bluuebunny/crossref_metadata_2025 huggingface dataset:
Manifesto of Deep Learning Architecture for Aspect Level Sentiment Analysis to extract customer criticism
Authors: Kushwaha N , Singh B , Agrawal S
Published: April 2024

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.
Estimación del rango de confort adaptativo para espacios públicos en climas cálidos: caso Cali
Authors: Morales Vivas Luz Marina , Osuna-Motta Iván
Published: April 2024

La pandemia del COVID evidenció la importancia de los espacios públicos para superar los desafíos de la sociedad. La pospandemia es el escenario propicio para revisar los criterios con los cuales diseñamos plazas y parques de nuestras ciudades. El estudio del confort higrotérmico se ha centrado en espacios interiores y es necesario que estos parámetros se trasladen hacia el exterior, donde la cultura del lugar, el clima y la materialidad deben vincularse para generar espacios que respondan al clima y a la población local. Con el propósito de determinar el rango de confort adaptativo en el espacio público para la ciudad de Cali, Colombia, se analizan seis espacios públicos con diversas características, combinando mediciones in situ con encuestas de satisfacción de los usuarios. Como resultado, se generan diagramas de confort para espacios exteriores con indicadores para el diseño de espacios públicos con condiciones óptimas para clima cálido semiárido y cálido semihúmedo.
Lineamientos de diseño para urbanizaciones en áreas de piedemonte, Mendoza, Argentina: optimización del desempeño térmico
Authors: Castillo Ana Laura , Correa Erica Norma , Cantón María Alicia
Published: April 2024

El desarrollo de urbanizaciones denominadas barrio parque –núcleos de viviendas unifamiliares con amplios espacios verdes– sobre áreas de piedemonte generan transformaciones climáticas y ambientales en el territorio. Su diseño, centrado en el aspecto paisajístico, no responde a las demandas del área de piedemonte de Mendoza, Argentina. Las características particulares del sector requieren estrategias que consideren un desarrollo adaptado a sus condiciones biofísicas para optimizar su comportamiento microclimático y mejorar la habitabilidad de los espacios públicos exteriores, mitigando el impacto ambiental de la urbanización. El presente trabajo propone y evalúa distintas alternativas de diseño asociadas al uso de la forestación como estrategia de sombreo para disminuir la acumulación de calor sobre las superficies del espacio público. La propuesta –que incorpora boulevard con especies de baja permeabilidad solar– demuestra mayor eficiencia térmica, reduciendo en 3,5°C la temperatura máxima, 1,5°C la mínima y 2,5°C la media.
New avenues for the treatment of immunotherapy-resistant pancreatic cancer
Authors: Silva Luis Guilherme de Oliveira , Lemos Fabian Fellipe Bueno , Luz Marcel Silva , Rocha Pinheiro Samuel Luca , Calmon Mariana dos Santos , Correa Santos Gabriel Lima , Rocha Gabriel Reis , de Melo Fabrício Freire
Published: April 2024

Pancreatic cancer (PC) is characterized by its extremely aggressive nature and ranks 14th in the number of new cancer cases worldwide. However, due to its complexity, it ranks 7th in the list of the most lethal cancers worldwide. The pathogenesis of PC involves several complex processes, including familial genetic factors associated with risk factors such as obesity, diabetes mellitus, chronic pancreatitis, and smoking. Mutations in genes such as KRAS, TP53 , and SMAD4 are linked to the appearance of malignant cells that generate pancreatic lesions and, consequently, cancer. In this context, some therapies are used for PC, one of which is immunotherapy, which is extremely promising in various other types of cancer but has shown little response in the treatment of PC due to various resistance mechanisms that contribute to a drop in immunotherapy efficiency. It is therefore clear that the tumor microenvironment (TME) has a huge impact on the resistance process, since cellular and non-cellular elements create an immunosuppressive environment, characterized by a dense desmoplastic stroma with cancer-associated fibroblasts, pancreatic stellate cells, extracellular matrix, and immunosuppressive cells. Linked to this are genetic mutations in TP53 and immunosuppressive factors that act on T cells, resulting in a shortage of CD8+ T cells and limited expression of activation markers such as interferon-gamma. In this way, finding new strategies that make it possible to manipulate resistance mechanisms is necessary. Thus, techniques such as the use of TME modulators that block receptors and stromal molecules that generate resistance, the use of genetic manipulation in specific regions, such as microRNAs, the modulation of extrinsic and intrinsic factors associated with T cells, and, above all, therapeutic models that combine these modulation techniques constitute the promising future of PC therapy. Thus, this study aims to elucidate the main mechanisms of resistance to immunotherapy in PC and new ways of manipulating this process, resulting in a more efficient therapy for cancer patients and, consequently, a reduction in the lethality of this aggressive cancer.
Esophageal cancer screening, early detection and treatment: Current insights and future directions
Authors: Qu Hong-Tao , Li Qing , Hao Liang , Ni Yan-Jing , Luan Wen-Yu , Yang Zhe , Chen Xiao-Dong , Zhang Tong-Tong , Miao Yan-Dong , Zhang Fang
Published: April 2024

