Proliferation Of Electric Vehicles Research Articles

Designing a transactive electric vehicle agent with customer’s participation preference

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner’s willingness to trade-off between savings and amenity in form of a slider, where the EV owner’s amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer’s transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed. It reveals that the V2G does not offer significant additional benefits with current battery prices, but could be promising if battery costs decline in the future.

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.

Security-Constrained Optimal Traffic-Power Flow With Adaptive Convex Relaxation and Contingency Filtering

The ongoing transportation electrification [e.g., the proliferation of electric vehicles (EVs)] strengthens the interdependence between power and transportation networks and brings tremendous operational challenges to the two critical infrastructure systems. This article proposes an N–1 security-constrained optimal traffic-power flow (SCOTPF) model that includes bilateral contingencies (closure of traffic link and outage of charging station) to coordinate the two networks toward N–1 secure and reliable. The partial user equilibrium (PUE) criterion is adopted to capture the driver’s rerouting behavior in response to the contingency. To facilitate the model tractability, we exploit an adaptive piecewise convex relaxation method based on the convex hull and McCormick envelope to derive a mixed-integer second-order cone programming (MISOCP) model and design a binding contingency identification algorithm to screen redundant contingencies. Finally, the optimal preventive operation strategy is efficiently obtained to reallocate the precontingency traffic-power flow and hence ensures the N–1 reliability and security of the coupled networks. Numerical results on two test systems validate both solution quality and computational efficiency of the SCOTPF. Meanwhile, interdependence between the two networks in contingency scenarios is thoroughly analyzed.

Pricing for Electric Vehicle Charging Stations Based on the Responsiveness of Demand

Electric vehicles (EVs) have the promising potential to be effective in mitigating greenhouse gas emissions in the transportation sector. Hence, the penetration of EVs in some countries experiences steady growth. However, the proliferation of EVs can cause negative impacts on the power distribution network (PDN) and the transportation network (TN). Thus, in this paper, a dynamic pricing strategy for electric vehicle charging stations (EVCSs) is proposed to facilitate EVCSs to earn higher profits while alleviating the potential negative impacts on both PDN and TN. First, a pricing strategy, considering the competition effect established based on the TN, is formulated to facilitate the EVCS to attract the defined competitive charging demand. Second, a two-step approach is proposed to mathematically formulate the responsiveness of demand towards the charging price. Third, EV user behaviors are incorporated based on both an admission control scheme and a queueing model to further adjust the charging demand. We have conducted simulations to verify the effectiveness of the pricing strategy in a PDN and TN coupled system, which contains approximately 2000 EVs. Results show that the proposed pricing strategy can facilitate the EVCS to gain 14.2% more net charging profit on average compared with the other three cases. Moreover, the operational stability of the EVCS can be ensured because the proposed pricing strategy can result in the least queueing length volatility with moderate profit variance compared with the other three cases. Furthermore, the proposed pricing strategy can achieve spatial load shifting by incentivizing EVs to alter their station-selection decisions to avoid possible power congestion in the electricity network.

Adaptive Integrated Planning of Electricity Networks and Fast Charging Stations Under Electric Vehicle Diffusion

The proliferation of electric vehicles (EVs) is an inevitable trend to realize transportation electrification. However, the increasing charging demand of EVs also brings challenges to future smart grids. To cope with the EV diffusion, this paper presents an adaptive fast charging station (FCS) strategy that considers the integrated network planning and quality of service (QoS) guarantee. First, based on the EV diffusion model and the proposed EV-integrated traffic assignment model, the charging demands at different regions and stages are forecasted. Based on the predicted spatial and temporal EV charging demand, a multistage stochastic distribution expansion planning model is presented, where the distribution network assets and FCSs are jointly considered. To guarantee the quality of the fast-charging services under EV diffusion, a QoS assessment is conducted based on the average waiting time and the average number of dissatisfied leaves. Finally, the proposed planning model allows several alternatives at both the distribution and transmission levels. The cost of electricity network expansion planning is considered as the adaptive cost arisen from the EV diffusion and the increase of the other electrical loads. We aim to find a planning strategy that can adapt to different penetration levels of EVs. The proposed framework and methodology are verified on the IEEE 24-bus power system encompassing 6 IEEE 33-bus distribution networks. It is found out that the proposed strategy shows superiority in both QoS and total costs.

