Electric Vehicles Research Articles

Ionic Gel Chemical Sensors with Damage Tolerance for Monitoring Lithium‐ion Battery Electrolyte Leakage and Battery Safety

AbstractGiven the frequent occurrence of lithium‐ion battery (LIB) incidents, gas sensors monitoring LIB safety are imperative yet deficient. Here, a new class of LIB electrolyte sensors based on ionic gels is presented, in which ions instead of electrons act as charge carriers and interact with analyte molecules for improved sensitivity. The ionic gels are readily synthesized through the photopolymerization of ionic liquids (ILs) incorporating C═C bonds with structurally analogous ILs devoid of C═C bonds. Through systematic investigation, PF6− is identified as the optimal ion carrier for LIB electrolyte sensing. Trace amounts of LIB electrolyte (20 nL) leakage can be detected within seconds. Interestingly, the devices exhibit decent damage tolerance. More importantly, multiple sensors with decent consistency can be batch‐fabricated on a large scale, which is essential for practical applications. Remarkably, the devices exhibit an extended operational lifespan, endowing them with significant potential for long‐term safety monitoring. The sensors are integrated into a monitoring system and demonstrate exceptional capability in differentiating the various stages of LIB thermal runaway, which paves the way for their potential applications in power storage stations and electric vehicles.

Divergent imaginings: Transitioning to decarbonised mobility in ‘post-coalonial’ County Durham

ABSTRACT What happens when the various parties involved in constructing decarbonised futures’ infrastructure diverge in their imaginaries? Much of the published research on the sociotechnical imaginaries relating to electric vehicles (EVs) describes the creation of the future of decarbonised transport as a process mired in conflict, with various interested parties represented as strenuously disagreeing in their assessment of the most efficacious solution. The aim of the article is to offer an alternative account, based upon data gathered through participant observation, interviews, and grey literature. It describes the sort of personal transportation futures currently being imagined in the United Kingdom. The focus is specifically on the installation of electric vehicle charge points. The author contrasts Whitehall’s national vision for this infrastructure with the ‘post-coalonial’ vision of officers of Durham County Council in North East England have articulated an alternative, a ‘post-coalonial’ vision, and finds that the vision of both the British civil service and Government of the United Kingdom focused on private ownership and commuting, while Durham County Council envisioned publicly accessible charge points that enabled various types of different journeys. Despite the striking differences the conclusion is that contrary to the findings of previous studies the existence of these divergent infrastructural imaginaries led not to conflict but to co-existence.

A Fuzzy Logic Control-Based Adaptive Gear-Shifting Considering Load Variation and Slope Gradient for Multi-Speed Automated Manual Transmission (AMT) Electric Heavy-Duty Commercial Vehicles

A Fuzzy Logic Control-Based Adaptive Gear-Shifting Considering Load Variation and Slope Gradient for Multi-Speed Automated Manual Transmission (AMT) Electric Heavy-Duty Commercial Vehicles

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

Electric vehicle drivers’ choices of expressway usage and peak avoidance: an empirical analysis considering the random effects among individuals

ABSTRACT Traffic congestion is a persistent challenge in urban areas, particularly with increased private car ownership post-COVID-19. Traditional administrative measures to manage traffic demand have proven unsustainable, necessitating more effective strategies. This study examines trajectory data from 3,064 commuting (CMT) and 2,690 non-commuting (Non-CMT) electric vehicles in Shanghai to analyze how travel purposes, distances, directions, and departure times influence expressway usage and peak avoidance decisions. It identifies significant differences in choices between CMT and Non-CMT users, highlighting the need for personalized travel management methods based on vehicle usage patterns. Route management strategies should focus on commuting trips for CMT vehicle users, while spatial control measures considering the trip distance and direction can reduce Non-CMT vehicle users’ dependence on expressways during peak hours. This research contributes to enhancing our understanding of the heterogeneity of travelers’ behaviors and offers personal and practical insights for managing traffic congestion sustainably in metropolitan cities.