Esophageal cancer ranks among the most prevalent malignant tumors globally, primarily due to its highly aggressive nature and poor survival rates. According to the 2020 global cancer statistics, there were approximately 604000 new cases of esophageal cancer, resulting in 544000 deaths. The 5-year survival rate hovers around a mere 15%-25%. Notably, distinct variations exist in the risk factors associated with the two primary histological types, influencing their worldwide incidence and distribution. Squamous cell carcinoma displays a high incidence in specific regions, such as certain areas in China, where it meets the cost-effectiveness criteria for widespread endoscopy-based early diagnosis within the local population. Conversely, adenocarcinoma (EAC) represents the most common histological subtype of esophageal cancer in Europe and the United States. The role of early diagnosis in cases of EAC originating from Barrett's esophagus (BE) remains a subject of controversy. The effectiveness of early detection for EAC, particularly those arising from BE, continues to be a debated topic. The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses. In areas with higher incidences, such as China and Japan, early diagnosis is more common, which has led to the advancement of endoscopic methods as definitive treatments. These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality. Early screening, prompt diagnosis, and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.
Distribution and Seasonal Variation of Phlebotomus and Sergentomyia Sand Fly Populations in Bhutan
Authors: Wangdi Tenzin , Senanayake Sanath , Dorjee Sithar , Tobgay Tashi , Zhou Guofa , Piyasiri Sachee B. , Weerasinghe W. M. Lakshika I. , Karunaweera Nadira
Published: April 2024

Leishmaniasis is considered one of the neglected tropical diseases in the world. Although Bhutan is a member of the visceral leishmaniasis elimination consortium in South Asia, not much attention has been accorded to the disease because of its low incidence. The vector that transmits Leishmania remains poorly understood. In this backdrop, sand-fly surveys were regularly conducted at multiple sites where leishmaniasis cases have been reported in Bhutan. Collections were made using CDC light traps and cattle-baited net traps in 15 villages from February 2019 to May 2022. Six species of Phlebotomus and four species of Sergentomyia were identified from these sites that included two Phlebotomus and three Sergentomyia species discovered for the first time in Bhutan. Sand-fly density varied significantly from village to village, and it showed strong seasonality with peak numbers collected from June to October and almost zero from December to February. Overall, sand-fly density was highest in the basements of the houses and were higher outdoors than indoors. Cattle-baited net traps collected few sand flies during the surveillance period. This work constituted the first systematic sand-fly population surveillance conducted in Bhutan and will provide a baseline for future vector ecology and Leishmania epidemiological studies.
Research on Vehicle Type Recognition Based on Improved YOLOv5 Algorithm
Authors: Liu XinHong
Published: April 2024

