Advanced Technologies for Energy Storage and Electric Vehicles

1. Introduction

The demand for energy in the world has been growing rapidly. The fast depletion of available natural resources such as coal and oil leads to the inability of conventional systems to meet growing energy demands equitably and sustainably. The current trends indicate clearly that the world will be facing constraints in the indigenous availability of conventional energy resources. Therefore, there is a requirement to efficiently and economically meet energy needs. In recent years, modern electrical power grid networks have become more complex and interconnected to handle the large-scale penetration of renewable energy-based distributed generations (DGs) such as wind and solar PV units, electric vehicles (EVs), energy storage systems (ESSs), the ever-increasing power demand, and restructuring of the power market [1,2]. During the past decades, the decarbonization of the power sector is at the heart of energy transformation roadmaps due to increasing environmental awareness throughout the world. Renewable energy sources (RESs) such as wind, solar, hydro, biomass, geothermal, etc., can generate power from natural sources to reduce energy shortage and emissions. As the penetration of RESs and EVs grows, it is imperative to understand and study the impacts and implications of a high penetration of these resources over the electrical grid. However, without combining with ESSs, these RESs and EVs cannot be used as long-term electrical solutions [3]. The variability and intermittency of these RESs and EVs is leading to larger uncertainty in power networks, apart from the uncertainty in the power demand, which implies a more complex operation and control.

ESSs have become inevitable as there has been a large-scale penetration of RESs and an increasing level of EVs. Energy can be stored in several forms, such as kinetic energy, potential energy, electrochemical energy, etc. This stored energy can be used during power deficit conditions. These storage systems provide reliable, continuous, and sustainable electrical power while providing various other benefits, such as peak reduction, provision of ancillary services, reliability improvement, etc. ESSs are required to handle the power deviation/mismatch between demand and supply in the power grid. For standalone as well as grid-connected systems, these ESSs are used for providing continuous power generation from the RESs [4]. Recently, the combining of multiple ESSs has increased as it provides more benefits than using a single ESS. Hybrid ESSs incorporate the characteristics of various energy storage elements to increase the system’s reliability and stability.

EVs have been used to overcome the problem of pollution and emissions. However, the proper infrastructure of electric vehicle (EV) charging plays a vital role to ensure a full round-trip can be completed. Several consumers face driving range anxiety, the availability of EV charging stations, and EV charging times as barriers to purchasing EVs as they take a longer time to charge. Recently, proposed wireless power transfer techniques can recharge EVs whilst they are running and effectively reduce the size of the battery pack, resulting in an increased overall reliability and efficiency [5]. Many researchers proposed new grid integration techniques, the optimal utilization of RESs as a solution for EV charging stations, and the integration of EVs.

2. Editorial Content

Sun et al. [6] presented a review of EV technology development in key fields, such as the battery, charging, the electronic motor, charging infrastructure, and emerging technology. The development of battery technology is very important for EV penetration. In addition to traditional lead-acid batteries, a wider range of battery types are being used in EVs. Nickel–metal hydrid batteries, ZEBRA batteries, and lithium-ion (Li-ion) batteries are employed as the power source of EVs because they have a higher specific energy, higher power density, and are more environmentally friendly. At present, Li-ion batteries are the most widely used. The usage of metal–air batteries and supercapacitors is still being researched but may be a target for all EVs. The charging of batteries can help relieve range anxiety. To solve the problem of charging EVs, many efforts have been undertaken. On-board chargers have been designed with the characteristics of being lightweight, of a small size, a high performance, and control simplicity. Inductive charging provides the possibility of charging without the limit of the physical cable connection. Charging becomes more flexible and the cost of the EV can also be reduced using the technology of dynamic charging. Battery swapping is another alternative for efficient and hassle-free charging methods. A battery swapping station (BSS) cannot only offer a battery swapping service but also provide energy and ancillary services to the distribution grid.

Han et al. [7] presented an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real-time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. The determined EF is dependent on the optimal co-state of Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. The effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of PHEVs, based on a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies, and compared with the existing strategy.

Rangarajan et al. [8] presented the challenges and opportunities associated with lithium-ion (Li-ion) batteries for EVs in the form of a brief review. The Li-ion battery has advanced to its current state of a high energy density, high cycle life, and high efficiency through high levels of research and has clear fundamental benefits. The Li-ion battery has emerged as the heart of electric cars, and the focus has now shifted to the automotive sector. Liquid crystal displays have evolved to meet the demands of automobiles. International research groups and the performance of the production of electric vehicles are used to discuss and inform vehicle-driven battery targets. However, research on new electrode materials continues to push the boundaries of cost, energy density, power density, cycle life, and safety.