Enhancing the energy and environmental efficiency of the city's infrastructure based on optimizing its fuel and energy balance

The article considers the direction of increasing the energy and environmental efficiency of the city's energy infrastructure through the modernization of existing thermal power plants (TPP) and combined heat and power (CHP) plants using combined-cycle gas turbine (CCGT) plants. Fuel and energy balances of electric power plants have been constructed, and the energy and environmental effects of the use of CCGT have been assessed. The modeling of energy and environmental indicators for the thermal circuits of the CCGT was conducted using the Thermoflex, GT PRO software (Thermoflow) and the ISC Manager program developed at the National Research University "Moscow Power Engineering Institute (MPEI)". The analysis of CCGT efficiency in urban energy applications is based on modeling and optimizing the city’s fuel and energy balance (FEB). For this purpose, the OptiTEB program developed at the Department of PTS at the National Research University "MРEI" is used. Constructing fuel and energy systems in cities and regions is a pressing task that enables the planning of a strategy for the development of the urban fuel and energy complex (FEC), with an assessment of the magnitude of harmful emissions, including greenhouse gases. The article presents the results of work on establishing scientific foundations for modern heat supply systems, exemplified by a mathematical model of the Moscow thermal power plant and its optimization, taking into account the projected development of electric transport infrastructure over the coming decades, improving the thermal protection characteristics of newly constructed buildings, and the potential increase in the use of renewable energy sources (RES). The growth in the share of electric transport should be linked to an increase in the share of renewable energy sources (RES). Simply increasing the number of electric vehicles without a significant rise in renewable energy usage in the city's energy balance will not result in a reduction of carbon dioxide and harmful substance emissions. Expanding electricity generation through CCGT, alongside the growth of renewable energy use in the city, can lead to a significant decrease in carbon dioxide emissions.

Energy Consumption Optimization for an Electric Delivery Vehicle

For nearly two centuries, electric drives have been used in transportation. Nevertheless, they were not always favored by designers. The century-long dominance of heat engines led to the disregard of numerous challenges associated with the operation of electric drive systems. One of these issues is the optimization of energy consumption by an electric vehicle. This publication proposes an electronic Energy Consumption Optimizer (ECO) that predictively uses information about the shape of the route and speed limits on its individual sections to control the motor speed and gear changes in the gearbox. This work presents the structure of the optimizer system and the developed control algorithms. Additionally, electric motor excitation control was used, which may have contributed to reducing the power and weight of the electric drive motor. Simulation studies carried out using WLTP test cycles and cycles from real road routes showed the potential to decrease energy consumption for vehicle movement by approximately 10%.

Carbon Footprint of Electric Vehicles—Review of Methodologies and Determinants

The carbon footprint of a product and organization is one of the most important environmental indicators in many sectors, including transport. Consequently, electric vehicles (EV) are being introduced as an alternative to achieve decarbonization targets. This article presents an overview of methodologies for assessing the carbon footprint of electric vehicles, including a review of concepts, methods, standards, and calculation models based on the life cycle of the carbon footprint. The article also includes a systematic review of the results of EV carbon footprint analyses. The analysis of current knowledge on the carbon footprint focuses on road transport vehicles: Battery Electric Vehicles (BEV), Fuel Cell Electric Vehicles (FCEV), Hybrid Electric Vehicles (HEV), and Plug-in Hybrid Electric Vehicles (PHEV). Additionally, a review of factors determining the carbon footprint assessment of electric vehicles, considering their entire life cycle, has been conducted.

Mathematical Modeling and Statistical Analysis of Novel Swarm Computing Based Charging Management Framework for Electric Vehicles

This paper presents a novel swarm computing-based charging management framework for electric vehicles (EVs), designed to optimize charging operations and enhance the grid's capacity to handle EV load without compromising system reliability. We introduce a mathematical model that incorporates swarm intelligence algorithms, particularly Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), to efficiently manage the charging schedules of EVs across multiple charging stations. The framework aims to minimize energy costs, balance load during peak hours, and reduce charging time by distributing the charging load in a more organized manner.A statistical analysis is conducted to validate the effectiveness of the proposed model. Using real-time data and simulation, the study evaluates various scenarios to analyze the impact of the swarm-based approach on overall grid stability and EV charging efficiency. The results indicate significant improvements in grid management, with a reduction in peak load demands and enhanced utilization of renewable energy sources. The analysis also demonstrates the scalability and adaptability of the model to different urban settings and EV penetration rates.The paper concludes with a discussion on the potential integration of this model into existing grid systems and its implications for future smart grid and EV infrastructure developments. The proposed framework not only contributes to more sustainable energy management practices but also opens new avenues for research in the domain of intelligent transportation systems and smart grid integration.

