Summary

High-capacity hybrid thermoacoustic electric generators (HTAEGs) are ideal in different small- and micro-scale energy systems, especially in space nuclear power systems. In this work, an HTAEG with a post-positioned gas spring is proposed. To demonstrate the superiority of the gas-spring-post-positioned design on high-capacity HTAEGs, an HTAEG prototype is modeled, built, and tested from the perspective of thermoacoustics accordingly. Experimental results demonstrate an output electric power of 15.0 kW. Furthermore, it could achieve the highest efficiency of 39.2% with an output electric power of 11.1 kW. Given that a 15-kW power output is an ultra-high level on a single-piston HTAEG of this type to date, and the achieved efficiency on the prototype is also encouraging, this work marks an important milestone in the development of high-capacity HTAEGs. It also demonstrates that the gas-spring-post-positioned design has significant advantages and enormous potential for application.

Introduction

The thermoacoustic electric generator (TAEG) emerges as a promising dynamic conversion technology due to its exceptional reliability, extremely long lifespan, versatility in heat source utilization, and high intrinsic efficiency.1 These attributes render it particularly well suited for space and submarine thermoelectric applications.2,3 Within the TAEG family, a hybrid one (traditionally called the free-piston Stirling engine,4 currently recognized as a generalized thermoacoustic engine5) that incorporates a solid mass to tune the internal acoustic field transcends the conventional gas-tuning systems in the aspects of power density and thermal-to-electrical efficiency.6

Most recently, dramatic progress, e.g., 16-year non-stop operation and over 39% thermal-to-electrical efficiency,7 has stoked up the enthusiasm for the hybrid thermoacoustic electric generator (HTAEG) in space application. However, the current achievements are all confined in a rather small power level (i.e., around 100 W), not sufficient to cover various application scenarios. Scaling up to tens of kilowatt power while maintaining high reliability simultaneously is formidably challenging, and so far, there is no public literature reported on it. The main obstacle is the spring force mechanism that is imperative to tune the acoustic field. For low- and medium-power HTAEGs (ranging from tens of watts to kilowatts), planar springs are commonly employed as the spring force mechanism for their excellent compactness and radial rigidity.8 These planar springs can be positioned either at the bounce space or compression space, resulting in two distinct designs for HTAEG. Meanwhile, both designs have been extensively utilized in numerous HTAEG prototypes, with their reliability well demonstrated through ground tests.7

Results and discussion

System configuration and numerical study
Figure 1 presents the schematics of an MGS-HTAEG and a PGS-HTAEG. The PGS-HTAEG is primarily a helium-filled pressure vessel that houses a hybrid thermoacoustic engine (HTAE) and a linear alternator (LA). The HTAE comprises an ambient heat exchanger (AHX), a regenerator, a hot heat exchanger (HHX), and a displacer. Meanwhile, the LA consists of an electromagnetic conversion circuit consisting of outer and inner stators, moving magnets, and coils, along with a power piston supported by gas bearings. Within the HTAEG, a combination of the AHX, the regenerator, and the HHX constitutes its thermoacoustic core, designed to transform external thermal energy into acoustic energy. By heating the HHX while rejecting heat from the AHX, helium within the regenerator undergoes oscillatory flow due to the thermoacoustic effect. Consequently, thermal energy is converted into acoustic energy. The displacer plays a pivotal role in modulating the acoustic field and facilitating the transfer of acoustic power. When the acoustic pressure fluctuation generated by the thermoacoustic core pushes the power piston to move reciprocating in the cylinder, the moving magnets mounted on the power piston move in a stationary coil accordingly, and the acoustic power is converted to electric power through electromagnetic induction.

Abstract

The fast transition to the electrification of the energy system, combined with an exponential growth of the market share of electric vehicles, is leading to a tight interrelation between electric energy production and transportation, two prominent sectors in fossil fuels consumption and greenhouse gas emissions. Accelerating this process, the management of electric fluxes, aiming at optimizing production and demand coupling, plays a crucial role in reaching the net-zero emission target. The proposed software platform is designed to optimally manage the energy fluxes for a solar powered parking lot, serving a fleet of electric vehicles; the real-time knowledge of energy production and demand, in conjunction with forecasted power generation, allows the maximization of renewable energy self-consumption, thus reducing the exchange with the external grid. The software platform can work either in design mode, allowing the dimensioning of the various parking lot components, or in real-time mode managing instantaneously the energy balance. As a case study, it is tested on the 2019 parking lot mobility data of a research center, assuming a complete transformation of the then existing fleet of employees’ cars to electric vehicles. A comparison of the resulting energy flows with those projected by an established commercial tool is performed, as well as a preliminary economic evaluation. Both consistency of the simulation results and favorable economics validate the presented smart charging algorithm and Internet of Things platform for the real-time energy management of a solar parking lot.