Analysis and Sizing of Charging Stations in Kota City

This paper focuses on optimization of charging station capacities and locations. The proliferation of electric vehicles is inextricably linked to the deployment of charging infrastructure. Currently, there are issues with locating and sizing when it comes to the building of electric vehicle charging stations. In order to find out the capacity of charging stations, a mathematical model was developed, which includes a site survey, EV density, electricity demand analysis, load modeling, costing analysis, EV battery charging time and power quality. The proposed approach was tested in MATLAB/Simulink environment.

Electric Vehicle Owners’ Perception of Remanufactured Batteries: An Empirical Study in China

The proliferation of electric vehicles (EVs) globally is remarkable progress in strides toward a low carbon society. However, the volume of end-of-life EV batteries will hit a critical mass in the future. Widespread adoption of EV battery remanufacturing is essential in achieving higher resource efficiency. The current study investigated Chinese EV owners’ perceptions of remanufactured EV batteries: 420 respondents in China who own and drive EVs participated in the survey. This study modeled respondents’ acceptance, purchase intention, and willingness-to-pay for remanufactured EV batteries by adapting the structural equation model (SEM). The results showed that consumers’ price consciousness and perceived benefits both directly influence their purchase intention of remanufactured batteries. Unlike previous studies, this study found that consumers’ perceived risks on remanufactured batteries do not directly influence their purchase intention. Instead, the influence of perceived risks on purchasing behavior is mediated by perceived benefits. The study also found that purchase intention affects willingness to pay and acceptance of remanufactured batteries. Drawing on our study results, this research suggests measures to promote markets for remanufactured EV batteries and provides corporate marketing options to accelerate proliferation of remanufactured batteries.

Techno-economic modelling for energy cost optimisation of households with electric vehicles and renewable sources under export limits

With the proliferation of electric vehicles (EVs), EV charging cost will become an integral part of household energy cost. This research proposes a novel household energy cost optimisation method for grid-connected homes with EV under power export constraints. It addresses the limitations of previous studies by incorporating more realistic variable EV charging characteristics, power export limits, degradation of battery energy storage (BES) and battery salvage revenue into a comprehensive techno-economic energy system model. Cost optimisation results are presented for four system configurations using relatively new time-of-use (ToU) tariff and real load and photovoltaic (PV) generation data for South Australian households. Sensitivity analysis for annual energy cost (AEC) is conducted by varying daily household load demand, PV/BES capacity, power export limits and PV/BES cost. Power flow and peak demand analyses are presented to the impacts of PV, BES and EV on household demand. Results show that PV with BES and EV is the most economical configuration for individual households, where the AEC can be reduced by up to 39.6% compared to a normal household without EV, PV and BES. The BES can effectively reduce household power and energy demands during peak periods by up to 80.4% and 89.1%, respectively.

Transformer-Based Model for Electrical Load Forecasting

Amongst energy-related CO2 emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.

An Efficient Vehicle-to-Vehicle (V2V) Energy Sharing Framework

The proliferation of electric vehicles (EVs), owing to their advantages over internal combustion engine vehicles, has introduced many challenges, due to the lack of charging infrastructure that can handle such a large surge of EVs. Therefore, alternative feasible charging solutions, such as EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence thanks to the bidirectional charger. However, there are several challenges hindering the adoption of V2V energy sharing solutions. The existing frameworks that detail the main aspects in energy management protocols emphasize solely on the EV integration with the grid, with the assumption of the grid availability and capability of supporting V2V energy sharing. In this article, a novel holistic energy management framework for an efficient V2V energy sharing is proposed. The proposed framework offers a complete overview of the different stages in the V2V charging problem and introduces possible solutions for each stage and its integration with other stages to form a comprehensive V2V solution, that is not only cost-effective but also maximizes user satisfaction and social welfare, while simultaneously fulfills the highest number of energy demands.