Electric vehicle adoption in Vietnam: usefulness and pro-environmentalism in an emerging market

ABSTRACT Future sustainable mobilities are emerging in public discourses in Vietnam, Southeast Asia’s third most populous country and one of the first three late developing countries to commit to net zero carbon by 2050. In response to the global push towards low-emission transport, Vietnam has flipped from being a significant fossil fuel producer to having a strategic plan for becoming a net zero carbon partner. This policy shift includes a top-down commitment to developing the electric vehicle (EV) market in Vietnam. This article maps academic literature and public discourses in the online news media about EV development and consumer attitudes to EV adoption in Vietnam’s domestic market. We find that, despite policy settings to achieve net carbon zero, economic drivers outweigh environmental concerns in the emerging EV market. Drawing on Baudrillard’s consumer society, we find that the use value of EVs is more important at the current time than the sign value associated with prestige, lifestyle and pro-environmental behaviours. The article concludes with recommendations for future research to unpack social and policy issues in the emerging EV market in Vietnam and Southeast Asia.

Differential Game Theory in Electric Vehicle Charging Optimization

Differential Game Theory is a strong way to solve the hard optimization problems that come up when charging electric vehicles (EVs) in smart grids. This method takes into account the strategic exchanges between many parties, such as EV users, charging station operators, and grid operators, each of whom has their own goals. In this study, we look at how differential game theory can be used to find the best charging plans for electric vehicles. Our goal is to lower total energy costs, make the grid less crowded, and get more energy from green sources. We take into account how power prices change, the different types of green energy that are available, and how EVs move around by modeling the charging process as a difference game. The game-theoretic method lets you come up with plans where each player maximizes their own reward while taking into account what other players do. To find the best price methods for different situations, such as peak and off-peak hours, different amounts of green energy usage, and different pricing plans, we use Nash equilibrium solutions. Our findings show that differential game theory can successfully balance the different needs of parties, which can lead to EV charging systems that work better and last longer. Furthermore, the suggested way not only makes smart grids work better, but it also helps lower carbon emissions by supporting the use of clean energy. In addition, this method helps us understand how to create reward systems that match individuals' goals with world improvement goals. This makes it possible for EV charging strategies to work together better. This study shows that differential game theory could be a very useful tool in the development of smart and environmentally friendly transportation systems.

LIFE-CYCLE AND ENERGY CONSUMPTION OF THE COMMERCIALLY AVAILABLE PASSENGER CARS

Today, passenger vehicle emissions alone represent 15% of global greenhouse gas emissions (GHG). The automobile industry is aware of this and has taken steps towards decarbonization over the past decade. Emissions related to the six phases of the life cycle were determined from the LCA database compiled on the basis of data collected on emissions during the life cycle of different types of vehicles, units, sizes and geographical areas. Typically, Internal Combustion Engine Vehicles (ICE) constantly emit CO2 while driving, whereas Battery Electric Vehicles (BEV) and Fuel Cell Electric Vehicles (FCEV) do not. The transition to an electric vehicle is only as clean as the energy source used to charge the vehicle. Each BEV car represents approximately 2.2 MWh/year of load on the country's electric power system, while due to the way energy is converted, the ICE car represents approximately 11.9 MWh/year of fuel-equivalent load.

Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.

Analysis of multidimensional impacts of electric vehicles penetration in distribution networks.

Moving towards a cleaner, greener, and more sustainable future, expanding electric vehicles (EVs) adoption is inevitable. However, uncontrolled charging of EVs, especially with their increased penetration among the utility grid, imposes several negative technical impacts, including grid instability and deteriorated power quality in addition to overloading conditions. Hence, smart and coordinated charging is crucial in EV electrification, where Vehicle-to-Grid (V2G) technology is gaining much interest. Owing to its inherited capability of bi-directional power flow, V2G is capable of enhancing grid stability and resilience, load balancing, and congestion alleviation, as well as supporting renewable energy sources (RESs) integration. However, as with most emerging technologies, there are still technical research gaps that need to be addressed. In addition to these technical impacts, other multidisciplinary factors must be investigated to promote EVs adoption and V2G implementation. This paper provides a detailed demonstration of the technical problems associated with EVs penetration in distribution networks along with quantifiable insights into these limitations and the corresponding mitigation schemes. In addition, it discusses V2G benefits for power systems and consumers, as well as explores their technical barriers and research directions to adequately regulate their services and encourage EV's owners to its embracement. Moreover, other factors, including regulatory, social, economic and environmental ones that affect EV market penetration are being studied and related challenges are analyzed to draw recommendations that aid market growth.

