Abstract

The front-end part of the DC charging module for electric vehicles commonly utilizes the Vienna rectifier, whose stable operation directly impacts the overall status of the charging module. Therefore, focusing on the characteristics of open-circuit faults in core components such as power switches and electrolytic capacitors of the Vienna rectifier, this paper proposes a diagnostic method based on Empirical Mode Decomposition (EMD) and Whale Optimization Algorithm (WOA) optimized Random Forest (RF) algorithm. Firstly, by constructing a simulation model of the Vienna rectifier, the waveform characteristics of the input current during open-circuit faults are summarized. The fault current signal is decomposed, and feature vectors are constructed using the EMD method. These feature vectors are then input into the classification model with optimized parameters using the WOAoptimizedRandom Forest. Simulation results demonstrate that this method achieves a high fault diagnosis rate and reduces diagnosis time, providing practical guidance for fault diagnosis in DC charging piles for automobiles.

Abstract

In order to ensure the safe and stable operation of electric vehicle (EV) charging stations and improve the accuracy of fault diagnosis, a fault diagnosis method based on an improved Backpropagation Neural Network (BP) is proposed. This method first preprocesses the operational dataset of the charging stations. Then, the preprocessed dataset is input into the BP model for training to learn the correlation between the normal and faulty states of the charging stations. Finally, an improved optimization technique is introduced to optimize the weights and thresholds of the BP model. This technique combines the Firefly Algorithm (FA) and the Northern Goshawk Optimization Algorithm (NGO) to obtain the optimal model by optimizing the BP model. Simulation results demonstrate that the proposed improved BP method has good computational advantages in terms of precision and recall rates. Compared to the traditional BP algorithm, the improved BP method achieves a 10.83% increase in diagnostic accuracy and can accurately diagnose the status of the charging stations

Abstract

The rapid growth of photovoltaic (PV) power generation has led to an increasing need for effective battery energy storage systems to address the intermittency and variability of PV output. This comprehensive review focuses on the optimization models used for battery sizing in photovoltaic power stations. It presents an in-depth analysis of various approaches, including mathematical programming, heuristic algorithms, and hybrid methods. The review examines the objective functions, constraints, and key parameters considered in these models. Additionally, it discusses the performance evaluation metrics and the impact of different factors such as load profiles, PV generation patterns, and cost structures on battery sizing decisions. The findings of this review provide valuable insights for researchers and practitioners aiming to optimize battery sizing in PV power stations to enhance system reliability and economic viability.

Abstract

The intermittency and instability of solar power generation present significant challenges for its effective integration into the grid. This project introduces an advanced Particle Swarm Optimization (PSO) based model for optimizing the configuration of photovoltaic (PV) and battery systems in power stations. The model tackles the intermittency and unpredictability of solar energy by incorporating an uncertainty set within the PSO algorithm, ensuring robust optimization under varying conditions. It also integrates an attention mechanism to enhance the accuracy of PV power output predictions. The study further develops an economic assessment model to evaluate the financial performance of different configurations, considering equipment, maintenance costs, and market prices. The project’s findings demonstrate the model’s effectiveness in improving both the operational efficiency and economic viability of PV-battery systems.

Abstract

The heat pump is a highly efficient and environmentally friendly technology for converting electricity into heat with efficiencies larger than one. Recently, air-source heat pumps (ASHPs) have seen widespread deployment across various climate zones for building heating and cooling purposes. Conversely, ground-source heat pumps (GSHPs) offer even greater efficiencies but have seen 15% of total heat pump installations. This article intends to conduct a comprehensive thermodynamic comparison analysis of adopting an Air-source heat pump (ASHP) and a ground-source heat pump (GSHP) for building heating and cooling in Beijing using TRNSYS simulation. The TRNSYS model leverages city-level building information to simulate the load profile of the building. Additionally, it incorporated the impact of occupants’ energy behavior including the window-opening schedule, heating and cooling temperature, effective area, and number of heat pumps to form 3 scenarios. As a result, the GSHP outperform the ASHP with around 30% higher efficiency and 45% reduction of total power consumption.

Abstract

Smart homes use devices that automate tasks like security, lighting, and temperature control. These homes let people control appliances remotely through the Internet of Things (IoT), adjusting to their schedules for better energy use. But as energy use rises, it causes more pollution, and climate problems, and puts more strain on energy sources. Therefore, it’s important to track energy use closely as the world moves into the use of renewable energy to avoid power outages, save money, and protect the environment especially because of the intermittent nature of renewable energy. This paper proposed an Ensemble-Tree Model Based on Bayesian Optimization or Solar Energy Generation Prediction in Smart Homes. First, three tree-like machine learning models training hyperparameters were optimized using the Bayesian optimization technique. Secondly, their output was concatenated based on Mean Aggregation Methods in mathematics. Lastly, the prediction was done based on k-fold cross-validation. The ‘smart-home-dataset with weather-information dataset is used while using the R-squared (R2), Mean Squared Error (MSE), and Mean Absolute Error (MAE) to observe how accurate the predictions are. Results show that the proposed model outperforms other machine learning models with R2 value of 0.988 as compared in this paper.