Skouras et al. [9] presented and analyzed the current state of the art in EVs along with various technological aspects, such as charging techniques and a wireless power transfer. Emerging issues, such as the connection of EVs to smart electrical grids (SEG) and autonomous driving requirements, are analyzed. The main features, the requirements of vehicle-to-grid (V2G) communications, as well as future developments and scenarios of electrification are also presented and analyzed. Moreover, integration issues with currently deployed fifth-generation (5G) mobile wireless networks are also outlined to ensure optimum transmission and reception quality in V2G communications and improved the user experience. This integration is also expanded in autonomous vehicles (Avs) technology (self-driving objects) since optimized information processing from various diverse sources is required to ensure advanced traffic management aspects.

One of the main obstacles to accepting Evs is the limitation of charging stations, which consist of high-charge batteries and high-energy charging infrastructure. Shahir et al. [10] proposed a transformer-less topology for boosting dc-dc converters with the higher power density and lower switch stress, which may be a suitable candidate for the high-power fast-charging battery chargers of Evs. Two operating modes of the proposed converter, continuous current mode (CCM), and discontinuous current mode (DCM) are analyzed in detail. Additionally, the critical inductances and design considerations for the proposed converter are calculated.

Divakaran et al. [11] presented a new design, preliminary development, and results for an inexpensive reusable, liquid-cooled, modular, hexagonal battery module that may be suitable for some mobile and stationary applications that have a high charge and or discharge rate requirements. The battery temperature rise was measured experimentally for a 6-parallel 18,650 cylindrical cell demonstrator module over complete discharge cycles at discharge rates of 1C, 2C, and 3C. The measured temperature rises at the hottest point in the cells, at the anode terminal, were found to be 6, 17, and 22 °C, respectively. The thermal resistance of the system was estimated to be below 0.2 K/W at a coolant flow rate of 0.001 Kg/s. The proposed liquid-cooled module appeared to be an effective solution for maintaining cylindrical Li-ion cells close to their optimum working temperature.

Temporelli et al. [12] presented a literature review to understand how large and variable the main impacts are due to automotive batteries’ life cycle, with particular attention to climate change impacts, and to support researchers with some methodological suggestions in the field of automotive batteries’ life cycle assessments (LCAs). In electric and hybrid vehicles’ LCAs, batteries play a central role and are in the spotlight of the scientific community and public opinion. Automotive batteries constitute, together with the powertrain, the main differences between EVs and internal combustion engine (ICE) vehicles. For this reason, many decision-makers and researchers wondered whether the energy and environmental impacts from battery production can exceed the benefits generated during the vehicle’s use phase. The results show that there is high variability in the environmental impact assessment. Nevertheless, either using the lower or upper bounds of this range, EVs result in being less carbon-intensive in their life cycle than the corresponding diesel or petrol vehicles.

Zhang et al. [13] presented a review of a comprehensive hierarchical classification scheme for energy management strategies (EMSs). In the first category, offline EMSs are presented based on the level of driving information under global optimization-based EMSs and rule-based EMSs. In the second category, online EMSs are layered as instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs. Since the presented scheme covers various approaches in terms of targeted solution objectives, optimality, and real-time implementation, an important number of literature studies are extensively overviewed. The principle of each approach along with its pros and cons are illustrated and compared within the design and operational characterization of the proposed scheme. Finally, a good number of emerging innovative EMSs and recent literature that have not been covered in previous review papers are summarized and important future trends for hybrid EVs are highlighted. This study is intended to serve as a comprehensive reference for researchers in the field of the development and optimization of EMSs.

Mohammad et al. [14] presented a comprehensive review of all aspects of modeling a grid-connected EV-PV (photovoltaic) system, viz., control architectures, charging algorithms, and uncertainty analysis. This work aims to provide an evaluation of these aspects to enable the researchers to model a grid-connected EV-PV system for carrying out impact or implementation studies of EV integration into the distribution system. The grid is represented by a distribution network as EV and PV both are on the distribution side. Throughout this work, the EV-PV system is considered a single entity (limited to the times when connected to the grid for charging or vehicle-to-grid), and the PV is considered a complementary energy source to charge EVs other than the grid.

