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World Electr. Veh. J., Volume 10, Issue 1 (March 2019)

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Cover Story (view full-size image) While most electric customers pay a standard flat rate, dynamic pricing plans have numerous [...] Read more.
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Open AccessArticle Flexibility of Electric Vehicle Demand: Analysis of Measured Charging Data and Simulation for the Future
World Electr. Veh. J. 2019, 10(1), 14; https://doi.org/10.3390/wevj10010014
Received: 23 January 2019 / Revised: 8 March 2019 / Accepted: 14 March 2019 / Published: 19 March 2019
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Abstract
This paper proposes a method for analyzing and simulating the time-dependent flexibility of electric vehicle (EV) demand. This flexibility is influenced by charging power, which depends on the charging stations, the EV characteristics, and several environmental factors. Detailed charging station data from a [...] Read more.
This paper proposes a method for analyzing and simulating the time-dependent flexibility of electric vehicle (EV) demand. This flexibility is influenced by charging power, which depends on the charging stations, the EV characteristics, and several environmental factors. Detailed charging station data from a Dutch case study have been analysed and used as input for a simulation. In the simulation, the interdependencies between plug-in time, connection duration, and required energy are respected. The data analysis of measured data reveals that 59% of the aggregated EV demand can be delayed for more than 8 h, and 16% for even more than 24 h. The evening peak shows high flexibility, confirming the feasibility of congestion management using smart charging within flexibility constraints. The results from the simulation show that the average daily EV demand increases by a factor 21 between the ‘Present-day’ and the ‘High’ scenario, while the maximum EV demand peak increases only by a factor 6, as a result of the limited simultaneity of the transactions. Further, simulations using the average charging power of individual measured transactions yield more accurate results than simulations using a fixed value for charging power. The proposed method for simulating future EV flexibility provides a basis for testing different smart charging algorithms. Full article
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Open AccessArticle Reducing the Environmental Impacts of Electric Vehicles and Electricity Supply: How Hourly Defined Life Cycle Assessment and Smart Charging Can Contribute
World Electr. Veh. J. 2019, 10(1), 13; https://doi.org/10.3390/wevj10010013
Received: 22 June 2018 / Revised: 5 February 2019 / Accepted: 5 March 2019 / Published: 8 March 2019
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Increasing shares of renewable electricity generation lead to fundamental changes of the electricity supply, resulting in varying supply mixes and environmental impacts. The hourly-defined life cycle assessment (HD-LCA) approach aims to capture the environmental profile of electricity supply in an hourly resolution. It [...] Read more.
Increasing shares of renewable electricity generation lead to fundamental changes of the electricity supply, resulting in varying supply mixes and environmental impacts. The hourly-defined life cycle assessment (HD-LCA) approach aims to capture the environmental profile of electricity supply in an hourly resolution. It offers a flexible connectivity to unit commitment models or real-time electricity production and consumption data from electricity suppliers. When charging EVs, the environmental impact of the charging session depends on the electricity mix during the session. This paper introduces the combination of HD-LCA and smart charging and illustrates its impacts on the life cycle greenhouse gas emissions of BEVs. Full article
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Open AccessArticle Location-Allocation of Electric Vehicle Fast Chargers—Research and Practice
World Electr. Veh. J. 2019, 10(1), 12; https://doi.org/10.3390/wevj10010012
Received: 31 January 2019 / Revised: 28 February 2019 / Accepted: 4 March 2019 / Published: 6 March 2019
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This paper conducts a comparative analysis of academic research on location-allocation of electric vehicle fast chargers into the pattern of the actual fast-charger allocation in the United States. The work aims to highlight the gap between academic research and actual practice of charging-station [...] Read more.
This paper conducts a comparative analysis of academic research on location-allocation of electric vehicle fast chargers into the pattern of the actual fast-charger allocation in the United States. The work aims to highlight the gap between academic research and actual practice of charging-station placement and operation. It presents evidence that the node-serving approach is, in fact, applied in the actual location-allocation of fast charging stations. However, little evidence suggests that flow-capturing, which has been much more predominantly applied in research, is being applied in practice. The author argues that a large-scale location-allocation plan for public fast chargers should be formulated based on explicit consideration of stakeholders, the objective, practical constraints, and underlining assumptions. Full article
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Open AccessArticle An Analysis of Consumer Incentives in Support of Electric Vehicle Uptake: An Australian Case Study
World Electr. Veh. J. 2019, 10(1), 11; https://doi.org/10.3390/wevj10010011
Received: 22 December 2018 / Revised: 13 February 2019 / Accepted: 22 February 2019 / Published: 4 March 2019
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Abstract
Transitioning from internal combustion engine vehicles (ICEVs) to innovative technologies, including electric vehicles (EVs), can be a crucial pathway to reducing Greenhouse Gas (GHG) emissions and other negative externalities arising from fossil-fueled cars used for personal transport. Government action to correct insufficient market [...] Read more.
