Deployment Of Electric Vehicles Research Articles

Traffic NO<sub>x</sub> Pollution Prediction and Health Cost Estimation Using Machine Learning: A Case Study of Toronto, Canada

Road traffic is a significant source of air pollution that has a harmful impact on human health. To reduce the health and environmental impacts of fossil fuel consumption in the transportation sector, many countries have implemented policies to promote the deployment of electric vehicles (EVs). A vital factor to consider when designing policies to support EV use is the monetized health impacts of fossil fuel consumption. This research aims to investigate the health benefit of replacing internal combustion engine vehicles (ICEVs) with zero-emission vehicles in the city of Toronto, Canada. A long short-term memory (LSTM) model is developed in this work to predict future NO<sub>x</sub> concentrations considering the effect of the traffic volume, weather, time of day, and historical NO<sub>x</sub> concentrations. The developed model is then used to predict long-term NO<sub>x</sub> concentrations and annual average NO<sub>x</sub> reduction from zero-emission vehicle deployment in four different scenarios in Toronto. Additionally, interpolation methods are used to predict the pollution reduction in all Dissemination Areas (DA) of Toronto, and a health cost assessment is conducted to estimate the health benefit in all the scenarios. The results of the modeling in this work show that the western areas of Toronto experience higher NO<sub>x</sub> concentration reduction in all scenarios. These reductions are the result of the higher correlation between traffic volume and pollution in those areas. The results also show that with a 10% reduction in ICEV traffic volume, 70 premature deaths can be prevented annually, equivalent to 560 million CAD in health benefits per year.

The electric-thermal coupling simulation and state estimation of lithium-ion battery

The widespread deployment of electric vehicles is not solely associated with the electrification reform of the transport sector, but also plays an important role in the low-carbon transformation for urban energy systems. As the main energy supply, lithium-ion battery invariably affects the performance, cost and safety of the electric vehicles. Strengthening the energy management of lithium-ion battery system is of great importance and necessity to guarantee its efficiency and safety. In this paper, the prototype simulation systems of the multi-level batteries covering cell, module and pack are built within MATLAB/Simulink platform to estimate and predict their electrical and thermal properties under various environment and working conditions. Moreover, the battery states including state of charge, state of health and state of power are jointly estimated based on the equivalent circuit model and minimalist electrochemical model. The simulation and experimental results show that the prototype simulation system can not only describe the electrical and thermal properties of the battery, but also estimate the battery state with great accuracy, which guarantees the safe and stable operation of the lithium-ion battery system.

Electric vehicle optimum charging-discharging scheduling with dynamic pricing employing multi agent deep neural network

Electric Vehicles (EVs) are environmentally friendly. Extensive progress makes EVs popularly deployed and adopted. Once EVs are connected to the smart grid, EVs can act as both variable load and energy supply systems. One major challenge in EV deployment is the management of charging stations with minimum waiting time and reduced EV customer electricity prices. Considering dynamic pricing and various EV features could provide optimum scheduling. To address this issue, we proposed dynamic pricing and optimized scheduling as constrained by a Markov decision process. The solution is obtained by a novel Multi-Agent Deep Neural Network (MADNN). A numerical experiment was conducted with real-time data using the Nissan Leaf model EV. The proposed MADNN uses queuing model and obtained the highest saving rate of 18.45% and an average profit of 340.5 $/kWh with a network convergence time of 520 s. This obtained result validates the effectiveness of the proposed EV scheduling algorithm.

