Traditional Internal Combustion Engine Vehicles Research Articles

Evaluation of potential electric vehicles load-induced damage on flexible pavements

The adoption of Electric Vehicles (EVs), driven by policies like the Zero Emission Vehicle (ZEV) mandate, is rapidly transforming transportation infrastructure. In 2021, over 1.25-million light-duty EVs were registered in the United States, signaling a shift that extends to heavy vehicle categories. This study evaluates the implications of this transition on road networks, focusing on the increased gross weight of EVs compared to traditional Internal Combustion Engine (ICE) vehicles and its impact on flexible pavement structures. The study investigates the deterioration patterns and potential damage to road infrastructures resulting from integrating EVs into the traffic mix, utilizing both the AASHTO 1993 and Mechanistic-Empirical (ME) pavement evaluation methodologies. The analyses reveal a significant acceleration in road degradation, necessitating urgent consideration of EV-induced stresses by transportation authorities. Recommendations for future research and practical strategies for mitigating these impacts are provided to guide policymakers and engineers in adapting to this evolving vehicular landscape.

A Review on Advanced Battery Thermal Management Systems for Fast Charging in Electric Vehicles

To protect the environment and reduce dependence on fossil fuels, the world is shifting towards electric vehicles (EVs) as a sustainable solution. The development of fast charging technologies for EVs to reduce charging time and increase operating range is essential to replace traditional internal combustion engine (ICE) vehicles. Lithium-ion batteries (LIBs) are efficient energy storage systems in EVs. However, the efficiency of LIBs depends significantly on their working temperature range. However, the huge amount of heat generated during fast charging increases battery temperature uncontrollably and may lead to thermal runaway, which poses serious hazards during the operation of EVs. In addition, fast charging with high current accelerates battery aging and seriously reduces battery capacity. Therefore, an effective and advanced battery thermal management system (BTMS) is essential to ensure the performance, lifetime, and safety of LIBs, particularly under extreme charging conditions. In this perspective, the current review presents the state-of-the-art thermal management strategies for LIBs during fast charging. The serious thermal problems owing to heat generated during fast charging and its impacts on LIBs are discussed. The core part of this review presents advanced cooling strategies such as indirect liquid cooling, immersion cooling, and hybrid cooling for the thermal management of batteries during fast charging based on recently published research studies in the period of 2019–2024 (5 years). Finally, the key findings and potential directions for next-generation BTMSs toward fast charging are proposed. This review offers an in-depth analysis by providing recommendations and potential solutions to develop reliable and efficient BTMSs for LIBs during fast charging.

A Two-Echelon Routing Model for Sustainable Last-Mile Delivery with an Intermediate Facility: A Case Study of Pharmaceutical Distribution in Rome

This paper introduces a two-echelon optimization model for the integrated routing of an electric vehicle (EV) and a traditional internal combustion engine vehicle (ICEV) in an urban environment. The scientific context of this study is sustainable urban logistics. The case study focuses on the distribution of pharmaceuticals in the metropolitan area of Rome. Distributing pharmaceuticals in large cities presents significant challenges, including heavy traffic congestion, the need for strict temperature control, and the maintenance of the integrity and timely delivery of sensitive medications. Furthermore, the complexity of urban logistics and adherence to regulatory requirements introduce additional layers of difficulty. Therefore, the implementation of fast and sustainable distribution mechanisms is crucial in this context. Specifically, the model seeks to minimize both total CO2 emissions and transportation costs while optimizing the use of an EV and an ICEV, all while ensuring that service level requirements are met. Computational results demonstrate the effectiveness of the proposed method in improving the sustainability of pharmaceutical distribution.

Electric automobility and the race to road transfer: ‘Formula E’ and ‘Extreme E’ in documentary film

As the need for changes in transportation grows, the transition to sustainable mobility is being envisioned in varying ways. Shifting from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs), along with changing mobility habits, will both be necessary. What has been neglected from the study is the role of sport in aiding this transition. Motorsport has long served dual roles of serving as a technological testbed and center for culture formation around street cars. Coupling one of the largest sporting platforms in the world with the missions of climate change awareness and technological advancement, two racing leagues are catalyzing the move to electric mobility. Formula E and Extreme E have released documentary films showcasing their future goals. To understand the role of electric racing in shaping the transition to sustainable mobility, this paper uses a grounded theory approach to identify relevant themes of the EV transition, while a narrative analysis is used to foreground the overall storytelling aspect of the ecocinematic films. The racing EV emerges as a site of collective collaboration: meeting the needs of the future within the framing of traditional motorsport, and merging traditionally masculinist narratives of automotive technology with prototypically feminized environmentalism.

