Plug-in Vehicles Research Articles

Advanced control strategies to manage electric vehicle drivetrain battery health for Vehicle-to-X applications

The demand for local power resilience is promoting the installation of renewable-based microgrids. Plug-in electric vehicles (PEVs) are rapidly increasing and can play a critical role in microgrid service provision. The capability of bi-directional charging and discharging will transition PEVs from a simple means of transportation to multi-purpose value generating assets. They can balance the inherently fluctuating generation of renewable energy resources and shape customer’s electricity demand, while still providing the core service of transportation. Value stacking through those additional services allows PEV owners to draw from a number of revenue streams to offset investment costs. However, accelerated battery degradation is often cited as concern when using PEV batteries for purposes other than driving, which presents a major barrier for broad market adoption. This study seeks to assess the economics and trade-offs of bi-directional use of PEVs when utilized as fleet vehicles. In particular, the usage patterns and benefits for PEV fleet deployment at three U.S. military bases is presented. The results show operational cost benefits for bi-directional application of up to 60.8 % annual return on investment without managing battery health, and up to 106.0 % when actively managing battery health through advanced control strategies. The one year payback period far exceeds the expected installation lifespan of at least ten years. Given the results, utilizing PEV fleets for additional services at U.S. military bases can largely offset the additional cost of bi-directional charging hardware and software compared to standard uni-directional equipment.

A multi-level multi-objective strategy for eco-environmental management of electricity market among micro-grids under high penetration of smart homes, plug-in electric vehicles and energy storage devices

This article develops a stochastic multi-level multi-objective strategy to facilitate the electricity market clearing process among micro-grids considering the environmental impacts of fossil fuel powered generation units. Various types of distributed renewable energy resources (DRERs), plug-in electric vehicles (PEVs) and internet-of-things (IoT)-enabled smart homes (SHs) are considered in the proposed concept. In addition, energy storage devices (ESDs) are installed in SHs, micro-grids and distribution network to handle the negative impacts of operational uncertainties arising from DRERs and load demand. Scenario-based stochastic (SBS) method is adopted to model the scenarios of uncertain parameters. In the proposed methodology, micro-grids build their bids/offers for market participation by using the weighted fuzzy method with regard to the SHs optimal programs and aiming to minimizing operational and emission costs. SHs have PEVs and ESDs, and control their controllable appliances via the IoT. The proposed methodology is formulated in linear form and is implemented on a modified IEEE 33-node distribution network comprising three micro-grids. The simulation results prove that the IoT infrastructure in SHs has led to their effective participation in the electricity market and subsequently lowering their costs by 9.13 %. Besides, the results reflect that the provision of vehicle-to-grid services by PEVs along with the optimal operation of ESDs not only lowers the market settlement price (MSP) by about 25 % during the high-demand period, but also reduces daily operation costs and carbon emissions by 32.85 % and 27.65 %, respectively.

Scenario-based fuel-constrained heat and power scheduling of a remote microgrid

This manuscript suggests elephant clan optimization (ECO) algorithm to solve heat and electric power scheduling of a remote microgrid (MG) for three different scenarios considering fuel constraints. ECO algorithm is a populace-based method motivated by the elephants’ behaviour and their societal organization. MG comprises diesel generators (DGs), small hydro power plants (SHPPs), wind turbine generators (WTGs), solar micro-cogeneration (SMC) unit, biomass-fuel-fired micro-cogeneration (BMC) unit, battery energy storage system (BESS), plug-in electrical vehicles (PEVs) and thermal energy storage system (TESS). BMC units and SMC units are incorporated alternately into the MG. Numerical results of a typical system are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). It is seen from the evaluation that ECO offers superior solution.