Aggregate demand response strategies for smart communities with battery-charging/switching electric vehicles

Demand response (DR) plays an important role in the peak shaving and load-shifting of a community, but the avalanche effect creates a huge obstacle in DR with uncoordinated usage of appliances and charging of electric vehicles (EVs). Recognizing the remarkably rising deployment of EVs, there is great potential to coordinate EVs' charging in a charging station or a battery switching station for providing DR. However, the potential benefits of the battery-switching EVs for providing DR and their impacts on the aggregate load of the community have not been fully studied. In this regard, the present study proposes two novel aggregate community-level DR strategies, i.e., a strategy employing battery-charging EVs and a strategy employing battery-switching EVs. The proposed strategies are compared with the conventional strategy that only incorporates community battery energy storage for enabling peak shaving. The cost optimization is conducted with optimization parameters including demand limits during different periods and the power and energy capacity of the battery energy storage system. A stochastic bottom-up demand model for households, a probabilistic model for predicting loads of EVs, and a redistribution time-of-use tariff are implemented in the optimization. A group with 20 households and 20 EVs in New York, USA is used in the case study. The results in the case study show that the total annual costs (electricity tariff cost and battery energy storage cost if any) are reduced by 63.3 %, 54.0 %, and 14.7 % under the battery-switching EV strategy, the battery-charging EV strategy, and the battery energy storage strategy compared to the base case (i.e., DR-free strategy), respectively. Additionally, the battery-switching EV strategy enables DR throughout the day with the maximum temporal flexibility. Last, the developed two EV strategies can provide a good stability of the household loads on the grid and avoid the avalanche effect.

Putting the foot down: Accelerating EV uptake in Kyrgyzstan

Road transport accounts for around 12% of global greenhouse gas (GHG) emissions. As many high-income countries are moving to decarbonise their road vehicle fleets, Kyrgyzstan – with rapid growth in passenger car ownership – is seeing a significant increase in transport-related GHG emissions and air pollution in urban areas. Whilst a transition to electric vehicles (EVs) is a key part of Kyrgyzstan’s Nationally Determined Contribution to the Paris Agreement, the potential for successful EV deployment in the region is under-researched. To fill this research gap, this paper presents an assessment of the potential for EV deployment in Kyrgyzstan. Firstly, we present an investigation of the policy and institutional landscape relating to transport and the promotion of EVs in Kyrgyzstan. Secondly, based on research of 50,000 car sales and interviews with 23 key stakeholders in the country, we present analysis of the existing Kyrgyz vehicle fleet. Thirdly, using information ascertained from the interviews and desk-based research, we conduct a Total Cost of Ownership assessment of EVs versus internal combustion engine vehicles (ICEVs) in the Kyrgyz context. We find that under the current conditions, EVs can have a lower total cost of ownership against similar ICEVs in Kyrgyzstan. However, it is evident that this is not typically the case: the analysis in this paper suggests that cost-competitiveness between EVs and ICEVs is more likely for larger and higher-powered vehicles, for which the ICEV version has a higher retail price. Finally, combining analysis of these results and analysis of the current conditions in Kyrgyzstan, trajectories for EV adoption in geographically and economically similar nations, and data from expert stakeholder interviews, we propose a set of policy recommendations to accelerate EV uptake in Kyrgyzstan.

Prediction of air pollution reduction benefits and atmospheric environmental quality improvement effects from electric vehicle deployment in Beijing, China

Prediction of air pollution reduction benefits and atmospheric environmental quality improvement effects from electric vehicle deployment in Beijing, China

Intelligent EV Charging for Urban Prosumer Communities: An Auction and Multi-Agent Deep Reinforcement Learning Approach

Recently, the deployment of electric vehicles supply equipment (EVSE) and its market is expanding rapidly to support the massive penetration of electric vehicles (EVs). However, to accomplish an effective EV charging mechanism for urban prosumer communities, it is imperative to tackle the challenges of distinct energy generation among the communities, dependency of the total purchasable energy price of each EV based on the distance between EV and EVSE, and extreme uncertainty among the energy demand and generation. Therefore, in this paper, the problem of EV charging of urban prosumer communities is studied. In particular, a joint optimization problem is proposed to maximize both the social welfare and EV charging achieved rate of the considered urban prosumer communities. Consequently, the formulated problem is decomposed into 1) truthful double auction problem for determining the unit price and winners by maximizing social welfare, and 2) EV auction losers charging problem for improving EVs charging achieved rate by purchasing energy from the power grid. Then the breakeven-based double auction (BDA) mechanism is proposed to find the unit price and EV winners’ for charging. Sequentially, a multi-agent deep reinforcement learning-based asynchronous advantage actor-critic algorithm with a long short-term memory layer (A3C-LSTM) is adopted to achieve the optimal grid energy buying decision for ensuring the charging of the losers. Finally, the experimental results demonstrate the efficacy of the proposed model that can increase the number of EV charging up to 57.31%, and prosumer communities have gained 86.04% of their income compared to baseline methods.

