Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

https://www.preprints.org/manuscript/201705.0090/v1/download

A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Review of wearable thermoelectric energy harvesting: From body temperature to electronic systems

The world population depends highly on fossil fuels, particularly for transportation and power generation. This dependency leads to oil price increases because of the depletion of fossil fuels. Burning of fossil fuels also increases greenhouse gas emissions that are greatly responsible for global warming. Thus, electric vehicles (EVs) are considered one of the premium solutions in the land transportation system because they can significantly reduce the dependency on crude oil and minimize transportation-related carbon dioxide emissions along with other pollutants. This study presents an extensive review of the three key areas of EV research, namely, EV charging technologies, the various impacts of EVs, and optimal EV charging station (CS) placement and sizing. Several technical publications related to EV charging technologies are highlighted, and the performance comparison of different EV technologies is discussed. A review of literature on these key areas reveals an increasing interest in these topics in the last decade, with the impacts of EV on the electric power system and the optimal placement and sizing of CS issues widely investigated. By providing an overview of these areas, this study demonstrates the current issues and challenges of widespread deployment of EVs in the market as well as the future research direction in this field. A total of 185 publications are arranged and appended for quick referencing.

A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles

The impending environmental issues and growing concerns for global energy crises are driving the need for new opportunities and technologies that can meet significantly higher demand for cleaner and sustainable energy systems. This necessitates the development of transportation and power generation systems. The electrification of the transportation system is a promising approach to green the transportation systems and to reduce the issues of climate change. This paper inspects the present status, latest deployment, and challenging issues in the implementation of Electric vehicles (EVs) infrastructural and charging systems in conjunction with several international standards and charging codes. It further analyzes EVs impacts and prospects in society. A complete assessment of charging systems for EVs with battery charging techniques is explained. Moreover, the beneficial and harmful impacts of EVs are categorized and thoroughly reviewed. Remedial measures for harmful impacts are presented and benefits obtained therefrom are highlighted. Bidirectional charging offers the fundamental feature of vehicle to grid technology. In this paper, the current challenging issues due to the massive deployment of EVs, and upcoming research trends are also presented. It is envisioned that the researchers interested in such areas can find this paper valuable and an informative one-stop source.

A Comprehensive Study of Implemented International Standards, Technical Challenges, Impacts and Prospects for Electric Vehicles

The Rogowski coil is an old device for current measurement. It has been modified and improved over a century and is still being studied for new applications. The Rogowski coil has various advantages over conventional magnetic current transformers (CTs). Not only can it be used instead of CT, but also it has various utilizations in other fields. This paper provides a brief review on different aspects of the Rogowski coil and its advancement procedure, during last decades. In this paper, the history of the coil is brifely reviewed and its bases and applications are discussed. The Rogowski coil is analyzed from different points of view, including different integration techniques in the output stage, models for the Rogowski coil, experimental methods for parameter measurement in models, and method for determining the damping resistor. Finally, a brief review over different applications of the coil ends this paper.

The Rogowski Coil Principles and Applications: A Review

The non-linear loads in domestic applications are becoming more common as the power electronic converters are being implemented more widely. This can lead to more significant load current distortions. This paper is presenting a case study results to describe the expected harmonic distortions and to provide models to estimate their magnitudes during day for different times of year. The results presented are from a residential area low-voltage network, which is also providing the heating power for the households. The discussion is presented about the daily harmonic current profiles, which are presented to vary to great extent, harmonic to harmonic and for the different times of the year. The daily profiles of the harmonics with more stable characteristics are described using daily patters trend models.

Harmonic load of residential distribution network — Case study monitoring results

Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Electromagnetic sensing for predictive diagnostics of electrical insulation defects in MV power lines

Partial discharge (PD) detection and localization in medium voltage (MV) cables connected in branched network configurations, is difficult by using the conventional methods. It is due to interconnected cables of different lengths and impedance which make the interpretation of PD signals quite complex as compared to single cable routes. This paper presents an online technique of PD localization for branched cable network. PD activity emits current pulses propagating away from the discharge location. Polarity of the detected PD pulses with reference to polarity of the supply voltage can determine the direction of arrival of PD pulses. Comparison of the polarity of PD pulses captured at different cable branches identifies the faulty section of the cable network. Evaluation of direction of arrival is based on directionally calibrated installed sensor. In this paper induction sensors are employed for implementation of the proposed technique. Integration of the proposed technique over a wider network can be very useful as an efficient diagnostic tool for improving the reliability of power supply.

Online partial discharge diagnostics in medium voltage branched cable networks

Wind energy forecasting is a very challenging task as it involves many variable factors from wind speed, weather season, location and many other factors. Its accurate prediction can be quite helpful in maintaining the balance between demand and supply, and issues related to the reliability of a power system. In this article, the Recurrent Neural Network (RNN) based forecasting algorithm is used for the three day-ahead predictions of energy generation from wind sources in Estonia. Then a comparison is made between the predicted energy generation of Estonian energy regulatory authority's algorithm and this RNN based algorithm. The simulation results show that our proposed algorithm has lower Root Mean Square Error (RMSE) value and it gives better forecasting.

Lauri Kutt

Papers

Harmonic load of residential distribution network — Case study monitoring results

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Electromagnetic sensing for predictive diagnostics of electrical insulation defects in MV power lines

Online partial discharge diagnostics in medium voltage branched cable networks

Wind Energy Forecasting Using Recurrent Neural Networks