Plug-in Vehicles Research Articles

Multivariate stochastic modeling of plugin electric vehicles charging profile and grid impact analysis

The electrification of the transportation sector presents a significant challenge to the power grid. The accurate modeling of the plug-in electric vehicle (PEV) charging load is essential since it can guide policymakers and grid operators to make informed decisions regarding grid reinforcement to facilitate the additional charging demand of PEVs. This research paper introduces a hybrid technique for estimating the aggregated PEV charging load based on a stochastic model and real-world dynamics. The proposed methodology includes multivariate stochastic modeling of PEV driving behaviors which is combined with the powertrain simulations performed on multiple models of PEVs operating under two different driving cycles to determine critical parameters such as energy consumption, the initial state of charge (SOC), and grid charging energy requirements. These parameters are used to estimate daily PEV charging demand based on real-world dynamics. The grid impact analysis is performed on the model of an actual distribution network, located at the National University of Sciences and Technology (NUST), through load flow analysis to identify the weakest nodes in the grid and the percentage of transformer loading due to additional PEV charging load. In addition, the research paper proposes a techno-economic grid reinforcement solution, performed on the Homer Pro, to minimize the detrimental impact of the additional PEV charging load on grid voltage stability. The proposed solution includes measures such as optimal placement of distributed generations (DGs), including photovoltaic (PV) panels, storage systems, and diesel generators, to ensure reliable and efficient charging infrastructure for PEVs.

Distributed demand response charging control of multiple plug‐in electric vehicle clusters

AbstractThis paper is devoted to the distributed demand response (DR) charging control of plug‐in electric vehicles (PEVs) for frequency regulation in power systems in terms of load following service. Specifically, PEVs are divided into different clusters according to their parking place under different energy management systems. Each EV cluster is modelled by a transport‐based load aggregate model with the input being the charging rate, and the output is the aggregate power. Based on the aggregate charging control model, a novel dynamic real‐time distributed pinning control algorithm is proposed to coordinate the charging rates such that the aggregate charging power of PEVs can follow a given reference power trajectory. The theoretical analysis shows that if the reference power profile is in the trackable area of all PEVs' charging power and the ramping rate is restrained by a predefined bounded constraint, then the demand response charging tracking control is solvable. Finally, simulation results on an EV system with twelve PEV clusters are presented to show the effectiveness of the proposed demand response control algorithm.

Prosumer-Based Optimization of Educational Building Grid Connected with Plug-in Electric Vehicle Integration using Modified Firefly Algorithm

Educational buildings have the potential to support government programs in efforts to reduce carbon emissions. Installing photovoltaics and providing charging stations can reduce the use of fossil fuels and increase the number of electric vehicle users. This paper aims to optimize educational buildings when implementing a prosumer scheme and integrating Plug-in Electric Vehicles (PEV) to meet building electricity demands. Optimization is carried out through two case studies, namely the application of a prosumer scheme with independent photovoltaic generators with and without PEV integration. The optimization process uses the Modified Firefly Algorithm. The results obtained by applying the prosumer scheme to educational buildings, the two case studies can produce LCOE cheaper than just buying electricity from the grid. Optimizing results show that photovoltaic installation and charging stations in educational buildings can be beneficial when implementing a prosumer scheme.

An improved differential evolution using self-adaptable cosine similarity for economic emission dispatch

