Energy Demand Of Electric Vehicles Research Articles

Charging Strategies for Electric Vehicles from Renewable Hybrid Systems

Objective: The aim of this study is to analyze charging strategies for electric vehicles (EVs) in a hybrid system connected to the conventional electricity grid, to optimize the use of renewable energies. Theoretical Framework: This paper presents the concepts of hybrid systems, photovoltaic power generation, and electric vehicle energy demand. It emphasizes the importance of integrating renewable energy into the transportation sector to reduce greenhouse gas emissions and promote sustainability. Method: The research was based on obtaining load curves and photovoltaic generation in different climatic conditions. The energy available for charging electric vehicles was calculated, considering the limitations of the hybrid system. Different charging power configurations were studied. Results and Discussion: The results demonstrate the feasibility of recharging EVs with renewable energy and highlight the operational flexibility provided by storage systems. Different climate scenarios affect energy availability for charging, highlighting the importance of adaptive strategies. Research Implications: This research contributes to developing sustainable strategies in the transportation sector by providing insights into integrating electric vehicles and renewable energy. Practical implications include reducing carbon emissions and decreasing dependence on fossil fuels. Originality/Value: This study stands out for its specific analysis of EV charging strategies in a hybrid context, providing new approaches for the efficient use of renewable energy in transportation. The integration of storage systems and adaptation to climatic conditions are innovative aspects that add value to the research.

Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach

The rise of electric vehicles (EVs) has significantly transformed transportation, offering environmental advantages by curbing greenhouse gas emissions and fossil fuel dependency. However, their increasing adoption poses challenges for power systems, especially distribution systems, due to the direct connection of EVs with them. It requires robust infrastructure development, smart grid integration, and effective charging solutions to mitigate issues like overloading and peak demand to ensure grid stability, reliability, and sustainability. To prevent local equipment overloading during peak load intervals, the management of EV charging demand is carried out in this study, considering both the time to deadline and the energy demand of EVs. Initially, EVs are prioritized based on these two factors (time and energy)—those with shorter deadlines and lower energy demands receive higher rankings. This prioritization aims to maximize the number of EVs with their energy demands met. Subsequently, energy allocation to EVs is determined by their rankings while adhering to the transformer’s capacity limits. The process begins with the highest-ranked EV and continues until the transformer nears its limit. To this end, an index is proposed to evaluate the performance of the proposed method in terms of unserved EVs during various peak load intervals. Comparative analysis against the earliest deadline first approach demonstrates the superior ability of the proposed method to fulfill the energy demand of a larger number of EVs. By ensuring sustainable energy management, the proposed method supports the widespread adoption of EVs and the transition to a cleaner, more sustainable transportation system. Comparative analysis shows that the proposed method fulfills the energy needs of up to 33% more EVs compared to the earliest deadline method, highlighting its superior performance in managing network loads.

Battery electric vehicle energy demand in urban energy system modeling: A stochastic analysis of added flexibility for home charging and battery swapping stations

The successive upsurge in the penetration rate of battery electric vehicles (BEV) in the transport sector stresses electricity grids. Concurrently, batteries in BEVs add flexibility, while the reintroduction of battery swapping stations challenges home charging with regards to added flexibility. To address research gaps regarding the role of BEV charging systems in urban decarbonization pathways, this paper introduces an innovative modeling approach that reflects the implications of home charging and battery swapping stations in urban environments and accounts for the stochasticity of vehicle use.The developed method is demonstrated using a case study that comprises a sample residential area equipped with rooftop PV. The comparison of home charging and battery swapping stations shows that both approaches allow significant increases of self-consumption of locally provided electricity over conventional opportunistic home charging, with battery swapping stations evincing the highest increase in flexibility. This increase in added flexibility might, however, not justify a system-wide introduction of battery swapping stations for BEVs, as required surplus batteries and industry-wide standardization entail added efforts over home charging.Policy recommendations derived by this work support building regulations regarding the installation of charging stations and the utilization of PV rooftop potentials considering the anticipated market penetration of BEVs and the possibility to share electricity within local energy hubs.

Optimizing Electric Vehicle Charging Schedules Based on Probabilistic Forecast of Individual Mobility

Abstract. The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if the charging of many EVs is not coordinated. Among the many strategies to cope with this challenge, next-day EV energy demand forecasting plays a key role. Existing studies have focused on predicting the next-day energy demand of EVs on the aggregated and individual levels. However, these studies have not yet extensively considered individual user mobility behaviors, which exhibit a high level of predictability. In this study, we consider several mobility features of individual users when forecasting the next-day energy demand of individual EVs. Three types of quantile regression models are used to generate probabilistic forecasts of energy demand, particularly the next-day energy consumption and parking duration. Based on the prediction results, two time-shifting smart charging strategies are designed: unidirectional and bidirectional smart charging. These two strategies are compared with an uncontrolled charging baseline to evaluate their financial benefits and peak-shaving effects. Our results show that human mobility features can partially improve the prediction of next-day individual EV energy demand. Additionally, users and distribution grids can benefit from smart charging strategies both financially and technically.

