Traditional Combustion Research Articles

Recent advancements and performance implications of hybrid battery thermal management systems for Electric Vehicles

Electric Vehicles (EVs) can contribute significantly toward the reduction of environmental emissions caused by traditional Internal Combustion Engines (ICEs), as well as it reduces the reliance on conventional fossil fuels. Lithium-ion batteries (LiBs) are generally preferred for EVs due to their superior energy density, specific power output, and longer lifespan. LiBs are most effective and achieve optimal performance within a specific temperature range of 15-35 °C. An efficient Battery Thermal Management System (BTMS) is necessary to dissipate the heat produced in the battery during continuous charging and discharging process, which ultimately determines the operating range and life cycle of the battery. The hybrid battery thermal management system is becoming increasingly popular as it tackles the downsides and capitalizes on the upsides of individual conventional battery thermal management systems. Although hybrid Battery Management Systems are often mentioned in review articles, there is a lack of detailed or specialized discussions specifically on hybrid BTMS for electric vehicles. This article summarizes the current state-of-the-art and recent advancements in hybrid battery thermal management of LiBs and discusses the performance implications of a hybrid battery thermal management system. The study extensively examines hybrid BTMSs, designed to enhance the thermal performance of traditional BTMSs to cope with the stringent thermal requirements in high ambient temperatures and fast charging conditions. Lastly, the article critically evaluates and discusses the different BTMSs, emphasizing the future prospects, challenges, and recommendations in the BTMS field. The review's findings will assist researchers in determining the future direction of next-generation LiB thermal management systems for high-power electric vehicles.

Heat Transfer and Thermal Efficiency in Oxy-Fuel Retrofit of 0.5 MW Fire Tube Gas Boiler

Industrial boilers cause significant energy wastage that could be mitigated with oxy-fuel combustion versus traditional air combustion. Despite several feasibility studies on oxy-fuel burners, they are widely avoided in industry due to major infrastructural challenges. This study measured the performance and heat transfer characteristics of each component in a 0.5 MW fire tube gas boiler after retrofitting it with an oxy-fuel burner. Comparisons were drawn across three combustion modes—air combustion, oxy-fuel combustion, and oxy-fuel flue gas recirculation (FGR). The Dittus–Boelter equation was employed to predict heat transfer in the fire tube for all combustion modes at full load (100%). Heat transfer in the latent heat section of the economizer was measured and compared with predictions using the Zukauskas equation. With this retrofit, oxy-fuel combustion improved the thermal efficiency by about 3–4%. In oxy-fuel combustion, the flow rate of exhaust gas decreased. When integrated into an existing fire tube boiler, the fire tube’s heat transfer contribution diminished greatly, suggesting the economic viability of a redesigned, reduced fire tube section. Additionally, a new design could address the notable increase in gas radiation from the fire tube in oxy-fuel and FGR, as well as aid in the efficient recovery of condensation heat from exhaust gases.

Electric Vehicle Steering System Fuzzy Modeling and Controller Design

Electric vehicle (EV) steering control becomes much more important in many senses such as driving comfort and safety. Unlike the traditional internal combustion engine powered vehicles that possess only one control input related to the steering wheel, the so‐called direct yaw moment control is yet another control input in EVs thanks to the dispersedly located in‐wheel motors. In view of controller design crucially depending on a proper mathematical model, this paper first focuses on the modeling of EV steering system with uncertainty in the system considered for the latter robust controller design. Then a Takagi‐Sugeno (T‐S) fuzzy model is obtained based on the EV's model, which is capable of varying vehicle velocity in a range. Finally, a robust controller with inputs constraint is designed based on the model, and the effectiveness is verified by computer simulations. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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.

Retrofitting of IC Engine to EV in Two-Wheeler

Abstract: The global focus on sustainable transportation has led to the emergence of retrofitting existing conventional bikes as a practical strategy to transition from traditional combustion engine vehicles to environmentally friendly alternatives. This research delves into the conceptualization, design, and implementation of retrofitting technologies aimed at enhancing the performance, efficiency, and environmental impact of conventional bikes. In the age of Technological Advancement, Innovations, and Development, the Electric Mobility trend has captured the attention of all industries. Electric vehicles are poised to revolutionize the automotive sector. Renowned industries are prioritizing electrification over conventional propulsion methods. In the near future, IC vehicles are expected to be eclipsed by their electric counterparts. This project aims to address the issue of public transportation by providing a cost-effective and eco-friendly alternative to existing vehicles. The proposed design involves replacing the entire drivetrain with a pure electric power source, necessitating modifications to the OEM Bike. This project boasts cost-effectiveness, lightweight construction, interchangeability, and complete Eco friendliness, significantly reducing the use of hazardous substances that could lead to accidents or fatalities. Furthermore, the study examines the environmental benefits of retrofitting, considering the life cycle analysis of modified bikes and their potential contribution to greenhouse gas emission reduction.