A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows

Transportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with the proliferation of EVs. The vehicle routing problem concerns the freight capacity and battery autonomy limitations in different delivery-service scenarios, and the challenge of best locating recharging stations. This work proposes a mixed-integer linear programming model to solve the electric location routing problem with time windows (E-LRPTW) considering the state of charge, freight and battery capacities, and customer time windows in the decision model. A clustering strategy based on the k-means algorithm is proposed to divide the set of vertices (EVs) into small areas and define potential sites for recharging stations, while reducing the number of binary variables. The proposed model for E-LRPTW was implemented in Python and solved using mathematical modeling language AMPL together with CPLEX. Performed tests on instances with 5 and 10 clients showed a large reduction in the time required to find the solution (by about 60 times in one instance). It is concluded that the strategy of dividing customers by sectors has the potential to be applied and generate solutions for larger geographical areas and numbers of recharging stations, and determine recharging station locations as part of planning decisions in more realistic scenarios.

Blockchain-Based Secure and Cooperative Private Charging Pile Sharing Services for Vehicular Networks

With the proliferation of electric vehicles (EVs), private charging pile (PCP) sharing networks are likely to be an integral part of future smart cities, especially in places with limited public charging infrastructure. However, there are a number of operational challenges associated with the deployment of PCPs in such a shared and untrusted environment. For example, how do we formulate efficient PCP sharing strategies in PCP sharing networks, while also taking into consideration the dynamic charging behaviors of EVs? Therefore, in this paper, we propose an energy <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u> lockchain- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u> ased secure P <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> P sharing scheme (BBC) for PCP sharing networks. First, an energy blockchain-based framework is designed for PCP sharing networks to facilitate energy sharing services for EVs and PCPs, using both distributed ledgers and cryptocurrency. Then, we devise a reputation-based secure PCP sharing algorithm to improve consensus efficiency with smaller signature sizes. In addition, a distributed reputation mechanism is constructed to assess the trustworthiness of consensus nodes in blockchain, based on ratings, behaviors, and fading. We also model the interactions among EVs and cooperative PCPs as a joint coalition-matching game, and obtain the optimal strategies of PCPs and EVs by analyzing the Nash-stable coalitional structure and stable many-to-one matching pairs. Extensive simulations and the real-world implementation demonstrate that the proposed approach improves the utility of EV users and renewable energy efficiency in PCP sharing networks.

Evaluating environmental impacts and economic performance of remanufacturing electric vehicle lithium-ion batteries

With the proliferation of electric vehicles (EV), large amounts of retired batteries need to be disposed, which poses emerging waste management challenges. Remanufacturing LIBs with materials recycled from used batteries is gaining increasing appreciation. Its environmental and economic benefits are still controversial and merit further examinations. This paper employs a life-cycle model and a process-based cost model to evaluate the greenhouse gas (GHG) emissions, water consumption, and the related costs of remanufacturing LIBs within the context of China, which is the biggest EV producer. Four types of LIBs, i.e. NCM111, NCM622, NCM811 and NCA, and three different recycling methods, i.e. Pyrometallurgical Recycling (PR), Hydrometallurgical Recycling (HR), and Direct Physical Recycling (DPR) are analyzed. The environmental impacts of remanufacturing LIBs are assessed at both national and provincial levels. Results show that compared with manufacturing LIBs with virgin materials, remanufacturing LIBs can significantly reduce GHG emissions, water consumption, and production costs. Among the three recycling methods, DPR has the biggest potentials for reducing GHG emissions, water consumption, and manufacturing costs with 29.27%–38.15%, 30.07%–41.19%, and 25.61%–36.63% reduction, depending on the different types of LIBs. Regarding battery technologies, remanufacturing NCM111 cell with DPR induces the least negative environmental impacts. Sensitivity analyses show that there are still large profit margins for remanufacturing LIBs with DPR process to bear the used LIB purchase price increase. Potential water-carbon conflicts are demonstrated for developing LIB remanufacturing industry due to different provincial electric power portfolios, which should be considered in future industry planning.

