Search Results (36)

Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO₄) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability. Full article

Catenary-powered networks are expected to play a pivotal role in urban energy transition, due to the larger deployment of electric public transport, in-motion-charging (IMC) vehicles, and catenary-backed electric vehicle chargers. However, there are technical challenges that must be overcome to ensure the successful utilization of existing networks without compromising vehicle performance or compliance with network standards. This paper aims to validate the use of battery energy storage systems (BESS) built from second-life batteries as a means of retrofitting catenary-powered traction networks. The objective is to increase the network robustness without creating a negative impact on its overall operational efficiency. Consequently, more electrification projects can be implemented using the same network infrastructure without substantial modifications. Furthermore, a power management scheme is presented which allows the voltage and current range allowed in the catenary network and the BESS maximum charging rate to be controlled from user-defined values. The proposed control scheme is adept at customizing the BESS size for the specific application under consideration. Validation is performed on a case study of the trolleybus system in Bologna, Italy. Full article

The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential disruptions in power generation. This paper addresses the problem of optimizing SoC estimation to enhance the reliability and efficiency of V2G scheduling and MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing the Levenberg–Marquardt algorithm and varying the number of hidden neurons to achieve better performance; performance was measured by the maximum coefficient of determination (R²) and the minimum mean squared error (MSE). Utilizing the NASA Prognostics Center of Excellence (PCoE) dataset, we validate the model’s capability to accurately predict the life cycle of EV batteries. Our proposed FFBPN model demonstrates superior performance compared to existing methods from the literature, offering significant implications for future V2G system developments. The comparison between training, validation, and testing phases underscores the model’s validity and precisely identifies the characteristic curves of FFBPN, showcasing its potential to enhance profitability, efficiency, production, energy savings, and minimize environmental impact. Full article

As the adoption of Electric Vehicles (EVs) accelerates, driven by increasing urbanization and the push for sustainable infrastructure, the need for innovative solutions to support this growth has become more pressing. Vehicle-to-Grid (V2G) technology presents a promising solution by enabling EVs to engage in bidirectional interactions with the electrical grid. Through V2G, EVs can supply energy back to the grid during peak demand periods and draw power during off-peak times, offering a valuable tool for enhancing grid stability, improving energy management, and supporting environmental sustainability. Despite its potential, the large-scale implementation of V2G faces significant challenges, particularly from a technological and regulatory standpoint. The success of V2G requires coordinated efforts among various stakeholders, including vehicle manufacturers, infrastructure providers, grid operators, and policymakers. In addition to the technical barriers, such as battery degradation due to frequent charging cycles and the need for advanced bidirectional charging systems, regulatory frameworks must evolve to accommodate this new energy paradigm. This review aims to provide a comprehensive analysis of V2G technology, focusing on different perspectives—such as those of users, vehicles, infrastructures, and the electricity grid. This study will also explore ex ante, ex post, and ongoing assessment studies, alongside the experiences of pioneer cities in implementing V2G. Full article

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles’ batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. Full article

Vehicle-to-everything (V2X) refers to the technology that enables electric vehicles (EVs) to push their battery energy back to the grid. The system’s V2X integration includes key functions like V2G, V2H, V2B, etc. This paper explores the preferences of Swedish EV drivers in contributing to V2X programs through an online questionnaire. Respondents were asked to answer questions in three contexts: (1) claims related to their EV charging, (2) V2G application by EV, and (3) V2H application by EV. The respondents were questioned about the importance of control, pricing, energy sustainability and climate issues, impact on the battery, the acceptability of V2X, range anxiety, financial compensation, as well as how and where they prefer to charge the EV. The results of the survey indicate that Swedish EV drivers are more interested in the V2H application than in V2G. Additionally, they express more concern about range anxiety than battery degradation due to the V2X application. Full article

Batteries are the core component of electric vehicles (EVs) and energy storage systems (ESSs), being crucial technologies contributing to carbon neutrality, energy security, power system reliability, economic efficiency, etc. The effective operation of batteries requires precise knowledge of the state of health (SOH) of the battery. A lack of proper knowledge of SOH may lead to the improper use of severely aged batteries, which may result in degraded system performance or even a risk of failure. This makes it important to accurately estimate battery SOH using only operational data, and this is the main topic of this study. In this study, we propose a novel method for online SOH estimation for batteries featuring simple online computation and robustness against measurement anomalies while avoiding the need for full cycle discharging and charging operation data. Our proposed method is based on incremental capacity analysis (ICA) to extract battery aging feature parameters and regression using simple piecewise linear interpolation. Our proposed method is compared with back-propagation neural network (BPNN) regression, a popular method for SOH estimation, in case studies involving actual data from battery aging experiments under realistic discharging and temperature conditions. In terms of accuracy, our method is on par with BPNN results (about 5% maximum relative error), while the simplicity of our method leads to better computation efficiency and robustness against data anomalies. Full article