An Interval Fuzzy Programming Approach to Solve a Green Intermodal Routing Problem for Timber Transportation Under Uncertain Information

This study investigates an intermodal routing problem for transporting wood from a storage yard of the timber harvest area to a timber mill, in which the transfer nodes in the intermodal transportation network have multiple service time windows. To improve the environmental sustainability of timber transportation, a carbon tax policy is employed in the routing to reduce the carbon emissions. Uncertain information on the capacities and carbon emission factors of the transportation activities in the intermodal transportation network is modeled using interval fuzzy numbers to enhance the feasibility of the routing optimization in the actual timber transportation. Based on the above consideration, an interval fuzzy nonlinear optimization model is established to handle the specific routing problem. Model defuzzification and linearization are then conducted to obtain an equivalent formulation that is crisp and linear to make the global optimum solution attainable. A numerical experiment is conducted to verify the feasibility of the proposed model, and it reveals the influence of the optimization level and service time windows on the routing optimization, and it confirms that intermodal transportation is suitable for timber transportation. This experiment also analyzes the feasibility of a carbon tax policy in reducing the carbon emissions of timber transportation, and it finds that the performance of this policy is determined by the optimization level given by the timber mill and is not always feasible in all cases. For the case where a carbon tax policy is infeasible, this study proposes a bi-objective optimization that can use Pareto solutions to balance the economic and environmental objectives as an alternative. The bi-objective optimization further shows the relationship between lowering the transportation costs, reducing the carbon emissions, and enhancing the reliability on capacity and budget by improving the optimization level. The conclusions provide managerial insights that can help the timber mill and intermodal transportation operator organize cost-efficient, low-carbon, and reliable intermodal transportation for timber distribution, and support sustainable forest logistics.

Retraction Note: Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements

Retraction Note: Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements

Feasibility Assessment of Hybrid Renewable Energy Based EV Charging Station in Libya

This study presents an assessment of the feasibility of implementing a hybrid renewable energy-based electric vehicle (EV) charging station at a residential building in Tripoli, Libya. Utilizing the advanced capabilities of HOMER Grid software, the research evaluates multiple scenarios involving combinations of solar and wind energy sources integrated with energy storage and the utility grid. This analysis provides a novel approach to enhancing urban energy systems with renewable technologies in a region traditionally reliant on fossil fuels. Key contributions of this study include the demonstration of an innovative integration strategy that combines solar and wind power with battery storage to ensure a reliable and efficient energy supply for EV charging. Furthermore, the study addresses the practical implications for local energy policy, suggesting that such hybrid systems can significantly enhance energy security and support sustainable urban development. The authors studied five scenarios using HOMER. The results reveals that the annual total costs and payback periods are as follows: for Scenario 1 (wind/utility grid), the expenditure totals US$1,554,416 and payback period of 4.8/5.8 years; for Scenario 2 (solar/wind/Utility grid), the amount is US$1,554,506 and payback period of 4.8/5.8 years; and for Scenario 3(solar/wind/storage/utility grid), it escalates slightly to US$1,554,731, all predicated on the utility grid tariffs and payback period of 4.8/5.8 years. Furthermore, in Scenario 4 (solar/utility grid), the annual total cost is significantly reduced to US$30,589 and a payback period of 8.1/14.3 years, while Scenario 5 (solar/storage/utility grid) incurs an even lower expenditure of US$28,572, again based on the utility grid tariffs and a payback period of 14.0 years. The findings contribute valuable insights into the scalability and adaptability of renewable energy solutions, providing a robust framework for policymakers and planners considering similar implementations in other regions. Overall, the research underscores the potential of integrated renewable energy systems to transform urban energy infrastructures, promoting a sustainable and resilient energy future. The HOMER Grid analysis shows that configurations with energy storage are more cost-effective in the long run, even though they require higher initial costs. It also offers important insights into the economic viability and optimization of hybrid renewable energy systems for an EV charging station in Tripoli, Libya. These results highlight the significance of making calculated investments in renewable energy infrastructure and supporting policies for the development of sustainable energy.