Collection policy analysis for retired electric vehicle batteries through agent-based simulation

With the massive deployment of Electric Vehicles (EVs), a large number of lithium-ion batteries are reaching retirement age, attracting great concerns about the industrial sustainability. Currently, several incentive measures are being introduced and expected to address this waste management challenge, but the exact effects remain unclear. Therefore, an agent-based model of EV battery collection system is established in this study, which characterizes the heterogeneous interactive relationship, behavior rules and decision-making function of three main agents including residents, formal and informal collection enterprises. Eventually, the policy implementation effects are quantitatively evaluated. The results show that: (i) the publicity policy with the strongest implementation level leads to a 38.11% increase in the collection rate, which is defined as the ratio of the number of battery packs collected through formal channels to the total; (ii) the collection price subsidy policy of 80 RMB/kWh improves the collection rate by 109.87%; (iii) with the target level and intensity level set at 50 kWh and 80 RMB/kWh, the collection target policy enhances the market share of formal collection enterprises by 1.08%; (iv) with the combination of these three collection policies, the policy mix enhances the collection rate by 3.81 times, which is greater than the implementation effect of any single policy. In sum, this research will promote the sustainable supply chain management of EV batteries. With a further model extension, it can also provide decision support for the policy-making of more countries.

A Paradox over Electric Vehicles, Mining of Lithium for Car Batteries

Lithium, a silver-white alkali metal, with significantly high energy density, has been exploited for making rechargeable lithium-ion batteries (LiBs). They have become one of the main energy storage solutions in modern electric cars (EVs). Cobalt, nickel, and manganese are three other key components of LiBs that power electric vehicles (EVs). Neodymium and dysprosium, two rare earth metals, are used in the permanent magnet-based motors of EVs. The operation of EVs also requires a high amount of electricity for recharging their LiBs. Thus, the CO2 emission is reduced during the operation of an EV if the recharged electricity is generated from non-carbon sources such as hydroelectricity, solar energy, and nuclear energy. LiBs in EVs have been pushed to the limit because of their limited storage capacity and charge/discharge cycles. Batteries account for a substantial portion of the size and weight of an EV and occupy the entire chassis. Thus, future LiBs must be smaller and more powerful with extended driving ranges and short charging times. The extended range and longevity of LiBs are feasible with advances in solid-state electrolytes and robust electrode materials. Attention must also be focused on the high-cost, energy, and time-demand steps of LiB manufacturing to reduce cost and turnover time. Solid strategies are required to promote the deployment of spent LiBs for power storage, solar energy, power grids, and other stationary usages. Recycling spent LiBs will alleviate the demand for virgin lithium and 2.6 × 1011 tons of lithium in seawater is a definite asset. Nonetheless, it remains unknown whether advances in battery production technology and recycling will substantially reduce the demand for lithium and other metals beyond 2050. Technical challenges in LiB manufacturing and lithium recycling must be overcome to sustain the deployment of EVs for reducing CO2 emissions. However, potential environmental problems associated with the production and operation of EVs deserve further studies while promoting their global deployment. Moreover, the combined repurposing and remanufacturing of spent LiBs also increases the environmental benefits of EVs. EVs will be equipped with more powerful computers and reliable software to monitor and optimize the operation of LiBs.