Transitioning from internal combustion engine vehicles (ICEVs) to innovative technologies, including electric vehicles (EVs), can be a crucial pathway to reducing Greenhouse Gas (GHG) emissions and other negative externalities arising from fossil-fueled cars used for personal transport. Government action to correct insufficient market incentives has been essential in countries working to enhance EV acceptance; however, to date in Australia, there has been little government support to enact EV uptake. This paper identifies barriers and incentives to EV adoption in Australia through a survey of pro-environmental motorists, including an experimental component to test information provision influences on attitude change. Results evidence that wide ranging factors influence vehicle choice including EVs. Purchase barriers are focused on lack of a comprehensive recharge network and high EV purchase price. Factors encouraging fully EV uptake showed affordable price (56%) increased vehicle range (26%) and an adequate recharge network (28%) were mentioned most often; only 13% specifically indicated environmental regard as influential. Information provided about EVs increased the likelihood of positive attitudes towards EV purchase and decreased uncertainty about the technology. Recommendations arising from this research could be considered by laggard countries that, like Australia, have yet to take significant action to encourage transition to EVs. Full article
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Open AccessConcept Paper Architecture and Protocols for Toll-Free Electric Vehicle Charging
World Electr. Veh. J. 2019, 10(1), 10; https://doi.org/10.3390/wevj10010010
Received: 25 August 2018 / Revised: 17 February 2019 / Accepted: 20 February 2019 / Published: 24 February 2019
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This paper proposes system architecture and protocols for the deployment of a toll-free electric vehicle charging service. The architecture enables the party initiating the electric vehicle (EV) charging to have their service request authorized by the system and paid for by a third [...] Read more.
This paper proposes system architecture and protocols for the deployment of a toll-free electric vehicle charging service. The architecture enables the party initiating the electric vehicle (EV) charging to have their service request authorized by the system and paid for by a third party. Full article
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Open AccessArticle Performances Analysis of a Novel Electromagnetic-Frictional Integrated Brake Based on Multi-Physical Fields Coupling
World Electr. Veh. J. 2019, 10(1), 9; https://doi.org/10.3390/wevj10010009
Received: 25 December 2018 / Revised: 15 February 2019 / Accepted: 19 February 2019 / Published: 21 February 2019
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Abstract
In this article, a novel electromagnetic-frictional integrated brake is proposed, and its structure and working principle are introduced. The geometric model and mathematical models of integrated brake were established, and the multi-field coupling mechanism of integrated brake were analyzed. With BYD Qin as [...] Read more.
In this article, a novel electromagnetic-frictional integrated brake is proposed, and its structure and working principle are introduced. The geometric model and mathematical models of integrated brake were established, and the multi-field coupling mechanism of integrated brake were analyzed. With BYD Qin as a reference vehicle, the boundary conditions of thermal load and force load of integrated brake were determined according to its structure and performance parameters. Based on the COMSOL software, numerical coupling calculations of electric, magnetic, thermal, and solid fields of integrated brake were carried out respectively in the emergency and downhill braking at a constant speed. The axial, circumferential, and radial temperature distributions of integrated brake disc were analyzed respectively, and they were compared with those of the traditional friction brake disc. The analysis results show that the proposed integrated brake can effectively improve the heat fading resistance of automotive brake during emergency and continuous braking. Under the two braking conditions, the temperature rise of friction brake was faster than that of an electromagnetic brake, and the effect of the electromagnetic brake on temperature rise of integrated brake was small. Full article
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Open AccessArticle Electric Car Chassis for Shell Eco Marathon Competition: Design, Modelling and Finite Element Analysis
World Electr. Veh. J. 2019, 10(1), 8; https://doi.org/10.3390/wevj10010008
Received: 26 November 2018 / Revised: 21 January 2019 / Accepted: 24 January 2019 / Published: 31 January 2019
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Abstract
The increasing demand for energy efficient electric cars, in the automotive sector, entails the need for improvement of their structures, especially the chassis, because of its multifaceted role on the vehicle dynamic behaviour. The major criteria for the development of electric car chassis [...] Read more.