Introduction

The steep growth of anthropogenic greenhouse gas (GHG) emissions related to the use of fossil resources requires fast and challenging solutions; transportation and electricity production sectors are responsible for around two thirds of global CO2 emissions. According to the numbers published by the U.S. Energy Information Administration (EIA) in its monthly energy review of August 2022 [1], the total consumption of primary energy in the U.S.A. in the year 2021 was 28.5 Million terawatt-hours (TWh) or 2.4546 Billion tonnes of oil equivalent (toe), 7.9 Million TWh (0.6782 Billion toe) of which are used by the transportation sector and 10.8 Million TWh (0.9267 Billion toe) by the electric power sector. 94% of the total energy used for transportation came from petroleum and natural gas and 6% from biofuels and electricity [2]. In 2021, according to EIA estimates [3], cars, light trucks, and motorcycles accounted for the largest shares of total U.S. transportation sector energy consumption, with light-duty vehicles (cars, small trucks, vans, sport utility vehicles, and motorcycles) accounting for 54.2%, commercial and freight trucks 4.5%, jets, planes, and other aircraft 8.7%, boat, ships, and other watercraft 4.6%, trains and buses 2.6%, the military sector 2.0%, pipelines 2.8%, lubricants 0.5%. Globally, carbon dioxide emissions associated with transportation accounted in 2021 for 7620 Mt; 600 Mt less than pre pandemic level by 2019 [4].

Since the onset of the industrial revolution, ever increasing amounts of GHG have been released into the atmosphere. While the energy mix and corresponding supply chains shifted from coal to oil and more recently towards natural gas, the prosperity of humankind still relies on fossil fuels and energy conversion systems based on fuels combustion. Even today, transportation and electricity production are heavily reliant on CO2 emitting technologies. But there is a silver lining, as technological improvements coupled with a strong cost reduction enabled the fast growth of the total installed energy capacity of renewable energy sources for electricity production (RES-E). Together with powerful battery energy storage systems (BESSs), they will impose drastic changes in these two historically conservative sectors. Today, the fast growth of electric vehicles (EVs) related technologies, as well as their forecasted market penetration, reaching the record of 6.6 million vehicles sold in 2021 [5], generates new scenarios impacting the electric energy production and transportation market. Based on existing climate-focused policy pledges and announcements, the International Energy Agency predicts for 2030 EVs to represent more than 30% of vehicles sold globally across all modes (excluding two- and three-wheelers) and the global electricity demand for EVs to reach 1100 TWh; then about 4% of the total final electricity demand [5].

Conclusions

This paper presented the SPEM IoT platform, capable of simulating, designing and real time managing the smart charging of EV fleets in a RES-E driven parking lot.

A deterministic rule-based algorithm was introduced for the fine-grained management of the energy fluxes between EVs and the different electric components of a solar parking lot. Since the algorithm does not utilize any assumption on the future mobility and energy demand it can handle unforeseen changes avoiding critical situations.

As it was shown, intra-day deferral serves to shift EV charging demand towards the time of maximum production, guided by MPC, constantly analyzing the momentary situation of 1) intermittent solar production, 2) stored energy and energy storage availability, 3) charging station occupancy and 4) solar production forecast.

An extensive case study was conducted, where the design mode of SPEM was applied to the historic situation of an Italian Research Center in 2019, assuming the substitution of all employee’s cars by EVs. As a benchmark for the validation of SPEM, HOMER® Grid, a commercial software for the modeling, design and simulation of electrical infrastructure, was used.

Abstract

1. Introduction

2. Literature review of battery modularity for electric vehicles

3. Approach to the concept of ‘replacing battery’ stations

4. Challenges of ‘battery replacement’ stations

5. Envisaged I4.0 solutions

6. Discussion

7. Conclusions and outlook

Acknowledgments

References

Abstract

     Energy and environmental impacts are at the top of the list of major global challenges to be addressed within the next few years. With transportation being one of the most pollutant sectors, focus has been directed towards Electric Vehicles, due to their significantly less dependency on fossil fuels. Nevertheless, electric mobility is currently characterized by a limited driving range, leading manufacturers to investigating new methods that could increase range and charging efficiency. Current research is mainly focused on increasing battery performance and reducing charging time. This study concentrates on presenting a novel approach, based on replacing battery stations, where the vehicle’s batteries are replaced with a fully charged one (available at the station), while new modular batteries could be made on the spot. The existing gas station networks could be used for the replacement of electric car batteries. This will be addressing the issue of time-consuming charging of batteries. Through the assimilation of Industry 4.0 Key Enabling Technologies, the respective challenges can be addressed. This is illustrated through a holistic framework, whereby the requirements for the context-aware design of the car itself are also given and a specific solution, based on the mechanical mounting of batteries is discussed..

Introduction

     Car manufacturers enter the space of the electric vehicle (EV) (Gnann et al. 2018; Nanaki 2021). This is highly correlated to the fact that the consumers find the green aspect very appealing (Rezvani, Jansson, and Bodin 2015). Reduction in the consumption of the emissions and the resources (Athanasopoulou, Bikas, and Stavropoulos 2018) are also in support of this trend. Moreover, the green agenda in Europe seems to have had a great impact on manufacturing in general (‘White Paper 2011 | Mobility and Transport’ 2021). Statistical figures show the correlation between market uptake and GDP, in the European Union (EU), with Germany, the United Kingdom and France having the highest EV market share in the EU for 2018, highlighting the importance of the EV market (‘Interactive Map – Correlation between Uptake of Electric Cars and GDP in the EU – ACEA – European Automobile Manufacturers’ Association’ 2021; Hanžič, Marksel, and Božičnik 2019). Electric vehicles, in particular, seem to be having a very specific related issue, that of the battery. Some intelligence (Arora, Shen, and Kapoor 2016; Albright and Kappel 2003) has been embodied in the design of the car and the battery, but the issue of its charging still remains, despite the fact that the stakeholders’ number has now been increased drastically, by offering many differentiating choices.