Sizing Methodology of Dynamic Wireless Charging Infrastructures for Electric Vehicles in Highways: An Italian Case Study

The necessity of pushing the road mobility towards more sustainable solutions has become of undeniable importance in last years. For this reason, both research and industry are constantly investigating new technologies able to make the usage of battery electric vehicles(BEV) as accessible and usable as traditional internal combustion engine vehicles (ICEV). One of the most limiting issues concerns the short range of electric vehicles, which complicates their use for long distances, such as for highway travels. A promising solution seems to be the “charge-while-driving” approach, by exploiting the inductive dynamic wireless power transfer (DWPT) technology. Nevertheless, such systems show different issues, first of all, high investment and maintenance costs. Furthermore, it is not clear how extensive a potential dynamic wireless charging infrastructure needs to be to make a real advantage for electric vehicle drivers. As a consequence, the aim of this paper is to introduce a new methodology to estimate the number and length of wireless charging sections necessary to allow the maximum number of electric vehicles to travel a specific highway without the need to stop for a recharge at a service area. Specifically, the methodology is based on a algorithm that, starting by real traffic data, simulates vehicle flows and defines the basic layout of the wireless charging infrastructure. This simulator can provide a decision support tool for highway road operators.

Stochastic assessment of electric powertrain whining noise under early-stage design uncertainties

Despite the advantage of being quieter than traditional internal combustion engine vehicles, electric vehicles are often distinguished by high-frequency tonal components, which can be perceived as unpleasant to the occupants and the environment. To ensure optimal acoustic comfort in electric vehicles, it is important to analyze the NVH behavior of e-powertrains during the early stages of the design process which poses inherent uncertainties, such as varying operating conditions, partial knowledge of design parameters, dispersion in measurement-based data, etc. To effectively address these uncertainties, it is necessary to use fast and comprehensive stochastic models during the design phase. In this work, a probabilistic framework is presented to estimate the electric powertrain’s interior whining noises considering the structure-borne contribution. Hence, two different stochastic metamodels are developed for efficient quantification and propagation of uncertainties from electric motor stage to powertrain mounting system. Multivariate Bayesian regression models help to incorporate prior knowledge on the uncertain parameters and generate the respective posterior distributions using Markov chains Monte Carlo (MCMC) techniques. For this particular application, the data is generated through weakly-coupled multi-physical domains estimated using semi-analytical approaches and combined with measured vehicle transfer functions. Importantly, the validation of each domain is conducted separately to ensure accurate representation. The results obtained from the developed probabilistic framework will aid in the early design stages by guiding engineers in making informed decisions to optimize NVH performance.

Machine Learning Applied to Electric Vehicle Routing Problem: Optimizing Costs for a Sustainable Environment

The global move towards Electric Vehicles (EVs) marks a crucial step towards sustainable transportation. However, effectively integrating EVs into the current infrastructure demands more than technological advancements. One of the key challenges is optimizing the routing of EVs to minimize costs and environmental impact. This editorial examines the role of Machine Learning (ML) in addressing the electric vehicle routing problem (ESVRP), highlighting its potential to transform cost optimization and sustainability in transportation. Routing is a fundamental part of transportation logistics, influencing efficiency, cost, and environmental impact. While traditional internal combustion engine vehicles have established routing systems, EVs present unique challenges such as limited battery capacity, longer refueling times, and fewer charging stations. These factors require advanced routing solutions that can dynamically adapt to various constraints.

Adoption of electric vehicles in corporate enterprises: enhancing sustainability, economic efficiency, and operational management

The global automotive industry is currently undergoing a transformation driven by a number of factors, including environmental concerns, sustainability targets, and the advent of innovative technologies. The adoption of electric vehicles represents a pivotal aspect of this transformation, offering individual and corporate users in the car rental sector a significant alternative to traditional internal combustion engine vehicles. The economic and operational advantages of electric vehicles, coupled with the opportunity for car rental companies to fulfil their environmental responsibilities, are accelerating the transformation of the automotive industry. This study presents a case study on the utilization of electric vehicles for long-term car leasing companies for the purpose of providing corporate internal services. The aim is to provide a comprehensive evaluation of the issue from multiple perspectives. The objective of this paper is to provide a comprehensive overview of the concept of electric vehicle leasing, encompassing a range of considerations pertinent to decision-making. These include environmental sustainability, economic advantages, user experience, and operational efficiency.

