Large-scale Deployment Of Electric Vehicles Research Articles

Charging Stations for Large-Scale Deployment of Electric Vehicles

The large-scale adoption of electric vehicles will require a charging infrastructure that meets the new needs that will arise. Currently, the charging infrastructure for electric vehicles is still in the early stages of development, not least because of the low number of electric vehicles in use. However, there are still many questions to be answered when it comes to standardization in terms of connectors, DC or AC charging, and power, as well as both operational and economic issues. Although this topic has been the subject of numerous studies over the last ten years, there are still gaps to be filled, particularly with regard to the mix of different recharging strategies (normal, accelerated, fast, induction-track, etc.), as well as the economic and operational aspects. Moreover, the relationship between users and private cars is changing rapidly, and charging behaviors are not yet well established.

Stabilized Positive Electrode Material to Enable High Energy/Power Density Lithium-Ion Batteries

Recently, the large-scale deployment of electric vehicles (EVs) and stationary battery systems has putforward urgent requests for rechargeable batteries with higher energy/power density and longerlifespans. As such, next-generation Li-ion batteries (LiBs) are expected to meet these requirements. Thepossibility of using Ni-rich high-capacity cathode materials (NMC) can help to achieve that goal, however,they still suffer from significant capacity fade via several chemical and mechanical degradation modes.One is crack formation within secondary particles which leads to parasitic reactions that evolve oxygenand potentially initiate the formation of inactive crystal structures. LiBs mechanical and chemicaldegradation mechanisms can be extended by applying protective coatings to the cathode’s surface. Manystudies explore atomic layer deposition (ALD) for this purpose. However, the complementary molecularlayer deposition (MLD) technique might offer the benefit of depositing flexible hybrid coatings that canaccommodate potential volume changes of the electrode during the battery cycling. This study reportsthe deposition of novel organic-inorganic films via the ALD-MLD method. Characterization analysis suchas FTIR, XPS, SEM-EDS, and TEM confirmed the film deposition on the surface of the NMC electrode.Besides, the electrochemical results showed the enhancement of the coated electrode, as evidenced byproviding a longer life with a 93% capacity retention after 150 cycles (3.0-4.4 V vs. Li/Li+), while theuncoated one retained only 83% of its initial capacity. Moreover, in situ dilatometry showed irreversiblevolume change for uncoated NMC while it was mostly reversible for coated ones during the cycling. Itrevealed the dilation behavior of the electrode, resulting in crack formation, which is significantlysuppressed for the coated samples. Stable cycling of Ni-rich cathode materials in crucial conditions suchas high voltage windows and elevated temperatures is still extremely challenging. To evaluate this, in situdilatometry was performed at elevated temperatures (40 °C and 55 °C) and higher voltage ranges (3.0-4.6V). The results confirmed that in both conditions, the dilation behavior of the coated sample is still undercontrol while the capacity is comparable to the uncoated sample. This revealed that the coatings presentgood lithium-ion kinetics and reduce electrolyte decomposition. Overall, this work showed the liability ofthe ALD-MLD films deposited on NMC electrodes to mitigate the degradation mechanisms and providesinsight into a method that seems promising as a protective and elastic coating for future high-energylithium-ion battery cathodes. Figure 1

A Novel Electric Vehicle Placement Index for Sustaining Power Quality in a Distribution Network: Proper Allocation of Electric Vehicle Charging Stations

Large-scale deployment of electric vehicles (EVs) has adverse effects on distribution network operations. Inappropriate installation of EV charging stations (EVCS) degrades the distribution network’s power quality, affecting smooth functioning. This article proposes a novel strategy for properly allocating EVCS in a distribution system by analyzing the impact of power quality issues, namely harmonics and loss of life of the distribution transformer. The analysis is carried out in a standard IEEE-33 bus radial distribution system and a real-time distribution system in Kerala, India. The impact of power quality issues is highlighted by perceiving the effect of slow/moderate and fast chargers in the system under study. The results substantiate the potency of the reliability index (RI) strategy. The severity level for planning the charging station (CS) in numerous buses in the distribution network without infrastructural upgrades is determined by employing a modified novel EV placement index (EVPI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> _m </i> ). The novelty of the index lies in the fact that it incorporates all of the operational parameters with a specific focus on the power quality issues in the distribution network. The severity level of the EVPI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> _m </i> maintains the power quality without increasing the rating of the components used or any infrastructural upgrades. The sensitivity analysis results prove the robustness of the modified EVPI-based strategy. Moreover, the proposed strategy is validated using real-time experiments on a hardware-in-loop real-time simulator, OPAL-RT OP4510. The results substantiate the efficacy of the strategy and the modified index.