Nour et al. [15] presented a review of the potential negative impacts of EV charging on electric power systems mainly due to uncontrolled charging and how through controlled charging and discharging, those impacts can be reduced and become even positive impacts. The impacts of uncontrolled EV charging on the increase in the peak demand, voltage deviation from the acceptable limits, phase unbalance due to the single-phase chargers, harmonics distortion, overloading of the power system equipment, and increase in power losses are presented. Furthermore, a review of the positive impacts of controlled EV charging and discharging, and the electrical services that it can provide, such as frequency regulation, voltage regulation and reactive power compensation, congestion management, and improving the power quality, are presented. Moreover, a few promising research topics that require further investigation in future research are briefly discussed. Furthermore, the concepts and general background of EVs, EVs market, EV charging technology, and the charging methods are presented.

Wang et al. [16] proposed an optimized power distribution method for hybrid electric ESSs for EVs. The hybrid energy storage system (HESS) uses two isolated soft-switching symmetrical half-bridge bidirectional converters connected to the battery and supercapacitor (SC) as a composite structure of the protection structure. The bidirectional converter can precisely control the charge and discharge of the SC and battery. Spiral wound SCs with mesoporous carbon electrodes are used as the energy storage units of EVs. This work analyzed the deficiencies of the topology and control strategies of traditional HESSs, improves the previous topology, and proposes an optimized power allocation strategy based on the current control of EVs, thereby accurately predicting future power requirements. In addition, based on the current control, the SOC control of SCs is added based on the driving speed of EVs, thereby reducing the overcharge and over-discharge of the battery, improving the climbing performance of the EV, improving the energy utilization rate, and reducing the battery aging effect.

Karimi et al. [17] reviewed the most advanced developed thermal management systems (TMSs) in EV applications. In this work, the target cell was the new hybrid lithium-ion capacitor (LiC) which is a combination of supercapacitors and lithium-ion (Li-ion) batteries. Such a hybrid technology can operate at high current rates in high-power applications. Nevertheless, the main issue in such high-power rates is hot spots during fast charging and discharging. Therefore, a robust TMS is needed to control the maximum temperature of the LiC cell/module. The proposed TMSs in this work for Li-ion batteries, especially lithium-titanate oxide (LTO) batteries, have been explained as well. The investigated TMSs are classified into active, passive, and hybrid cooling methods.

Tavares Nascimento et al. [18] evaluated the viability of battery energy storage systems (BESS) in emerging economies. This work takes into account the replacement of the grid or diesel generator for the costly periods to meet the demand of consumers that takes advantage of time-of-use rate plans. To evaluate the BESS entry scenario, the work employs the cost variation through the lifecycle regarding different economic and technical parameters. Four different scenarios with different energy sources combinations are analyzed. Despite efforts to increase the BESS implementation, access to the technology is still not affordable for a large amount of consumers. A large-scale system, such as the one analyzed, is expensive for consumers located in emerging economies, and this reality is aggravated by a currency that is devalued against others and import costs. The advantage of the time-of-use rate throughout the day does not establish an affordable perspective, even for a high variation of tariffs and fuel costs. This work balances the costs of buying energy from other sources with the costs of purchasing and maintaining a BESS.

Ram et al. [19] demonstrated a simulation of a hybrid energy storage system consisting of a battery and a fuel cell in parallel operation. The proposed system consists of an electrolyzer along with a switching algorithm. The electrolyzer consumes electricity to intrinsically produce hydrogen and store it in a tank. This implies that the system consumes electricity as input energy as opposed to hydrogen being the input fuel. The hydrogen produced by the electrolyzer and stored in the tank is later utilized by the fuel cell to produce electricity to power the load when needed. Energy is, therefore, stored in the form of hydrogen. A battery of a lower capacity is coupled with the fuel cell to handle transient loads. A parallel control algorithm is developed to switch on/off the charging and discharging cycle of the fuel cell and battery depending upon the connected load.

Zhao et al. [20] developed a novel business model to enable virtual storage sharing among a group of users to promote the efficient utilization of energy storage. Specifically, a storage aggregator invests and operates the central physical storage unit by virtualizing it into separable virtual capacities and selling it to users. Each user purchases the virtual capacity and utilizes it to reduce energy costs. This work formulates the interaction between the aggregator and users as a two-stage optimization problem. In stage 1, over the investment horizon, the aggregator determines the investment and pricing decisions. In stage 2, in each operational horizon, each user decides the virtual capacity to purchase together with the operation of the virtual storage.