E-Fuel: An EV-Friendly Urgent Electrical Charge-Sharing Model with Preference-Based Off-Grid Services

Electric-powered vehicles (EVs) allow for an environmentally friendly and economic alternative to fuel-running ones. However, such an alternative is expected to impose further usage hikes and periods of instability on cities’ power systems. From their perspective, cities need to scale their infrastructure grids to allow for adequate power resources to feed such new power-hungry consumers. Indeed, for such a green alternative to proceed, our power grids need to be ready to cope with any unexpected hikes in the power consumption rates without compromising the stability of the services provided to our homes and workplaces. Operators’ steps in this path are still modest, and the coverage of EV charging stations is still insufficient as they are trying to avoid any further costs for upgrading their infrastructures. The lack of price consideration for the charging services offered at charging stations may result in EV drivers paying higher costs compared to traditional fuel vehicles to charge their EVs’ batteries, hindering the economic incentive of owning such sorts of vehicles. Hence, it may take a while for sufficient coverage to exist. Although for drivers the adoption of EVs represents a city-friendly alternative with affordable expenses, it usually comes with range anxiety and battery charging concerns. In this work, we are presenting e-Fuel, a charge-sharing model that allows for preference-based mobile EV charging services. In e-Fuel, we are proposing a stable weight-based vehicle-to-vehicle matching algorithm, through which drivers of EVs will be capable of requesting instant mobile charge-sharing service for their EVs. In addition to being mobile, such charging services are customized, as they are chosen based on the drivers’ preferences of price-per-unit, charging speed, and time of delivery. The developed e-Fuel matching algorithm has been tested in various environments and settings. Compared to the benchmark price-based matching algorithm, the resulting matching decisions of e-Fuel come with balanced matching attributes that mostly allow for 6- to 7-fold shorter service delivery times for a minimal increase in service charges that vary between 9% and 65%.

Review of Economic, Technical and Environmental Aspects of Electric Vehicles

Electric vehicles (EVs) have seen significant advancements and mainstream adoption, prompting in-depth analysis of their economic, technical, and environmental impacts. Economically, while EVs offer lower operational costs than internal combustion engine vehicles, challenges remain, particularly for urban users reliant on public charging stations and the potential implementation of new road taxes to offset declining fuel tax revenues. Technically, electric motors in EVs have fewer moving parts, but battery management and cybersecurity complexities pose new risks. Transitioning from Nickel-Manganese-Cobalt (NMC) to Lithium-Iron-Phosphate (LFP) batteries reflects efforts to enhance thermal stability and mitigate fire hazards. Environmentally, lithium extraction for batteries has profound ecological impacts, including for water consumption and pollution. Battery production and the carbon footprint of the entire lifecycle remain pressing concerns, with battery recycling and second-life applications as crucial mitigation strategies. Smart integration of EVs with the energy infrastructure introduces challenges like grid stability and opportunities, such as smart, intelligent, innovative charging solutions and vehicle-to-grid (V2G) technology. Future research should develop economic models to forecast long-term impacts, advance battery technology, enhance cybersecurity, and conduct comprehensive environmental assessments to optimise the benefits of electromobility, addressing the multidimensional challenges and opportunities presented by EVs.

Techno-Economic Analysis of Grid-Connected Highway Solar EV Charging Station

AbstractSolar electric vehicle (EV) charging stations offer a promising solution to an environmental issue related to EVs by supplying eco-friendly electricity. Herein, we designed and analyzed a grid-connected highway solar EV charging station for 2022, 2030, and 2050 under two scenarios: Current policy scenario with restricted grid sales and policy mitigation scenario allowing grid sale. Future systems consider changes in EV charging station, grid CO2 emissions, carbon prices, and renewable costs. In 2022, high PV and battery costs led to a grid-only system. By 2030 and 2050, significant reductions in PV costs enabled systems with substantial PV capacity, especially under the policy mitigation scenario. Economic analysis showed that enabling grid sales reduces net present cost (NPC) by 40% in 2030 and 35% in 2050 compared to the current policy scenario, with significantly lower levelized cost of energy. CO2 emissions from EV operation are projected to be 44% and 3% of 2022 levels by 2030 and 2050, respectively. The policy mitigation scenario further reduces CO2 emissions by 22% in 2030 and 25% in 2050 compared to the current policy scenario. Sensitivity analyses reveal that increased grid sale capacity leads to higher renewable penetration and lower NPC but also increased grid congestion, highlighting the need for efficient grid management. Policymakers should consider revising regulations to support higher PV penetration and manage grid congestion. This study supports the transition to renewable systems and underscores the importance of policy measures in achieving sustainable energy goals.