Charging Strategy For Electric Vehicles Research Articles

Electric vehicle charging strategies for Urban freight transport: concept and typology

ABSTRACT Charging services play an important role in encouraging the growth of the electric freight vehicle (EFV) market. Understanding charging strategies and their influence on charging behavior is crucial in the deployment of charging services. Unfortunately, existing charging strategy definitions and types are incoherent and incomplete. This conceptual paper synthesizes the perspectives found in literature on the charging strategy concept, and provides a definition based on Orlikowski’s structurational model of technology. A two-level charging strategy typology is proposed. The first distinguishes between decision-making modes: schedule- or trigger-based decisions. The second identifies when and where charging is used during a vehicle operating cycle. The result is three schedule-based charging strategies: downtime, opportunity, and intrusive charging, and one trigger-based emergency charging. The selection of strategy depends on the EFV operations, the availability of charging systems and services, as well as the driver behaviour. The influence of the strategy on enhancing the EFV, the carrier operations and business model is discussed. Charging behaviour, especially in location and timing, is shown to be strongly dependent on the strategy. While downtime charging is considered the default strategy, carriers should employ a variety of complementary strategies to enhance the capability of their vehicles and improve the financial viability of the operations. Carriers should also have an emergency charging strategy on hand in case of unforeseen situations.

Charging stations‐oriented electric vehicle charging strategy based on battery characteristics

SummaryIn recent years, electric vehicles (EVs) receive intensive attention due to their environment‐friendly nature and outstanding energy efficiency. However, there are still obstacles in EVs' popularity. Battery related issues are most concerned from users' perspective. While the evolution in battery technology may help to address the aforementioned problems eventually, other solutions are in urgent demand for the time being. In this paper, we propose an innovative charge scheduling method that could significantly improve EV users' charging experience with the nowadays battery technology. Specifically, the proposed method takes into account the characteristics of the batteries to be charged as well as the constraints of the external power grid; thus, a plan can be devised for efficiently allocating the power of the charging station to the EVs through cyber‐physical system. Our method prioritizes jobs based on their marginal utilities to maximize user satisfaction with limited power resource. The power allocation in our method is managed globally and constrained by specific rules to avoid waste or overload in the power grid. Lastly, we simulate several power scheduling methods in charging stations, which validates that the user satisfaction under our method is higher than that of other traditional method.

Two‐layer optimal charging strategy for electric vehicles in old residential areas

Two‐layer optimal charging strategy for electric vehicles in old residential areas

Multi‐objective orderly charging strategy for electric vehicles based on interactive network model

In order to solve the shortcomings of traditional orderly charging strategy, such as the inability to accurately respond to EV load, the difficulty to formulate an effective charging strategy and the lack of positive response from users. In this paper, a novel interactive network model of distribution network and electric vehicles is constructed. The optimization factor is assigned to the charging station, so that the scheduling for electric vehicles is no longer random. Further based on the time‐of‐use price for the charging, the multi‐objective orderly charging model from the point of both users and power grid is established. The minimum charge cost and the minimum variance of load are taken as the multi‐ objective function with the constraints of charging demand and rated capacity of transformer. Finally, the simulation by NSGA‐II on different numbers of electric vehicles shows that the proposed orderly charging strategy can significantly reduce the charging cost and load peak valley for a certain scale of electric vehicles charging. It is beneficial to ensure the safety and stability of the distribution network and improve users satisfaction. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Smart electric vehicle charging strategies for sectoral coupling in a city energy system

The decarbonization of city energy systems plays an important role to meet climate targets. We examine the consequences of integrating electric cars and buses into the city energy system (60% of private cars and 100% of public buses), using three different charging strategies in a modelling tool that considers local generation and storage of electricity and heat, electricity import to the city, and investments to achieve net-zero emissions from local electricity and heating in 2050. We find that up to 85% of the demand for the charging of electric cars is flexible and that smart charging strategies can facilitate 62% solar PV in the charging electricity mix, compared to 24% when cars are charged directly when parked. Electric buses are less flexible, but the timing of charging enables up to 32% to be supplied by solar PV. The benefit from smart charging to the city energy system can be exploited when charging is aligned with the local value of electricity in the city. Smart charging for cars reduces the need for investments in stationary batteries and peak units in the city electricity and heating sectors. Thus, our results point to the importance of sectoral coupling to exploit flexibility options in the city electricity, district heating and transport sectors.