As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety,optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle type recognition algorithms, this paper proposes an improved vehicle type recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed. Secondly, the attention mechanism in MobileNet-V3 is improved, and a more efficient coordinate attention module is embedded to enhance the model ‘s attention to key features, so as to further improve the accuracy of vehicle type recognition. Finally, in order to better integrate different levels of features, the weighted bidirectional feature pyramid network BiFPN structure is introduced. By processing the top-down and bottom-up paths at the same time, the feature maps of different resolutions are fused, so that the model can obtain richer feature information at multiple scales, thereby improving detection efficiency and accuracy. The experimental results show that the improved algorithm has a vehicle type recognition accuracy of 96% on the BIT-Vehicle dataset. Compared with the original YOLOv5 model, the number of model parameters is reduced by 47%, and the detection speed is increased by 54.6%. Therefore, the improved algorithm in this paper can better meet the requirements of small parameter quantity and high detection efficiency, and is suitable for real-time vehicle type recognition.
Validation and Analysis of Driving Safety Assessment Metrics in Real-world Car-Following Scenarios with Aerial Videos
Authors: Lu Duo , Haines Sam , Jammula Varun Chandra , Rath Prabin Kumar , Yu Hongbin , Yang Yezhou , Wishart Jeffrey
Published: April 2024

Data-driven driving safety assessment is crucial in understanding the insights of traffic accidents caused by dangerous driving behaviors. Meanwhile, quantifying driving safety through well-defined metrics in real-world naturalistic driving data is also an important step for the operational safety assessment of automated vehicles (AV). However, the lack of flexible data acquisition methods and fine-grained datasets has hindered progress in this critical area. In response to this challenge, we propose a novel dataset for driving safety metrics analysis specifically tailored to car-following situations. Leveraging state-of-the-art Artificial Intelligence (AI) technology, we employ drones to capture high-resolution video data at 12 traffic scenes in the Phoenix metropolitan area. After that, we developed advanced computer vision algorithms and semantically annotated maps to extract precise vehicle trajectories and leader-follower relations among vehicles. These components, in conjunction with a set of defined metrics based on our prior work on Operational Safety Assessment (OSA) by the Institute of Automated Mobility (IAM), allow us to conduct a detailed analysis of driving safety. Our results reveal the distribution of these metrics under various real-world car-following scenarios and characterize the impact of different parameters and thresholds in the metrics. By enabling a data-driven approach to address driving safety in car-following scenarios, our work can empower traffic operators and policymakers to make informed decisions and contribute to a safer, more efficient future for road transportation systems.
Optimizing the Dispense Time of a Battery from the Swappable Charging Station
Authors: Gera Chiranjeevi , Holavanahalli Shashank
Published: April 2024

In the emerging economies, there is a growing adoption of electric vehicles into fleet vehicles. With the steady increase in this business area, there is a demand for the innovation in the battery charging methodologies. The swappable charging method is one such charging method that is gaining prominence. Battery swapping involves replacing an EV’s depleted battery with a fully charged one. This approach can significantly reduce wait times for drivers, as swapping batteries typically takes only few minutes, similar to the time it takes to refuel an ICE vehicle. With battery swapping, EV owners can avoid concerns related to battery degradation, since they receive a fully charged, well-maintained battery during each swap. Research is being done either to reduce the cost of operation of Battery Swapping station (BSS), or to reduce the waiting time for the users by charging fast. But focusing on the cost reduction, BSS may not be able to meet the demand of the users and by focusing only on the fast charging, the health of the battery will be under stake. The objective of the present work is to optimize the charging process in the BSS to reduce the waiting time for the users along with prolonging the battery life. This paper addresses the above-mentioned issues by tailoring the charging profile specific to the internal health state of the battery. These internal health states are obtained from a hybrid health model of battery, which is combination of physics based aging model and machine learning correction model. This paper starts with the time estimation for the complete charging of the battery which includes precooling, charging and post-cooling. Using a digital twin of the battery, efficient time estimation is achieved considering different average currents as the input. Then it provides insight on the various charging patterns along with their advantages and disadvantages, which is necessary for selecting the charging profile for the battery pack. Considering the state of health of the battery and the internal states of the battery pack, the charging profile is further optimized. The BSS uses this optimized charging profile. This work is developed in MATLAB/Simulink.
Vehicle E/E Architecture and Key Technologies Enabling Software-Defined Vehicle
Authors: Jiang Shugang
Published: April 2024

