Green Vehicle Research Articles

Using Cognitive IOT In Vehicle System for On Actual Time Monitoring of CO2 Emission with Temperature and Alcohol Detector

One of the pressing environmental challenges we face today is the phenomenon of global warming, primarily driven by the release of greenhouse gases. Among these gases, carbon dioxide stands out as a significant contributor, with its emissions leading to a warming effect on the Earth's surface. Protecting our environment requires us to address and manage these changes effectively, presenting a considerable challenge. For long-term control of carbon dioxide emissions at their source, there is a growing recognition of the need for preventive and control technologies. This paper aims to employ IoT technology, utilizing Raspberry Pi's CO2 sensitivity, to monitor emissions from various sources such as public transportation, industries, and forest fires. Continuous monitoring of carbon dioxide levels within a city will help identify areas with the highest pollution levels. Additionally, this initiative seeks to implement an intelligent system for early detection of forest fires, also known as wildfires. These uncontrolled fires in natural areas pose significant threats to both ecosystems and human resources. It's noteworthy that wildfires emit more CO2 gas than initially assumed, impacting state climate targets. Integrating these efforts with IoT enhances security and enables various services, including the deployment of a Simple Notification Service (SNS) to alert mobile users about high CO2 levels in specific areas. Furthermore, this model aims to extend its capabilities to include the detection of temperature in heated vehicle components, such as engines, using temperature detectors. Moreover, alcohol detectors will be utilized to monitor alcohol consumption by vehicle drivers, enhancing safety measures on the road. Keywords: SNS (Simple Notification Service), IoT (Internet of Things), GPS (Global Positioning System), GVG (Green Vehicle Guide), MAQUMON (Mobile Air Quality Monitoring Network).

Technology progress and clean vehicle policies on fleet turnover and equity: insights from household vehicle fleet micro-simulations with ATLAS

ABSTRACT This paper documents the design and application of ATLAS (Automobile and Technology Lifecycle-Based ASsignment), a comprehensive household vehicle transaction and technology adoption micro-simulator in the San Francisco Bay Area. ATLAS evolves the fleet mix of individual households by simulating the vehicle transaction and choice decisions in response to co-evolving demographics, land use, and vehicle technology simulations. While most existing literature has focused on the aggregate clean vehicle uptake, this paper differentiates distributional effects and decomposes the underlying mechanisms across heterogeneous sub-populations of households. Using scenarios and sensitivity simulations that vary vehicle technology and policy assumptions, we find that Zero Emission Vehicles (ZEVs) penetrate into higher income groups at a faster rate than into lower income groups, which is intuitive and aligns with expectations. Interestingly, the relative income disparity in ZEV ownership shrinks over time across all scenarios, with a ZEV mandate coupled with declining battery cost leading to the greatest reduction in disparity of ZEV ownership by 2050. Federal, state, and local financial incentives influence the redistribution of ZEV uptake across income groups and contribute to narrowing income disparity. Vehicle transaction frequency and new versus used market dynamics are found to be important factors contributing to the income disparity.

A lightweight genetic algorithm with variable neighborhood search for multi-depot vehicle routing problem with time windows

A multi-depot green vehicle routing problem with time windows considering customer satisfaction (MDGVRPTW-CS) was constructed. To optimize it, we proposed a lightweight genetic algorithm with variable neighborhood search (LGAVNS), which takes the genetic algorithm as the upper algorithm and the variable neighborhood algorithm as the neighborhood search algorithm. In our constructed algorithm, the neighborhood search operators were applied to the optimal gene instead of all genes. Additionally, an enhanced crossover operator was developed to facilitate the effective transmission of gene information. Finally, we extended and optimized the parameters of the algorithm. Experimental results demonstrated that our approach exhibited significant advantages in terms of optimization effectiveness and convergence speed compared to six state-of-the-art algorithms, particularly when addressing MDGVRPTW-CS in large-scale scenarios. Moreover, the lightweight nature of our algorithm can be utilized for optimizing both the number and location of depots while further exploring the optimization potential of MDGVRPTW-CS.