The peak load shaving assessment of developing a user-oriented vehicle-to-grid scheme with multiple operation modes: The case study of Shenzhen, China

The vehicle-to-grid (V2G) technology creates a cost-effective alternative solution for the integration of electric vehicles (EVs) into smart grids. Two mainstreams of operation modes exist for the V2G implementation, viz. the centralized and decentralized V2G operation mode. In previous studies, researchers used to assume that EV users involved would participate in a special mode for the coordinated V2G strategy. In fact, the requirements of EV users on the V2G operation vary from individual to individual, depending on the type of EV usage, the charging preference of EV users, and the urgency degree of EV charging. Therefore, it is essential to develop a user-oriented V2G scheme with multiple operation modes to make more EVs participate in the V2G operation for coordinated charging. In this paper, the peak shaving capacity of a user-oriented V2G scheme with multiple operation modes is surveyed compared to the conventional charging mode. The city of Shenzhen, China, is taken as a case study to evaluate the power demand of EV charging during peak hours for different scenarios. In order to obtain the coordinated EV charging strategy, the global/divided scheduling optimization model is developed for the centralized/decentralized V2G strategy. The global scheduling method devotes to minimizing the total EV charging costs whereas the divided scheduling method aims to minimize the individual EV charging costs based on the time of use pricing system. Results reveal that the power demand during peak periods is decreased by 0.93 GW or 5.89 % for a modest case in a user-oriented V2G scheme in contrast to the conventional charging mode.

Study on Battery Charging Strategy of Electric Vehicles Considering Battery Capacity

This paper proposes an improved fast charging strategy for electric vehicles (EVs) by considering available battery capacity. According to previous research and battery experiment reports, the energy capacity of batteries is not fixed, and it can decrease temporarily depending on the magnitude of charging or discharging power. This paper addresses the decreased capacity in proportion to the magnitude of the charging power which leads to a reduction in the driving range. For effective and practical use for EV users, the change in battery capacity is expressed through an equation, and a new state of charged indicator is proposed. To improve the conventional charging method that uniformly supplies power regardless of the battery capacity, this paper proposes an optimal charging strategy injecting constant power. The proposed charging strategy provides an optimal charging power reference to minimize costs considering charged energy, charging time, and usable energy loss based on billing system of EV charging. To verify the effectiveness of the proposed charging method, the optimal charging power reference for each battery is calculated based on capacity and characteristics, and the total cost is compared. The results show that the performance of the proposed charging strategy is effective in minimizing both the reduced battery capacity and economic burden on EV users.

Optimal Charging Strategy of Electric Vehicles with Consideration of Battery Storage

The high penetration of electric vehicles (EVs) will increase burden of a power grid. However, the expansion of capacity of distribution facilities is not always possible, especially in some old residential community. This paper proposes to use an optimal charging strategy of EVs with additional battery energy storage (BES) to improve the charging capabilities in a residential community. By modeling the EV charging behavior, the required charging capacity is evaluated using Monte Carlo method and the BES size is determined as the difference between the required capacity and the distribution capacity. An optimal charging strategy is then proposed to reduce the charging cost and ensure the safe running of distribution network.

Selecting the Optimal Charging Strategy of Electric Vehicles Using Simulation Based on Users’ Behavior Pattern Data

Low-carbon-emitting vehicles, such as plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs), are becoming increasingly common as an emerging solution to environmental problems. As the number of EVs increases in a market, EV charging requires significantly more electricity use and charging strategies could have a significant impact on the peak load and electricity demand in the power system, as well as the electricity bill for each household. The purpose of this paper is to develop a model to find a user-specific optimal EV charging strategy using actual data of users' behavior patterns in daily life. Toward this end, we deduce the time when each user utilizes electrical appliances and drives an EV based on actual user behavior data. The daily electricity consumption of a household by time is then calculated according to three different charging strategies. Comparing these results, we can obtain the optimal charging strategy for users to minimize their electricity bills. As a result of a simulation, it is found that, when each user follows her optimal charging strategy, the total electricity bill can be decreased by 35% and the peak-to-average ratio by 12% compared to when every user's charging is uncontrolled. The results show that the charging EVs according to the optimal strategy obtained by our model would significantly affect not only the user's electricity bill but also the whole power grid system.