How Cost-effective are Electric Vehicle Subsidies in Reducing Tailpipe-CO2 Emissions? An Analysis of Major Electric Vehicle Markets

We estimate the cost-effectiveness of plug-in electric vehicle (PEV) subsidies in reducing tailpipe-CO2 emissions in China, the U.S., and nine European countries. We find that the per-tonne cost of tailpipe-CO2 avoided increases linearly with the government-subsidized percentage of the PEV price. Costs are relatively higher in the Netherlands and Denmark, which subsidized high-priced PEVs including plug-in hybrids, and lower in the U.S., where PEVs replaced higher-emissions cars. Chinese PEV subsidies have a short-run static cost of up to $1,600 per tonne, far exceeding the social cost of carbon, suggesting that subsidies are more a part of China’s industrial policy than its carbon policy. When subsidy-induced PEV sales and power generation emissions are considered, the ordering of countries based on the cost-effectiveness of subsidies changes. The long-run dynamic subsidy cost is expected to be lower, as current subsidies may drive future innovation and sales, and due to grid decarbonization.

Placement and Capacity of EV Charging Stations by Considering Uncertainties With Energy Management Strategies

At the present context, Plug-in electric vehicles (PEVs) are gaining popularity in the automotive industry due to their low CO2 emissions, simple maintenance, and low operating costs. As the number of PEVs on the road increases, the charging demand of PEVs affects distribution network features, such as power loss, voltage profile, and harmonic distortion. Furthermore, one more problem arises due to the high peak power demand from the grid to charge the PEVs at the charging station (CS). In addition, the location of CS also affects the behavior of EV users and CS investors. Hence, this paper applies CS investor, PEV user, and distribution network operator who could approach to CS's optimal location and capacity. Integrating renewable energy sources (RESs) at the charging station is suggested to lower the energy stress on the grid. Moreover, to keep down the peak power demand from the grid and utilize renewable energy more efficiently, energy management strategies (EMS) have been applied through the control of charging and discharging of the battery storage system (BSS). In addition, vehicle to grid (V2G) strategy is also applied to discharge the EV battery at charging station. Furthermore, the uncertainties related to PEV charging demand and PV power generation are addressed by the Monte Carlo Simulation (MCS) method.

Transactive Charging Control of Electric Vehicles Considering Voltage Unbalance and Transformers’ Loss of Life

This paper presents a novel transactive energy (TE) management model for real-time coordination of plug-in electric vehicles (PEVs) in the distribution network. The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers’ loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model.

Achieving reliable coordination of residential plug-in electric vehicle charging: A pilot study

Achieving reliable coordination of residential plug-in electric vehicle charging: A pilot study

Do plug-in hybrid adopters switch to battery electric vehicles (and vice versa)?

Plug-in electric vehicle (PEV) adopters, which include owners of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have the choice to replace their vehicle with a BEV, PHEV, or conventional vehicle. These replacement choices are important to consider since they can impact PEV market growth. We investigate factors associated with the choice to replace a BEV or a PHEV with a PHEV, BEV, or conventional vehicle using survey responses from 1,446 PEV adopters. Most BEV adopters’ replacement choices are a newer BEV, for around 1/10th the replacement is a PHEV, and around ¼ replace their BEV with a conventional vehicle. PHEV adopters’ replacement choices are split more evenly between BEVs, PHEVs, or conventional vehicles. Replacement choices are correlated with vehicle attributes (e.g., range), satisfaction with vehicle attributes (e.g., charging convenience), home charging access, gas price, demographics, and attitudes toward technology, the environment and travel.

Do electric vehicle charger locations respond to the potential charging demands from multi-unit dwellings? Evidence from Los Angeles County

With the transportation sector being the largest contributor of greenhouse gas emissions in the United States, states are actively planning the deployment of plug-in electric vehicles (PEVs). Mass adoption of PEVs requires attracting potential buyers living in multi-unit dwellings (MUDs). Given the current low adoption rate of PEVs in MUDs and MUD residents’ reliance on public charging, this paper studies whether there is a positive correlation between the number of public Level 2 (L2) chargers and MUD density (measured by total square footage of MUD per capita) across census block groups (CBGs) in LA County. The results show that high MUD-density CBGs and low MUD-density CBGs do not differ much in terms of the number of chargers. The charger-to-PEV-ratio range for MUD residents derived in this paper is below the ideal charger-to-PEV-ratio range in the literature. A direct policy implication is that more charging infrastructure should be made available to MUD residents. This includes public L2 chargers near MUDs, which are discussed in this paper, and onsite MUD charging, which is still at an early stage.