Multi-objective green vehicle scheduling problem considering time window and emission factors in ship block transportation

Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China’s shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard’s block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise’s block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers.

Analyzing Operation Management Methods for Defect Mitigation in the Mass Production of Green Car Parts

Analyzing Operation Management Methods for Defect Mitigation in the Mass Production of Green Car Parts

Quenching optimization of a hot stamping line for aluminum automotive components

The rising cost of fuel and harder environmental regulations driving the need for more eco-friendly vehicles have compelled automotive manufacturers to search for innovative lightweight solutions. Consequently, hot stamped aluminum alloys are gaining prominence due to their exceptional specific strength and improved formability compared to cold formed components. Unfortunately, the current state of knowledge in the field lacks the necessary depth to establish a reliable, fast and optimized process for aluminum hot forming. To address this gap, a collaborative effort between Mondragon Unibertsitatea (knowledge provider), Fagor Arrasate (hot stamping line supplier), and Batz (tool manufacturer) has been initiated. The collaborative endeavour aims to conduct semi-industrial trials involving the hot stamping of an authentic automobile bumper employing high-strength 6xxx and 7xxx aluminum alloys. The hot stamping trials have been performed under different in-die quenching times and mechanical properties of the stamped bumpers have been empirically tested. These results have helped to define the optimal quenching time. With that aim, first, a thermophysical and rheological characterization of the aluminum has been performed, followed by the development of a model to predict mechanical properties of aluminum alloys. This predictive model, together with the aluminum material data, has been then integrated into the simulation framework to enable accurate forecasting of mechanical properties and, consequently, the identification of the optimal quenching duration. The results revealed that quenching times as brief as 1 second could be employed while maintaining acceptable mechanical properties in a rapid cycle hot stamping process. These expedited quenching times have the potential to boost production by an impressive 70 % when compared to the conventional hot stamping process applied to the equivalent steel bumper.

Optimization of TLPS Bonding Process and Joint Property using Ni-Sn Paste for High Temperature Power Module Applications

Recently, as interest in eco-friendly vehicles such as electric and hybrid vehicles increases, the demand for power semiconductors, a key component, is also increasing. Power semiconductors convert, distribute, and control power and operate in harsh environments such as high temperature and high pressure. In order to ensure stable reliability in such harsh environments, research on a technology that can stably form joints even at high temperatures is essential. Transitional liquid phase (TLP) bonding was proposed as a high-temperature power semiconductor chip bonding technology, which has the advantage of forming an intermetallic compound (IMC) phase with a high melting point at the joint. However, it takes a long time to convert the joint into full IMC phase. Therefore, in this study, in order to shorten the process time, a paste was manufactured by mixing high-melting point Ni metal powder and low-melting point Sn metal power, and a joint was formed through a TLPS (Transition liquid phase sintering) bonding using the paste. Pastes of different compositions were prepared by adjusting the ratio of Ni and Sn powders. The chip and substrate were bonded through a thermocompression (TC) bonding process, and the highest shear strength was obtained at a bonding temperature of 250 ℃ for 10 min. Heat treatment was performed at 200 ℃ for up to 500 h to evaluate the high temperature long-term reliability of the joints. The Ni-Sn TLPS bonded joints remained reliable joints after a long-term aging test at a high temperature of 200 ℃.

A Green Vehicle Adoptions and Its Impediments: A Bibliometric Review

The green vehicle is responsible for considerably lower emissions than conventional internal combustion engine vehicles and is one of the important components of meeting global commitments on climate change. However, the consumers’ adoption is far beyond expectations. To this end, this paper contributes to the extant literature on green vehicle adoption and its impediments by assessing the publication trends and network visualization leveraging the bibliometric analysis method. Meanwhile, key research clusters are outlined and visualized to analyse the underlying knowledge structure. The 347 articles collected from the Scopus database were later analysed using bibliometric tools, VOSviewer, and Harzing’s Publish or Perish. The results presented in this paper assist researchers with a structured understanding and knowledge of green vehicle adoption research and its impediments.