Conclusions and outlook

     The investigation of I4.0 KETs, under the concept of battery modularity and the development of intermediate battery replacement stations, could have high potential on addressing significant barriers, related to the time-consuming battery charging. The particular study focuses on bringing together the modular batteries concept, which is still at a very early stage, with the new concept of intermediate battery replacement stations.

     From a product development perspective, modularization correlates with a radical change in the automotive industry. The modular battery pack design and distribution, under the concept of battery swapping stations, could lead to improved network capabilities and to manufacturing mobility. Modularity could be considered as a new milestone in the automotive industry, targeting at the optimization of global production networks and operational flexibility. Latest achievements in the field of complex assembly decision-making and the use of advanced technologies in design under the concept of I4.0, have rendered the necessary feasibility and adaptability possible, towards the implementation of modular concepts in the industrial sector (Remco, Halman, and Hofman 2016).

Abstract

Nomenclature

1. Introduction

2. Vehicle model and driver model

3. Design of DYC for energy-efficiency

4. DYC for stability and stability judgement

5. Performance of the DYC for energy-efficiency and the DYC for stability

6. Switching principle

7. Results of combining DYC for energy-efficiency and DYC for stability

8. Conclusion

References

Abstract

     A direct yaw moment control (DYC) for energy-efficiency and a DYC for stability of electric vehicles (EVs) are proposed. The DYC for energy-efficiency is active during non-safety-critical cornering manoeuvres to improve the energy-efficiency of EVs. The DYC for stability is active during safety-critical manoeuvres to keep the vehicle stable. A combination of the DYC for energy-efficiency and the DYC for stability is studied. A stability judgement based on the yaw rate and slip angle is designed for evaluating the criticality of the vehicle’s working state. A switching principle for alternating between the DYC for energy-efficiency and the DYC for stability is designed. During non-safety-critical cornering manoeuvres, it is shown that the DYC for energy efficiency can save considerable percentage of energy compared to both equal torque driving and the DYC for stability. During cornering manoeuvres containing both non-safety-critical parts and safety-critical parts, the simulation results in this work show that the combination of the DYC for energy-efficiency and the DYC for stability can give 12% to 18% energy savings compared to the DYC for stability only for the vehicle and manoeuvres studied.

Introduction

     Electric vehicles (EVs) are an important component of a fossil-fuel-free future and the number of EVs on the roads is increasing rapidly. There are several solutions for electrified powertrains, one of which is in-wheel motor technology. In the case of 4 in-wheel motors (4IWMs), the propelling power of each wheel can be independently controlled. Therefore, 4IWM EVs can provide more control flexibility than traditional centralised driving vehicles. For example, direct yaw moment control (DYC) can easily be implemented in a 4IWM EV.

     The DYC usually follows a hierarchical structure consisting of a high-level yaw moment controller and a low-level torque distribution controller. Based on a reference model, the high-level controller can determine the stability yaw moment which provides an effective way to stabilise the vehicles. Tahami et al. [1] used a fuzzy logic controller, which adopted the yaw rate error (compared to the desired yaw rate) and this yaw rate error rate of change as inputs and the torque difference between the right and left wheels as the output to generate the DYC, for the purpose of enhancing the vehicle stability. Raksincharoensak et al. [2] proposed two types of desired yaw rate: one for the lane-keeping function and the other for vehicle stability control to improve the vehicle handling and stability. A driver steering behaviour recognition algorithm, using the steering wheel angle and the steering wheel velocity as inputs, was designed to determine the type of desired yaw rate. Geng et al. [3] took, besides the desired yaw rate, the desired body slip angle into account and adopted the linear quadratic regulator method to calculate the optimal yaw moment.

Conclusion

     A DYC for energy-efficiency based on the desired lateral acceleration is designed for non-safety-critical cornering manoeuvres. A DYC for stability tracking the desired yaw rate is designed for safety-critical cornering manoeuvres. In order to improve the energy efficiency and keep the vehicle safe during cornering manoeuvres which contain both non-safety-critical parts and safety-critical parts, a DYC is proposed which combines DYC for energy-efficiency and DYC for stability, which has been evaluated by simulations. A stability judgement, consisting of the yaw rate and body slip angle, is suggested and a switching principle for alternating between DYC for energy-efficiency and DYC for stability has been developed.

     The following five driving strategies have been analysed: 4WETD, 2WETD, DYC for energy-efficiency, DYC for stability and combined DYC. During non-safety-critical cornering manoeuvres, the DYC for energy-efficiency can make a considerable percentage of energy saving compared to 4WETD, 2WETD and DYC for stability. It has been shown that during cornering manoeuvres containing both non-safety-critical parts and safety-critical parts, only the DYC for stability and combined DYC can keep the vehicle safe and besides the combined DYC consumes less energy than the DYC for stability.