Comprehensive Comparison of Traditional Engines and Emerging Alternatives

As the natural environment deteriorates, electric vehicles will gradually replace internal combustion engines that use traditional fossil fuels. This paper compares traditional engines to alternatives regarding efficiency, emissions, price, and market share. In brief, alternative engines have advantages over traditional internal combustion engines in terms of efficiency, emissions, and long-term overhead, as evidenced by rising market share. In 2022, the share of electric vehicles in global sales has reached 14%. Compared to traditional internal combustion engine vehicles, electric vehicles have a higher well-to-wheel efficiency, up to more than twice of internal combustion engine vehicles. From a price perspective, electric vehicles have a higher original cost. However, lower maintenance costs allow electric vehicles to achieve the same cost as equivalent combustion engines in 5-8 years. Electric vehicles typically have low well-to-wheel and fuel emissions and are more sustainable. In the future, more research and development are needed for electric vehicles to make them suitable for most cities worldwide. At the same time, research into the traditional internal combustion engine should be addressed, as cleaner, more efficient engines such as the HCCI engines are already available for civilian use.

Review of Fuel-Cell Electric Vehicles

This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future.

Blockchain based crowdsourcing framework for Vehicle-to-Vehicle charging

Electric Vehicles (EVs) have become a promising solution to entirely replace traditional internal combustion engine vehicles. EV owners face the “Range Anxiety” problem which is the fear that their remaining charge would not be enough to reach their destination. Vehicle-to-Vehicle (V2V) charging is currently being used in exchanging energy between EVs. Most of the existing energy V2V frameworks depend on a centralized server that manages the energy exchange. However, these solutions may suffer from privacy leakage and misbehaving platforms. To overcome these limitations, the paper proposes a blockchain-based crowdsourcing framework for V2V charging. The framework features include registering users, maintaining user information, clustering requesters (charging tasks), collecting preferences lists from requesters and providers, and lastly selecting the requester-provider charging pair as well as the corresponding meeting point. These features are implemented using three types of smart contracts: User Management Contract, Task Management Contract, and Solution Management Contract while guaranteeing the transparency of the system and overcoming the main limitations of the centralized solutions. Experiment results with real-life dataset show that the proposed framework achieves a comparable performance at reasonable paid costs and execution times.

The transition to electrified vehicles: Evaluating the labor demand of manufacturing conventional versus battery electric vehicle powertrains

The shift from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) has raised concerns about displacement of automotive manufacturing labor. Prior studies have mixed findings, and have been hindered by a lack of shop floor-level data on the labor hours required for ICEV and EV manufacturing. We collect detailed data from leading automotive manufacturers on the production process steps and labor inputs required to build key ICEV and battery electric vehicle (BEV) powertrain components. Our novel data set covers 252 process steps, with data on a further 78 process steps from the existing literature. We build a production process model that estimates the labor hours required to produce ICEV and BEV powertrain components at different production volumes and labor efficiency levels. We find that, accounting for production and assembly of battery packs, the labor intensity required for the manufacturing of BEV powertrain components is larger than for ICEV powertrain components. This difference in labor intensity holds even when comparing the highest-efficiency estimates for BEV production with the lowest efficiency-estimates for ICEVs. This finding depends on our shop-floor operations modeling approach and could not be derived from recent approaches focusing on part counts. Our results imply that vehicle electrification could lead to more automotive manufacturing jobs, at least in the short- to medium-term. While there are potential jobs to support transitions for incumbent automotive workers, the feasibility of these transitions will depend on geographic co-location of new battery production capacity with current ICEV production sites, and on matching between the skills of the automotive workforce and the demands of new EV jobs.

Battery Management in Electric Vehicle Routing Problems: A Review

The adoption of electric vehicles (EVs) has gained significant momentum in recent years as a sustainable alternative to traditional internal combustion engine vehicles. However, the efficient utilization of batteries in EVs, coupled with the growing demand for sustainable transportation, has posed complex challenges for battery management in the context of electric vehicle routing problems in a broad sense, which includes vehicle routing problems, team orienteering problems, and arc routing problems, all of them using EVs. This paper presents a comprehensive review of the state-of-the-art approaches, methodologies, and strategies for battery management in each of the aforementioned optimization problems. We explore the relevant factors influencing battery performance and the interplay between routing, charging, and energy management in the context of EVs. The paper also discusses the advances in optimization algorithms, vehicle-to-grid integration, and intelligent decision-making techniques aimed at enhancing the range, reliability, and sustainability of EV operations. Key findings indicate a paradigm shift towards addressing uncertainties, dynamic conditions, and synchronization challenges inherent in large-scale and dynamic routing problems within the context of EVs that require efficient battery management.