Examining the cost-effectiveness of electric vehicle policy in India

ABSTRACT Deployment of electric vehicles (EVs) is being widely used by governments across the world as a policy tool to mitigate the catastrophic effects of global warming and climate change. However, the demand for EVs has been slow in many large automotive markets, including India. To increase the uptake of EVs in India, the central government has introduced a new consumer incentive program and a special tax rate for EVs. In this paper, we estimate the cost-effectiveness of these EV promotion programs in achieving CO2 emission reduction for each State in India. We use emission data of the chosen vehicle and CO2 emission intensity data of each State’s power grid for computation. The results are then compared to other benchmark investments in climate change mitigation. The findings indicate that subsidizing EVs may not be the most cost-efficient method to achieve CO2 reduction.

Electric vehicle deployment & fossil fuel tax revenue in Mexico to 2050

We assess the changes in Mexico's privately owned vehicle fleet and tax losses (fuel duty on gasoline) associated to the deployment of electric vehicles (EVs) through a diffusion model. Our projections for 2050 focus on a) electricity use of EVs, b) gasoline use and c) revenue for Mexico's treasury. The model uses population growth and vehicles sales to project two items: a) EV diffusion and b) substitution rates of ICVs with EVs. The equation uses vehicle km travelled and average consumption of fuel to calculate gasoline use of vehicles.The results show that revenues decline by 21.6% per year 2019–2050 owing to technological change in comparison to a gasoline-based scenario. The scenario without EVs points that Mexico's Treasury revenues grow by 100% by 2050 in comparison to revenues collected under the EV diffusion scenario.By 2032 motorization based on EVs will reach 50% of the saturation level producing revenue losses for Mexico's treasury between 70 and 105 billion US$ on a cumulative basis by 2050. Policy implications include: the need to adopt a new tax on electricity, a subsidy policy for EVs and for its infrastructure, a registration tax and an appropriate sales tax as well as strengthening the electric mobility strategy of the country. These taxes help recoup the lost tax revenue of Mexico's treasury.

GMIX: Graph-based spatial–temporal multi-agent reinforcement learning for dynamic electric vehicle dispatching system

The past decade has witnessed a significant growth of electric vehicles (EVs) deployment in public and private transportation sectors. Dynamic electric vehicle routing aims to plan the routes of EVs to serve dynamically generated customers’ requests while ensuring the battery level by visiting the recharge stations. This paper studies the dynamic electric vehicle routing problem with simultaneous pick-up and delivery and soft time windows (DEVRP-STW). We design a graph-based spatio-temporal multi-agent reinforcement learning (GMIX) framework consisting of a spatio-temporal graph attention network (ST-GAT) and a value decomposition-based multi-agent reinforcement learning algorithm (Graph-QMIX). Graph-QMIX derives a multi-agent soft policy gradient based on QMIX and Soft Actor–Critic and utilises a graph-based mixing network proposed to enhance the interaction of agents. Extensive experiments including a case study using real-world taxi data from New York City and a simulation study are conducted. The proposed GMIX outperforms the baseline algorithms on the request–response ratio (RRO), the average travelling distance per completed request (ATR), the out of energy ratio (OER) and the average waiting time per completed request (AWR).

A comprehensive framework for analysis and evaluation of factors responsible for sustainable growth of electric vehicles in India

