Abstract

This paper examines common barriers limiting the implementation of small-scale biogas systems in rural Southern Africa through a literature study and a case study of six small-scale biogas digesters installed in Maputo province in Mozambique. The study strives to understand why the implementation rate of small-scale biogas systems in rural Southern Africa is so limited despite favourable conditions. The literature study identified several common barriers to the successful implementation of small-scale biogas in rural Southern Africa related to financial, technical, socio-cultural, and institutional issues. The case study results show that only one digester was operational, and five failed and were abandoned. Low technical ability of constructors results in poor-quality installations. Lack of technological know- how and local capacity for operation and maintenance of digesters are primary reasons for the failure of the digesters. Possible solutions are to intensify research, demonstration, dissemination and application of small- scale biogas; adapt the design of biogas digesters to the local context, meeting the needs of users and using locally available materials; sharing knowledge and information about small-scale biogas technology and its potential would contribute to improving the rate of successful implementation in Southern Africa.

Abstract

The surge in electric vehicle (EV) popularity necessitates innovative approaches for estimating the state of health (SOH) of EV lithium-ion batteries. This study introduces a transformer-based online SOH estimation model that leverages actual EV driving data, marking a departure from conventional methods that rely on lab-experimented battery cycle data. Our model comprises a transformer encoder and processes the raw sequences of battery voltage, current, state of charge, and vehicle speed. Despite the inherent noise in the EV battery readings while driving, the model shows high accuracy, with a mean absolute error of 1.31% and a root mean square error of 2.08%. Furthermore, this study unveils through self-attention map analysis that the model attends the stationary period of EVs to estimate the SOH. Although this study has a limitation in the dataset which lacks a wide range of driving route patterns, it still demonstrates the significant potential of transformer models in online SOH estimation for EVs while also providing valuable insights for future data collection.

Abstract

With the increasing complexity of future flight conditions and the rapid development of aviation electrification, variable cycle and high-power extraction have become important directions of aero engines. This paper proposes the Hybrid Cycle Engine (HCE) conception which is the combination of heat engine, thruster and generator based on the traditional aero engines. Besides the basic thrust requirements, HCE can export high electric power, adjust the operating point of aero engine and increase thrust. The HCE breaks through the limitation of the Brayton cycle via the hybrid cycle including thermodynamics and electrodynamic. In this paper, the performance simulation model is established based on the principle of the thermoelectric hybrid cycle. The sensitivity analysis of power extraction of high and low pressure rotor under design conditions is carried out to explore the matching mechanism of the hybrid cycle. This paper provides research support for the realization of adaptive integrated energy management under all working conditions and taps the performance potential of the hybrid cycle.

Abstract

A reliable battery thermal management system plays a crucial role in the safe, efficient, and long-term operation of a high-performance lithium battery system. This study evaluates the temperature rise, pressure drop, capacity loss, and cyclical cost of an air-cooled battery system consisting of 90 cylindrical battery cells placed in a staggered arrangement in the module. The effect of spacing between the adjacent cells and inflow velocity is investigated for the battery system operating at high charge/discharge rates of 3C and 5C. The results demonstrate that the hybrid model, which consists of the battery life model integrated with the simplified modeling approach for the thermal evaluation of battery packs, provides a cost-effective tool for multi-objective analysis and optimization of air-cooled battery packages. The results reveal that the air-based cooling system has the potential to fulfill the safety standards in all studied cases, and employing battery modules with larger cell spacing at a constant inflow velocity may reduce the maximum temperature, pressure drop, and cyclical cost by up to 2.14%, 93.36%, and 35.69%, respectively, while extending the lifespan of the battery system by up to 55.45%. However, it is found that the air-based cooling system approaches its limit of thermal performance at high inflow velocities. A novel index (MCR index) is proposed in this paper to characterize the limitations associated with adjusting cell spacing for air-based battery cooling systems. It is observed that for systems with an MCR index beyond 600, the effect of cell spacing on thermal performance becomes negligible. This can be used as a useful guideline for optimizing air-based battery thermal management systems or integrating them with other cooling methods.