Abstract

Introduction

Literature review

Electric vehicles — key concepts

Overview of hybrid three‑wheeler

Current state of the art of electric three‑wheeler

Policy gap

Methods

Duration

Rickshaw cost

Usage statistics

Expenditures and earnings

Sustainability analysis

Discussion

Reduced GHG emissions for environmental sustainability

Development of indigenous technology

Government easy loan scheme

Poverty amelioration for economic sustainability

Easily available charging stations

Promoting gender equality

Conclusions

Declarations

References

Abstract

     Transportation sector is one of the major contributors to GHG emissions, and it is essential to investigate the role of electric vehicles in economic and environmental performance of developing countries. In this research work, the authors seek to investigate the usage pattern of three-wheeler vehicle by taking a Pakistan-based survey approach and analyzing the responses from various three-wheeler drivers across the country. The survey results indicate that the adoption of hybrid and electric three-wheelers in Pakistan would be welcomed by the users due to increased economic benefits, despite having a high initial capital cost. On an average basis, the regular three-wheeler is run for more than 5 days a week, 101–150 km daily across more than 10 h, and enables the owner to make around PKR 20,000–25,000 (~ US $119–US $149) after approximately PKR 15,000 (~ USD $89) is spent every month on fuel and maintenance. The adoption of hybrid or electric three-wheelers can easily improve the monthly earnings by 50%. In addition to this, the GHG emissions from the transportation sector will be considerably lowered around 3–6 tonnes of CO2 emissions per year per three-wheeler. At an investment return period of 13–16 months, the electric three-wheeler is a highly lucrative opportunity.

Introduction

     The industrial revolutions brought about a paradigm shift in the quality of lives of individuals; the world became a global village and the technological advancements resulted in extensive economic gains. However, the environmental impact of the economic gains was often overlooked and it eventually culminated in the pressing issue of climate change where the annual temperatures are forecasted to rise by 0.5–4.5 °C over the next century (GoP, 2016) if stringent measures are not taken. In this context, it is incumbent upon the stakeholders and policymakers to identify the areas where the carbon footprint can be reduced through conscious eforts. Transportation sector is one of the major contributors to environmental pollution1 and the conventional internal combustion engines require stringent protocols for curbing the environmentally detrimental greenhouse gas emissions. Therefore, countries have promoted the use of electric vehicles (Kwon et al., 2020; Daina et al., 2017) to reduce the environmental impact of transportation, in addition to numerous clean energy initiatives. The commercially available electric vehicles are broadly classifed into two categories: (i) plug-in hybrid electric vehicles or PHEVs and (ii) battery electric vehicles or BEVs and share the common aim of reducing the GHG emissions by reducing or eliminating the use of fossil fuels (Cofman et al., 2017). The worldwide adoption of hybrid technology vehicles has been an outcome of spreading awareness followed by relevant actions among governments, consumers, and producers.

Conclusions

     In this research work, the authors have sought to understand the usage pattern of three-wheeler vehicles and the general consensus on the adoption of electric threewheelers. Using a survey-based approach, data from 534 three-wheeler drivers were collected from the major metropolitan cities of Pakistan and it was observed that there exists an opportunity to introduce electric three-wheelers to achieve holistic sustainability. Many researchers have discussed the importance of adoption of electric vehicles and their economic benefts; however, the case of threewheelers from driver/owner viewpoint has remained an unexplored avenue. To the best of authors’ knowledge, this is the frst study that describes the usage pattern of three-wheelers and performs the sustainability analysis of adoption of three-wheeler vehicles in Pakistan, and it can be said with confdence that the results can be extended to other countries with similarity in socioeconomic demography. The higher price point of the electric or hybrid threewheeler will not afect the buyer as the investment can easily be recouped within 13 months if the three-wheeler is bought on lump sum basis, whereas the return period is 16 months if the electric or hybrid three-wheeler is bought through a government loan scheme. The adoption of electric three-wheelers will result in improved environmental performance as on average 3–6 tons of CO2 emissions is produced from a single three-wheeler throughout the year at a driving cycle of 101–150 km a day for 6 days a week. With more than 100,000 three-wheelers running across the country,

Abstract

     With the emergence of battery-based electric vehicles, transportation systems gradually leave using fossil fuel-based combustion engines. Due to their reasonable performance, Lithium-ion batteries have become one of the major batteries used for electric vehicles. Although these batteries are being used in most companies, their high production cost, rare raw material, and short life cycle have raised important incentives for their recovery process. However, locating a recovery center for end-of-life Lithium-ion batteries is a multi-aspect decision making problem influenced by many criteria. For this purpose, a novel integrated decision-making model is developed based on Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH) for calculating the criteria weights and Weight Aggregated Sum Product ASsessment (WASPAS) methods under the fuzzy environment with Dombi norms for evaluating the alternatives to address recovery center location selection problem considering technical as well as environmental, economic, and social aspects. To show the reliability and applicability of the developed method, a real-world case study in Istanbul is investigated. The developed method is used to evaluate six potential locations for the possible establishment of a recovery center. Results showed that Tuzla district is the most suitable location for opening a recovery center for end-of-life Lithium-ion batteries. Tuzla is in a very good position in terms of proximity to suppliers, transportation and location. To illustrate the robustness of the obtained results, extensive sensitivity analysis tests are performed.

Introduction

     Electric vehicles (EVs) are an appealing solution for the decarburization of the transportation sector (Romero-Ocaño et al., 2022, Zhang et al., 2020). It is estimated that more than 125 million EVs will be on the road worldwide by 2030 (Hua et al., 2020). Lithium-ion batteries (LiBs) have exponential growth and a key portion of industry investments (Chen et al., 2019, Cui et al., 2022). An automobile Lithium-ion battery (ALiB) is a major component of an EV (Pelletier et al., 2017, Ramoni and Zhang, 2013). ALiBs provide the required energy storage for EVs due to the superiority of high energy density, high output voltage, low self-discharge rate, and long cycling life (Tang et al., 2019, Wang et al., 2022). They are composed of a cathode, an anode, an electrolyte, and a separator (Olivetti, Ceder, Gaustad, & Fu, 2017). The useful lifetime of ALiBs is 120,000–240,000 km (Onat, Kucukvar, Tatari, & Zheng, 2016).