Optimal allocation of FCSs and PV units in smart grids considering traffic and generation dispatch

Worldwide, transportation electrification and photovoltaics (PVs) have become vital components of modern and future planning problems. Therefore, the large-scale deployment of electric vehicles (EVs) requires reliable fast-charging stations (FCSs). However, random allocation of FCSs and PV adversely impacts the performance and operation of the traffic flow and power networks. Thus, this paper proposes a new planning approach for the optimal allocation of FCSs and PVs in large-scale smart grids and transportation networks. Unlike existing approaches, the well-known Dynamic Network Assignment Simulation for Road Telematics (DYNASMART) is updated and employed in the proposed planning approach to accurately simulate the EVs within the traffic stream. The average waiting time of the EVs at the charging stations (including charging time) and the total annual costs are utilized as conflicting objective functions to be minimized. To effectively solve this comprehensive model with conflicting objectives, a bi-layer multiobjective optimization based on metaheuristic (NSGA-II) and deterministic (GAMS) algorithms is established. The outer layer optimizes the allocation of FCSs and PV accurately, while the inner one optimally dispatches the generated power of the conventional generation units. The proposed planning approach is tested on a 26-bus electrical transmission network and 92-node transportation system. The simulation results demonstrate the efficiency of the proposed approach. The optimal allocation of FCSs and PVs with 80% EVs penetration increases the net profit by 85.36% while maintaining a maximum of 30 minutes for both charging and waiting time.

Stacking regression technology with event profile for electric vehicle fast charging behavior prediction

Large-scale deployment of electric vehicles (EVs) poses a huge challenge to the operation of the distribution network. As a possible mobile energy carrier, the interaction between EVs and distribution networks can provide some opportunities for power operation. Where to charge and how to charge have become an important research topic in EV charging scheduling. Previous studies mainly focused on slow-charging behavior analysis rather than fast-charging behavior. Here, we provide an in-depth understanding of EV user fast-charging behavior in public stations based on more than 220,000 real-world charging records with the Variational-Bayesian Gaussian-mixture model. Characteristics related to charging energy and charging duration are mainly considered in the cluster model, especially dwelling duration after charging is taken into account to better support the decision of charging recommendation strategy and charging power allocation. Inspired by the future application scenario of the charging behavior cluster of previous studies, we propose a charging behavior prediction framework considering behavior catalogues with stacking regression technology. The results show that the proposed framework improves the prediction accuracy of charging behavior and can effectively evaluate the priority of charging behavior.

Effects on environmental impacts of introducing electric vehicle batteries as storage - A case study of the United Kingdom

This paper examines the potential environmental impact of using electric vehicle batteries as storage in relation to an energy system as it moves towards the goal of net-zero emissions in 2050. The electrified transportation sector is an inevitable step towards a more sustainable energy system to meet climate change mitigation. Large-scale deployment of electric vehicles increases electricity demand whilst simultaneously presenting an opportunity to use electric vehicle batteries to shift peak demand through vehicle to grid, battery swapping, and reuse of retired vehicle batteries. The environmental consequence of using electric vehicle batteries as energy storage is analysed in the context of energy scenarios in 2050 in the United Kingdom. The results show that using an electric vehicle battery for energy storage through battery swapping can help decrease investigated environmental impacts; a further reduction can be achieved by using retired electric vehicle batteries. Using an electric vehicle battery for energy storage through a vehicle to grid mechanism has the potential to reduce environmental impacts if the impact of cycle degradation is minimal compared with calendar degradation. This balance is dependent upon the lithium-ion chemistry, temperature and mileage driven.

Feasibility Investigation for Residential Battery Sizing Considering EV Charging Demand

Photovoltaic (PV) systems along with battery energy storage systems (BESS) are an increasing trend for residential users due to the increasing cost of energy and environmental factors. Future sustainable grids will also have electric vehicles (EVs) integrated into these residential microgrids. However, this large-scale deployment of EVs and PV systems could mean several problems in terms of power quality, hosting capacity and as well economic implications. This paper aims to provide input to more optimal design and management of domestic PV and BESS for residential users with EVs. In this work, a measurement-based data set from a low-voltage distribution network in a rural area has been used. Investigation sees different household and PV-EV penetration levels to propose the BESS capacity and use cases. An economic analysis has been performed to check the feasibility of the proposed systems. The payback period is found to be between 13 to 15 years of the proposed systems.