The automotive industry is currently undergoing a significant transformation characterized by technological and commercial trends involving autonomous driving, connectivity, electrification, and shared service. Vehicles are becoming an integral part of a much broader ecosystem. In light of various new developments, the Software-Defined Vehicle (SDV) concept is gaining substantial attention and momentum. SDV emphasizes the central role of software in realizing and enhancing vehicle functions, enriching features, improving performance, adapting to surrounding environment and external conditions, customizing user experience, addressing changing customer needs, and enabling vehicles to dynamically evolve over their entire life cycle. The advancements in vehicle Electrical/Electronic (E/E) architecture and various key technologies serve as the technical foundation for the emergence of SDV. This paper gives a definition of the SDV concept, provides views from different aspects, discusses the progress in vehicle E/E architecture, especially zone-based architecture with centralized computation, and various technologies including High-Performance Computing (HPC) platform, standardized vehicle software architecture, advanced onboard communication, Over-The-Air (OTA) update, and cybersecurity etc. that collectively enable the realization of SDV.
Engineering Requirements that Address Real World Hazards from Using High-Definition Maps, GNSS, and Weather Sensors in Autonomous Vehicles
Authors: Masterson Alexandra , Patil Pritesh , Brown Nicolas , Asher Zachary , Fanas Rojas Johan , Siems-Anderson Amanda , Walker Curtis , Rabinowitz Aaron
Published: April 2024

Evaluating real-world hazards associated with perception subsystems is critical in enhancing the performance of autonomous vehicles. The reliability of autonomous vehicles perception subsystems are paramount for safe and efficient operation. While current studies employ different metrics to evaluate perception subsystem failures in autonomous vehicles, there still exists a gap in the development and emphasis on engineering requirements. To address this gap, this study proposes the establishment of engineering requirements that specifically target real-world hazards and resilience factors important to AV operation, using High-Definition Maps, Global Navigation Satellite System, and weather sensors. The findings include the need for engineering requirements to establish clear criteria for a high-definition maps functionality in the presence of erroneous perception subsystem inputs which enhances the overall safety and reliability of the autonomous vehicles. In conjunction, global navigation satellite system consistently provides highly accurate positional information, thereby enabling precise navigation and trajectory. Additionally, a requirement was formulated that mandates the integration of weather sensors into the autonomous vehicles perception subsystem to collect precise weather condition data. These findings show the significance of implementing engineering requirements utilizing resilient engineering as a fundamental aspect of evaluating perception sensor performance in real-world scenarios. By incorporating these requirements into autonomous vehicles development we can improve the safety and reliability of these vehicles and accelerate the adoption of autonomous vehicle technology.
Effective Application of Model-Based Software Engineering Technology to Automotive Embedded Software
Authors: Kolesnikov Max
Published: April 2024

The major driving factors for adoption of model-based software engineering (MBSE) practices are increasing complexity, time-to-market pressures, and certification requirements. Adoption of MBSE technology can add tremendous value to an automotive software development organization, however to realize maximum efficiency it is critically important to scale and customize these practices appropriately. This paper offers a practical, tool vendor-independent framework for application of MBSE technology to automotive embedded software development, based on tendencies and outcomes observed while working with automotive OEMs and Tier 1 suppliers of various sizes. The core of the presented approach is to first define the MBSE technology strategy and then to operationalize the strategy. There are three main elements that constitute the MBSE technology strategy of a software development organization. The first element is technology scope – it defines the balance between MBSE and traditional coding methods. This is rarely a binary decision, and it takes into account not only the application domain, but also the culture of communication between engineering functions internal and external to the organization. The second element is integration paradigm, i.e., whether to integrate software in model or code domain. This is typically driven by balance between effort, performance, and program objectives. The third element is software stack design. Commonly discussed in the web and mobile space, it is sensible to address software stack design specifically as it applies to automotive embedded software. Stack design choices are directly tied to business objectives of an organization, as they frequently (and sometimes implicitly) define critical interfaces, draw ownership boundaries, and inform make vs. buy decisions. Finally, once the MBSE technology strategy is defined, it is operationalized by leveraging traditional software DevOps practices, modified and extended to support MBSE methods in the automotive embedded space.
Low-Temperature Aging Effect on Safety of Lithium-Ion Batteries Subjected to Intrusion: A Comparative Study of 18650 and Pouch Cells
Authors: Spettmann Christopher , Shin Jonghyeon , Qu Yunlong , Liu Yuanjie , Xia Yong
Published: April 2024