     Approximately 11 million ALiBs are expected to be sold worldwide by 2020 (Li et al., 2018; Alamerew & Brissaud, 2020). Due to the degradation in capacity and quality, the service life of these complex multiple material products, which belong to class 9 of dangerous goods, is 5–10 years (Li et al., 2018; Chen et al., 2019, Tang et al., 2019, Alamerew and Brissaud, 2020, Li et al., 2020). ALiB is replaced when the capacity has reached 70–80 % of its initial capacity (Alamerew and Brissaud, 2020, Hua et al., 2020, Ramoni and Zhang, 2013).

Conclusions

     The emergence of EVs has been a noticeable point for transportation systems to transform from fossil fuel-based vehicles to cleaner vehicles which require lower energy costs and also produce lower negative environmental, economic, and social impacts. The utilization of ALiBs is of great importance for EVs, but more and more valuable resources are being depleted without appropriate recovery. Therefore, countries should consider establishing recovery centers for ALiBs as soon as possible. However, locating a recovery center is a complex and multi-aspect decision-making problem. For this purpose, we developed a novel decision-making approach based on the MACBETH-D-WASPAS model under the fuzzy environment. The proposed integrated fuzzy decision-making approach empowers experts in the field of LiB management to enhance their decision-making capabilities and select the most suitable location for an EoL ALiB recovery center. Besides, the real-life case study of Istanbul is provided to show the feasibility and applicability of the developed methodology for solving the recovery center location selection problem. Results showed that Sariyer and Büyükçekmece are top first and second locations that a recovery center for an EoL ALiB recovery center. On the other hand, results pointed out that Ümraniye is the least preferred location for establishment of an EoL ALiB recovery center.

Abstract

     A significant transformation occurs globally as transportation switches from fossil fuel-powered to zero and ultra-low tailpipe emissions vehicles. The transition to the electric vehicle requires an infrastructure of charging stations (CSs) with information technology, ingenious, distributed energy generation units, and favorable government policies. This paper discusses the key factors when planning electric vehicle charging infrastructure. This paper provides information about planning and technological developments that can be used to improve the design and implementation of charging station infrastructure. A comprehensive review of the current electric vehicle scenario, the impact of EVs on grid integration, and Electric Vehicle optimal allocation provisioning are presented. In particular, this paper analyzes research and developments related to charging station infrastructure, challenges, and efforts to standardize the infrastructure to enhance future research work. In addition, the optimal placement of rapid charging stations is based on economic benefits and grid impacts. It also describes the challenges of adoption. On the other hand, future trends in the field, such as energy procurement from renewable sources and cars’ benefits to grid technology, are also presented and discussed.

Introduction

     The escalation in need for conventional energy sources has caused multiple outcomes that negatively affect the environment. Resources are depleted, and CO2 is released in high amounts, causing the greenhouse effect and undesirable global warming (Wang and Cheng, 2020). As a result of the Paris Agreement, CO2 emissions were reduced, and the planet’s temperature was controlled (Saerbeck et al., 2020). Clean energy resources and related technologies have been developed to mitigate these problems. Although technological advancements have significantly reduced greenhouse gas emissions from transportation, about one-quarter of these emissions come from the sector (Napoli et al., 2019). According to Outlook (2010), the growing population and freight movements will contribute to a 77% increase in transportation by 2055. Due to the above reasons, research and adoption of electric vehicles (EV) deserve exorbitant attention. By emitting very low or no tailpipe emissions and making very little noise, electric vehicles significantly reduce traffic congestion and contribute to a healthier living environment (Sanchez-Sutil et al., 2015, Abid et al., 2022, Huang et al., 2022, Chakir et al., 2022, Lan et al., 2022, Soares et al., 2022, Guo and Zhao, 2015). As a result of this transition to zero-emission vehicles, the automotive industry is switching to zero-emission vehicles (Bräunl et al., 2020, Domínguez-Navarro et al., 2019). Approximately 1.5 million new battery electric vehicles (BEV) have been added to the global fleet of BEVs (Martins et al., 2021) in 2019, with approximately 4.8 million BEVs in use globally.

Conclusion

     The popularity of electric vehicles is expected to grow significantly in the next decade due to technological progress, charging infrastructure, and grid integration. Furthermore, further technical improvements such as intelligent charging infrastructure, reliable communication systems, and coordinated charging systems are essential for EVs with distributed generators to achieve the maximum benefits. A future grid technology based on the Energy Internet can enable the electrical grid to become fully automated with advanced energy management systems. A discussion of EV charging and grid integration infrastructure is presented in this paper. EVs and their charging infrastructure must have unified norms and standards worldwide to gain popularity in the market. Future researchers will be given a clear picture of the specification requirements for EV charging and grid integration based on our discussion of the most prominent standards. In addition, different aspects of the existing charging and grid integration infrastructure are examined rigorously in terms of their advantages and disadvantages, including power, communication, control, and coordination. Besides suggestions for future research, this paper also includes perspectives on overcoming current challenges. It is evident from the discussion of the prospects of EVs that a review of this area is necessary. With this review, researchers and engineers will clearly understand the current state of EV charging and grid integration research.