The Indian transport sector powered by fossil fuels is primarily responsible for creating severe issues like greenhouse gas emissions, foreign fuel dependency, economic burden, and chronic health effects. To mitigate these severe issues, electric vehicles (EVs) are positioned as an alternate green and clean technology, which can potentially enable the efficient transition to a sustainable low-carbon emission transportation system and preservation of natural scarce resources. Despite announcing favorable policy measures to encourage EV adoption, the multiplicity of potential factors with mutual interaction has resisted its penetration in several countries. Though researchers have identified the critical factors, the question “How do these factors mutually interact among themselves?” has remained largely unanswered in empirical research. Unpacking the relationship between the factors will empower manufacturers and policymakers in strategic planning, and devising suitable measures in controlling the factors. The primary goal of this research is to undertake a thorough assessment and give a brief understanding of the various factors responsible for sustainable growth of EVs market in India. Factors are identified from past academic literature and experts' interviews. Study further empirically validated the identified factors. Then integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL) – Interpretive Structural Modeling (ISM) approach has been used to demonstrate the interrelationship and hierarchal structure of the factors. The present study will be useful to the manufacturers, policymakers to focus on the gray area so that they can expedite the growth of EVs deployment in India.

Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system.

Potential clean energy transition pathways in the U.S. Virgin Islands using carbon sensitive policy options

This study uses the LEAP–OSeMOSYS modeling tool to examine exploratory decarbonization scenarios in the U. S Virgin Islands (USVI). The method combines quantitative modeling of data gathered from utilities and power generators with qualitative information gathered through engagement with experts and diverse business and civil society stakeholder. The results show the effects of two utility scale energy systems - on the islands of St Thomas /St John and St Croix - and future optimized capacity addition with solar, liquefied petroleum gas and wind, whereby renewable energy targets can be achieved over time. Results are also modelled for electric vehicle deployment over time, on the islands. The models take into consideration the effects of extreme weather event impacts on electricity demand; and electric vehicles deployment on the grid, where grid destabilization is a potential risk. These findings can serve as inputs for updating comprehensive island energy strategies in the USVI to meet carbon emissions reduction goals.

A novel AI approach for optimal deployment of EV fast charging station and reliability analysis with solar based DGs in distribution network

The transportation sector is one of the most prevalent fossil fuel users worldwide. Therefore, to mitigate the impacts of carbon-dioxide emissions and reduce the use of non-environmentally friendly traditional energy resources, the electrification of the transportation system, such as the development of electric vehicles (EV), has become crucial. For impeccable EVs deployment, a well-developed charging infrastructure is required. However, the optimal placement of fast charging stations (FCSs) is a critical concern. Therefore, this article provides a functional approach for identifying the optimal location of FCSs using the east delta network (EDN). In addition, the electrical distribution network’s infrastructure is susceptible to changes in electrifying the transportation sector. Therefore, actual power loss, reactive power loss, and investment cost are three areas of consideration in deploying FCSs. Furthermore, including FCSs in the electricity distribution network increases the energy demand from the electrical grid. Therefore, this research paper recommends integrating solar-based distributed generations (SDGs) at selected locations in the distribution network, to mitigate the burden of FCSs on the system. Hence, making the system self-sustaining and reliable. In addition, the reliability of the distribution system is also analyzed after deploying the FCSs and SDGs. Furthermore, six case studies (CS) have been proposed to deploy FCSs with or without DG integration. Consequently, the active power loss went from 1014.48 kW to 829.68 kW for the CS-6.

An assessment of barriers and solutions for the deployment of electric vehicles in the Brazilian market

The growing concerns about the energy and environmental crisis drive the development of a sustainable society. The electric vehicle (EV) is a potentially clean technology that can contribute to the decarbonization of the transportation sector. However, EVs face obstacles to being diffused and are not prevalent in the global vehicle fleet. Thus, this research identifies the attractiveness, barriers, and solutions for the diffusion of EVs. Semi-structured interviews were conducted with key agents of the Brazilian supply chain of this technology. Results showed that EVs have essential attractions, but there are barriers to their diffusion in the Brazilian market related to EV cost, charging infrastructure, and public policies. The main solutions to overcome these barriers and expand EV adoption involve: (i) increase of emission consumer awareness through promotional campaigns by government and industry, (ii) tax and non-tax incentives, e.g., reduction of taxation on EV purchase or parts of it, reduction of annual fees or special transit permits for EV, and (iii) technological advances, notably in terms of battery performance and availability and efficiency of charging stations. This research comprises aspects of EV diffusion in Brazil but can serve as guidance for other countries with emerging economies or regions where EV adoption is still incipient.