This study investigates the impact of cycling aging on the safety performance of lithium-ion batteries, specifically 18650 cells and pouch cells. These cells are cycled at 0 °C with charging rates of 2 C and 0.8 C, respectively, upon reaching different states-of-health, and their mechanical-electrical-thermal responses are analyzed post-indentation tests. The compressive behavior of anodes and cathodes at different states-of-health is also examined. The failure mechanisms of battery components are discussed based on indentation results at cell level, compression results of components, electrochemical impedance spectroscopy, and visual observations. The study reveals that aged 18650 cells exhibit increased stiffness (evidenced by left-shifted force-displacement curves) during cell indentation, while the compressions test results of aged electrodes show decreased stiffness (right-shifted force-displacement curves) which is similar to the stiffness behavior of the pouch cells. As aging progresses, the internal components of 18650 cells tend to fracture earlier during indentation, followed by a soft short circuit mode. A hard short circuit mode was observed at the peak force during indentation. In contrast, pouch cells, after a short circuit, demonstrate a more rapid voltage drop and release electrical energy in a shorter duration when aged. This leads to an elevated temperature throughout the entire battery cell. The changes in response for both cell types are primarily from the anode. The study confirms that low-temperature aging leads to dendrite formation on the anode, potentially causing severe short circuit or even thermal runaway. Dendrite formation is evidenced in post-mortem analysis for aged anodes of both cell types.
Optical Investigation of Lean Combustion Characteristics of Non-Uniform Distributed Orifice Passive Pre-Chamber on a High Compression Ratio GDI Engine
Authors: Tang Yuanzhi , Lou Diming , Fang Liang , Fan Benzheng , Wu Xijiang , Wang Zhiyu , Zhang Yunhua , Tan Piqiang , Hu Zhiyuan
Published: April 2024

The passive pre-chamber (PC) is valued for its jet ignition (JI) and is suitable for wide use in the field of gasoline direct injection (GDI) for small passenger cars, which can improve the performance of lean combustion. However, the intake, exhaust, and ignition combustion stability of the engine at low speed is a shortcoming that has not been overcome. Changing the structural design to increase the fluidity of the main chamber (MC) and pre-chamber (PC) may reduce jet ignition performance, affecting engine dynamics. This investigation is based on non-uniformly nozzles distributed passive pre-chamber, which is adjusted according to the working medium exchange between PC and MC. The advantages and disadvantages of the ignition mode of PC and SI in the target engine speed range are compared through optical experiments on a small single-cylinder GDI engine. The results show that with the increase of λ from 1.0 to 1.6, the promotion effect of PCJI on dynamic performance gradually decreases, and it is beneficial for the λ=1.0 condition close to the stoichiometric combustion at low speed. However, not all pre-chambers can improve lean burn performance. In addition to many existing conclusions on structural design, the combustion performance is also closely related to the relative arrangement of PC and MC.
CFD Modeling of Conventional and Pre-Chamber Ignition of a High-Performance Naturally Aspirated Engine
Authors: Nodi Alessandro , Sforza Lorenzo , Lucchini Tommaso , Onorati Angelo , Buttitta Marco , Marmorini Luca
Published: April 2024

The abatement of carbon dioxide and pollutant emissions on motorbike spark-ignition (SI) engines is a challenging task, considering the small size, the low cost and the high power-to-weight ratio required by the market for such powertrain. In this context, the passive pre-chamber (PPC) technology is an attractive solution. The combustion duration can be reduced by igniting the air-fuel mixture inside a small volume connected to the cylinder, unfolding the way to high engine efficiencies without penalization of the peak performance. Moreover, no injectors are needed inside the PPC, guaranteeing a cheap and fast retrofitting of the existing fleet. In this work, a 3D computational fluid dynamics (CFD) investigation is carried out over an experimental configuration of motorbike SI engine, operated at fixed operating conditions with both traditional and PPC configurations. The employed CFD methodology is based on a unique flamelet-based combustion model, regardless the selected ignition strategy. First, 3D gas exchange simulations were performed to achieve realistic conditions for the power-cycle analysis. A specific care was given to the analysis of the PPC scavenging process, which is crucial for a reliable estimation of the exhaust gases stratification near the ignition position. Then, the combustion process was simulated with both ignition strategies, clarifying in particular the dependency of the hot-gases ejection process from the flow field inside the PPC. Finally, a numerical-experimental comparison was carried out in terms of pressure and heat release trends, demonstrating the reliability of the employed CFD methodology in the design of high-performance SI engines.
Combustion and Emission Characteristics of an Ammonia-Hydrogen Engine under Passive- and Active-Jet Ignition
Authors: Qi Yunliang , Wang Wei , Wang Zhi
Published: April 2024