전주기 관점에서 살펴본 일본의 사용후 배터리 안전규제 특징 분석

The importance of the used battery field is growing as it is a market that is bound to grow as secondary batteries rapidly emerge, but technology development and industrial revitalization are hindered by issues such as safety management. The study analyzes the characteristics of Japan’s used battery regulations from a life-cycle perspective and seeks to provide implications for establishing similar policies in Korea. The analysis results are as follows. First, Japan is managing battery data by establishing a digital platform considering the entire value chain and life cycle. Second, Japan is strengthening its demonstration project for reuse of used batteries. Third, Japan is promoting technology development with ‘standards’ in mind to secure the battery supply chain. As safety is a key issue in the future transition process from internal combustion engine vehicles to eco-friendly vehicles, research on the topic of safety must continue to be conducted in the future. This study is significant in that it provides implications by reviewing systems and policies that Korea can refer to through an analysis of the case of Japan.

Fire Risk Mitigation Strategies for Electric and Hydrogen Vehicle Car Parks

As the use of eco-friendly vehicles, such as electric and hydrogen vehicles, is rapidly increasing, the related infrastructure is also expanding, and they have become common in parking areas. The fire safety design of a car park is based on the existing data of internal combustion engine vehicles; therefore, the characteristics of eco-friendly vehicles must be reflected. This study investigated the heat release rate of internal combustion engine vehicles, electric vehicles, and hydrogen vehicles using real-scale fire test data and analyzed the effects of curb weight and battery capacity. The fire growth rate and maximum heat release rate of electric and hydrogen vehicles were similar to those of internal combustion engine vehicles, except for the batteries and hydrogen gas, which are the energy sources of electric and hydrogen vehicles, respectively. When ignited from the battery of an electric vehicle, the fire grew rapidly. When the thermally activated pressure relief device was activated in the storage vessel of a hydrogen vehicle, the heat release rate instantaneously increased to 14.5 MW. To improve fire safety in car parks, the curb weight and fire resistance structure must be designed to reflect the increase in plastic interior materials and battery capacity of vehicles, and firefighting facilities must be installed based on vehicles with greater fire severity.

Optimizing sustainable multimodal distribution networks in the context of carbon pricing, with a case study in the Thai sugar industry

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs.Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3 % and facilitate the building of another factory to increase exports.

An integrated Bi-objective green vehicle routing and partial disassembly line problem for electronic waste: an industrial case study

ABSTRACT Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model’s objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the Augmented ℇ-Constraint Method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight Method) and COPRAS (Complex Proportional Assessment) is used.

Research on electric vehicle charging load prediction method based on spectral clustering and deep learning network

With the increasing prominence of environmental and energy issues, electric vehicles (EVs) as representatives of clean energy vehicles have experienced rapid development in recent years, and the charging load has also exhibited statistical characteristics. Accurate prediction of EV charging load is crucial to improve grid load dispatch and intelligent level. However, current research on EV charging load prediction still faces challenges such as data reliability, complexity and variability of charging behavior, uncertainty, and lack of standardization methods. Therefore, this paper proposes an electric vehicle charging load prediction method based on spectral clustering and deep learning network (SC-CNN-LSTM). Firstly, to address the insufficient amount of EV charging load data, this paper proposes to use Monte Carlo simulation to sample and simulate historical load data. Then, in order to identify the internal structure and patterns of charging load, the sampled and simulated dataset is clustered using spectral clustering, dividing the data into different clusters, where each cluster represents samples with similar charging load characteristics. Finally, based on the different sample features of each cluster, corresponding CNN-LSTM models are constructed and trained and predict using the respective data. By modifying the model parameters, the prediction accuracy of the model is improved. Through comparative experiments, the proposed method in this paper has significantly improved prediction accuracy compared to traditional prediction methods without clustering, thus validating the effectiveness and practicality of the method.