As increasing demand of electric energy and deterioration of environment pollution in global, economic emission dispatch (EED) problem has become one of the most important problems in power systems. In order to improve the quality of life and further reduce emissions, plug-in electric vehicle (PEV) has become a popular transportation. But the frequent and random charging of PEVs will bring potential risk that undermines the stability of the power grid. Therefore, dynamic economic emission dispatch considering plug-in electric vehicles (DEED-PEV) as a new hot-spot issue has gradually become more and more important in the power system. In this paper, an improved differential evolution using self-adaptable cosine similarity (DE-SCS) is proposed for EED and DEED-PEV problems. First, a dynamic cosine similarity calculated replaces the F of the differential evolution algorithm (DE) to scale the perturbation by self-adaptability. Second, a result-driven selection operation for multiple mutation strategies is implemented in each iteration. Third, a modified environment selection changes the usual way to ranking in the vectors pool during the update on Pareto front. Last, an evaluation mechanism that involves two series of solution sets and three types of comparing approaches is conducted on our proposed algorithms in the experiment. DE-SCS improves the equilibrium between exploration and exploration of the DE. The cosine similarity makes the convergence more self-adaptive. The modified environment selection maximizes the worth of previous generation vectors and brings the randomness into the next iteration. DE-SCS matching various environmental selection are tested on twelve unconstrained multi-objectives problems, six EED cases and six DEED-PEV cases containing charging load, discharging power, valley filling, and peak shaving. Experimental results confirm that DE-SCS is capable of obtaining such excellent and feasible solutions that it has good potential to deal with EED and DEED-PEV problems.

Development of Machine Learning Based State-of-Charge Prediction Model for Plug-in Electric Vehicle's Relocation in Sharing System

This paper presents a Plug-in electric vehicle (PEV) relocation strategy in a sharing system considering machine learning based state-of-charge (SOC) prediction model. The proposed work involves the machine learning approaches like the Adaboost algorithm and XGBoost algorithm for PEVs SOC prediction in relocation problems for the first time. Also, the low-capacity batteries of PEVs are considered as it is affordable by the common people with less charging time. It is also believed that the large number of low capacities PEVs based taxi owners in metropolitan cities will be attracted towards relocation-based sharing system considering machine learning based SOC prediction model. The customer request for sharing PEVs will be reflected in the management system at the beginning. Assuming all the PEVs with a certain level of SOC during pre-relocation are connected to the management system. The customer request for PEVs service will be assigned to the nearest PEVs depending on the predicted value of SOC by machine learning approaches. The machine learning-based predicted value of PEVs SOC will help the management system to identify the nearest PEVs for the customer sharing system. The present work steps are listed here. Firstly, the PEVs with higher predicted SOC value by machine learning have been shared with the customer from the nearest location considering descending order of their SOC. Further, the rest of the PEVs will be assigned to the nearest charging station by the PEV sharing management system. Secondly, the customer request is assigned from the station to relocate PEVs with higher SOC values in descending order. Different case studies are assumed including one practical scenario of Mumbai, India. The optimization problem of multi-depot vehicle routing problem (MDVRP) with the minimization of relocation cost has been solved by IBM CPLEX optimizer in this work. Finally, the proposed work efficacy has been demonstrated with some validated results including less relocation costs for all the case studies under consideration with lower capacity based PEVs.

Investigating heterogeneous preferences for plug-in electric vehicles: Policy implications from different choice models

Plug-in electric vehicles (PEVs) adoption has been a promising strategy to address climate change and improve energy security. Understanding consumer preferences for PEV attributes and policies is critical to accelerate mass PEV penetration. This study examines consumer preference heterogeneity in adopting PEVs with mixed logit (MXL), latent class (LC), and latent class-mixed logit (LC-MXL) models based on a stated preference survey in Virginia, U.S. Consistently, all three models indicate that providing a monetary incentive is most effective in increasing the overall PEV market share, followed by deploying more charging infrastructure, while improvement in battery range shows a weak impact. Furthermore, the choice models that capture preference heterogeneity provide more nuanced results on the effectiveness of policies on a specific consumer segment and for a specific vehicle powertrain type. Lastly, when considering various model evaluation measures (e.g., model fit, prediction accuracy, and behavioral interpretation), results show that no model is unanimously superior to the other models. Rather, altogether they uncover a more comprehensive picture of EV preference structure. Findings provide insights into the usefulness of each choice modeling framework for future PEV adoption research. Also, it informs policymakers to be aware of alternative models which can provide different perspectives on policy implications.