In the context of carbon neutrality, ammonia is considered a zero-carbon fuel with potential applications in the transportation sector. However, its high ignition energy, low flame speed, and high natural temperature, indicative of low reactivity, make it challenging to be applied as a sole fuel in engines. In such a scenario, the use of another zero-carbon and highly reactive fuel, hydrogen, becomes necessary to enhance the combustion of ammonia. Furthermore, jet ignition, a method known for improving engine combustion performance, may also hold potential for enhancing the combustion performance of ammonia engines. To explore the applicability of jet ignition in engines, this study conducted experimental research on a single-cylinder engine. Two ignition methods were employed: passive jet ignition of premixed ammonia-hydrogen at a compression ratio of 11.5, and active jet ignition of pure ammonia using hydrogen jet flame at a compression ratio of 17.3. Experimental results indicated that, under passive jet ignition conditions, as the proportion of ammonia energy increased, the engine's combustion phase was delayed, combustion duration was extended, and peak cylinder pressure and peak heat release rate decreased. Additionally, as the proportion of ammonia energy increased, the engine's NOx emissions decreased, while unburned ammonia emissions increased. The engine achieved the highest indicated thermal efficiency at an 80% ammonia blending ratio. Under active jet ignition conditions, the jet flame could stably ignite pure ammonia under lean conditions. However, under lean conditions, the low reactivity of pure ammonia led to prolonged combustion duration, reduced ammonia combustion efficiency, and a significant increase in unburned ammonia emissions, resulting in decreased thermal efficiency.
Highway Exhaust Emissions of a Natural Gas-Diesel Dual-Fuel Heavy-Duty Truck
Authors: Dev Shouvik , Qi Aidu , Anderson Andrew , Dahlseide Austin , Smith Brett , Lussier Simon-Alexandre , Guo Hongsheng , Rosenblatt Deborah
Published: April 2024

Diesel-fueled heavy-duty vehicles (HDVs) can be retrofitted with conversion kits to operate as dual-fuel vehicles in which partial diesel usage is offset by a gaseous fuel such as compressed natural gas (CNG). The main purpose of installing such a conversion kit is to reduce the operating cost of HDVs. Additionally, replacing diesel partially with a low-carbon fuel such as CNG can potentially lead to lower carbon dioxide (CO2) emissions in the tail-pipe. The main issue of CNG-diesel dual-fuel vehicles is the methane (CH4, the primary component of CNG) slip. CH4 is difficult to oxidize in the exhaust after-treatment (EAT) system and its slip may offset the advantage of lower CO2 emissions of natural gas combustion as CH4 is a strong greenhouse gas (GHG). The objective of this study is to compare the emissions of an HDV with a CNG conversion kit operating in diesel and dual-fuel mode during highway operation.

Road tests were conducted on a three-axle Class-8 highway semi-trailer tractor hauling a two-axle loaded box trailer. The gross combined weight of the tractor-trailer was 34,470 kg (~76,000 lbs). The tractor was powered by an inline 6-cylinder, direct injection diesel engine with EAT system, and met EPA 2010 emission regulations. The primary components of the conversion kit were: CNG tank, regulator, and mixing manifold with solenoid CNG injectors. CNG was injected into the intake manifold of the engine downstream of the intercooler. The CNG injection map was based on the throttle position, engine speed, load, and intake boost pressure. Portable emissions measurement systems (PEMS) were used to analyze the exhaust gas before and after the EAT system. The vehicle’s onboard diagnostic (OBD) data was also recorded concurrently. The highway test route was 74 km long and the average road speed was ~102 km/h.

