In the recent years, lithium-ion batteries have become the battery technology of choice for portable devices, electric vehicles and grid storage. While increasing numbers of car manufacturers are introducing electrified models into their offering, range anxiety and the length of time required to recharge the batteries are still a common concern. The high currents needed to accelerate the charging process have been known to reduce energy efficiency and cause accelerated capacity and power fade. Fast charging is a multiscale problem, therefore insights from atomic to system level are required to understand and improve fast charging performance. The present paper reviews the literature on the physical phenomena that limit battery charging speeds, the degradation mechanisms that commonly result from charging at high currents, and the approaches that have been proposed to address these issues. Special attention is paid to low temperature charging. Alternative fast charging protocols are presented and critically assessed. Safety implications are explored, including the potential influence of fast charging on thermal runaway characteristics. Finally, knowledge gaps are identified and recommendations are made for the direction of future research. The need to develop reliable onboard methods to detect lithium plating and mechanical degradation is highlighted. Robust model-based charging optimisation strategies are identified as key to enabling fast charging in all conditions. Thermal management strategies to both cool batteries during charging and preheat them in cold weather are acknowledged as critical, with a particular focus on techniques capable of achieving high speeds and good temperature homogeneities.

Lithium-ion battery fast charging: A review

Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers’ attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

Deep Metric Learning: A Survey

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

Wind Energy is a fast developing source of energy since 1996. Despite its advantages, this energy could never be a primary source of electric power to be integrated into the grid even in high wind areas, such as Great Plains, due to its intermittent behaviour. This intermittency will generate intermittent power to grid, which leads to instability, unreliability and power quality problem onto the grid system. One of the widely accepted methods to overcome this problem is by coupling the wind turbine with the energy storage system. This paper reviews the ability of four different types of the energy storage system to mitigate the power fluctuated into the grid, especially during low wind speed. This paper also explains the operating principles and the different methods of charging and discharging the energy storage. The ability of permanent magnet synchronous generator (PMSG) in dealing with variable wind speed also will be discussed.

Review of storage schemes for wind energy systems

The pulse-based charging method for battery cells has been recognized as a fast and efficient way to overcome the shortcoming of a slow charging time in distributed battery cells, which is regarded as a connection of cells such as the Internet of Things (IoT). The pulse frequency for controlling the battery charge duration is dynamically controlled within a certain range in order to inject the maximum charge current into the battery cells. The optimal frequency is determined in order to minimize battery impedance. The adaptation of the proposed pulse duty and frequency decreases the concentration of the polarization by sensing the runtime characteristics of battery cells so that it guarantees a certain level of safety in charging the distributed battery cells within the operating temperature range of 5–45 °C. The sensed terminal voltage and temperature of battery cells are dynamically monitored while the battery is charging so as to adjust the frequency and duty of the proposed charging pulse method, thereby preventing battery degradation. The evaluation results show that a newly designed charging algorithm for the implemented charger system is about 18.6% faster than the conventional constant-current (CC) charging method with the temperature rise within a reasonable range. The implemented charger system, which is based on the proposed dynamic frequency and duty control by considering the cell polarization, charges to about 80% of its maximum capacity in less than 56 min and involves a 13 °C maximum temperature rise without damaging the battery.

/pdf/pulse-based-fast-battery-iot-charger-using-dynamic-frequency-57fjxmibsa.pdf

Pulse-Based Fast Battery IoT Charger Using Dynamic Frequency and Duty Control Techniques Based on Multi-Sensing of Polarization Curve

Electrocardiogram signal analysis is based on detecting a fiducial point consisting of the onset, offset, and peak of each waveform. The accurate diagnosis of arrhythmias depends on the accuracy of fiducial point detection. Detecting the onset and offset fiducial points is ambiguous because the feature values are similar to those of the surrounding sample. To improve the accuracy of this paper’s fiducial point detection, the signal is represented by a small number of vertices through a curvature-based vertex selection technique using polygonal approximation. The proposed method minimizes the number of candidate samples for fiducial point detection and emphasizes these sample’s feature values to enable reliable detection. It is also sensitive to the morphological changes of various QRS complexes by generating an accumulated signal of the amplitude change rate between vertices as an auxiliary signal. To verify the superiority of the proposed algorithm, error distribution is measured through comparison with the QT-DB annotation provided by Physionet. The mean and standard deviation of the onset and the offset were stable as − 4.02 ± 7.99 ms and − 5.45 ± 8.04 ms, respectively. The results show that proposed method using small number of vertices is acceptable in practical applications. We also confirmed that the proposed method is effective through the clustering of the QRS complex. Experiments on the arrhythmia data of MIT-BIH ADB confirmed reliable fiducial point detection results for various types of QRS complexes.