Abstract

Transportation electrification is an important strategy of addressing the rapid growth of carbon emissions from the transportation sector. However, rapid and widespread adoption of electric vehicles (EVs) may result in adverse effects such as increased grid emissions, power equipment degradation, and reduced grid efficiency. This study employs HOMER software to develop a hybrid EV charging station model with deferrable charging, offering a potential solution to these issues. To determine the most effective energy configuration, a multi-scenario simulation using real-world charging load data is performed. Findings indicate that hybrid charging stations equipped with smart charging technology can significantly alleviate these negative impacts by reducing peak loads, cutting carbon emissions, and enhancing cost efficiency. In these simulations, the optimal solution for public charging involves integrating wind power with grid electricity. This approach yields an annual production of 1,567.5 MWh, achieves an levelized cost of energy (LCOE) of $0.051/kWh, and results in a negative net emission through the sale of excess electricity to the grid. Furthermore, comparative simulations illustrate three key points. First, the system can sell back the excess electricity to achieve a negative net emission of -266 t carbon dioxide per year. Secondly, in the public charging scenario, managed charging has a significant effect on reducing peak loads (92% reduction in the maximum peak load), lowering grid emissions by selling back excess clean electricity (-74.5t per year), and optimizing the energy cost ($0.030/kWh reduction in LCOE). Lastly, in the domestic charging scenario, on-demand charging may outperform the deferrable charging. This paper highlights the limitations of REs and smart charging, the need for region-specific strategies and energy configurations, potentially contributing valuable insights to the fields of charging strategies and energy systems.

Abstract

Addressing global challenges such as climate change, energy crises, housing shortages, and regional inequality, this paper explores sustainable building design workflow in Yunnan’s rural area. This study developed a data-driven approach to achieve climate-adaptive, affordable rammed earth house design. It assesses environmental impacts based on cradle-to-grave life cycle assessment and assesses indoor comfort and energy efficiency through dynamic building performance simulations. The study culminates in guidelines for rammed earth house design based on extensive data analysis. Several design plans for the rammed earth house project were developed and evaluated, together with a conventional brick house. The outcome of our study offers a sustainable housing design workflow that could inspire similar initiatives in regions with comparable social and climatic conditions.

Abstract

Facade photovoltaic (FaPV) can significantly enhance the capabilities of building-integrated photovoltaic (BIPV) systems as prosumers, while also mitigating the additional electricity demands of electric vehicles (EVs). This study presents a comprehensive analytical framework for optimizing the operation of BIPV-EV systems and evaluating their environmental and economic benefits with a case study in Xiong’an New Area. The Economy-Energy-Environment(3E) parameters are measured for multiple scenarios, and sensitivity analyses are applied to validate the impacts of the changes in electricity prices and BIPV costs on the BIPV-EV system. The simulation results demonstrate that BIPV-EV systems effectively reduce carbon emissions and electricity costs. The installation of FaPV can increase power generation by 67.60% compared to the BIPV system using RPV stand-alone. Regarding urban decarbonization, the adoption of BIPV system can lead to a reduction in CO2 emissions by 41.91% and 34.99%. In each scenario, the lowest levelized cost of energy (LCOE) scenarios in the short-term and long-term are RPV+FaPV (SE)-EV and RPV+FaPV (SW)-EV, where BIPV generates 48.10% and 31.90% of the total electricity generation, respectively. In terms of EV power consumption, The BIPV-EV system can decrease the grid sell ratio by 15.38% compared to the single BIPV scenario. This framework analyzes the potential of integrated systems with EVs under various BIPV scenarios. In cities with nascent BIPV and EV markets, the framework can be used as a strategic tool for urban planners to plan BIPV-EV systems that improve sustainability and energy efficiency.

Abstract

For hydrogen polymer electrolyte membrane fuel cell (PEMFC), the structure design of the bipolar plate (BP) and gas diffusion layer (GDL) greatly affects the water and gas transport and the performance of the PEMFC. Starting with the length and cross-section size of the flow channel (FCH), the thickness of the GDL, the average porosity and the degree of porosity gradient in the flow direction, this paper studies the effect of the collaborative matching optimization of the BP and GDL structure on the performance of the PEMFC. Firstly, five variables including average porosity, GDL thickness, porosity gradient degree, FCH cross-section and length are selected as matching factors (five-level numbers for each variable), and two performance parameters including maximum power density and pressure drop are used as performance evaluation indexes. Secondly, the orthogonal table is established by orthogonal experimental design. The range analysis and variance analysis are carried out on the Orthogonal test results after the data is obtained through simulation. Finally, the Entropy weighting method is used to evaluate the two performance evaluation indexes of 26 groups of data. The results show that Case 18 has the highest comprehensive evaluation index, with an evaluation index of 0.06055 and the base group is 0.04655. Compared to the base case, the maximum power density of case 18 is increased by 19.8%.