Abstract

Introduction

Domestication of the smart home charger

Method and pilot description

Results and interpretation: domesticating smart charging—four motivations

Discussion

Conclusions

Availability of data and materials

References

Acknowledgements

Funding

Author information

Ethics declarations

Additional information

Rights and permissions

About this article

References

Background

Norway is currently in the process of replacing internal combustion engine (ICE) vehicles with electric vehicles (EVs). A steadily increasing number of EVs being charged in the evening when the demand peaks are already high has added to the strain on local electricity grids. Smart charging is proposed as a solution to peak-load challenges of this kind, as it can delay charging until evenings and nights when demand peaks are generally low. However, current knowledge of the preferences and motivations of EV owners regarding the installation and use of smart chargers is lacking. Accordingly, the study aims to contribute an improved understanding in this respect.

Methods

The authors conducted a qualitative study based on semi-structured interviews, in which they gained information on householder’s experiences following their participation in a pilot project to test smart charging of EVs. The interviews explored the interviewees’ motivation for participating in the project, their experiences of using and charging EVs, and their thoughts on flexibility in the timing of EV charging. Interviews were recorded and transcribed verbatim, after which a domestication theory approach was employed to perform an in-depth analysis of the interview data.

Results

The analysis of the interviews revealed that the study participants had four motivations for participating in the smart charging demonstration project and engaging in the further use of smart charging: (1) the fire-safety aspect and speed of charging devices for EVs was better than charging from a socket; (2) they derived a broader interest in and more joy from using smart home technology; (3) attractive practical and economic benefits; and (4) the flexibility offered by smart charging had the potential to have a positive impact on physical comfort.

Conclusions

The study revealed key domestication processes unfolding in the Norwegian energy transition with the increasing numbers of EVs and the corresponding grid peaks. The findings clearly indicate that users’ motivations go much further than simple economic rationalization, which should be of interest to policymakers, smart home developers, and all others that work with end-user flexibility in the grid.

1- Introduction

2- An analytical electromagnetic model of the “Sen” transformer

3- Simulation case studies

4- Conclusions

References

Abstract

In flexible AC transmission systems, the “Sen” transformer (ST) is an economical and effective method for voltage regulation and power flow control, which can achieve independent control of active and reactive power. The ST is a three-phase multi-winding transformer based on the on-load tap-changing technology. In order to reveal the internal characteristics of the ST, this paper proposes an analytical electromagnetic model considering the multi-winding coupling in the ST with the three-phase three-limb structure. Firstly, according to the electromagnetic coupling relationship in the ST, a magnetic equivalent circuit model consisting of self-inductance coefficients and mutual inductance is derived via the unified magnetic equivalent circuit (UMEC). Secondly, an electric circuit model for the internal voltage and current of the ST is established based on the electrical connection between the ST and the external system. Finally, an analytical electromagnetic model considering multi-winding coupling for the ST is obtained by combining the magnetic equivalent circuit model and the electric circuit model. Two case studies have been carried out on a three-phase three-limb model of the ST with the help of MATLAB. The effectiveness of the proposed model is verified by comparing the obtained analytic results with the existing results from time-domain simulation about the series compensating voltage of the ST. In addition, the voltages and currents of windings in the ST in the conditions of different transformer core structures and ferromagnetic materials are investigated for evaluating the influence of magnetic coupling on the results.

Introduction

With the large-scale integration of renewable energy sources, it has been becoming more and more difficult for the voltage regulation and the optimal power flow control in complex power networks with UHV (Ultra High Voltage) transmission lines as the backbones[1,2]. The unified power-flow controller (UPFC) is an important way to solve the problems of voltage regulation and power flow control [3,4]. However, the high installation and operation costs prevent the UPFC from widespread applications. The “Sen” transformer (ST), as proposed in [5], has the ability of controlling active and reactive power flow independently. Compared with UPFC, the ST is lower in cost, higher in reliability and efficiency [6–8], although reducing the accuracy and dynamic performance [9]. The ST is a typical electromagnetic unified power flow controller, and is usually based on a three-phase multiwinding transformer. By changing the position of secondary winding taps, the ST can generate different voltages, achieving the adjustment of power flow. The adjustment performances between the “Sen” transformer and phase angle regulator (PAR) were compared in [10,11]. The results showed that the overall reactive power of the ST was lower than that of the PAR for enhancing the required amount of active power flow. Therefore, the “Sen” transformer is expected to be a device that will have widespread applications in flexible AC transmission systems (FACTS). As a matter of fact, the ST is a technology based on transformers and tap-changers. From the perspective of the development of electrical topologies and structural variants, many researchers have optimized the topology of the ST for the purpose of improving its power flow control performance. For example, some novel topologies of the ST were proposed in [12–15] for the sake of achieving continuous power flow control of existing transmission lines and improving the utilization rate of existing transmission lines. These topologies include the hybrid electromagnetic unified power flow controller (HEUPFC), the improved hybrid unified power flow controller (IHUPFC) and the power transistor-assisted “Sen” transformer (TAST) which consists of a high-capacity ST and a small capacity UPFC.