Impact of Electric Vehicles toward Energy Reliability of the Jawa-Madura-Bali Electrical Power System

There is an observed limitation in the number of electric vehicles using batteries as power sources (e.g., plug-in electric vehicles) in Indonesia. While the number of said electric vehicles is increasing in response to the government’s commitment to shift the transportation energy source towards electric energy for a more sustainable environment, this change is followed by an inevitable impact on the current electric network and infrastructure. Indonesia’s government predicted that there will be around 20 million 2-wheelers and 4 million 4-wheelers ICE by the year 2030 with an additional load of 29.300 MW to be expected. Lessons learned from other countries have shown that the abrupt changes in the supply-demand energy for the electric vehicle, such as the deficit/surplus of energy or the duck curve phenomena, create an urgency to prevent the said phenomena and the potential financial loss that might have cross-sectoral impacts. Through an analysis of the Jawa-Madura-Bali system, an independent system that can produce 30.970 MW by the year 2020 [1], the paper focuses on the impact of this system in developing an adaptive infrastructure to address increased power needs for electric vehicles in Indonesia. Literature review and data analysis shows that the impact of Electric Vehicles on the Energy Reliability of the Jawa-Madura-Bali Electrical Power System is very pronounced. The amount of energy needed for the grid to be reliable in the EV transition is significantly higher than the natural growth of the grid energy supply. It is feasible that the increasing electricity demand from EVs could be offset by adding more renewable energy sources to the grid. Renewable energy sources, such as solar and wind, (both centralized and decentralized) can help to offset the insufficient total energy supply growth from the conventional power producer. One approach to address these challenges is to continue the development of mixed renewable and non-renewable energy sources, allowing the grid to draw on a variety of sources as needed to meet demand. This development can help to ensure a reliable and stable supply of electricity to meet the needs of electric vehicles and other customers. A supply-demand simulation and prediction, which could be a very helpful tool in the conceptualization of a management plan for the energy system to mitigate the imbalance of supply and demand of electric energy supply should be developed.

Distributed plug-in electric vehicles charging strategy considering driver behaviours and load constraints

Distributed plug-in electric vehicles charging strategy considering driver behaviours and load constraints

Development and Application of an Energy Management System for Electric Vehicles Integrated with Multi-input DC-DC Bidirectional Buck-Boost Converter

The rise in environmental pollution, demand for fossil fuels, and higher fuel economy vehicles has raised concerns about the creation of new and efficient transportation vehicles in recent days. These days, most developments in electric vehicles concentrate on making the vehicles more pleasant to ride in. Nonetheless, the emphasis now should be on energy and its most efficient use. To do this, you must give your attention to the origin of the automobile. The answer to this problem may be found in hybrid energy storage systems (HESS). This work is concerned with the design and implementation of an effective energy management system in electric vehicles (EVs) equipped with an active HESS consisting of a battery and a super capacitor via the incorporation of load sharing into this hybridization under a variety of load demand scenarios. To address the demands of high fuel efficiency vehicles, automotive firms are focusing on the development of diesel-engine operated vehicles, electric vehicles, fuel-cell vehicles, plug-in electric vehicles, and hybrid electric vehicles. A Multi-input Bidirectional Buck-Boost (MIB3) DC-DC converter is proposed in this dissertation to provide a greater conversion ratio to the input DC voltage. The multi-input converter recommended has fewer components and a simpler control method, making it more trustworthy and cost-effective. This converter also has bidirectional power flow functionality, making it suitable for charging the battery during regenerative braking in an electric or hybrid vehicle. Three different energy sources are used in the suggested topology: a photovoltaic (PV) panel, a battery, and an ultra-capacitor

AN EFFECTIVE METHOD OF ELECTRICAL VEHICLE CHARGING BY WIRELESS POWER TRANSFER

Electric vehicles are a viable solution for lowering greenhouse gas emissions. Electric vehicles not only reduce fossil fuel dependency, but they also minimise ozone damaging compounds and support large-scale renewable deployment. Despite extensive research on the qualities and attributes of electric vehicles, as well as the nature of their charging infrastructure, electric vehicle manufacturing and network modelling continue to evolve and be constrained. Various non-contact Plug-In Electric Vehicle charging methods are covered in this paper. They're either in the works or are already being used as flexible aftermarket choices in the light-duty vehicle industry. Wireless power transfer (WPT) is a commonly used name for wireless charging that includes inductive power transfer and magnetic resonance coupling. Wireless technology is still in its infancy, and there is a lack of standardisation, particularly in terms of interoperability, centre frequency selection, magnetic fringe field suppression, and power flow regulation methods.