Thermo-electric modeling and analysis of lithium-ion battery pack for E-mobility

AbstractElectric Vehicles (EVs) have emerged as a viable and environmentally sustainable alternative to traditional internal combustion vehicles by utilizing a clean energy source. The advancement and expansion of electric cars rely on the progress of electrochemical batteries. The utilization of Lithium-Ion Batteries is widespread primarily because of its notable energy density. Changes influence the performance of these batteries in temperature. The Thermal Management System of the battery is one of the very important systems in EVs to improve the performance and life of the battery. The geometrical spacing of the cell modules is considered identical for a more accurate comparison of temperature distribution. For better cooling and heat dissipation, the battery pack’s two sides are kept entirely open to facilitate the inflow of air. In this work, active BTMS solutions are selected and analyzed using the development of three-dimensional free, open-source OpenFOAM computational fluid dynamics simulations for accurate thermal modeling and hotspot zones in cylindrical battery packs. The outcome of the simulations is compared using parameters like temperature distribution in battery cells, battery modules, and heat generation. Among all the cell temperature zones, the temperature maximum is near the sixth cell of the module depth. OpenFOAM results validated with the existing literature’s experimental and Ansys results. Air cooling is utilized for cooling performance because of its relatively simple structure and lightweight.

A Simulation Study on a Premixed-charge Compression Ignition Mode-based Engine Using a Blend of Biodiesel/Diesel Fuel under a Split Injection Strategy

Environmental pollution from transportation means and natural resource degradation are the top concern globally. According to statistics, NOx and PM emissions from vehicles account for 70% of total emissions in urban areas. Therefore, finding solutions to reduce NOx and PM emissions is necessary. Changing the engine's internal combustion method is considered promising and influential among the known solutions. One of the research directions is a combustion engine using the Premixed Charge Compression Ignition (PCCI) method combined with biofuels to improve the mixture formation and combustion process, reducing NOx and PM emissions. Therefore, this study presents the mechanism of the formation of PM and NOx emissions in the traditional combustion and the low-temperature combustion process of internal combustion engines. Besides, the theoretical basis of flame spread during combustion is also introduced. The key feature of this research is that it has modeled the combustion process in diesel engines under the PCCI modes. This was accomplished using blends of waste cooking oil (WCO)-based biodiesel and diesel fuel, as well as the ANSYS Fluent software. The results showed that PCCI combustion using B20 fuel can significantly reduce NOx and PM emissions, although HC and CO emissions tend to increase, and thermal efficiency tends to decrease. In further studies, different modes of the PCCI combustion process should be thoroughly examined so that this process can be implemented in practice to reduce pollutant emissions.

Management of Globalization of Motorsport and Future Development

The motorsport industry has conducted a remarkable global transformation in the past few years and reach some success in terms of popularity and finance. Formula 1, in particular, has seen a boost in viewership thanks to innovative management and the influence of social media, especially the "Drive to Survive" series. However, this success has come with a set of complex challenges. One key issue is the choice of propulsion systems. While electric power gain traction in the automotive sector, motorsport faces a rather difficult choose. Traditional internal combustion engines, a hallmark of the sport, risk falling out of sync with environmental standards. Transitioning entirely to electric power might alienate loyal fans. A middle ground could lie in sustainable and synthetic fuels, reducing carbon dioxide emissions while preserving the essence of racing. Diversity, equity, and inclusion are pressing concerns. Underrepresentation of individuals from minority backgrounds persists, accompanied by racial discrimination. Female representation remains rare. Addressing these challenges requires systemic changes, starting with equal opportunities in education and support for aspiring motorsport enthusiasts from minority backgrounds. In conclusion, while the motorsport industry faces complex issues, it has a bright future if it makes informed decisions and embraces technological development. Sustainable fuels, equitable regulations, and diversity initiatives promise a more inclusive and sustainable motorsport landscape.

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.

Investigation of Charging Technologies for Electric Vehicles

In today's world, internal combustion engine vehicles are widely used. These vehicles cause harmful emissions as they use fossil fuels for engine propulsion and vehicle movement. To address this issue, electric vehicles have been considered as a solution. In electric vehicles, electric motors are used instead of traditional internal combustion engines and the necessary electrical energy is supplied from battery packs. In this way, electric vehicles do not cause harmful emissions to the environment. If the energy required for electric vehicle charging stations is obtained from renewable energy systems, electric vehicles will be zero-emission vehicles. Battery packs, that provide energy for electric vehicles, typically consist of rechargeable Lithium-ion battery groups and they can be charged using fast charging technology. In this study, the future of transportation, electric vehicles and the standards and technologies used in electric vehicle charging stations have been examined. Additionally, an electric vehicle system has been designed using MATLAB/Simscape.