The increasing demand for energy efficient electric cars, in the automotive sector, entails the need for improvement of their structures, especially the chassis, because of its multifaceted role on the vehicle dynamic behaviour. The major criteria for the development of electric car chassis are the stiffness and strength enhancement subject to mass reduction as well as cost and time elimination. Towards this direction, this work indicates an integrated methodology of developing an electric car chassis considering the modeling and simulation concurrently. The chassis has been designed in compliance with the regulations of Shell Eco Marathon competition. This methodology is implemented both by the use of our chassis load calculator (CLC) model, which automatically calculates the total loads applied on the vehicle’s chassis and by the determination of a worst case stress scenario. Under this extreme stress scenario, the model’s output was evaluated for the chassis design and the FEA method was performed by the pre-processor ANSA and the solver Ansys. This method could be characterized as an accurate ultrafast and cost-efficient method. Full article
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Open AccessArticle Probabilistic Prediction Algorithm for Cycle Life of Energy Storage in Lithium Battery
World Electr. Veh. J. 2019, 10(1), 7; https://doi.org/10.3390/wevj10010007
Received: 17 October 2018 / Revised: 18 January 2019 / Accepted: 18 January 2019 / Published: 28 January 2019
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Abstract
Lithium batteries are widely used in energy storage power systems such as hydraulic, thermal, wind and solar power stations, as well as power tools, military equipment, aerospace and other fields. The traditional fusion prediction algorithm for the cycle life of energy storage in [...] Read more.
Lithium batteries are widely used in energy storage power systems such as hydraulic, thermal, wind and solar power stations, as well as power tools, military equipment, aerospace and other fields. The traditional fusion prediction algorithm for the cycle life of energy storage in lithium batteries combines the correlation vector machine, particle filter and autoregressive model to predict the cycle life of lithium batteries, which are subjected to many uncertainties in the prediction process and to inaccurate prediction results. In this paper, a probabilistic prediction algorithm for the cycle life of energy storage in lithium batteries is proposed. The LS-SVR prediction model was trained by a Bayesian three-layer reasoning. In the iterative prediction phase, the Monte Carlo method was used to express and manage the uncertainty and its transitivity in a multistep prediction and to predict the future trend of a lithium battery’s health status. Based on the given failure threshold, the probability distribution of the residual life was obtained by counting the number of particles passing through the threshold. The wavelet neural network was used to study the sample data of lithium batteries, and the mapping relationship between the probability distribution of the residual life of lithium batteries and the unknown values were established. According to this mapping relation and the probability distribution of the residual life of lithium batteries, the health data could be deduced and then iterated into the input of the wavelet neural network. In this way, the predicted degradation curve and the cycle life of lithium batteries could be obtained. The experimental results show that the proposed algorithm has good adaptability and high prediction efficiency and accuracy, with the mean error of 0.17 and only 1.38 seconds by average required for prediction. Full article
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Open AccessArticle Charge for Less: An Analysis of Hourly Electricity Pricing for Electric Vehicles
World Electr. Veh. J. 2019, 10(1), 6; https://doi.org/10.3390/wevj10010006
Received: 19 October 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 17 January 2019
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Abstract
By motivating Electric Vehicle (EV) owners to charge their vehicles when power supply exceeds demand, dynamic pricing can improve system load shape and capacity utilization, reduce consumer costs, and cut pollution. We compare what perfectly rational EV drivers would pay to charge their [...] Read more.
By motivating Electric Vehicle (EV) owners to charge their vehicles when power supply exceeds demand, dynamic pricing can improve system load shape and capacity utilization, reduce consumer costs, and cut pollution. We compare what perfectly rational EV drivers would pay to charge their vehicle on ComEd’s hourly pricing program with costs associated with the utility’s flat-rate energy price. We find that ComEd’s hourly pricing program would have saved EV owners significantly over its flat-rate tariff in both 2016 and 2017, with cost reductions from 52 percent to 59 percent. Using price signals to manage charging is almost certainly one of the best (and cheapest) strategies to implement in order to achieve the traditional regulatory goals of a safe, reliable, and affordable service while advancing system efficiency, enhancing environmental sustainability, and facilitating the integration of distributed energy resources. Full article
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Open AccessArticle Implementation Schemes for Electric Bus Fleets at Depots with Optimized Energy Procurements in Virtual Power Plant Operations
World Electr. Veh. J. 2019, 10(1), 5; https://doi.org/10.3390/wevj10010005
Received: 18 December 2018 / Revised: 11 January 2019 / Accepted: 14 January 2019 / Published: 17 January 2019
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For the purpose of utilizing electric bus fleets in metropolitan areas and with regard to providing active energy management at depots, a profound understanding of the transactions between the market entities involved in the charging process is given. The paper examines sophisticated charging [...] Read more.
