The transportation sector is one among the key sources of greenhouse gas emissions (GHGs) leading to climate change and global warming. Energy transition through electrified transportation is one of the solutions to tackle the issues. Electric vehicles (EVs) offer significant environmental and economic advantages against the conventional Internal Combustion Engine (ICE) vehicles. EVs are called mobility loads and their connectivity to the utility grid for charging is unpredictable. The large penetration of such unpredictable loads into the utility grid will lead to undesirable impacts on the utility service. This paper highlights the importance of managing and optimizing the charging schedules. The optimization of EV charging has diverse aspects, and the perspectives of EV charging differ among consumers, aggregators, and utility services. Proper planning and management of EV charging is essential to achieve harmony amongst these stakeholders. A comprehensive review on the objectives of electric vehicle charging optimization from various perspectives is presented and discussed in this paper. EV charging optimization techniques including mathematical programming, meta heuristics algorithms and machine learning techniques are explored. The main objectives, constraints, strength, and limitations of different charging optimization techniques are analyzed in detail. A brief discussion on the communication strategies for data exchange in EV charging framework is presented and the need for a communication security constrained EV charging scheduling is also emphasized.

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications.

Protonic ceramic fuel cells (PCFCs) are one of the promising and emerging technologies for future energy generation. PCFCs are operated at intermediate temperatures (450–750 °C) and exhibit many advantages over traditional high-temperature oxygen-ion conducting solid oxide fuel cells (O-SOFCs) because they are simplified, have a longer life, and have faster startup times. A clear understanding/analysis of their specific working parameters/processes is required to enhance the performance of PCFCs further. Many physical processes, such as heat transfer, species transport, fluid flow, and electrochemical reactions, are involved in the operation of the PCFCs. These parameters are linked with each other along with internal velocity, temperature, and electric field. In real life, a complex non-linear relationship between these process parameters and their respective output cannot be validated only using an experimental setup. Hence, the computational fluid dynamics (CFD) method is an easier and more effective mathematical-based approach, which can easily change various geometric/process parameters of PCFCs and analyze their influence on its efficiency. This short review details the recent studies related to the application of CFD modeling in the PCFC system done by researchers to improve the electrochemical characteristics of the PCFC system. One of the crucial observations from this review is that the application of CFD modeling in PCFC design optimization is still much less than the traditional O-SOFC.

In this paper, a two-input, single-output (TISO) DC-DC converter for electric vehicle charging applications with solar photovoltaic (PV) as one of the sources is discussed. A novel, simple, and effective control strategy with maximum power point tracking (MPPT) to maximize power from PV while maintaining a constant bus voltage is proposed. A 100 V, 100 W TISO DC-DC converter is designed and comprehensive simulations are conducted under different conditions. The system characteristics are validated by comparing the results with a conventional P&O algorithm and multiple step size P&O algorithm. The proposed control strategy can precisely generate control signals to track MPPT while maintaining the bus voltage by controlling the output of a fixed DC source according to the changes in PV generation. The overall efficiency of the proposed approach is 98.67%, and the average output voltage is 99.09 V under selected conditions. The overall average output voltage ripple is 0.22% with the proposed approach, while it is 2.91% and 1.55% with the conventional P&O and multiple step size P&O MPPT techniques, respectively. Further, a 500 V, 17 kW high-power converter is designed and a simulation is carried out. The high-power converter provides an average output voltage of 496.92 V with an overall efficiency of 98.72%, and the average output voltage ripple is 0.16%. The results obtained from the simulation under the selected conditions lead to the conclusion that the converter has better efficiency and less variation in the output voltage at higher power levels with the proposed control technique.