Optimal Game-Based Energy Management with Renewable Energy for Secure Electric Vehicles Charging in Internet of Things

The integration of smart grid and Internet of Things (IoT) has been facilitated with the proliferation of electric vehicles (EVs). However, due to EVs’ random mobility and different interests of energy demand, there exists a significant challenge to optimally schedule energy supply in IoT. In this paper, we propose a secure game theoretic scheme for charging EVs supplied by mobile charging stations (MCSs) in IoT, considering the dynamic renewable energy source. Firstly, the charging system composed of MCSs is developed to implement the charging service. Secondly, when the secure charging scheme of EV users is designed, the utility function of each entity in the charging system is formulated to express the trading relationship between EV users and MCSs. Moreover, with consideration of the competition and cooperation, we propose a Stackelberg game framework with sub-noncooperative optimization. Thirdly, the existence and uniqueness of both Stackelberg equilibrium (SE) and Nash equilibrium (NE) are theoretically analyzed and proved. Through the presented distributed energy scheduling algorithm, we can achieve the optimal solution. Finally, numerical results demonstrate the effectiveness and efficiency of our proposal through comparison with other existing schemes.

An efficient soft-switched vienna rectifier topology for EV battery chargers

The requirement of high power rated, efficient and high power density grid connected converters has increased due to their extended use in multiple applications such as battery chargers. The proliferation of electric vehicles (EVs) in the transportation market is essentially associated with the performance and reliability of battery chargers. An efficient, compact and fast battery charger is indispensable in order to provide a way to charge an EV in a short time. In view of this fact, this article proposes a soft-switching three-level T-type vienna rectifier, which can be used as a front-end power stage converter in an on-board EV battery charger. The proposed topology achieves high efficiency and high power density by employing soft-switching strategy, which is achieved by incorporating a simple auxiliary network in the proposed circuit. The reduced component stresses and simple control strategy make it an appealing candidate for EV industries to develop this front-end PFC rectifier as a fast battery charger. The high switching operation with reduced power losses, regulated DC at the output, low-harmonic grid current and power factor correction operation are achieved with the proposed topology. The construction, operating principle and simulation analysis of the proposed converter are discussed in detail. In order to show the stand-point of the proposed scheme, a fair comparison of the proposed soft-switched converter with an existing soft-switched 3-level neutral-point diode-clamped converter is carried out in terms of the number of components and complexity in the PWM signals generation. Additionally, an efficiency comparison of the suggested converter is carried out with some existing well-known rectifier topologies at different power loads. The practical implementation of the proposed power converter scheme is checked by building a laboratory-prototype to perform an experimental analysis.

Validation of optimal electric vehicle charging station allotment on IEEE 15-bus system

Introduction. The diminishing conventional energy resources and their adverse environmental impacts compelled the researchers and industries to move towards the nonconventional energy resources. Consequently, a drastic paradigm shift is observed in the power and transportation sectors from the traditional fossil fuel based to the renewable energy-based technologies. Considering the proliferation of electric vehicles, the energy companies have been working continuously to extend electric vehicle charging facilities. Problem. Down the line, the inclusion of electric vehicle charging stations to the electric grid upsurges the complication as charging demands are random in nature all over the grid, and in turn, an unplanned electric vehicle charging station installation may cause for the system profile degradation. Purpose. To mitigate the problem, optimum allocation of the charging stations in existing power distribution system in a strategic manner is a matter of pronounced importance in maintaining the system stability and power quality. In this paper, optimum allocation of electric vehicle charging stations in IEEE 15-bus system is studied in order to minimize the highest over and under voltage deviations. Methodology. Primarily, voltage stability analysis is carried out for identification of the suitable system nodes for the integration. Voltage sensitivity indices of all the system nodes are calculated by introducing an incremental change in reactive power injection and noting down the corresponding change in node voltage for all nodes. Henceforth, dynamic load-flow analysis is performed using a fast and efficient power flow analysis technique while using particle swarm optimization method in finding the optimal locations. Results. The results obtained by the application of the mentioned techniques on IEEE 15-bus system not only give the optimum feasible locations of the electric vehicle charging stations, but also provide the maximum number of such charging stations of stipulated sizes which can be incorporated while maintaining the voltage profile. Originality. The originality of the proposed work is the development of the objective function; voltage stability analysis; power flow analysis and optimization algorithms. Practical value. The proposed work demonstrates the detailed procedure of optimum electric vehicle charging station allotment. The experimental results can be used for the subsequent execution in real field.