A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China.

Electric vehicle deployment shows promising potentials in promoting cleaner energy utilization and reducing carbon emission. Due to increasing carbon neutral pressure and market competition from transportation sector, government agencies and public bodies (GAPBs) have emphasized the significance of electric vehicle adoption through supplier selection. Consequently, GAPBs must consider a reasonable criteria system and a comprehensive supplier selection framework and rationally select the electric vehicle supplier that matches their practical needs in terms of economic, social, environmental, and technical factors. This paper provides insights into electric vehicle supplier selection (EVSS) from the perspective of GAPBs using an integrated multi-criteria decision-making (MCDM) framework based on best–worst method (BWM) and fuzzy ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Initially, 14 critical factors from economic, social, environmental, and technical dimensions are identified as the criteria by literature review and experts’ opinions. Then, a comprehensive decision framework using the integrated MCDM approach is proposed. To validate the applicability and feasibility of the proposed framework, a case study is launched and analyzed. It emerges that bad environmental record, cost, quality, service, and environmental initiatives are the most important criteria in EVSS for GAPBs with the weight values of 0.1995, 0.1172, 0.1219, 0.0708, and 0.2553. The comparative analysis and the sensitivity analysis are performed for verifying the reliability of the proposed framework. The work helps to understand the electric vehicle supplier selection criteria and makes methodological decision-making support for GAPBs.

What Place for Electric Vehicles as a Research Object and a Practical Alternative to Internal Combustion Engine Vehicles in Africa? Toward a Research Agenda Based on a Systematic Literature Review and a Census of Electromobility Projects

The environmental crisis of global warming and rising oil prices have contributed to the spread of electric vehicles (EVs) since the 2010s. As the global EV fleet grows, a broad scientific literature on EVs is emerging. This literature, while abundant, is still concentrated on very limited geographical regions, mainly Europe, North America, and China, offering little analysis of, or comparison with, markets outside these areas, especially Africa. In both EV deployment and scientific production, Africa could seem to be lagging behind, yet there is some growing momentum for EVs in Africa. This paper examines the place of EVs as a research object and practical alternative to conventional vehicles in Africa. It is based on a twofold methodological approach combining a systematic review of scientific literature and an overview of electric mobility projects on the continent. The results reveal a growing interest in EVs in Africa over the past decade, in both publications and projects. Nevertheless, this growth masks strong disparities in the distribution of projects and papers. In addition, academic research is only weakly related to ongoing electric mobility projects in Africa, with few field experiments in the methodological approaches used by the authors, and, above all, several thematic aspects absent from EV research.

Optimized power dispatch for smart building(s) and electric vehicles with V2X operation

Smart buildings are being built as a synergetic deployment of electric vehicles (EVs) and renewable energy sources. Smart charging of EVs and vehicle-to-everything (V2X) technologies are seen as way forward in this context in terms of achieving economic, technological, and environmental advantages. This paper proposes a framework for multi-objective techno-economic optimization for profit maximization of multiple inter-connected buildings (with bilateral contracts) and scheduling of EVs. The optimization problem is modeled as mixed integer linear programming problem, which is solved using CPLEX solver in ILOG optimization studio. The primary building owns the photovoltaic system coupled with storage and charging infrastructure for the fleet of EVs. The optimized charging of EVs at affordable rates using local resources at primary building assists the grid in managing the EVs’ load during peak hours. Results indicate that the primary building gains up to 62% daily profit after factoring in solar, storage, and charging station deployment costs. Additionally, secondary buildings (without solar, storage and charging facilities) earn up to 20% cost savings depending upon the nature of bilateral contracts with primary building. The results further suggest that fleet of EVs gains 35%–65% savings in charging cost based on lower charging rates and V2X operations with primary and secondary buildings.