Online electric vehicle charging with discrete charging rates

Abstract Due to the increasing penetration of electric vehicles (EVs) in the distribution grid, coordinated control of their charging is required to maintain a proper grid operation. Many EV charging strategies assume that the EV can charge at any rate up to a maximum value. Furthermore, many strategies use detailed predictions of uncertain data such as uncontrollable loads as input. However, in practice, charging can often be done only at a few discrete charging rates and obtaining detailed predictions of the uncertain data is difficult. Therefore, this paper presents an online EV scheduling approach based on discrete charging rates that does not require detailed predictions of this uncertain data. Instead, the approach requires only a prediction of a single value that characterizes an optimal offline EV schedule. Simulation results show that this approach is robust against prediction errors in this characterizing value and that this value can be easily predicted. Moreover, the results indicate that incorporating practical limitations such as discrete charging rates and uncertainty in uncontrollable loads can be done in an efficient and effective way.

Electric vehicle charging strategy study and the application on charging station placement

Optimal placement of charging stations for electric vehicles (EVs) is critical for providing convenient charging service to EV owners and promoting public acceptance of EVs. There has been a lot of work on EV charging station placement, yet EV drivers’ charging strategy, which plays an important role in deciding charging stations’ performance, is missing. EV drivers make choice among charging stations according to various factors, including the distance, the charging fare and queuing condition in different stations etc. In turn, some factors, like queuing condition, is greatly influenced by EV drivers’ choices. As more EVs visit the same station, longer queuing duration should be expected. This work first proposes a behavior model to capture the decision making of EV drivers in choosing charging stations, based on which an optimal charging station placement model is presented to minimize the social cost (defined as the congestion in charging stations suffered by all EV drivers). Through analyzing EV drivers’ decision-making in the charging process, we propose a k-Level nested Quantal Response Equilibrium charging behavior model inspired by Quantal Response Equilibrium model and level-k thinking model. We then design a set of user studies to simulate charging scenarios and collect data from human players to learn the parameters of different behavior models. Experimental results show that our charging behavior model can better capture the bounded rationality of human players in the charging activity compared with state-of-the-art behavior models. Furthermore, to evaluate the proposed charging behavior model, we formulate the charging station placement problem with it and design an algorithm to solve the problem. It is shown that our approach obtains placement with a significantly better performance to different extent, especially when the budget is limited and relatively low.

Optimal charging strategy for electric vehicles in residential charging station under dynamic spike pricing policy

Nowadays, much attention has been drawn to environmental protection. Traveling by electric vehicles (EVs) instead of conventional fuel cars is strongly supported by national governments for the sustainable development of urban transportation. However, the increasing EV charging load in residential areas have brought a heavy burden to the distribution transformer. Therefore, coordinated charging of EVs in the residential charging station (RCS) is essential to relieve the power supply pressure. In this paper, an optimal charging strategy based on dynamic spike pricing (DSP) policy is proposed to reduce the charging cost of EVs and ensure the normal operation of the distribution transformer. First of all, the load model of EVs on four kinds of typical days is established with consideration of the seasonal and holiday characteristics of EV charging demands. Then, a new DSP policy based on Time-of-Use (TOU) mechanism is designed with an additional spike time period and a spike price to transfer peak loads in rush hours. To protect EV users from financial losses and prevent transformer overload, an optimal charging model is formulated to minimize the charging cost of EVs with considering the power margin of the distribution transformer. Ultimately, the genetic algorithm (GA) is used to solve the model. The simulation results show that the optimal charging strategy proposed in this paper is effective in peak shaving and reducing charging cost.

Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain

Abstracts In order to improve the consumption capacity of renewable energy, an ordered charging control method of electric vehicles based on Markov chain prediction is proposed considering the uncertainty of the electric vehicle charging load. The quantity of electric vehicles in different parking lots and different time periods is predicted by the Markov chain to calculate the electric vehicle charging load of the photovoltaic charging station. Taking the minimum power sum of a photovoltaic charging station, the minimum cost of users and the maximum consumption of the renewable energy as the objective functions, an ordered charging control model with the interest and willingness of electric vehicle users is established. The peak-to-valley time period is divided by the particle swarm optimization algorithm, and the genetic algorithm is used to solve the model. The simulation tests of the electric vehicle driving data provided by the Unite State Travel Survey website verify the correctness and effectiveness of the proposed method, the effects of different peak and valley periods and the responsiveness of electric vehicle users on the ordered charging control of electric vehicles are compared and analyzed. The simulation results show that the prediction accuracy of the charging load obtained by the Markov chain is significantly higher than that of Monte Carlo, and when the responsiveness of electric vehicle users reaches 90%, the total charging cost can be reduced by up to 16.89%. The ordered charging strategy of electric vehicles proposed in this paper can effectively reduce the peak-to-valley difference, increase the consumption of renewable energy, and reduce the charging cost of electric vehicle users.

Impact of Electric Vehicle Charging Strategy on the Long-Term Planning of an Isolated Microgrid

Isolated microgrids, such as islands, rely on fossil fuels for electricity generation and include vehicle fleets, which poses significant environmental challenges. To address this, distributed energy resources based on renewable energy and electric vehicles (EVs) have been deployed in several places. However, they present operational and planning concerns. Hence, the aim of this paper is to propose a two-level microgrid problem. The first problem considers an EV charging strategy that minimizes charging costs and maximizes the renewable energy use. The second level evaluates the impact of this charging strategy on the power generation planning of Santa Cruz Island, Galapagos, Ecuador. This planning model is simulated in HOMER Energy. The results demonstrate the economic and environmental benefits of investing in additional photovoltaic (PV) generation and in the EV charging strategy. Investing in PV and smart charging for EVs could reduce the N P C by 13.58%, but a reduction in the N P C of the EV charging strategy would result in up to 3.12%.

The influence of electric vehicle charging strategies on the sizing of electrical energy storage systems in charging hub microgrids

Economic, ecologic, and social benefits support the rapid diffusion of grid-connected microgrids (MG). Economic feasibility still stands out as the primary goal of commercial MGs. A stationary electrical energy storage system (ESS) is often a central component of MGs, facilitating islanding and cost-effective management of main grid use. Therefore, previous research has focused on the sizing of stationary ESS. The advent of large-scale electric vehicle (EV) charging hub MGs (CHMGs) such as the one along the freeway A8 near Augsburg, Germany, profoundly changes the economically optimal capacity of stationary ESS. While it is well conceived that EVs can be aggregated and then compensated for stationary ESS, research still lacks quantifiable evidence and methodological guidance on how the charging strategy (immediate, controlled, bidirectional) influences the economically optimal capacity of the stationary ESS. To address this gap, this paper proposes a method that includes a mixed-integer linear programming model for scheduling decisions under various conceivable ESS capacities and provides scenario analyses on the EV charging strategies as well as on ESS cost. Thereby, the method thus identifies the economically optimal capacity of the ESS. The results show that in the considered CHMG near Augsburg, the stationary ESS sizing decision is relevant in all but extreme scenarios. In particular, the economically optimal stationary ESS capacity soars if more than 65% of the EVs begin to charge immediately and the storage costs falls below 150 EUR/kWh. In contrast, smaller portions of controlled charging EVs can already drastically reduce stationary ESS. Remarkably, this paper also gives quantitative evidence that investments in bidirectional charging do not to pay off in the CHMG near Augsburg.

Analysing the impacts of a large-scale EV rollout in the UK – How can we better inform environmental and climate policy?

Electrifying transport to meet local pollution and overall net zero carbon ambitions is now a key UK policy focus, but this will have important impacts on the energy system, the economy, and the environment. Understanding the changes that the electrification of transport will bring is crucial for developing sustainable policies for net zero goals and a just transition. A literature is emerging to analyse the impact of a large-scale penetration of electric vehicles (EVs), but generally limiting focus to the implications for the electricity network. In this paper, we aim to provide insight on the wider energy system impacts of the expected EV rollout in the UK, in terms of fuel changes, energy costs, CO2 emission reduction and network investments; and how different EV charging strategies increase or mitigate the impacts of the expected large-scale penetration of EVs. Results show that non-smart and/or decentralised charging will require considerably larger investments on the network to accommodate new EV demand. Network reinforcement costs are passed to the consumer via increased electricity prices and, albeit reduced, emissions shift from the transport to the power sector. These results show the importance of considering the whole energy system and the wider economy, to avoid carbon leakage and to maximise the effectiveness of policies.