A Novel Reliability Index Based Approach for EV Charging Station Allocation in Distribution System

Large-scale deployment of electric vehicles (EVs) is deleterious for distribution system operations. Unplanned installation of EV charging station (EVCS) deteriorates the voltage stability, reliability, and power quality of the distribution system leading to customer discontentment. This article proposes a novel approach for the proper placement of EVCS in a distribution network maintaining the system performance. The design of a test station with vehicle-to-grid (V2G) capability is outlined in this work. The test station is developed with five charging columns taking into consideration the standards and guidelines outlined in the Government of India EV policy. The charging station is connected to the power grid by a dc bus to which numerous EVs are attached. EVCS placement in the IEEE-33 bus radial distribution system is performed by developing six test scenarios. The test case results substantiate the potency of the reliability-index based approach for allocating EVCS both slow and fast chargers in a distribution network. Further, the maximal threshold value for placing the EVCS in various buses in a distribution system is determined using a novel electric vehicle placement index (EVPI). The novelty of the approach lies in the fact that it can include all operational parameters influenced owing to EV deployment, thereby maintaining the overall system performance without any physical restructuring of the network. The efficacy of the approach and the severity level of EVCS placement are validated for a real-time distribution system in Kerala, India.

Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicles (EVs)

Facing global climate change challenges entails a sustainable development of transportation. Governments and automobile manufacturers are highly aware of how a large-scale deployment of Electric Vehicles (EVs) can reduce greenhouse gas (GHG) emissions and mitigate global warming. This study aids the design of the adaptation strategies of the automotive industry to meet global goals on climate change by means of a fuzzy-set qualitative comparative analysis (fsQCA), which makes it possible to measure the level of actors’ satisfaction. This allows identification of a combination of factors leading to the outcome while dealing with uncertain environments due to the heterogeneous nature of actors with conflicting of interests. The methodology has been successfully applied to a case study, thus providing greater transparency, fairness, social equity, and consensus among actors.

Electromobility in Poland and Slovakia. Benchmarking of Electric Vehicles for 2019

Since the entry into force of the Paris Agreement in 2015, and with the publication of the Intergovernmental Panel on Climate Change report on the consequences of 1.5 degrees of global warming, the issue of reducing greenhouse gas emissions in a cost-effective manner and within the timeframe outlined has become a matter of urgency. The transport sector, which accounts for a quarter of total GHG (Greenhouse Gas) emissions in the 28 EU Member States, is no exception. Due to the serious environmental impacts of transport, new mobility concepts are being implemented at both national and international levels. One of these is the large-scale deployment of electric vehicles, including those powered exclusively by Battery Electric Vehicle (BEV) batteries. They are quiet and virtually emission-free and, in terms of safety, have the feature that, in the event of an accident, they reduce the risk of detonating the vehicle and of burning or burning out the passengers. This article presents the current state of electromobility in Poland and Slovakia with an indication of light electric cars BEV and the most important factors stimulating its development.

Research on Optimization of Charging Strategy Control for Aged Batteries

Rapid charging of lithium-ion batteries has become a major bottleneck for the large-scale deployment of electric vehicles. In this article, a charging optimization method is proposed for lithium ion batteries based on their aging characteristic, which takes charging time and temperature rise into consideration. An electric-thermal battery model is utilized to estimate the battery temperature during charging. A search for the optimal charging current pattern is performed using a particle swarm optimization (PSO) algorithm combined with fuzzy reasoning. The experiments are conducted on batteries with different aging states to verify the proposed method. Besides, the proposed method is compared with traditional constant current-constant voltage (CC-CV) charging method. Results show that the proposed charging strategy has advantages on maximum temperature rise maintenance and charging speed improvement for aged batteries with unknown aging state. The proposed charging method provides a way for aged batteries charging considering charging time and temperature rise.