Results showed that up to 34% of the diesel consumption could be replaced by CNG. When compared to diesel-only, the CO2 and total hydrocarbon emissions of the dual-fuel case were lower and higher, respectively. Engine-out black carbon emissions were lower for the dual-fuel case in comparison to diesel, while tail-pipe nitrogen oxides (NOx) emissions were higher. Distinct differences in the exhaust temperature profiles were observed as well.
Investigating Route Gradient and Thermal Demand on Hydrogen Fuel Cell Electric Bus Energy Consumption
Authors: O'Boyle Conor , Blades Luke Aubrey William , McGrath Teresa , Early Juliana , Harris Andrew
Published: April 2024

In 2022 in the UK, the transport sector was the largest single contributing sector to greenhouse gas emissions, responsible 34% of all territorial carbon dioxide emissions [1]. In the UK there is growing uptake in zero emission powertrain technologies, with the most promising variants based on battery electric or hydrogen fuel cell electric configurations. Given the limited number of fuel cell electric buses currently in operation in Europe, vehicle models and simulations are one of the few methods available to estimate energy consumption and provide the necessary increased confidence in operating range. This paper investigates the impact of route characteristics, thermal demand and coefficient of performance of different heat source configurations on the operational energy consumption of fuel cell electric buses. Using a MATLAB/Simulink model, the total energy demand of a vehicle operating in different route/elevation profiles is considered. The findings from this study show that implementing a waste heat recovery system, which recovers excess heat produced by the fuel cell system, in a traditional heat pump based HVAC system can reduce the amount of excess fuel consumption required for providing HVAC services by up to 70% over the course of a duty cycle, so long as high levels of waste heat can be recovered. It also shows that through the use of waste heat recovery systems it is possible to replace the heat pump based HVAC system with a much simpler positive temperature coefficient heater system and still achieve or improve upon the performance of a traditional heat pump based HVAC system.
Application of a CFD Methodology for the Design of PEM Fuel Cell at the Channel Scale
Authors: Bulgarini Margherita , Della Torre Augusto , Montenegro Gianluca , Baricci Andrea , Grimaldi Amedeo , Mereu Riccardo , Marocco Luca , Collaku Aldo , Savoldi Laura
Published: April 2024

Polymer electrolyte membrane (PEM) fuel cells will play a crucial role in the decarbonization of the transport sector, in particular for heavy duty applications. However, performance and durability of PEMFC stacks is still a concern especially when operated under high power density conditions, as required in order to improve the compactness and to reduce the cost of the system. In this context, the optimization of the geometry of hydrogen and air distributors represents a key factor to improve the distribution of the reactants on the active surface, in order to guarantee a proper water management and avoiding membrane dehydration. To this purpose, the adoption of CFD simulation tools can provide a useful insight into the physical phenomena which determine the efficient operation of the fuel cell (e.g. transport of mass, heat, species, electrons and ions, electrochemical reactions, water formation and removal), providing a valuable support for the design and the optimization of the device at the channel scale. In this work, an open-source simulation library, based on the OpenFOAM code, is applied to the detailed simulation of a basic module of a PEM fuel cell arranged with simple parallel channels. The simulation methodology is based on a multi-region and multi-physics approach, where the different components of the fuel cell (namely air and fuel channels, gas diffusion layers, catalyst layers, bipolar plates) are modeled resorting to different computational grids defining different local domains, on which the specific governing equations are solved. Transport phenomena in all of the local domains are coupled and solved simultaneously. The model is firstly validated resorting to experimental data acquired on a specific test bench installed at Politecnico di Milano. Then, a detailed analysis of the flow field is conducted in order to provide guidelines for the optimization of the distributor geometry. Finally, the influence of the channel design on the fuel cell performances is investigated, highlighting the influence of the rib-to-channel width geometrical parameter on the reactants diffusion and water removal.
High load Operation of Lithium-Ion Batteries – Modeling Study on a LiFePO
4
Graphite Cell
Authors: Wurzenberger Johann , Lechner Christoph , Triebl Robert
Published: April 2024