Optimal power tracking for autonomous demand side management of electric vehicles

Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.

Electric Vehicle Dynamic Wireless Charging System: Optimal Placement and Vehicle-to-Grid Scheduling

Electric vehicle (EV) dynamic wireless charging system has become an emerging application in the area of the intelligent transportation system (ITS). However, an integrated design of the EV dynamic wireless charging system requires the considerations of both the economical and technical perspectives of a smart city. Specifically, most of the existing researches unilaterally consider the application of either placement strategy for power tracks (PTs) or dynamic vehicle-to-grid (V2G) scheduling. In this article, we propose a multistage system framework to account for an integrated EV dynamic wireless charging system in a smart city. First of all, an optimal placement strategy for PTs is developed based on city traffic information and EV energy demand. Then, having the optimal locations of PTs through the previous stage approach, the proposed dynamic V2G scheduling scheme is formulated to coordinate the schedules of EVs with the provision of daytime V2G ancillary services. Our simulation results present that the proposed multistage system model achieves improvements on both the placement strategy and V2G scheduling scheme. In addition, relatively low economic system costs can be obtained through our proposed model.

Real-World Application of Sustainable Mobility in Urban Microgrids

The impact of microgrid applications expands with the increasing share of renewable energy resources in the energy supply and the progressing electrification of the transport sector. The additional energy demand of electric vehicles (EVs) may cause increased peak loads, leading to growing burdens on the power system. In this regard, microgrids allow to cover the energy demand of EVs utilizing local renewable energy resources and a battery storage system, thereby lowering the utilization of the power system. This article discusses a self-consumption operational strategy for a real-world microgrid at the EUREF-Campus in Berlin, Germany. A rule-based control algorithm for the sustainable energy supply of EV charging stations is implemented and validated. Measurements are recorded with high resolution over three years from 2017 to 2019. The performance of the microgrid operation is assessed with regards to three distinct key indicators: Self-consumption, autarky, and emission rate. The influence of selected operating parameters under real-world and ideal operating conditions are investigated. The results indicate that self-consumption and autarky rates are sensitive to the rated power capacity of a battery storage system rather than to its rated energy capacity. Moreover, the reduction of emissions through battery deployment for EV charging amounts up to 37 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\,$</tex-math></inline-formula> % under ideal operating conditions. This underlines the potential of the microgrid concept for the sustainable application of EVs in urban areas.

Intelligent electric vehicles charging coupled demand response of isolated microgrid

AbstractIn recent times, the shrinkage of fossil fuel reserves and increase in greenhouse emissions have compelled to switch to suitable eco‐friendly solutions. Electric vehicles (EVs), being one such solution, have been the center of attraction for the industries as well as the researchers. This paper deals with the inclusion of plug‐in EVs (PEVs) in isolated microgrid (I‐MG). An intelligent demand‐side management approach has been proposed for co‐ordination of the power generation from distributed generators, battery energy storage systems (BESSs), and other forms of distributed energy resources (DERs) to fulfill the daily load demand as well as energy demand of EVs. Medium voltage I‐MG integrated with DERs, BESSs and PEVs has been used for the study and the analysis has been done to justify the need for smart charging of PEVs in an I‐MG. The proposed technique combines smart charging and demand response which simplifies the consumer’s concerns and provides a cost‐effective solution.

Energetic Performances Booster for Electric Vehicle Applications Using Transient Power Control and Supercapacitors-Batteries/Fuel Cell

In this paper, a hybrid electric power supply system for an electric vehicle (EV) is investigated. The study aims to reduce electric stress on the main energy source (fuel cell) and boost energetic performances using energy sources with high specific power (supercapacitors, batteries) for rapid traction chain solicitations such as accelerations, decelerations, and braking operations. The multisource EV power supply system contains a fuel cell stack, a lithium batteries module, and a supercapacitors (Sc) pack. In order to emulate the EV energy demand (wheels, weight, external forces, etc.), a bidirectional load based on a reversible current DC-DC converter was used. Fuel cell (Fc) stack was interfaced by an interleaved boost converter. Batteries and the Sc pack were coupled to the DC point of coupling via buck/boost converters. Paper contribution was firstly concentrated on the distribution of energy and power between onboard energy sources in consonance with their dynamic characteristics (time response). Second contribution was based on a new Sc model, which takes into consideration the temperature and the DC current ripples frequency until 1000 Hz. Energy management strategy (EMS) was evaluated by simulations and reduced scale experimental tests. The used driving cycle was the US Federal Test Procedure known as FTP-75.