Active Distribution System Reinforcement Planning With EV Charging Stations—Part I: Uncertainty Modeling and Problem Formulation

Due to the associated uncertainties, the large-scale deployment of electric vehicles (EVs) and renewable distributed generation is a major challenge faced by the modern distribution systems. The first part of this two-paper series proposes a scenario-based stochastic model for the multistage joint reinforcement planning of the distribution systems and the electric vehicle charging stations (EVCSs). The historical EV charging demand is first determined using the Markovian analysis of EV driving patterns and charging demand. A scenario matrix, based on the heuristic moment matching method, is then generated to characterize the stochastic features and correlation among historical wind and photovoltaic generation, and conventional loads and EV demands. The scenario matrix is then utilized to formulate the expansion planning framework, aiming at the minimization of the investment and operational costs. The proposed expansion plan determines the optimal construction/reinforcement of substations, EVCSs, and feeders, in addition to the placement of wind and photovoltaic generators, and capacitor banks over the multi-stage planning horizon. In the second companion paper, the effectiveness and scalability of the proposed model is assessed through case studies in the 18-bus and the IEEE 123-bus distribution systems, respectively.

A chaotic particle swarm optimization algorithm for solving optimal power system problem of electric vehicle

Transportation of electrification has become a hot issue in recent decades and the large-scale deployment of electric vehicles has yet to be actualized. This article proposes a powertrain parameter optimization design approach based on chaotic particle swarm optimization algorithm. To improve the driving and economy performance of pure electric vehicles, chaotic particle swarm optimization algorithm is adopted in this study to optimize principal parameters of vehicle power system. Vehicle dynamic performance simulations were carried out in the Cruise software, and the simulation results before and after optimization were compared. Simulation results show that optimized vehicles by chaotic particle swarm optimization can meet the expected dynamic performance and the driving range has been greatly improved. Meanwhile, it is also viable that the parameters of the optimal objective function can achieve the purpose of balancing the driving performance and economic performance, which provides a reference for the development of vehicle dynamic performance.

Enhanced Electric Vehicle Integration in the UK Low-Voltage Networks With Distributed Phase Shifting Control

Electric vehicles (EV) have gained global attention due to increasing oil prices and rising concerns about transportation-related urban air pollution and climate change. While mass adoption of EVs has several economic and environmental benefits, large-scale deployment of EVs on the low-voltage (LV) urban distribution networks will also result in technical challenges. This paper proposes a simple and easy to implement single-phase EV charging coordination strategy with three-phase network supply, in which chargers connect EVs to the less loaded phase of their feeder at the beginning of the charging process. Hence, network unbalance is mitigated and, as a result, EV hosting capacity is increased. A new concept, called Maximum EV Hosting Capacity (HC max ) of low voltage distribution networks, is introduced to objectively assess and quantify the enhancement that the proposed phase-shifting strategy could bring to distribution networks. The resulting performance improvement has been demonstrated over three real UK residential networks through a comprehensive Monte Carlo simulation study using Matlab and OpenDSS tools. With the same EV penetration level, the under-voltage probability was reduced in the first network from 100% to 54% and in the second network from 100% to 48%. Furthermore, percentage voltage unbalance factors in the networks were successfully restored to their original values before any EV connection.

Usage pattern analysis of Beijing private electric vehicles based on real-world data

Developing electric vehicles, as one of the most effective measures in constructing clean transportation, has been vigorously prompted by China's government recently through series of beneficial policies. Thus, both the sales and manufacturing of electric vehicles have witnessed prosperous growth in the last decade, especially in Beijing, whose pure electric vehicles ownership has exceeded 170,000 by the end of 2017 and ranked first in China. However, large-scale deployment of electric vehicles may also bring about troublesome problems concerning the construction of charging infrastructure and stability of electric grid. A comprehensive analysis of usage pattern of electric vehicles, especially the majority for private usage, is useful in predicting the charging load and understanding the driving characteristics, thus guiding location of charging facilities and assisting energy management of electric grid. By collecting operational data of forty-one private electric vehicles with 33,041 trips and 4738 charging events, sixteen characteristic parameters including charge consumption, state of charge before/after charging, single-trip distance, daily distance travelled, specific energy consumption, etc. are analyzed in detail. The analysis results are useful in facilitating charging infrastructures construction, management of state gird, evaluation of emerging vehicular technology and so forth in Beijing and even other metropolises with similar situation.

Stackelberg game‐based demand response in multiple utility environments for electric vehicle charging

An upsurge in the electricity demand seems inevitable due to the large-scale deployment of electric vehicles (EVs). Demand response, which is a potential avenue to curb this demand, aims at reduction of power generation costs and electricity bills by allowing control of electricity consumption through electricity prices. This study proposes a holistic approach to combining the behaviour of EV users and customers with other elastic loads participating in demand response to make the scenario more realistic. In this study, various cases with single and multiple utility companies (UCs), which try to set the prices in such a way so as to maximise their profits, have been considered. A Stackelberg game model has been designed to address this conflict of interests between the UCs and the customers. This study considers different utility functions for different types of customers in order to meet their energy requirements meanwhile maximising the profits of the UCs at the Stackelberg equilibrium. The impact of the increase in competition is also studied.