Different charging infrastructures along with smart charging strategies for electric vehicles

The accelerating energy demand, growing concern regarding global warming and climate change has paved the path of electrification of the transport sector. Large scale adoption of Electric Vehicles (EVs) call for availability of sustainable and easily accessible charging infrastructure. The sporadic energy demand, different battery storage capacity and diverse penetrating patterns of electric vehicles have significantly raised the load elasticity on a power grid. Smart-grid environment promises to assist the addition of EVs into national grids by enabling both EV-charging and discharging (G2V and V2G) load. This will modify the load profile and reduce cost.This paper discusses comprehensively three basic infrastructures by which charging of EVs can be done. These infrastructures are studied and compared on the basis of some parameters. It has been found that distributed infrastructure shows best results for the charging of electric vehicles. The other two infrastructures prove costlier and increase power demand. Also, this paper examines three specific smart charging strategies and the impact of each strategy on the power system load profile and realization cost. Simulation results establish the superiority of smart charging over dumb charging.

Light electric vehicle charging strategy for low impact on the grid.

The alarming increase in the average temperature of the planet due to the massive emission of greenhouse gases has stimulated the introduction of electric vehicles (EV), given transport sector is responsible for more than 25% of the total global CO2 emissions. EV penetration will substantially increase electricity demand and, therefore, an optimization of the EV recharging scenario is needed to make full use of the existing electricity generation system without upgrading requirements. In this paper, a methodology based on the use of the temporal valleys in the daily electricity demand is developed for EV recharge, avoiding the peak demand hours to minimize the impact on the grid. The methodology assumes three different strategies for the recharge activities: home, public buildings, and electrical stations. It has been applied to the case of Spain in the year 2030, assuming three different scenarios for the growth of the total fleet: low, medium, and high. For each of them, three different levels for the EV penetration by the year 2030 are considered: 25%, 50%, and 75%, respectively. Only light electric vehicles (LEV), cars and motorcycles, are taken into account given the fact that batteries are not yet able to provide the full autonomy desired by heavy vehicles. Moreover, heavy vehicles have different travel uses that should be separately considered. Results for the fraction of the total recharge to be made in each of the different recharge modes are deduced with indication of the time intervals to be used in each of them. For the higher penetration scenario, 75% of the total park, an almost flat electricity demand curve is obtained. Studies are made for working days and for non-working days.

Optimal Electric Vehicle Charging Strategy With Markov Decision Process and Reinforcement Learning Technique

Electric vehicles (EVs) have rapidly developed in recent years and their penetration has also significantly increased, which, however, brings new challenges to power systems. Due to their stochastic behaviors, the improper charging strategies for EVs may violate the voltage security region. To address this problem, an optimal EV charging strategy in a distribution network is proposed to maximize the profit of the distribution system operators while satisfying all the physical constraints. When dealing with the uncertainties from EVs, a Markov decision process model is built to characterize the time series of the uncertainties, and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy. Finally, numerical results verify the effectiveness of the proposed method.

Demand responsive charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

The uncertainties caused by the high penetration of renewable energy sources (RESs) and electric vehicles (EVs) challenge the normal operation of the distribution system. In order to mitigate the negative impact of fluctuations of RES outputs, a smart charging strategy of EVs is presented in this paper. First, a novel uncertainty model using set pair analysis is proposed for the prediction of RES outputs, which provides a different choice of RES modeling method. Second, EVs are modeled as demand-responsive loads by introducing stochastic dynamic pricing. Then, two fluctuation indexes are defined to measure the volatility of RES outputs, and the charging cost is adopted as an economic index for protecting EV users from financial losses. Finally, an optimal charging model is established to minimize the volatility indexes and charging costs. The genetic algorithm is used to solve the model, the seasonality of RES outputs and the spatial-temporal characteristics of EV charging loads are discussed in the simulation under different conditions. Simulations are conducted in the modified IEEE 33 test system, results show that the proposed charging strategy is effective in alleviating the output fluctuations of RESs. The charging cost of EVs can be reduced by 7.6% and 10.3% respectively in winter and summer.