Sustainability of Electric Vehicles Using Artificial Intelligence

Electric vehicles (EVs) are widely regarded as a more environmentally sustainable transportation option compared to traditional internal combustion engine vehicles. However, concerns persist regarding the sustainability impacts of EV battery manufacturing, specifically the mining of lithium used in lithium-ion batteries. This literature review examines the potential for artificial intelligence (AI) to improve the sustainability of lithium mining and battery manufacturing for EVs. An extensive literature search yielded insights into the environmental impacts of lithium mining such as high water usage, carbon emissions, and soil contamination. These impacts present a challenge to the sustainability narrative of EVs. However, researchers have identified numerous applications for AI across the lithium-ion battery supply chain that could mitigate these effects. From optimizing mineral exploration and mining operations to recycling spent batteries, AI solutions show promise to reduce the lifecycle impacts of lithium-ion batteries. Additional research is still needed to quantify these potential improvements and implement AI responsibly. Furthermore, technology alone cannot address the complex socio-economic challenges associated with lithium mining. Ultimately, a combination of technological innovation and ethical, inclusive resource management will be required to truly transition to sustainable EV mobility. Keywords: electric vehicles, lithium-ion batteries, lithium mining, sustainability, artificial intelligence.

Adaptive Integrated Thermal Management System for a Stable Driving Environment in Battery Electric Vehicles

With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes.

Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning

The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among these are the formidable foes of range anxiety and battery health management. Range anxiety is a real issue felt by many potential EV owners worry about becoming stuck because their battery has run out before reaching their destination. This psychological barrier is very noticeable and makes present and future EV owners doubtful. In addition, the longevity and health of EV batteries are essential to their continued effectiveness and affordability. The driving range and operating efficiency of the vehicle are directly affected by the gradual degradation of the battery due to several factors like aging, charging patterns, and temperature. This research presents an integrative and holistic approach to address these pressing issues, enhancing and elevating the whole EV ownership experience by combining Electric Vehicle Health Monitoring (EVHM) with Electric Vehicle Range Prediction (EVRP) and Route Planning (EVRP). Combining these three essential elements creates an all-encompassing plan created to not only lessen these enormous obstacles but also accelerate the switch to electric vehicles by giving consumers the knowledge and assurance they require for a smooth, eco-friendly, and sustainable mobility in the future.

Zero emission freight transport and impacts on last mile delivery


 
 
 This literature review explores mainly the challenges and potential solutions in urban freight transportation, for last-mile logistics. The growing number of delivery vehicles in urban areas has led to problems such as traffic congestion, competition for parking spaces, noise, and air pollution. To address these issues, the paper focuses on the last mile problem in transportation logistics and the potential of disruptive innovations, a potential of disruptive innovations such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs) as alternatives to traditional Internal Combustion Engine (ICE) vehicles. It highlights the drastic operational differences between aerial drones and ADRs and the need for changes in operational procedures to fully leverage these technologies. The study also discusses routing strategies and delivery routes, emphasizing the shift from one-route delivery to a point-to- point model with the advent of autonomous vehicle technologies. The paper concludes that while these technologies can reduce costs and emissions, their effectiveness depends on the specific urban setting and operational factors. 
 
 

A REVIEW ON ELECTRIC VEHICLE

Plug-in hybrid electrics vehicles (PHEVs) have been extensively studied, and advancements in power electrics and energy storage have made them quite competitive with traditional internal combustion engine vehicles (ICEVs). By using optimized control strategies and energy management systems (EMS), PHEVs can become even more efficient. In my own words, PHEVs are vehicles that combine the benefits of electric and gasoline power to provide a good driving range and fuel economy. Battery and super capacitor technologies are also being explored to increase the energy capacity of PHEVs. It's exciting to see how these advancements are shaping thefuture of transportation

(Keynote) Rapid Thermal Management for Fast Charging of Energy-Dense Lithium-Ion Batteries

Charging time trauma refers to the pain experienced by electric vehicle (EV) owners when the ‘refill’ time is significantly longer than that of traditional internal combustion engine vehicles. Worsening this trauma is the inconsistency of EV charging; for example, the same EV may take five times longer to charge on a cold day. Faster charging will alleviate this pain point and aid in the mass adoption of EVs, crucial for meeting near-term carbon emission goals. Reaching this goal will require smarter and more rapid thermal management to provide significantly faster safe charging.The results from the 2022 Nature article “Fast charging of energy-dense lithium-ion batteries” (DOI 10.1038/s41586-022-05281-0) where 265 Wh/kg lithium-ion pouch cells were charged from 0-70% in less than 12 minutes, 2,000 times in a row before end-of-life, will be presented. These cells used only proven materials, including a liquid electrolyte made from industrially-used components, and energy-dense, EV-grade electrodes. Key to success was smart cell thermal management. More recent advancements, including multi-cell battery-level data, will also be discussed. Figure 1

Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand

Electric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.