With the surge of electric vehicles, fast charging has become one of the major challenges for the development of Li-ion and Li metal batteries. The degradation of battery electrodes at fast charging has been identified as among the gating factors. While there have been extensive studies on anode and cathode degradation modes, not sufficient efforts have been made to dive deep into the kinetics of battery charging and its influence on electrode degradation, especially during fast charging. This review presents a comprehensive yet concentrated perspective into such issues. By tracing back to the kinetic origins of battery charging, it is revealed that the intrinsic properties of electrode active materials and the microstructures of electrode are of great importance in determining electrode kinetics. Most of the electrode degradation modes are closely related to the high overpotentials and the spatial inhomogeneity in Li concentration and pertinent characteristics, which are results of the sluggish electrode kinetics during fast charging. Approaches to mitigate electrode degradation are summarized from the aspect of improving electrode kinetics and circumventing detrimental side reactions. Full article

Innovations in electric vehicle technology have led to a need for maximum energy storage in the energy source to provide some extra kilometers. The size of electric vehicles limits the size of the batteries, thus limiting the amount of energy that can be stored. Range anxiety amongst the crowd prevents the entire population from shifting to a completely electric mode of transport. The extra energy harnessed from the kinetic energy produced due to braking during deceleration is sent back to the batteries to charge them, a process known as regenerative braking, providing a longer range to the vehicle. The work proposes efficient machine learning-based methods used to harness maximum braking energy from an electric vehicle to provide longer mileage. The methods are compared to the energy harnessed using fuzzy logic and artificial neural network techniques. These techniques take into consideration the state of charge (SOC) estimation of the battery, or the supercapacitor and the brake demand, to calculate the energy harnessed from the braking power. With the proposed machine learning techniques, there has been a 59% increase in energy extraction compared to fuzzy logic and artificial neural network methods used for regenerative energy extraction. Full article

Electric vehicles (EVs) and photovoltaic (PV) systems have been progressively incorporated into the grid in recent years principally due to two factors: reduced energy costs and lower pollutants. Numerous studies have investigated how integrating PV and EVs into the grid may affect specific people. It is crucial to understand that the electricity grid will experience the combined effects of PV–EV integration as PV and EV penetration increases. The primary motivation for PV’s integration with Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) services is the aim to reduce charging costs from discharging; moreover, another prerequisite must be satisfied before PV arrays will be able to provide V2G services. The range between the driving limit and EV battery degradation should be reasonable. The way EVs charge and discharge will be impacted by these factors. Numerous analyses are required in order to control the power between various source and load scenarios. In order to balance grids and manage frequency, controllers such as Improved Particle Swarm Optimization (IPSO), Improved Ant Colony Optimization (IACO), and Improved Mayfly Optimization (IMO) are used. As a result, V2G/G2V helps feed electricity back into the grid. By providing the proper duty cycle ratio, the proposed controller regulates converter switching. This study allowed for the performance analysis and operation simulation of a grid-connected PV/EV/Grid system. The purpose of this system was to maximize PV self-consumption while maintaining power quality characteristics like harmonics, grid voltage/current, and power factor. Full article

Accurate capacity estimation of onboard lithium-ion batteries is crucial to the performance and safety of electric vehicles. In recent years, data-driven methods based on partial charging curve have been widely studied due to their low requirement of battery knowledge and easy implementation. However, existing data-driven methods are usually based on a fixed voltage segment or state of charge, which would be failed if the charging process does not cover the predetermined segment due to the user’s free charging behavior. This paper proposes a capacity estimation method using multiple small voltage sections and back propagation neural networks. It is intended to reduce the requirement of the length of voltage segment for estimating the complete battery capacity in an incomplete charging cycle. Firstly, the voltage segment most possibly covered is selected and divided into a number of small sections. Then, sectional capacity and skewness of the voltage curve are extracted from these small voltage sections, and severed as health factors. Secondly, the Box–Cox transformation is adopted to enhance the correlation between health factors and the capacity. Thirdly, multiple back propagation neural networks are constructed to achieve capacity estimation based on each voltage section, and their weighted average is taken as the final result. Finally, two public datasets are employed to verify the accuracy and generalization of the proposed method. Results show that the root mean square error of the fusion estimation is lower than 4.5%. Full article