Design of Two Fuel Cell Buses for Public Transport According to Two Different Operating Scenarios: Urban and Motorway

<div>The transport sector is one of the major parties responsible for carbon dioxide (CO<sub>2</sub>) and pollutants emissions in Europe. For this reason, one of the main commitments of the European Commission is its decarbonization by 2035/2040. To achieve this target, during the last decades, different propulsion technologies were developed such as hybrid electric vehicles (HEVs), plug-in electric vehicles (PHEVs), and battery electric vehicles (BEV). The first two proposals can be considered as bridging technology between the internal combustion engine (ICE) and the BEV because they offer at the same time comparable performance as conventional powertrains and improved efficiency. However, both technologies are struggling with the tightening of pollutants and CO<sub>2</sub> limits. On the other hand, the BEV can offer zero emissions at the tailpipe, but it suffers from limited range capabilities and the lack of fast-charging infrastructures. Within this context, the fuel cell vehicle (FCV) appears as an interesting opportunity because it offers zero tailpipe emissions and equivalent refuelling time of the ICE. This article evaluates through mathematical simulations the performance of two fuel cell electric buses (FCEBs), which are supposed to work respectively in urban and highway driving conditions. The urban bus is equipped with a single fuel cell (FC) module of 85 kW-Net and an electric motor (EM) of 225 kW. The intercity bus is equipped with two FC modules with a total power of 170 kW-Net and two EMs of 225 kW each. A sensitivity to the battery capacity from 20 kWh to 40 kWh was performed for both FECBs. The power split between the FC module and the high-voltage battery was optimized with the Equivalent Consumption Minimization Strategy (ECMS). The two FCEBs were tested considering different portfolios of cycles: in the case of the urban bus in Braunschweig and the Standardized On-Road Test Cycles SORT1 and SORT2 were assumed as a reference, while cycles like the Highway Fuel Economy Test (HWFET), European Transient Cycle (ETC), and cruising at 100 km/h were assumed as reference for the intercity. Simulation results highlighted that the increase of battery capacity in the case of the urban bus from 20 kWh to 30 kWh reduces hydrogen (H<sub>2</sub>) consumption by 11% along the Braunschweig cycle. On the other hand, in the case of the intercity bus, the fuel consumption is less affected by the increase of capacity in the same range. In this case a reduction of 4.7% is estimated for the HWFET cycle, and it is less than 1% in the case of cruising conditions.</div>

Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles

Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the grid (V2G), the fluctuations in the grid can be mitigated, and the benefits of balancing peaks and filling valleys can be realized. However, the complexity of DED has increased with the emergence of the penetration of plug-in electric vehicles. This paper proposes a model that takes into account the day-ahead, hourly-based scheduling of power systems and the impact of PEVs. To solve the model, an improved chaos moth flame optimization algorithm (CMFO) is introduced. This algorithm has a faster convergence rate and better global optimization capabilities due to the incorporation of chaotic mapping. The feasibility of the proposed CMFO is validated through numerical experiments on benchmark functions and various generation units of different sizes. The results demonstrate the superiority of CMFO compared with other commonly used swarm intelligence algorithms.