Effective Deterministic Methodology for Enhanced Distribution Network Performance and Plug-in Electric Vehicles

The rapid depletion of fossil fuel motivates researchers and policymakers to switch from the internal combustion engine (ICE) to plug-in electric vehicles (PEVs). However, the electric power distribution networks are congested, which lowers the accommodation of PEVs and produces higher power losses. Therefore, the study proposes an effective deterministic methodology to maximize the accommodation of PEVs and percentage power loss reduction (%PLR) in radial distribution networks (RDNs). In the first stage, the PEVs are allocated to the best bus, which is chosen based on the loading capacity to power loss index (LCPLI), and the accommodation profile of PEVs is developed based on varying states of charge (SoC) and battery capacities (BCs). In the second stage, the power losses are minimized in PEV integrated networks with the allocation of DG units using a recently developed parallel-operated arithmetic optimization algorithm salp swarm algorithm (AOASSA). In the third stage, the charging and discharging ratios of PEVs are optimized analytically to minimize power losses after planning PEVs and DGs. The outcomes reveal that bus-2 is the most optimal bus for accommodation of PEVs, as it has the highest level of LCPLI, which is 9.81 in the 33-bus system and 28.24 in the 69-bus system. The optimal bus can safely accommodate the largest number of electric vehicles, with a capacity of 31,988 units in the 33-bus system and 92,519 units in the 69-bus system. Additionally, the parallel-operated AOASSA mechanism leads to a reduction in power losses of at least 0.09% and 0.25% compared with other algorithms that have been previously applied to the 33-bus and 69-bus systems, respectively. Moreover, with an optimal charging and discharging ratio of PEVs in the IEEE-33-bus radial distribution network (RDN), the %PLR further improved by 3.08%, 4.19%, and 2.29% in the presence of the optimal allocation of one, two and three DG units, respectively. In the IEEE-69-bus RDN, the %PLR further improved by 0.09%, 0.09%, and 0.08% with optimal charge and discharge ratios in the presence of one, two, and three DG units, respectively. The proposed study intends to help the local power distribution companies to maximize accommodation of PEV units and minimize power losses in RDNs.

Optimized Deep Reinforcement Learning for Smart Charging Scheduling of Plug-in Electric Vehicles

In recent decades, fossil fuels have been the major source of energy but concerns about price fluctuations, security, and environmental issues. Plug-in electric vehicles (PEVs) should be connected to the power grid for battery charging. With a wide range of cars, the power system performance in the distribution network. The uncontrolled charging of the battery can cause issues such as overvoltage, overload, unbalanced load, instability, harmonics, and increased loss. Hence, efficient charging methodologies need to be developed for the power grid. The objective of this article is to maximize the total revenue of the charging stations and schedule the charging stations for PEV charging with reduced cost. First, the system model with its objectives such as demand, constraints, and fitness values are computed. Next, efficient charging path planning is proposed using the deep Q network, which is a reinforcement learning model. This can assist the PEV user in selecting the charging station and planning its path. Lastly, the particle sailfish optimization from our previous research work has been used to solve the charging station optimal solution. The simulation results analysis shows the efficiency of the proposed model in reducing the charging cost and time and improves the charging scheduling of PEVs.