The actual research in terms of energy focuses drastically on the use of green energy resources. Hydropower systems have been the most known green sources for years. However, the hydropower systems, which are seasonal and most exploited, do not cover the speed of increasing daily demand. The injection of solar power could be a supporting alternative, but it is only in daylight, weather dependent and intermittent. Therefore, a storage system is required. The batteries are the quick recourse. Not only the energy sector, but also the transport systems are not left behind; they are striving to turn green. Therefore, they are turning to electric vehicles (EVs) and electric moto-bicycles (EMBs). On the other hand, this option tends to be a sharply increasing demand that can be a burden to the grid, i.e., the increase in the EVs and EMBs implies increases in power demand, grid components and pressure on the grid. Fortunately, the EVs use batteries to store energy for their use. Therefore, the EVs are the power storage system, they become part of the power management system and they can save the power surplus. With the injection of PV solar power, there is no need for an extra storage system, as the EVs are charged from the grid and store the solar energy that can be used later after sunset. The bi-directional off-board charger is a solution as it allows the grid to charge the vehicle (G2V) and the vehicle to send power back to grid (V2G). The inclusion of EVs in power management introduces the concept of vehicle-to-vehicle (V2V) when one EV can charge another, and the vehicle-to-load (V2X) where the EV can supply power to EMBs or any load. The V2G, G2V, V2X, the inclusion on solar energy to the grid and the behavior of the grid in that scenario will be illustrated in this paper. Full article

Electric vehicles and energy storage systems are technologies in the stage of intensive development. One of the innovative ways to use electric cars is the Vehicle to Grid (V2G) concept. V2G charging points are characterized by the ability of bidirectional energy flow while charging EV/BEV (Electric Vehicles/Battery Electric Vehicles). In periods of low energy consumption and the presence of the highest shares of renewable sources, the cleanest electricity is drawn from the grid at the lowest prices and stored in a “mobile warehouse”, which is an electric car. During the reported peaks in electricity demand and the presence of high tariffs, the previously stored energy may be sold back to the distribution network operator. Thanks to this application, the technology determines the highest profitability of the system and assigns EV/BEV the ability to manage electricity flows, while improving the energy balance of the economy. The prospects for the spread of V2G have increased along with the growing requirements for domestic economies, closely related to the significant share of renewable energy sources. The vision of connecting EV/BEV with the power grid creates completely new ways of managing energy and makes it possible to build smart agglomerations in line with the Smartcity idea. Especially since Turkey is one of the countries promoting this idea. The scientific aim of the study is to maximize the aggregator’s profits for V2G by creating a coalition with renewable energy producers and combining the capacities of many EVs and offering their total capacities to the electricity markets. The subject of the research was to obtain extensive knowledge about the vehicle–grid interactions taking place in the Turkish power system. For this purpose, an analysis is conducted to determine the optimal preferred operating points and the amount of regulation proposals that maximize the profit of the EV users while satisfying the constraints of each stochastic parameter. The results show the system benefits from the implementation of the algorithms are significant to optimal bidirectional V2G impacts on distribution systems with high penetration of EVs. The research can find practical applications in assessing the role of electric vehicles and their integration in the vehicle–grid system in power systems. At the same time, pointing to the benefits related to the implementation of such solutions for Turkey and other countries in the field of electromobility, stability of energy systems, and energy independence through the possibility of achieving the desired synergy effect. Full article

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system. Full article

The electric vehicle (EV) industry is quickly growing in the present scenario, and will have more demand in the future. A sharp increase in the sales of EVs by 160% in 2021 represents 26% of new sales in the worldwide automotive market. EVs are deemed to be the transportation of the future, as they offer significant cost savings and reduce carbon emissions. However, their interactions with the power grid, charging stations, and households require new communication and control techniques. EVs show unprecedented behavior during vehicle battery charging, and sending the charge from the vehicle’s battery back to the grid via a charging station during peak hours has an impact on the grid operation. Balancing the load during peak hours, i.e., managing the energy between the grid and vehicle, requires efficient communication protocols, standards, and computational technologies that are essential for improving the performance, efficiency, and security of vehicle-to-vehicle, vehicle-to-grid (V2G), and grid-to-vehicle (G2V) communication. Machine learning and deep learning technologies are being used to manage EV-charging station interactions, estimate the charging behavior, and to use EVs in the load balancing and stability control of smart grids. Internet of Things (IoT) technology can be used for managing EV charging stations and monitoring EV batteries. Recently, much work has been presented in the EV communication and control domain. In order to categorize these efforts in a meaningful manner and highlight their contributions to advancing EV migration, a thorough survey is required. This paper presents existing literature on emerging protocols, standards, communication technologies, and computational technologies for EVs. Frameworks, standards, architectures, and protocols proposed by various authors are discussed in the paper to serve the need of various researchers for implementing the applications in the EV domain. Security plays a vital role in EV authentication and billing activities. Hackers may exploit the hardware, such as sensors and other electronic systems and software of the EV, for various malicious activities. Various authors proposed standards and protocols for mitigating cyber-attacks on security aspects in the complex EV ecosystem. Full article