Considering the combinatorial effects of on-site distributed generation and battery-to-X option availability in electric vehicle battery swap station operation

Amongst different options to supply the energy demand of electric vehicles, battery swap based operational concept may be a promising solution for wider integration of electric vehicles. However, wider adoption of battery swap solution heavily relies on implementation of effective business models and new operational concepts. In this study, the combinatorial effects of photovoltaic system based distributed generation unit as well as battery-to-X availability in an EV battery swap station operation are analysed via a Mixed-Integer Linear Programming framework based energy management system model. The aim of this study is to ensure a highly flexible energy management system model for such a structure composed of Battery-to-Battery, Grid-to-Battery, Battery-to-Grid, Photovoltaic-to-Battery, Photovoltaic-to-Grid transactions options. Besides, the Quality-of-Service level and State-of-Health condition variation of the battery stock in the swap station are also considered as constraints of the overall system model. The results reveal the benefits of constructing such a highly flexible operation model with the mentioned quality and operational sustainability oriented constraints. • Providing an energy management concept with different transaction possibilities. • A MILP framework based energy management system model. • Considering B2B, G2B, B2G, PV2B, and PV2G options simultaneously. • Considering the QoS and SoH as the decision making constraints simultaneously.

A hybrid policy gradient and rule-based control framework for electric vehicle charging

Recent years have seen a significant increase in the adoption of electric vehicles, and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations. The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices. Exploiting this flexibility, however, requires smart control algorithms capable of handling uncertainties from photo-voltaic generation, electric vehicle energy demand and user’s behaviour. This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building. The control objective is to maximize self-consumption of locally generated electricity and consequently, minimize the electricity cost of electric vehicle charging. The performance of the proposed framework is evaluated on a real-world data set from EnergyVille, a Belgian research institute. Simulation results show that the proposed control framework achieves a 62.5% electricity cost reduction compared to a business-as-usual or passive charging strategy. In addition, only a 5% performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.

Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.

Intelligent energy flow management of a nanogrid fast charging station equipped with second life batteries

In this paper we investigate a public Fast Charge (FC) station nanogrid equipped with a Photovoltaic (PV) system and an Energy Storage System (ESS) using second-life Electric Vehicle (EV) batteries. Since the nanogrid is intended for installation in urban areas, it is designed with a very limited connection with the grid to assure peak shaving and encourage PV autoconsumption. To demostrate the effectiveness of this approach to FC stations, an Energy Management System (EMS) is developed to manage the energy demand uncertainty of EVs and the power gap between the grid connection and the FC service. In particular, we propose a machine learning procedure for the automatic synthesis of a suitable (fuzzy) rule-based EMS. Indeed, we posit that a prediction based EMS would result not effective because of the stochastic and intermittent behavior of the FC load, and that a crisp rule-based system, defined by expert knowledge, would be too limited to capture uncertain behavior. The concept is demonstrated in a simulated environment inspired by the “Smart Columbus” project, implementing a mixed deterministic-stochastic process to simulate EV energy demand. In particular, different EV fleets and PV sizes are considered for EMS training, offering insights into the optimal size of PV system and nanogrid system effectiveness. The proposed approach is evaluated by comparing the EMS performance with related optimal benchmark solutions, evaluated by considering known a priori the overall PV and FC demand. The results show that the EMS performance is approximately within 10% of the benchmark optimal value.

Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems

This paper proposes a comprehensive methodological framework to investigate the potential role of the grid-connected battery swapping station (BSS) with vehicle-to-grid (V2G) capability in improving the reliability of supply in future distribution networks. For this aim, we first develop an empirical model for describing the energy demand of electric vehicles (EVs) and their resultant available generation capacity (AGC) that can be utilized for BSS operation. Then, on this basis, a quantitative method to quantify the effect of grid-connected BSS on distribution system reliability is proposed. In order to capture the uncertainties associated with EV users’ behaviors, Latin Hypercube Sampling (LHS) methods were utilized to obtain the time series of the BSS traffic flow and initial State of Charge (SOC) of each EV battery, according to the probability distribution of corresponding uncertain factors whose statistics are obtained from real-world historical data. Compared with existing works in this research field, the main contributions of this paper are threefold. (i) A comprehensive and efficient method to assess the reliability benefits of BSS with an explicit consideration of BSS characteristics (including physical structure, charging strategy, and swapping model) is proposed, which is in contrast to most of the extant studies that only focus on the EV fast-charging paradigm and thus provide a practical tool to analyze the potential value of BSS resources in future distribution systems. (ii) The randomness of EV user behaviors in BSS operation is explicitly modeled and considered. (iii) The LHS-based sequential simulation is used to improve the accuracy and convergence performance of the evaluation, as compared to the traditional Sequential Monte Carlo Simulation (SMCS) method. To verify the effectiveness of the proposed approach, numerical studies are conducted based on a modified IEEE 33-bus distribution network. The simulation results show that with V2G capabilities, BSS can improve reliability to a certain extent and reduce the adverse impact on the reliability of the distribution network. In addition, EV resources should be orderly managed and exploited; otherwise, uncoordinated charging activities could impose a negative impact on the reliability performance of distribution networks. Finally, it is also shown that under the same sampling time, LHS-based sequential simulation could be better than SMCS in the accuracy and convergence speed of the procedure.

Improving the polysulfide barrier by efficient carbon nanofibers coating on separator/cathode for Li-S batteries

High-power high-density lithium rechargeable batteries are necessary to meet the energy demand of electric vehicles and high-power stationary grids. Here, we present a straightforward method for obtaining carbon nanofibers (CNFs) as a polysulfide barrier in Li-S cells, resulting in a significant increase in cell performance. CNFs were coated both on the separator and on the cathode. The CNF-coated cathode showed a specific capacity of 1010 mA h g−1 after 250 cycles at 0.2 C, with a capacity fading of 0.021% per cycle. In addition, at 1 C, it delivered 946 mA h g−1, thus presenting a fast Li+ transport with a good capacity. This result turns CNFs-coated cathodes into a promising system for obtaining high-performance Li-S cells.

Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range

A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed.

Supervision Strategy to Mitigate the Effect of Electric Vehicles (EVs) Charging Load on Power Distribution System Operations

In a fast charging station (FCS) electrical energy is supplied to rapidly charge electric vehicles (EVs). EV charging station acts as the load of the power distribution system, and the large-sized charging activity may bring an effect on the power distribution system. In this paper, an approach to forecast the characteristic of power consumption due to EVs charging has been studied by using Monte Carlo and Artificial Neural Network (ANN) method. The traveling distance of EV users, the state of charge (SOC) of EV battery, and the arrival time to the FCS have been considered in this study. The proposed mathematical equation has been used to determine the number of FCS in the area. Finally, a Fuzzy supervisor design methodology has been applied to model supervision strategy in order to reduce the effect on the power distribution system by considering the electric energy demand of EV and number of EV to be charged. Thus, the obtained results indicate that the effect of EVs charging on the power distribution system increases voltage fluctuation and power losses. A supervision strategy can mitigate the effect of EVs charging on the power distribution system.

Space-Aware Modeling of Two-Phase Electric Charging Stations

In order to match the energy demand of electric vehicles to the capacity of the power grid, it is fundamental to understand the occupancy of charging stations and to react accordingly. A Markov model of a fast charging station for lithium-ion (Li-ion) batteries, i.e., the most prevalent type today, is proposed. Li-Ion batteries present a two-step charging profile, making energy management particularly challenging. A wide range of situations is covered by considering three types of scenarios with and without waiting lines. The analytical results obtained from the steady-state solution of the Markov model reveal the behavior of multiple variables of interest: availability of the charging station to accept new customers (in terms of space and energy), number of customers, energy consumption, and power utilization. From the results, indicators for assessing the quality of service of the charging station are derived. Based on these indicators, customers may decide either to wait or to head toward another station. The owners of the stations, in turn, can predict the impact of investments in space and energy provisioning, when devising capacity planning strategies.

Spatiotemporal Energy Demand Models for Electric Vehicles

Aiming to improve the energy management in large areas for sustainable transportation, this paper models the energy demand of electric vehicles (EVs) in an urban area as a function of location and time. One important consideration for the energy supply of a charging infrastructure is the characterization of the local charge energy demand as a function of time. Spatiotemporal formulations are derived for a number of scenarios, ranging from individual vehicles to global area demands to describe their three-dimension dynamical features. Furthermore, the perceived charging demand is modeled by considering real situations, e.g., parking time, remaining battery energy, and arrival and departure patterns. The proposed charging urgency model describes the probability of charging actions and then computes the perceived energy need in an urban area. Additionally, detailed simulations and analyses have been performed based on a simulated city to show the essential features of the introduced energy demand models.