Fuzzy Q-learning-based approach for real-time energy management of home microgrids using cooperative multi-agent system

The development of plug-in electrical vehicles (PEVs) and distributed energy resources in home microgrids (H-MGs) is gaining attention in recent years. Accordingly, it is vital to apply a suitable energy management system to provide the required energy of the PEV and ensure the health of the energy storage systems in the H-MG. This paper proposes a continuous real-time cooperative multi-agent system (MAS) for H-MG. Real-time MAS interacts with H-MG agents to perform as independent learners applying a distributed and cooperative reinforcement learning method, while state variables are shared to coordinate their real-time behavior. Therefore, the fuzzy Q-learning (FQL) method is investigated to control the agents in the continuous state space and achieve an efficient solution. The experimental results highlight the effectiveness of the suggested control strategy to guarantee the energy supply and reduce the electricity market price. The results of the simulation demonstrate that using the proposed cooperative MAS with the real-time FQL method in the energy management of the H-MG will reduce the electricity market price by 10%, increase the health of the storage system by 35.67%, and enhance the utilization of the PV system to charge PEV for 8% at the peak load demand.

A comparative techno-economic assessment of bidirectional heavy duty and light duty plug-in electric vehicles operation: A case study

The proliferation of electric vehicles (EVs) all around the world offers both challenges and opportunities to build a sustainable city and transportation system. Bidirectional charging capabilities at workplace charging facilities (e.g., as part of a microgrid) have made the overall economic optimization more attractive on one hand, but also more complex on the other hand. This paper investigates the cost optimization problem for bidirectional charging at a workplace microgrid connected to two different buildings to determine the optimal framework for a combination of both heavy-duty and light-duty electric vehicles (HDEV and LDEV). A deep learning-based model has been developed to forecast the 15-minute solar generation and building power consumption. Real-time travel profile data has been used to represent the temporal uncertainty of electric vehicle charging. The cost optimization problem is formulated as a Mixed Integer Programming (MIP) model which also addresses battery life degradation. Furthermore, a comprehensive economic analysis has been carried out to analyze the payback period, peak reduction, and cost savings for two different buildings at the same workplace with both on-board and off-board charger configurations. It has been found that HDEV is a better cost-effective solution in comparison to LDEV in terms of energy cost reduction and payback periods. Net metering capability leads to higher energy savings and peak reductions in most cases.

Co-Optimization of Eco-Driving and Energy Management for Connected HEV/PHEVs near Signalized Intersections: A Review

Currently, road transport constitutes a considerable proportion of global fossil fuel consumption, as well as CO2 and pollutant emissions. To mitigate transportation energy consumption, two primary approaches have emerged: the large-scale adoption of Hybrid Electric Vehicles (HEVs) and Plug-In Electric Vehicles (PHEVs), as well as the implementation of eco-driving strategies, which present an immediate and low-cost solution. In this context, this paper provides a comprehensive review of these two technologies and their integration for connected HEV/PHEVs. We summarize the framework of recent approaches to incorporate fusion road information in single-vehicle and multi-vehicle scenarios, respectively, wherein we compare their advantages, their disadvantages, and their effectiveness in reducing energy consumption. Additionally, we reflect on the future development directions of cooperative optimization in EMS and eco-driving strategies from various perspectives. This comprehensive review underscores the importance and potential impact of these approaches in addressing environmental challenges in transportation systems, thereby offering useful insights for new researchers and practitioners in this area.

Optimal day-ahead scheduling of renewable energy-based virtual power plant considering electrical, thermal and cooling energy

For synchronized functioning of distributed renewable energy resources, virtual power plant (VPP) is manifested as an upshot for maximizing net profit of the partakers taking into account uncertainties. Here, optimal day-ahead scheduling for VPP has been performed for meeting electrical, thermal and cooling demands. VPP comprises small hydro power plants, wind turbine generators, solar micro-cogeneration units, biomass-fuel-fired micro-cogeneration units, battery energy storage system, plug-in electric vehicles, electric chiller, absorption chiller, ice storage conditioner and thermal energy storage system. SMC units and BMC units are incorporated alternately. Demand response program is applied for electric, thermal and cooling demands to flatten demand curves and to improve system flexibility and decrease cost. The scheduling problem is solved by utilizing bottlenose dolphin optimizer (BDO), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf (GWO) optimization so as to maximize net profit of VPP operator. The maximum profit obtained from BDO is about 5.25 % and 9.82 % more than the maximum profit obtained from HPSO-TVAC and GWO respectively.

Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms.

Machine Learning-Based Sizing of a Renewable-Battery System for Grid-Connected Homes With Fast-Charging Electric Vehicle

This paper develops a sizing model of solar photovoltaic (SPV), small wind turbine (SWT) and battery storage system (BSS) for a grid-connected home with a fast-charging plug-in electric vehicle (PEV). The home trades energy with the main grid under time-of-use tariffs for selling and purchasing electricity that affects the energy management. In this paper, a practical rule-based operation strategy is developed for the grid-connected home with fast-charging PEV that enables efficient and cheap energy management. The sizing problem is solved using a supervised machine learning algorithm, which is a feed forward neural network, by minimizing the cost of electricity. While the developed renewable-battery sizing model is general, it is examined using actual data of insolation, wind speed, temperature, load, grid constraints, as well as technical and economic data of BSS, SPV, SWT, and PEV in Australia. Uncertainty analysis is investigated based on ten scenarios of data for wind speed, temperature, load, insolation, and PEV. The effectiveness of the proposed model with fast-charging PEV is verified by comparing to slow charging and uncontrolled fast-charging models, as well as two other machine learning methods and a metaheuristic algorithm. It is found that the proposed model decreases the cost of electricity by 10.1% and 19.6% compared to slow charging and uncontrolled fast-charging models for the grid-connected home with PEV.

Effective Utilization of Available PEV Battery Capacity for Mitigation of Solar PV Impact and Grid Support with Integrated V2G Functionality

Abstract: Utilizing battery storage devices in plug-in electric vehicles (PEVs) for grid support using vehicle-to-grid (V2G) concept is gaining popularity. With appropriate control strategies, the PEV batteries and associated power electronics can exploited for solar photovoltaic (PV) impact mitigation and grid support. However, as the PEV batteries have limited capacity and the capacity usage is also constrained by transportation requirements, an intelligent strategy is necessary for an effective utilization of the available capacity for V2G applications. In this paper, a strategy for an effective utilization of PEV battery capacity for solar PV impact mitigation and grid support is proposed. A controllable charging/ discharging pattern is developed to optimize the use of the limited PEV battery capacity to mitigate PV impacts, such as voltage rise during midday or to support the evening load peak. To ensure an effective utilization of the available PEV battery capacity when used for travel (which is the main usage of the PEVs) or when interventions in the charging operation is caused by passing clouds, a strategy for dynamic adjustments in PEV charging/discharging rates is proposed. The effectiveness of the proposed strategy is tested using a real distribution system in Australia based on practical PV and PEV data.

ZigBee-Based Self-Service Charging Framework for Plug-in Electric Vehicles

This research presents a self-service charging structure for plug-in type electric vehicles, which includes multiple charging stations, a data service centre, and a mobile communication service centre. Each charging station is equipped with ZigBee networking devices, a charging station computer monitoring centre, and several chargers. The ZigBee networking equipment establishes a wi-fi connection with a ZigBee terminal node device in the electric vehicle. Each charger comprises a charger information management unit, an IC magnetic card identification device, a touch screen, a printer, and a charging device. The proposed self-service charging structure overcomes the issues of unstable electrical connection and high networking complexity associated with both wired and wi-fi communication procedures. It enables user-friendly, safe, auto, and intelligent charging processes without the need for manual intervention. Additionally, the structure supports multiple charging service modes. The research also presents a self-service charging procedure for plug-in type electric vehicles.

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.