Abstract

To meet the difficulties of the current energy environment, hydrogen has enormous potential as a clean and sustainable energy source. Utilizing hydrogen’s potential requires accurate hydrogen production prediction. Due to its capacity to identify intricate patterns in data, Machine learning alongside deep learning models has attracted considerable interest from a variety of industries, including the energy industry. These algorithms are inherently black boxes, which makes it difficult to comprehend and interpret their predictions, particularly in important sectors like hydrogen generation. First, this study conducted an extensive experiment using 4 machine learning regression models and a novel deep learning model based on Keras API for hydrogen production prediction based on the co-gasification of biomass and plastics datasets. Secondly, this study investigates the application of explainable AI models including Shapley Additive Explanation (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Explain Like I’m Five (ELi5) in predicting hydrogen production. We explore the significance of these models in providing insights into the underlying mechanisms and factors influencing hydrogen production processes hence improving our understanding of the relationships between input factors and hydrogen production outputs. This will allow for better-informed decision-making and process optimization in the energy industry. Our results demonstrate the interpretability and transparency of these models, highlighting their potential to raise the accuracy and dependability of forecasts of hydrogen generation. These models provide a useful resource for stakeholders to make informed decisions and enhance the use of hydrogen as a sustainable energy source by bridging the gap between predicted accuracy and interpretability.

Abstract

Recycling low and medium heat is important for saving energy and reducing pollutant emissions. The continuous thermally regenerative electrochemical cycle (C-TREC) have attracted attention because of their ability to continuously output electrical energy. In this study, a two-dimensional stationary model coupling flow and electrochemical reactions is developed to investigate the flow and C-TREC electrochemical reactions during the charging and discharging process of a continuous thermally regenerative electrochemical cycle. The effects of porous electrode geometry, porosity, current density, overpotential, and temperature difference between hot and cold sources on the performance of C-TREC are analyzed. The study specifies the operation strategy during charging and discharging.

Abstract

This paper proposes a 24/7 Carbon-Free Electric Fleet digital twin framework for modeling, controlling, and analyzing an electric bus fleet, co-located solar photovoltaic arrays, and a battery energy storage system. The framework consists of forecasting modules for marginal grid emissions factors, solar generation, and bus energy consumption that are input to the optimization module, which determines bus and battery operations at minimal electricity and emissions costs. We present a digital platform based on this framework. For a case study of Stanford University’s Marguerite Shuttle, the platform reduced peak charging demand by 99%, electric utility bill by $2779, and associated carbon emissions by 100% for one week of simulated operations for 38 buses. When accounting for operational uncertainty, the platform still reduced the utility bill by $784 and emissions by 63%.

Abstract

The current vehicle-to-grid (V2G) project pilots generally face the problem of low user participation willingness, mainly due to the lack of detailed consideration of trade mechanisms and incentive policies. To address the potential threat posed by the large-scale application of electric vehicles (EVs) to the power grid system, an analysis of the promotion strategies of V2G technology among EV owners is deemed necessary. In this study, a new simulation framework for V2G adoption and heterogeneous trade mechanism evaluation based on social network theory is constructed. The diffusion process of V2G adoption and charging/discharging behavior is simulated under three trading mechanism scenarios: Time-of-Use (ToU) pricing + fixed service fee (ToU-F), regulated pricing + fixed service fee (Reg-F), and dynamic pricing + fixed service fee (Dyn-F). The research results indicate that (1) In terms of V2G adoption scale, both the Reg-F and Dyn-F scenarios have reached the maximum number of adopters, increasing by 41.8% compared to the ToU-F scenario. The main reason is that the former two trading mechanisms achieve a larger price difference, creating more opportunities for charge and discharge arbitrage. (2) Regarding EV load regulation, the discharge amount of EVs under the Reg-F and Dyn-F scenarios is much higher than that under the ToU-F scenario. The Dyn-F scenario further avoids drastic fluctuations in load. (3) In terms of benefit distribution, only under the Reg-F scenario have both the aggregator and V2G adopters gained higher profits.

Abstract

Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and planning of PV systems, enabling efficient energy management and grid integration. However, uncertainties caused by fluctuating weather conditions and complex interactions between different variables pose significant challenges to accurate PV power forecasting. In this study, we propose PV-Client (Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting) to address these challenges and enhance PV power forecasting accuracy. PV-Client employs a linear module to learn trend information in PV power, and employs an ENhanced Transformer module to capture complex interactions in PV systems. Experiments with the real-world PV power dataset have confirmed the SOTA performance of PV-Client in PV power forecasting.

Abstract

This study explored the relationship between the RGB color space and the temperature of hydrogen flames with thermal reflection principles. The prediction model based on CCD and infrared (IR) image were compared. The model based on RGB difference as inputs for CCD image showed an average relative error of 9.8314%. The model based on infrared image RGB values showed an average relative error of 6.6652%.