A multi-criteria framework for electric vehicle charging location selection using double hierarchy preferences and unknown weights

The adverse effects of traditional vehicles on the environment increase the demand for clean vehicles, such as electric vehicles (EVs). The correct positioning of the charging points for such vehicles certainly promotes the acceptance and spread of EVs. Indeed, selecting optimal locations for electric vehicle charging stations (EVCS) is crucial for shaping a sustainable future. This study introduces an integrated methodology under a double hierarchy linguistic context with a criteria importance through an inter-criteria correlation (CRITIC) technique for experts' reliability determination, an attitudinal Cronbach's method for criteria weight estimation, and a novel multi-criteria technique considering the compromise ranking of alternatives from distance to ideal solution (CRADIS) formulation for optimal EVCS location selection. Based on the results, criteria such as service capability, ecological impact, land cost, and traffic density are the most crucial, with Manapparai, India, as the optimal location for a new EVCS construction. Further, a crucial finding is that the social dimension is substantial compared to the economy and environment dimensions for EVCS location selection. The novelty of the paper is that (i) uncertainty and expression of choices in natural language form for locations for EVCS are modeled effectively using a double hierarchy structure, (ii) experts' weights are obtained methodically by considering hesitation and interactions among experts, (iii) interdependencies among criteria and importance of experts are considered during criteria weight determination, and (iv) locations are ranked by not only considering criteria type but also resembles closely to the human-centric decision process. A detailed sensitivity analysis is further conducted to prove the proposed approach's effectiveness and stability. In the context of sustainable transportation, the work could contribute to the relevant literature through a powerful decision-making tool.

Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

ABSTRACT This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes (“green delivery”). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption.

Modelling of hybrid biocomposites for automotive structural applications

The demand for environmentally friendly materials is at its peak, and government legislations have become stricter and no longer tolerate violations. With biocomposites emerging as structural components instead of being hidden as non-structural applications and interiors, the automotive and motorsport sector started considering them for structural body parts. Natural fibres abundance, commercial availability, renewability, low density, low cost, and high tensile strength make biocomposite materials excellent candidates for eco-friendly vehicles. Several studies reported the utilisation of biocomposites as high-performance components and structural applications. However, current computer-aided engineering and modelling tools are insufficient to explore the vast field of possibilities during biocomposite materials selection to accurately predict the components' behaviour and analyses. Therefore, this study focuses on creating a material model to predict the behaviour of biocomposite materials. Additionally, the model is numerically implemented to illustrate the deformation and bending stiffness capabilities of a motorsport monocoque chassis structure application. A micromechanics modelling combining rate-dependant constitutive laws and multi-site interactions of inclusions is developed for studying the nonlinear response of composite materials. To avoid numerical instabilities when increments of time become very small, a regulation procedure concerning the visco-plastic function is adopted in the computation of the consistent tangent modulus. Based on the Generalised Mori–Tanaka (GMT) scheme, the effective properties are obtained for the nonlinear composite. The accuracy of the model is evaluated and validated by comparison results from the open literature. Finally, the developed constitutive equations are implemented as a user-defined material UMAT in a Finite Element code, leading to an application on a bio-based composite for the bamboo/flax fibre-reinforced epoxy hybrid composite materials.

Reducing emissions from production and distribution in three-echelon supply chains