Energy management optimization in smart railway stations with the ability to charge plug-in hybrid electric vehicles

The increasing development of traffic in the world has led to an increase in the consumption of electrical energy in electric vehicles (EVs). One of the most widely used electric vehicles is the electric train. Thus, energy optimization becomes more crucial in electrical railways to reduce consumption and total electricity bill as well as environmental issues. Consequently, electrical railway energy management must be technically and economically efficient and effective. This paper proposes an energy efficiency optimization framework for intelligent railway stations that provide plug-in electric vehicle charging parking capacity use, renewable energy sources (REs), and regenerative braking energy (RBE). The structure of the proposed convex programming (CP) model is used for fast and efficient optimization of decision variables, Equipment size, and the cost function of the station for reducing the power purchased from the Grid. Furthermore, by presenting meta-heuristic algorithms, the state variables of the energy storage system (ESS) and Plug-in hybrid electric vehicles (PHEVs) battery have been optimized. The results obtained from the proposed method prove that the operation cost of the station decreases by 61.4 %.

Optimal self-tuning fractional order fuzzy load frequency control considering sustainable energy sources and electric vehicle

Considering uncertainties associated with sustainable energy sources in modern power systems, one of the main obstacles for load frequency control (LFC) is that various publications regard the parameters of FOPID controllers as fixed after optimisation. To tackle this challenge, an adaptive control structure may be a viable option for LFC. In this paper, in a two-zone hybrid power system that includes a thermal power plant, green energy sources, fuel cells, and plug-in electric vehicles, an optimal self-tuning fractional order fuzzy (OSTFOF) controller for LFC is proposed. Pathfinder algorithm (PFA) has been utilised for optimal tuning of parameters of the OSTFOF controller, including the upper bands of output membership functions, the input scaling factors, and the order of integral and derivative operators. In the proposed controller, the values of proportional, integral, and derivative gains are adaptively obtained. In the thermal power plant, non-linear factors, such as governor dead band (GDB) and generation rate constraint (GRC), are considered. Four different scenarios have been considered to evaluate the performance of the proposed OSTFOF controller, and results have been compared with PI, PID, and FOPID controllers. The simulations’ results indicate the excellent performance indices of the suggested OSTFOF controller compared to other controllers.

Dynamic optimal power flow for multi‐operator renewable energy‐based virtual power plants

AbstractRecently, infiltration of distributed energy resources (DERs) is augmented considerably to upsurge network flexibility, better economic indicator, and reduced power loss. But integration of different DERs may cause challenges in power grid. To overwhelmed these challenges and obtain maximum advantage of DERs, virtual power plant's concept has been emerged. Virtual power plants (VPPs) has the capacity to partake in electricity market and rivalry of VPPs to achieve more profit, deregulated multi‐operator markets are developed. This paper suggests dynamic optimum power flow (DOPF) for multi‐operator VPPs considering demand side management (DSM) and uncertainty of renewable energy sources. VPPs with different proprietorships are interconnected with each other by tie lines. Each VPP has small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plant (BPPs), and plug‐in electric vehicles (PEVs). VPP 1 comprises IEEE 33‐bus system, VPP 2 comprises 15‐bus system, and VPP 3 comprises IEEE 69‐bus system. Bottlenose dolphin optimizer (BDO), HPSO‐TVAC, and GWO have been applied to solve DOPF problem and maximize the net profit of multi‐operator VPPs.

Integrated Multipurpose Power Electronics Interface for Electric Vehicles

A novel integrated multipurpose power electronics interface (IMPEI) for the new generation of plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs) is proposed in this paper. The IMPEI is a reconfigurable power electronics interface (PEI) that integrates the onboard charger with the drive inverter allowing the same interface to be used for various modes of operation such as propulsion and regenerative braking as well as vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations with grid flexibility. For each mode of operation, the IMPEI is reconfigured into a previously existing power converter topology. The principles of operation and coordination of various modes of operation of the IMPEI are explained in this paper. Based on component count, operating modes, and control complexity, a comparison of the IMPEI and recently proposed integrated PEIs (IPEIs) is provided. Specifications of BMW i3 are used as the benchmark for comparing size, cost, and efficiency. This paper discusses and validates experimental results.

