability to reduce pressure pulsations by up to 80 % in terms of maximum pressure amplitude and 100 % in terms of fatigue cycle in both low and high-frequency ranges, up to ten times the runner frequency, based on pressure analysis. The optimal rod protrusion ranges from 5 to 20 % of the runner outlet diameter function of the operating load. The impact of the rods’ protrusion on the turbine structure appears negligible from the accelerometer measurements performed on the draft tube and spiral casing. The hydraulic efficiency is reduced by up to 1 %. These findings are significant across a wide range of part-load operations, from 40 % to 60 % load, indicating the potential to extend the operational range of existing Francis turbines. The research presented here is a novel attempt to enhance the existing Francis turbines with a new degree of freedom using protruding rods.

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO₂ emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO₂ emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.

This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

The proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain the accuracy of multi-step prediction performance. A multi-step WPF (MS-WPF) approach based on a time series bi-level empirical mode decomposition (BLEMD) method and BiLSTM neural network is proposed in this paper to improve the WPF accuracy of regional wind power generators. Since the uncertainty is always generated through coupled factors from both wind and weather-to-power conversion, the linearity feature is first introduced as an aspect apart from the frequency in the proposed approach to decompose the wind power time sequence data. The proposed BLEMD introduces Pearson product-moment correlation coefficient to evaluate the linearity of time series and a linearity-based decomposition algorithm is designed accordingly. To further enhance the precision and release computation burdens, a DL-based prediction strategy, including a BiLSTM network, a CNN-BiLSTM network, and a mean weight estimation method are implemented to predict the components separately. The proposed method only relies on local data, greatly reducing the data acquisition and computation cost. The precision of the proposed MS-WPF is verified by a 2.5 kW wind turbine with horizons from 5 s to 30 s, a 1.5 MW wind turbine with horizons from 10 min to 1 h, and a 51 MW wind farm with horizons from 1 h to 6 h. The comparative experimental results with other cutting-edge methods indicated that the proposed MS-WPF has superior prediction accuracy and stable performance for multi-step prediction.

Fossil diesel is a significant global fuel; however, environmental concerns and resource scarcity necessitate alternative fuels. Third-generation microalgae biodiesel (MB), despite its carbon neutrality, leads to higher oxides of nitrogen (NOx) emissions and poor engine performance. Renewable diesel (RD), also known as hydrotreated vegetable oil (HVO), could reduce these issues. A quasi-dimensional multi-zone combustion model is employed in this study to look into how different fuel blends of diesel, C. cohnii MB, and algae-derived RD affect the performance, combustion, and emissions of a compression ignition (CI) engine. The study uses a 2000 rpm engine arrangement with different engine loads, and validates the computational analysis with an experimental test engine arrangement. The study aims to explore the potential of sustainable biofuels in CI engines. D70MB30 (70 vol% diesel, and 30 vol% MB) fuel blend leads to a higher ignition delay period (IDP) while lowering peak cylinder pressure (PCP) and peak heat release rate (PHRR) compared to D100; however, when RD is added to diesel-MB fuel blends, it reduces IDP and increases PCP and PHRR. Brake specific fuel consumption (BSFC) for the D70MB30 blend rises by 5.28–8.9 %, while brake thermal efficiency (BTE) reduces by 0.98–4.27 %. However, RD in MB blends reduces BSFC by 1.57–3.41 % and marginally enhances BTE, resulting in greater fuel economy and efficiency. D70MB30 fuel blend results in higher specific carbon dioxide (CO₂) emissions and oxides of nitrogen (NOx) emissions while lowering particulate matter (PM) and smoke emissions compared to neat diesel due to its high intrinsic oxygen content. In contrast, RD with the MB blend reduces specific CO₂ and NOx emissions; however, it increases PM and smoke emissions due to the NOx-PM trade-off. The optimum results occur with a full load and D70MB15RD15 (70 vol% diesel, 15 vol% MB, and 15 vol% RD) fuel blend. Compared to the D70MB30 blend, D70MB15RD15 reduces BSFC, specific CO₂, and NOx by 2.15 %, ∼1%, and 8.37 %, respectively, while increasing PM emissions at 100 % load.

The widespread deployment of green hydrogen plays a crucial role in decarbonizing economies. Achieving global market competitiveness for green hydrogen necessitates not only competitive production costs, high generation capacity, and favorable political-economic conditions but also cost-effective transportation solutions. This is particularly vital for energy-intensive industrial nations like Germany, which will increasingly rely on hydrogen imports. This study assesses Germany due to its significant industrial demand and strategic location in Europe. Norway, Spain, and Morocco were chosen for their potential as major hydrogen exporters based on geographical proximity and renewable energy resources. Australia serves as a reference scenario for evaluating differing transportation costs depending on the distance. The transportation of compressed gaseous hydrogen via new and retrofitted natural gas pipelines, or liquefied hydrogen and liquid organic hydrogen carriers via maritime routes, currently represent the most promising alternatives. This paper conducts a techno-economic analysis on the transportation of green hydrogen from these countries to Germany by 2050. This year is pivotal as it aligns with Europe’s ambitious decarbonization goals, by which time a robust hydrogen market is anticipated to be established. The analysis employs the concept of Levelized Transportation Cost, evaluating costs across actual transportation routes, to provide insights into the most economically viable methods for hydrogen transport in a future decarbonized Europe. Various scenarios were designed to explore future developments. The analysis finds that for all countries examined, pipeline transportation of compressed hydrogen presents the lowest costs (0.08 €/kg to 1.34 €/kg), rendering it preferable to maritime transport options − with costs for liquefied hydrogen ranging between 1.73 €/kg and 3.40 €/kg, and for liquid organic hydrogen carriers between 2.33 €/kg and 7.29 €/kg. Transportation from Norway across all examined supply chains yields the lowest costs, followed by Spain.