Lan et al. [21] proposed a machine learning-based approach for energy management in renewable microgrids considering a reconfigurable structure based on the remote switching of tie and sectionalizing. The proposed method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). To mitigate the charging effects of HEVs on the system, two different scenarios are deployed: one coordinated and the other intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is proposed. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. The simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios.

Aljohani et al. [22] presented the dynamic simulations of the hour-to-hour operation of the distribution feeder to measure the grid’s reaction to the EVs charging and discharging process. The available energy stored in the EVs can be utilized to free the distribution system from some of the congested loads at certain times or to allow the grid to charge more EVs at any time of the day, including peak hours.

Triviño et al. [23] reviewed the technologies applied to the wireless charge of EVs. In particular, it focuses on the technologies based on the induction principle, the capacitive-based techniques, and those that use radiofrequency waves and laser power. As described, the convenience of each technique depends on the requirements imposed on the wireless power transfer. Specifically, one can state that the power level, the distance between the power source and the EV or whether the transfer is executed with the vehicle on the move or not, or the cost are critical parameters that need to be taken into account when deciding which technology to use. In addition, each technique requires some complementary electronics. This work reviewed the main components that are incorporated into these systems and it provided a review of their most relevant configurations.

Cunanan et al. [24] provided a comprehensive review to examine the working mechanism, performance metrics, and recent developments of the aforementioned heavy-duty diesel vehicles (HDVs) powertrain technologies. A detailed comparison between the three powertrain technologies, highlighting the advantages and disadvantages of each, is also presented, along with future perspectives of the HDV sector. Overall, diesel engines in HDVs will remain an important technology in the short-term future due to the existing infrastructure and lower costs, despite their high emissions, while battery electric HDV technology and hydrogen fuel cell HDV technology will be slowly developed to eliminate their barriers, including costs, infrastructure, and performance limitations, to penetrate the HDV market.

Hildermeier et al. [25] identified the greatest value from integrating EVs into the power grid, that being that the power grid can be generated by charging EVs when and where it is most beneficial for the power system while ensuring consumers’ mobility needs are met at an affordable cost. An emerging body of research on EV grid integration focuses on modeling the cost of integration under various scenarios, but few studies look at the existing promising practices that are based on the policy tools in use today. This work conducted a qualitative review of policies for EV grid integration in the European Union (EU) and United States markets. The work also explores the implications of these practices for policymakers and regulators in the EU.

Hasan et al. [26] reviewed several topics including EV systems, energy management systems, challenges and issues, and the conclusions and recommendations for future work. EV systems discuss all components that are included in producing the Li-ion battery. The energy storage section contains batteries, supercapacitors, fuel cells, hybrid storage, power, temperature, and heat management. Energy management systems consider battery monitoring for current and voltage, battery charge–discharge control, estimation and protection, and cell equalization. This work also discusses some of the critical aspects of Li-ion batteries, including temperature and safety, life-cycle and memory effects, environmental effects, and recycling processes.

Chandran et al. [27] presented the state of charge (SoC) estimation of Li-ion battery systems using six machine learning algorithms for EVs application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). The error analysis of the model is carried out to optimize the battery’s performance parameter.

Van Mierlo et al. [28] provided an overview of the current state of the art in EV developments and innovation, related to the vehicle components, their charging infrastructure, and interaction with the grid, but also related to battery material sciences and power electronics engineering up to environmental assessments, market considerations, and synergies with shared and autonomous vehicles. This work also provided recommendations for future developments and trends. The EV purchase price and driving range have improved, due to the current optimization of battery technologies and their system interfaces. This will be further improved by making use of innovative solid-state batteries.

3. Closing Remarks and Future Challenges

The papers in this Editorial reveal an exciting research area, namely the “Advanced Technologies for Energy Storage and Electric Vehicles” that is continuing to grow. This editorial addressed various technology development of EVs, the life cycle assessment of EV batteries, energy management strategies for hybrid EVs, integration of EVs in the distribution network, advanced machine learning-based energy management of renewable microgrids, and the wireless power transfer technologies applied to EVs. However, there exist several future challenges for developing advanced technologies for energy storage and EVs, including optimal location and sizing of EV charging stations, benefits maximization of the parking lot owner, maximizing the aggregator profit, minimizing EV charging costs, minimizing the total operating cost of the system, maximize the revenue/social welfare of the grid operator, the operation of hybrid energy storage systems, etc. I believe that the papers that are reviewed in this Editorial will have a practical importance for the development of advanced technologies for energy storage and EVs.