/pdf/efficient-fiducial-point-detection-of-ecg-qrs-complex-based-5cugg5l98n.pdf

Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation.

Dispersed power generation systems are expected as important electric power supply systems for the next generation. Wind power generation system (WPGS) is widely being introduced in the worldwide power utilities. The WPGS output power fluctuates due to wind speed variations. Hence, if a large number of wind power generators are connected to the grid system, their output can cause serious power quality problems, that is, frequency and voltage fluctuations may happen. In order to solve these problems, the smoothing control of wind power generator output is very important. In addition, Superconducting Magnet Energy Storage (SMES) is surely one of the key technologies to overcome these fluctuations. The SMES can compensate even small disturbances. With these points as background, this paper deals with power quality enhancement of grid-connected WPGS by pitch control and SMES. EMTDC and RTDS based simulations are performed for a small island power system including WPGS and the results are discussed in detail.

Power quality enhancement of grid-connected wind power generation system by SMES

As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the limitations of fixed hardware functions, relative to cloud computing. Recently, as application fields and collected data types diversify, and, in particular, applications requiring complex computation such as artificial intelligence (AI) and signal processing are applied to edges, flexible processing and computation capabilities based on hardware acceleration are required. In this paper, to meet these needs, we propose a new IoT platform, called a metamorphic IoT (mIoT) platform, which can various hardware acceleration with limited hardware platform resources, through on-demand transmission and reconfiguration of required hardware at edges instead of via transference of sensing data to a server. The proposed platform reconfigures the edge's hardware with minimal overhead, based on a probabilistic value, known as callability. The mIoT consists of reconfigurable edge devices based on RISC-V architecture and a server that manages the reconfiguration of edge devices based on callability. Through various experimental results, we confirmed that the callability-based mIoT platform can provide the hardware required by the edge device in real time. In addition, by performing various functions with small hardware, power consumption, which is a major constraint of IoT, can be reduced.

mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications.

Light detection and ranging (LiDAR) sensors help autonomous vehicles detect the surrounding environment and the exact distance to an object’s position. Conventional LiDAR sensors require a certain amount of power consumption because they detect objects by transmitting lasers at a regular interval according to a horizontal angular resolution (HAR). However, because the LiDAR sensors, which continuously consume power inefficiently, have a fatal effect on autonomous and electric vehicles using battery power, power consumption efficiency needs to be improved. In this paper, we propose algorithms to improve the inefficient power consumption of conventional LiDAR sensors, and efficiently reduce power consumption in two ways: (a) controlling the HAR to vary the laser transmission period (TP) of a laser diode (LD) depending on the vehicle’s speed and (b) reducing the static power consumption using a sleep mode, depending on the surrounding environment. The proposed LiDAR sensor with the HAR control algorithm reduces the power consumption of the LD by 6.92% to 32.43% depending on the vehicle’s speed, compared to the maximum number of laser transmissions (Nx.max). The sleep mode with a surrounding environment-sensing algorithm reduces the power consumption by 61.09%. The algorithm of the proposed LiDAR sensor was tested on a commercial processor chip, and the integrated processor was designed as an IC using the Global Foundries 55 nm CMOS process.

https://www.mdpi.com/1424-8220/20/19/5706/pdf

Daejin Park

Papers

Pulse-Based Fast Battery IoT Charger Using Dynamic Frequency and Duty Control Techniques Based on Multi-Sensing of Polarization Curve

Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation.

Power quality enhancement of grid-connected wind power generation system by SMES

mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications.

Accuracy-Power Controllable LiDAR Sensor System with 3D Object Recognition for Autonomous Vehicle.