Multi-Feature Fusion-Based Mechanical Fault Diagnosis for On-Load Tap Changers in Smart Grid With Electric Vehicles

With the proliferation of electric vehicles (EVs), the supporting facilities and infrastructure become new components in conjunction with conventional electrical appliances. These novel appliances, e.g., charging piles and energy storage devices, bring new features as well as challenges to the existing power grid. To enhance the accuracy of mechanical fault identification for on-load tap changers (OLTCs) in smart grid with EVs, a feature selection method for OLTC mechanical fault identification is proposed in this paper. This method relies on the multi-feature fusion and the joint application of the K-nearest neighbors algorithm (KNN) and the improved whale optimization algorithm (IWOA). By multi-feature fusion, the high-dimensional set of time-domain and frequency-domain characteristics as well as energy and composite multi-scale permutation entropy can be constructed. As a result, the maximum correlation minimum redundancy (mRMR) principle can be used to screen the sensitive feature subsets. Finally, IWOA is used to optimize the sensitive feature subsets, and KNN is used to classify the different types of optimal feature subsets. The experimental results show that the proposed method is at least 8% more accurate than the existing methods. The high-accuracy nature of the proposed method can accelerate the promotion of EVs and the establishment of intelligent transportation environments.

Towards Efficient Battery Swapping Service Operation Under Battery Heterogeneity

The proliferation of electric vehicles (EVs) has posed significant challenges to the existing power grid infrastructure. It thus becomes of vital importance to efficiently manage the Electro-Mobility for large demand from EVs. Due to limited cruising range of EVs, vehicles have to make frequent stops for recharging, while long charging period is one major concern under plug-in charging. We herein leverage battery swapping (BS) technology to provide an alternative charging service, which substantially reduces the charging duration (from hours down to minutes). Concerning in practice that various battery is generally not compatible with each other, we thus introduce battery heterogeneity into the swapping service, concerning the case that different types of EVs co-exist. A battery heterogeneity-based swapping service framework is then proposed. Further with reservations for swapping service enabled, the demand load can be anticipated at BS stations as a guidance to alleviate service congestion. Therefore, potential hotspots can be avoided. Results show the performance gains under the proposed scheme by comparing to other benchmarks, in terms of service waiting time, etc. In particular, the diversity of battery stock across the network can be effectively managed.

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

Coordinated Planning of Transportation and Electric Power Networks With the Proliferation of Electric Vehicles

The interdependency of transportation and electric power networks is becoming tighter due to the proliferation of electric vehicles (EVs), which introduces additional difficulties in the planning of the two networks. This paper presents the enhanced solution for the coordinated planning of multiple facilities in the two networks, including electric power lines, transportation roads, energy storage systems and fast charging stations. In order to calculate the optimal solution for the proposed coordinated planning problem, we introduce the applications of linear optimization theory including Karush–Kuhn–Tucker conditions, the big M method, and a linear expression of power loss to transform the nonlinear planning problem into a mixed-integer quadratically constrained programming (MIQCP) formulation, which is solved by commercial solvers. The proposed MIQCP formulation is decomposed into two corresponding subproblems by Lagrangian relaxation to represent transportation and electric power networks. The case studies validate the proposed planning model and demonstrate that the proposed solution can enhance the coordinated network planning with the proliferation of EVs.