Design of a Method for Analyzing Power Systems: Mathematical Models of Electric Vehicles and Distributed Generators

Electric vehicles (EVs) have significantly increased in recent years due to a growing awareness of the effects of polluting gases produced by combustion engines. The use of electric vehicles produces no emissions, making them a much more environmentally friendly alternative to traditional combustion engines. In addition, the price of EVs has decreased significantly in recent years, making them more affordable for consumers. The use of distributed generation (DG), an alternative source of energy for power systems, should also be considered. This study proposes a methodology for incorporating DGs and EVs into existing power systems. Power can be generated by DG directly where needed, while excess energy can be stored by EVs. Existing power systems can be made more efficient and cost-effective by combining these technologies. The methodology was applied to a 6-node IEEE system and a 33-node IEEE distribution system. It was found that DG can reduce network losses and voltage drops associated with EV penetration and that DG improves voltage drops.

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.

Combustion efficiency control method of circulating fluidized bed boiler based on adaptive genetic algorithm

In order to improve the traditional boiler combustion efficiency control method, which has the problem of low efficiency after control, a new combustion efficiency control method of circulating fluidized bed boiler based on adaptive genetic algorithm is proposed in this paper. Firstly, the iterative principle of adaptive genetic algorithm is analyzed; Secondly, the combustion process model of circulating fluidized bed boiler is constructed by BP neural network method; Finally, based on the above model, the optimal control objective function of adaptive genetic algorithm is constructed, and the objective function is solved according to the iterative principle of adaptive genetic algorithm to complete the control of combustion efficiency of circulating fluidized bed boiler. The experimental results show that compared with the traditional control method, the combustion efficiency of the boiler controlled by this method is always more than 98%.

Sustainable Vehicles for Decarbonizing the Transport Sector: A Comparison of Biofuel, Electric, Fuel Cell and Solar-Powered Vehicles

Climate change necessitates urgent action to decarbonize the transport sector. Sustainable vehicles represent crucial alternatives to traditional combustion engines. This study comprehensively compares four prominent sustainable vehicle technologies: biofuel-powered vehicles (BPVs), fuel cell vehicles (FCVs), electric vehicles (EVs), and solar vehicles. We examine each technology’s history, development, classification, key components, and operational principles. Furthermore, we assess their sustainability through technical factors, environmental impacts, cost considerations, and policy dimensions. Moreover, the discussion section addresses the challenges and opportunities associated with each technology and assesses their social impact, including public perception and adoption. Each technology offers promise for sustainable transportation but faces unique challenges. Policymakers, industry stakeholders, and researchers must collaborate to address these challenges and accelerate the transition toward a decarbonized transport future. Potential future research areas are identified to guide advancements in sustainable vehicle technologies.

Combustion Efficiency of Carbon-neutral Fuel using Micro-Combustor Designed for Aerospace Applications

Recent advancements in the field of micro combustor research are growing for achieving high-performance systems in micro power generation and microelectromechanical devices. To mitigate the hazardous emissions from carbon fuels, as an alternative, zero-carbon-free fuels ammonia, and hydrogen are being explored in micro combustion processes. The distinctive feature of a micro combustor lies in its significantly higher area to volume ratio in comparison with traditional combustion systems, leading to accelerated combustion reaction rates. However, the small size of micro combustors poses a challenge in achieving efficient mixing of highly reactive fuels like hydrogen and ammonia with oxidizers. The unique properties of micro combustors can lead to differences in the combustion behavior of hydrogen and ammonia compared to larger-scale combustion systems. Hence, examining the performance of carbon-free fuels in micro combustors is crucial for the advancement of clean energy combustion systems. A numerical investigation on a Y-shaped micro-combustor was carried out to identify the aspects of non-premixed combustion of ammonia/air and hydrogen/air. The findings reveal that in the case of hydrogen combustion, stable flames were reached, even at low equivalence ratios. Therefore, the distinct combustion properties of hydrogen and ammonia result in varying NOx emissions, with hydrogen generally leading to higher NOx levels due to its higher flame temperature and increased thermal NOx production.

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.

Data-driven approaches for predicting performance degradation of solid oxide fuel cells system considering prolonged operation and shutdown accumulation effect

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized backpropagation neural network model for analysis. This model exhibits the best performance degradation evolution prediction compared to the long short-term memory and particle swarm optimization backpropagation neural network methods. This finding is important for monitoring and controlling the operational status of stack.

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.