Reducing carbon emissions is one of the key goals of contemporary supply chains and logistics. This paper aims to formulate a novel three-echelon (supplier-3PL-retailer) supply chain model comprising green technology investment, variable demand, variable lead time, a dynamic shipping strategy, and an option for vehicle selection. The demand function considers the product's freshness index and maximum lifetime, along with the availability of stocks. Besides, the delivery from the 3PL to the retailer is dynamically determined based on previous shipment information. A green vehicle is selected using a multi-criteria decision-making (MCDM) technique based on q-alternatives with n-characteristics for the transportation process and mathematically we link this selection process to traditional three echelon supply chain optimization problem. The results show that combining green investment with an appropriate dynamic shipping strategy by using a selected green vehicle reduces the supply chain's emissions and total integrated cost. Furthermore, reducing lead time without compromising other supply chain factors stimulates demand of customers and produces fewer emissions with a significant reduction in the total integrated cost. A real-case-based study shows the performance of the proposed model and method.. The study illustrates that 47.744% of the carbon emissions in Scenario 1 and 47.53% in Scenario 2 are reduced when an emission reduction strategy is implemented. In terms of total integrated costs our proposed model could save 4.85% cost in Scenario 1 while 4.96% for Scenario 2, which is significant.

Proactive Emergency Response Strategies for First Responders to Hydrogen Gas Leakages in Vehicles

The widespread use of fossil fuels in automobiles has become a concern, particularly in light of recent frequent natural disasters, prompting a shift towards eco-friendly vehicles to mitigate greenhouse gas emissions. This shift is evident in the rapidly increasing registration rates of hydrogen vehicles. However, with the growing presence of hydrogen vehicles on roads, a corresponding rise in related accidents is anticipated, posing new challenges for first responders. In this study, computational fluid dynamics analysis was performed to develop effective response strategies for first responders dealing with high-pressure hydrogen gas leaks in vehicle accidents. The analysis revealed that in the absence of blower intervention, a vapor cloud explosion from leaked hydrogen gas could generate overpressure exceeding 13.8 kPa, potentially causing direct harm to first responders. In the event of a hydrogen vehicle accident requiring urgent rescue activities, the appropriate response strategy must be selected. The use of blowers can aid in developing a variety of strategies by reducing the risk of a vapor cloud explosion. Consequently, this study offers a tailored response strategy for first responders in hydrogen vehicle leak scenarios, emphasizing the importance of situational assessment at the incident site.

Low carbon integrated vehicles and buildings

This paper defines vehicles and buildings as main sources of United Kingdom (UK) carbon dioxide (CO2) and seeks to cut such emissions using green hydrogen made from combined wind and solar energy. Combustion vehicles powered by fossil petroleum emit near half of UK climate-warming CO2 while buildings heated by natural gas provide a third. First, current UK grid problems are defined: Electricity, gas and petroleum grids. Refueling green vehicles has been a particular problem. Then experiments on the private wire community of Keele University show how green hydrogen could integrate both green vehicles and buildings. Next, the model supply chain is planned and tested. Finally, experiments and calculations are outlined, analyzing the optimum system design criteria proposed. We conclude that economic green hydrogen can displace petroleum in vehicles, while powering buildings instead of natural gas. Also, the prospect in 2024 is that profits can be made all along the green hydrogen supply chain, such that new businesses involved in local private clean communities can cost less than the National Grid monopoly and other dominant fossil energy companies.

A variable neighborhood search for the green vehicle routing problem with two-dimensional loading constraints and split delivery

We address the Green Vehicle Routing Problem with Two-Dimensional Loading Constraints and Split Delivery (G2L-SDVRP), which extends the split delivery vehicle routing problem to include customer demands represented by two-dimensional, rectangular items. We aim to minimize carbon dioxide (CO2) emissions instead of travel distance, a critical issue in contemporary logistics activities. The CO2 emission rate is proportional to fuel consumption and measured in terms of the vehicle’s total weight and traveled distance. We propose the first metaheuristic for the G2L-SDVRP, based on a variable neighborhood search approach that designs effective routes and guarantees the feasibility of loading constraints using various strategies, such as lower bound procedures, the open space heuristic, and a constraint programming model. We evaluate the performance of our approach through computational experiments using benchmark and newly created instances. The results indicate that the proposed approach is effective. It achieves improved solutions for 21 out of 60 instances in relatively short computing times when compared to existing methods for the G2L-SDVRP. Furthermore, our approach is competitive on benchmark instances of a related variant, namely the Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints, improving the best-known solutions for 50 out of 180 instances.