Energy impact assessment of electric vehicle insertion in the Brazilian scenario, 2020 – 2050: a machine learning approach to fleet projection

The transportation sector stands out on the world stage as one of the largest consumers of energy and the trend towards electrification of the light vehicle fleet is proving to be challenging, given the impacts generated by growing demand of energy and mainly in the generation of electricity. In Brazil, the electricity generation mix comprises around 83% of renewable sources, basically composed of hydraulic, wind and biomass sources, making it reasonable to consider the electrification of the transportation sector as one of the measures to be adopted in the promotion of sector sustainability. This article contributes to energy planning through the long-term projection of the Brazilian fleet of light vehicles, and the simulation of their impacts on energy demand, in three scenarios of insertion of plug-in electric vehicles. In this sense, statistical methodologies applied to historical data series and supervised Machine Learning algorithms for curve fitting were used, as well as the well-to-wheels approach in the fuel life cycle to estimate the energy demand. The projections included three scenarios for the insertion of electric vehicles with annual increases in demand for electricity generation which would correspond to 0.6%, 0.9% and 4.2% of additional electricity generation demand in the year 2050, with reference to the year 2020. The electrification of the fleet also proved to be advantageous in terms of reducing the use of fossil fuels and promising in terms of sustainability in the transport sector. The results of these studies will provide fundamental information for energy planning, as well as for public policy decision-making.

Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

Utilizing controlled plug-in electric vehicles to improve hybrid power grid frequency regulation considering high renewable energy penetration

This study proposes an incorporated strategy based on energy storage systems (ESSs) like plug-in electric vehicles (PEVs) with load frequency control (LFC) to enhance the frequency stability during high penetration of renewable energy sources (RESs). Furthermore, an innovative-cascaded controller is used in the LFC loop, and PEVs are considered in the two identical areas' multi-source power grids. The proposed control structure is based on using two terms, the first term is the fractional-order-proportional-derivative (FOPD), and the second cascaded term is a fractional-order-proportional-integral (FOPI) to form the cascaded (FO-(PD-PI)) controller. Additionally, the gains of the proposed controller are well designed, utilizing an improved version of the manta ray foraging optimization algorithm (MRFO) labeled as leader MRFO (LMRFO). The effectiveness of the proposed FO-(PD-PI) controller based on the LMFRO algorithm is validated by comparing its performance with that of other control approaches such as (i.e., the proportional-integral-derivative (PID) controller, the tilt-integral-derivative (TID) controller, and the fractional-order-PID (FOPID) controller) based on different optimizers. Different load perturbation patterns, a high penetration level of RESs, and the communication delay time are broadly implemented for ensuring the superiority of the proposed controller in penalizing the power grid perturbations. Furthermore, for validating the efficiency of the proposed controller, an IEEE 39 bus is used. As a result, the proposed integrated strategy is a viable and effective control scheme for modern power grids with high-RESs penetration.

Control strategy for plug-in electric vehicles with a combination of battery and supercapacitors

Abstract Research proposes an optimal power distribution approach for application of electric vehicle (EV) with use of hybrid energy storage system (HESS). HESS protection structure includes dual isolated-based soft-switching symmetrical coupling with half-bridge bidirectional converters to the system of battery and supercapacitors (SCs). The bidirectional converter properly controls the charging process of the battery and SC as well. Besides, spiral wound SCs of mesoporous electrode material have been used in EVs. In the drive cycle of EVs, the operation of SC relates to the functionality of the allocated scheme under “peak load transfer” at 2i sc ∼ 3i bat current profile carried out. New energy allocation strategies under SOC control enable SC charging and discharging at peak currents of around 4i bat. The comparison of the mode of the battery system showed that the performance acceleration built under EVs has been improvised at a certain rate of 50% with a loss of energy minimised to 69%. As a result, the technique adapts different load curves, thus enhancing the utilisation of energy with reduced aging of the battery. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, for testing vehicle performance, and various energy management system have been assessed based on hydrogen consumption, overall efficiency, state of charge of SCs and batteries, stress on hybrid sources, and DC bus stability. The proposed strategy reduces hydrogen consumption by 8.7% compared to other strategies.