1- Introduction

2- Problem formulation

3- Case studies

4- Validation of the proposed algorithm in the laboratory

5- Conclusion

References

Abstract

Relay coordination ensures the appropriate isolation of healthy feeders from the faulty areas in a power system. The dynamic nature of renewable sources such as wind generators (WGs) can create variations in fault levels which affect the relay settings. This causes coordination problems, which can lead to the faulty operation of overcurrent relays (OCRs). The settings of the relay need to be modified appropriately in line with the variations of fault levels, which in turn change with the wind dynamics. As a solution, wind forecast based technique for the coordination of the OCRs in wind-integrated substations is suggested in this work. In the proposed algorithm, wind speed and its direction are predicted and the settings of the relay are predetermined using an optimization technique. This reduces the computation time required for the algorithms in the current period. The algorithm is implemented in the modified IEEE 9-bus system with wind farms and also in a typical wind-integrated substation. Further, it is validated through an experimental setup in the laboratory. The results were found to be promising and the algorithm can be applied to any substations integrated with wind farms for avoiding relay mal-operations.

Introduction

Wind power generation is emerging as the fastest developing technology providing the largest share of the distributed generations. The total installed capacity of power generation from the wind has been increasing annually at an average of about 20% across the world [1,2]. The integration of wind farms with power networks is intermittent, according to the operating conditions of the WGs [3,4]. Wind farms are usually connected to the distribution feeders and then integrated into the grid. The most commonly used protection scheme for the feeders is the inverse type of overcurrent protection. The OCRs in the distribution feeders should be coordinated with their backup relays in the main feeders which connects the distribution feeders to the grid [5,6]. The settings of the OCRs are fixed at the rated capacities of the connected load and the wind farm. The penetration of wind farms into the power grid changes the conventional distribution system’s short-circuit power causing malfunctions and coordination problems in OCRs, as reported in [7]. Therefore, the settings of the relays need to be modified according to the various operating condition of the WGs. The methods commonly adopted to determine the relay settings are the conventional approaches and optimization techniques [8]. The conventional method is to predetermine all fault currents during abnormal conditions and system contingencies.

1- Introduction

2- Problem formulation

3- Proposed models

4- Experimental results

5- Conclusions

References

Abstract

A factor that directly impacts the lifespan of a power transformer is the hot-spot temperature, and its monitoring is vital to prevent faults, reduce costs, keep the safety, and provide a reliable service to consumers. In this paper, we propose two forecasting models to predict the hot-spot temperature of power transformers. The first is the implementation of Set-Membership in the evolving Participatory Learning with Kernel Recursive Least Squares. And the second is a combination of the evolving Participatory Learning with Kernel Recursive Least Squares and the improved version of the Set-Membership concept, named Enhanced Set-Membership. Both Set-Membership and the Enhanced Set-Membership approaches are implemented to update the rate of change of the arousal index, which is a parameter that controls the creation of rules. A data set collected from an experimental transformer is adopted to evaluate the model’s performance. The obtained results are compared with the performance of the original evolving Participatory Learning with Kernel Recursive Least Squares and with the performance of other classical models suggested in the literature. The proposals have lower errors and a competitive number of final rules, suggesting that the models are efficient approaches to modeling complex data with high accuracy.

Introduction

The power transformer is a critical equipment in power distribution [1]. It is responsible for stepped-up the voltage before to be transmitted over long distances to reduce waste, and stepped-down the voltage to provide the energy to consumers safely [2,3]. Due to the composition of a power transformer, it is the most expensive apparatus in energy distribution [4]. In the case of a power transformer’s failure, when the recovering process is possible, it is slow and inefficient [5]. Thereof, monitoring is vital to prevent faults, reduce costs, keep the safety, and provide a reliable service to consumers [6,7]. The annual spent on power transformers’ monitoring hardware will increase more than $ 642 million in eight years until 2020, according to [8,9], indicating the importance of the power transformers in power distribution. Internal failures are about 10% of the total faults, and, among them, winding and bushing defects represent approximately 44% [10]. The bushing is a fragile component constituted of four parts: insulation, conductor, connection clamp, and accessories[11,12]. In the present work, we considered power transformers composed of Resin-bonded paper bushings (RBP) [13]. The principal factor in bushing failures is the hot-spot temperature, representing 32% of the total causes [11]. The hot-spot temperature is the highest temperature near to the top of the power transformers highvoltage (HV)/low-voltage (LV) windings [7,14] and represents the main limiting factor in the load capacity of the transformer [14], since increases in this temperature reduces the lifespan of the insulation and may determine the end life of the power transformer [15]. As the estimation of the hot-spot is a complex task, many models have been proposed in the literature with the purpose of estimating the hot-spot temperature of power transformers. Among them, the most commonly used in practice is the model based on the IEEE Standard C57.91-2011 [16] which is based on transient heating equations and specific thermal characteristics and parameters of power transformers. This deterministic model is imprecise due to assumed simplifications, and consequently, the power transformer must operate below the maximum capacity to prevent damages [7].

1- Introduction

2- System modelling

3- Surge protective device

4- Stochastic analysis

5- Conclusion

References

Abstract

Vacuum circuit breaker operation causes transient overvoltages that may lead to severe damages to transformer insulation. Since the parameters affecting these overvoltages have stochastic nature, a statistical analysis may provide detailed insight into the overvoltages from the point of view of insulation coordination applications. In this paper, a statistical analysis of the overvoltage variations of the step-up transformer during vacuum circuit breaker operation is conducted. Some variables, including switching angle, current chopping, high-frequency current quenching capability, and wind turbine power, are assumed as random variables. Besides, some indicators representing the switching overvoltage characteristics such as the amplitude, the number of restrikes, and the rate of rise are extracted. By performing a probabilistic analysis, the destructiveness due to switching overvoltages on typical transformer insulation can be investigated. The sensitivity of different surge protective devices and their effectivities are also analyzed and statistically evaluated. The applied methodology is very useful because of the stochastic nature of the switching overvoltages. In this work, the impact of protective equipment on each of the overvoltage indicators is analyzed. It is also found that considering only the amplitudes of transient overvoltages is not enough to draw conclusions about the safety of the transformer when exposed to frequent switching surges.