A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current–constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLABTM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.

Research on the Battery Charging Strategy With Charging and Temperature Rising Control Awareness

Fast charging of lithium-ion batteries is an essential problem that constrains the large-scale deployment of electric vehicles. To solve this problem, a new charging strategy is proposed in this paper. Three original contributions are made in this paper: 1) development of a novel multistage constant heating rates optimization method that reduces both the charging time and charging temperature increase, with the tradeoff between the charging time and charging temperature increase analyzed using the genetic algorithm (off-line strategy); 2) reduction of the charging time via ensuring a state of charge region by balancing the charge capacity and charge time; and 3) demonstration that the proposed method can be used under different temperatures by comparing the proposed method to the average constant current constant voltage (CCCV) under different temperatures; the comparison results suggest that the charging time of proposed method is reduced by 1.9%, 5.3%, 8.56%, and 9.54% compared to the average CCCV method under ambient temperature, 10 °C, 25 °C, and 40 °C, respectively. Moreover, the proposed method temperature rise is reduced by 48.6%, 28.3%, 67.3%, and 17.9% compared to the average CCCV method under ambient temperature 10 °C, 25 °C, and 40 °C, respectively.

Charging optimization in lithium-ion batteries based on temperature rise and charge time

Lithium-ion battery fast charging issues have become a main bottleneck of large-scale deployment of electric vehicles. This paper develops a polarization based charging time and temperature rise optimization strategy for lithium-ion batteries. An enhanced thermal behavior model is introduced to improve the solution accuracy at high charging current, in which the relationship between polarization voltage and charge current is addressed. Genetic algorithm (GA) is employed to search for the optimal charging current trajectories. The effects of weighting coefficients of charging time and temperature rise on battery charging performance are discussed. The charging time of the optimized charging protocol is reduced by 50%, and the associated temperature rise is almost identical, compared to 1/3C constant current-constant voltage (CC-CV) charging. Aging experiments demonstrate that the proposed charging method has a similar capacity retention ratio to that of 0.5CC-CV charging after 700 cycles, thereby accomplishing a good balance between charging speed and lifetime.

FUNCIONAMIENTO ÓPTIMO DE BATERÍAS DE VEHÍCULO ELÉCTRICO EN REDES INTELIGENTES CONSIDERANDO LA TECNOLOGIA VEHICULO-A-RED

RESUMEN: En un futuro previsible se supone el despliegue a gran escala de vehiculos electricos (EVs). La cuantiosa carga y descarga intermitentes reducen la capacidad y la vida de la bateria debido a la tecnologia vehiculo-red (V2G) empleada en las redes inteligentes. Por eso, el modelado de la operacion y mantenimiento de la bateria es clave para evaluar la utilizacion de EVs. En este estudio, se analizaron el marco de aplicacion V2G y el proceso general para definir el funcionamiento de la bateria. Se estudio el modelo de operacion y mantenimiento de la bateria bajo tres estados de trabajo (esto es, carga, descarga y circulacion) y un estado de fallo. La bateria se deterioro a lo largo del tiempo y requirio un mantenimiento correctivo. Por algunas limitaciones dadas, se modelo y analizo el costo promedio a largo plazo aumentando y disminuyendo el proceso geometrico. La expresion analitica de la funcion de la tasa de costo se obtuvo del modelo propuesto de operacion y mantenimiento. La correspondiente operacion optima se determino tambien numerica o analiticamente para minimizar el costo promedio a largo plazo. En el modelo de operacion y mantenimiento propuesto la bateria fue reemplazada por una nueva identica tras el Nsimo fracaso. Se presenta un sistema de simulacion numerica para ilustrar el modelo y metodo de funcionamiento optimizado. La amplia presentacion de los resultados de la simulacion muestran mejoras de rendimiento significativas usando el modelo propuesto, en comparacion con el metodo convencional de carga/descarga coordinada por redes inteligentes. Puede ser aplicado en el sistema de gestion de la bateria y resuelve los problemas de costos causados por el EVs. Palabras clave: Vehiculo electrico, Bateria, Vehiculo-Red, Costo Medio, Proceso Geometrico