For the purpose of utilizing electric bus fleets in metropolitan areas and with regard to providing active energy management at depots, a profound understanding of the transactions between the market entities involved in the charging process is given. The paper examines sophisticated charging strategies with energy procurements in joint market operation. Here, operation procedures and characteristics of a depot including the physical layout and utilization of appropriate charging infrastructure are investigated. A comprehensive model framework for a virtual power plant (VPP) is formulated and developed to integrate electric bus fleets in the power plant portfolio, enabling the provision of power system services. The proposed methodology is verified in numerical analysis by providing optimized dispatch schedules in day-ahead and intraday market operations. Full article
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Open AccessArticle Risk Identification and Analysis for PPP Projects of Electric Vehicle Charging Infrastructure Based on 2-Tuple and the DEMATEL Model
World Electr. Veh. J. 2019, 10(1), 4; https://doi.org/10.3390/wevj10010004
Received: 24 December 2018 / Revised: 11 January 2019 / Accepted: 11 January 2019 / Published: 16 January 2019
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Abstract
Risk management is critical to the success of electric vehicle charging infrastructure public–private partnership (EVCI-PPP) projects, as risks are present throughout the whole life cycle of projects. However, in EVCI-PPP projects, risk factors are often interdependent and, consequently, the interrelationships among factors affect [...] Read more.
Risk management is critical to the success of electric vehicle charging infrastructure public–private partnership (EVCI-PPP) projects, as risks are present throughout the whole life cycle of projects. However, in EVCI-PPP projects, risk factors are often interdependent and, consequently, the interrelationships among factors affect the risk management, which is ignored in the existing studies. To identify the risk factors of EVCI-PPP projects and analyze their internal influence relations, this paper develops a risk identification and analysis model of EVCI-PPP projects based on the 2-tuple linguistic representation model and the decision-making trial and evaluation laboratory (DEMATEL) model. First, a risk factor set is established including 22 criteria involved in 5 dimensions of political/legal risk, economic/market risk, social/environment risk, project/technical risk, and managing risk. Next, the 2-tuple model is introduced to integrate the decision makers’ evaluation information in a linguistic environment, and the direct relation matrix is calculated. Then, the cause–effect relations and a significant degree of risk factors are interpreted using the extended DEMATEL technique. The results show that economic/market risk is the most significant factor of EVCI-PPP projects, and 22 criteria are classified into 14 cause factors and 8 effect factors. Finally, suggestions are provided for decision-makers to ensure the success of EVCI-PPP projects. Full article
(This article belongs to the Special Issue Charging Infrastructure for Electric Vehicles)
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Open AccessEditorial Acknowledgement to Reviewers of World Electric Vehicle Journal in 2018
World Electr. Veh. J. 2019, 10(1), 3; https://doi.org/10.3390/wevj10010003
Published: 10 January 2019
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Abstract
Rigorous peer-review is the corner-stone of high-quality academic publishing [...] Full article
Open AccessArticle Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data
World Electr. Veh. J. 2019, 10(1), 2; https://doi.org/10.3390/wevj10010002
Received: 13 December 2018 / Revised: 29 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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In this paper, a general quasi-steady backward-looking model for energy consumption estimation of electric vehicles is presented. The model is based on a literature review of existing approaches and was set up using publicly available data for Nissan Leaf. The model has been [...] Read more.
In this paper, a general quasi-steady backward-looking model for energy consumption estimation of electric vehicles is presented. The model is based on a literature review of existing approaches and was set up using publicly available data for Nissan Leaf. The model has been used to assess the effect of ambient temperature on energy consumption and range, considering various reference driving cycles. The results are supported and validated using data available from an experimental campaign where the Nissan Leaf was driven to depletion across a broad range of winter ambient temperatures. The effect of ambient temperature and the consequent accessories consumption due to cabin heating are shown to be remarkable. For instance, in case of Federal Urban Driving Schedule (FUDS), simplified FUDS (SFUDS), and New European Driving Cycle (NEDC) driving cycles, the range exceeds 150 km at 20 °C, while it reduces to about 85 km and 60 km at 0 °C and −15 °C, respectively. Finally, a sensitivity analysis is reported to assess the impact of the hypotheses in the battery model and of making different assumptions on the regenerative braking efficiency. Full article
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Open AccessArticle Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm
World Electr. Veh. J. 2019, 10(1), 1; https://doi.org/10.3390/wevj10010001
Received: 8 November 2018 / Revised: 10 December 2018 / Accepted: 17 December 2018 / Published: 3 January 2019
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Abstract
Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, [...] Read more.
Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF reflects the maximum instantaneous output power of the battery. When discarded, it is able to show the degree of performance degradation of the power battery when also taken as a performance evaluation parameter. In this paper, the variables closely related to SOF have been selected to conduct the fuzzy inference system, which is optimized by the fuzzy c-means clustering algorithm, to estimate the SOF of the power lithium ion battery, whose relations can be proved by experimental data. Our simulation results and experimental results demonstrate the feasibility and advantages of the estimation strategy. Full article
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World Electr. Veh. J. EISSN 2032-6653 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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