Introduction

VCBs are commonly used in offshore wind farms due to their ability to eliminate high fault currents and their low maintenance costs. Due to wind speed variations, numerous switching actions (energization and de-energization) commonly occur in wind farms every day. VCB switching transients reach the step-up transformer through the short cable connected to the wind turbine. In case, the amplitude of a switching transient voltage rises above the BIL; it can lead to transformer winding failures. Previous statistical studies related to VCB opening were only focused on the amplitude of transient overvoltages regardless of their rise times [1–4] However, some transformer failures due to VCB operations have indicated that the leading cause of the failure was the high du/dt [5]. Recently performed detailed research showed that due to fast transients and during resonance conditions, the voltages with high amplitudes are non-linearly distributed along the windings [6], and the high du/dt accelerates failure mechanisms because of high interturn overvoltages in the transformer windings; causing coil-to-coil failures in the top or in the middle of the winding. Besides, the critical voltage of the transformer BIL remarkably decreases with the reduction of the rise time [7,8] and the amplitude [7] of the transient voltage applied. Moreover, the experimental studies confirm the effects of lightning impulses on oiled paper [9] and very fast transients on cast-resin insulation [10]. Accordingly, the rate of rise and the number of transient surges should be considered alongside their amplitude in a stochastic analysis in order to accurately assess the effects of the switching overvoltages on transformer insulation lifetime. The characteristics of the transient overvoltages occurring due to VCB operation depend on various parameters. The current chopping phenomenon in VCBs being one of the key parameters that contribute to the reignition overvoltages. Experimental tests conducted in Ref. [11] however, showed the low dependency of this parameter on the operation instant.

1- Introduction

2- Frequency response analysis

3- Measurement of transformer winding deformation based on frequency response method

4- Frequency response analysis based on mathematical statistical indicators

5- Conclusion and future work

References

Abstract

Since the power transformers are indispensable in power systems, their reliable operation are of significance. Winding deformation is one of the most common failures within transformers. At present, many methodologies have been proposed to detect these faults, and frequency response analysis (FRA) technique is most frequently used because of its low cost, convenience, simplicity and effectiveness. However, there is no standard and reliable code to interpret the mechanical deformations from FRA traces as so far. In this study, an actual transformer experimental platform was used to simulate variable winding deformation faults in order to standardize the interpretation of winding fault degree from FRA data. The measured FRA raw data are processed from the perspective of statistics, and some numerical indices are found to be more suitable for the diagnosis of transformer winding deformation degree.

Introduction

The stable, reliable, safe and high quality power supply concerns the lives of hundreds of millions of people. As one of the most important pieces of equipment in power system, the stable operation of power transformer plays a critical role in the safe operation of the power system [1,2]. Statistics indicates that 25 percent of transformer faults were caused by winding mechanical deformation. Since the low voltage impulse (LVI) method was first introduced by Lech and Tyminski from Poland in 1966, many methodologies have been successively proposed to detect transformer winding deformation, for instance, the short circuit impedance (SCI) method, frequency response analysis (FRA) method and vibration method. SCI is based on the comparison and analysis of two short circuit impedance tests. In FRA, the frequency response traces are compared with its fingerprint to diagnose the occurrence of internal faults within power transformers. The vibration diagnosis method utilizes the characteristics that the transformer tank vibration will change when the winding fails [3]. Recently, some scholars present other improved techniques, namely, the sweep frequency impedance (SFI) method, which is the combination of SCI and FRA [1], the online impulse frequency response analysis (online IFRA) [4], the electromagnetic wave method [5] and the Lissajous locus method [6]. Above all, FRA is an effective and economic diagnostic technique for detection of mechanical deformations inside a power transformer [7]. FRA is sensitive to failures in the windings and iron core. What’s more, it can provide reliable information about the geometry of active parts of the power transformer without any needs to dismantle the unit. FRA uses input voltage as the input signal and another voltage measured at the other terminal of transformer winding as the output signal, besides, the measured current could also be used as output signal. In most cases, the output voltage is used. However, a standard and widely accepted code for interpretation of FRA traces can be improved; at present, experienced personnel are still needed to diagnose winding faults by using visual inspection [8], which is still a matter of great concern. At present, there are variable FRA interpretation methods, for instance, the equivalent electric model simulation method [9], the artificial intelligence method [10], and the mathematical statistics method [11]. In the first method, finite element analysis (FEA) of transformer winding is often used to build its equivalent electric model [12], to aid in interpreting the FRA curves. N. Hashemnia has studied the impact of winding axial displacement and radial deformation fault on equivalent electric parameters and FRA curves based on FEA [8,13], to improve the detection of winding faults. Besides, Z. W. Zhang has built a hybrid model based on FEA and multi-transmission line (MTL) to study the FRA curve beyond 1 MHz [14]. In the artificial intelligence method, M. Bigdeli has applied support vector machine (SVM) algorithm to classify the winding faults [15].