Modeling of lithium iron phosphate electrodes calls for appropriate extensions of established model approaches. An electrochemical pseudo two-dimensional and a single-particle model are enhanced to address the phase separating behavior of this material with a variable solid state diffusion model. A particle size distribution model tackles the heterogeneity of the electrode microstructure. Both models are embedded in a framework to describe multi-layer electrode designs featuring segregated material properties. The models are parameterized following literature replicating a good match with measured discharge curves at low, medium and high currents. A simplified version of the rigorous model shows the effort of reparameterization, the computational advantage of model order reduction techniques, the model accuracy and application scope. The impact of the electrode morphology is investigated using the full model by simulating variants of porosity and particle size distribution in single and bi-layered electrodes. The simulations reveal porosity changes, and electrode layering plays a minor role as solid diffusion in lithium iron phosphate particles dominates the cell behavior. This is underlined by results from varied particle size distributions that strongly influence the predicted cell capacity.
Development and optimization of jet impingement on dimpled plate for maximizing cooling performance of an inverter
Authors: LEE Hyeseung , Yang Ilsuk , Jeong Hojin , Park Minkyu
Published: April 2024

A need to develop a cooling method with high cooling performance like jet impingement is increased as high power of an inverter is required. Jet Impingement on the dimpled plate would increase thermal performance than that of flat plate. Many previous researchers have dealt with the multi jet impingement on flat plate and some results of the study on dimpled plate evaluate the effect on heat transfer coefficients on several limited cases, making it difficult to apply them to inverter designs. Therefore, in this paper, heat transfer performance, pressure drop, and robustness at micro-scale of jet impingement on the dimpled plate were investigated in detail and the correlations of each performance were proposed. Finally, the optimal design was presented. The cooling performance was influenced by the jet array and the effect of depth and width of the dimples. The former can be expressed in terms of the Reynolds number, the ratio of height to nozzle diameter(H/D), the ratio of pitch to diameter(S/D). The latter can be expressed in terms of the ratio of dimple depth to dimple diameter(t/Dd) and the ratio of dimple width to pitch(SD/S). Although the heat transfer coefficient of the shallow dimple is larger than that of the deep dimple, the heat transfer coefficient decreases when the width of the dimple becomes too wide. The correlations were proposed and presented to the characteristics of heat transfer depending on jet array and the dimple. Uncertainty was introduced to evaluate the robustness. The optimal design was derived by swinging variables based on the correlations and considering the robustness of performance maximizing heat transfer under pressure drop below 300mbar. Compared to the base design, the thermal resistance of optimum design was improved by 6.0% from 0.2350K/W to 0.2210K/W. The correction-based optimization results were consistent with the 3D CFD results.
Development of Benchmarking Methods for Electric Vehicle Drive Units
Authors: Schauer Ethan , Moskalik Andrew , Kargul John , Stuhldreher Mark , Butters Karla , Barba Daniel , Drallmeier Joseph , Gross Michael
Published: April 2024

As part of the U.S. Environmental Protection Agency’s (EPA’s) continuing assessment of advanced light-duty automotive technologies in support of regulatory and compliance programs, a development project was started to study various test methods to benchmark Electric Drive Units (EDUs) consisting of an electric motor, inverter and a speed-reduction gearset.

Several test methods were identified for consideration, including both in-vehicle testing of the complete EDU and stand-alone testing of the EDU and its subcomponents after removal from the vehicle. In all test methods explored, sweeps of speed and torque test points were conducted while collecting key EDU data required to determine efficiency, including motor torque and speed, direct current (DC) battery voltage and current into the inverter, and three-phase alternating current (AC) phase voltages and currents out of the inverter and into the electric motor. In addition, EDU operational data were collected to ensure consistent repeatable benchmarking conditions, including electric motor/drive temperatures, oil supply pressure, and available Controller Area Network (CAN) bus data.

This paper documents the electric vehicle benchmarking test methods which were evaluated and developed by the EPA and compares the results from the selected test methods and configurations examined. The efficiency data measured by the EPA is also compared to published efficiency data from the technology manufacturer.
Commercial Vehicle's Longitudinal Deceleration Precise Control Considering Vehicle-Actuator Dynamic Characteristics
Authors: Zhu Bing , Ren Zhenxuan , Zhao Jian , Lin Yinju , Chen Zhicheng
Published: April 2024

The installation of the Electronic Braking System (EBS) could effectively improve braking response speed, shorten braking distance, and ensure driving safety of commercial vehicles. However, during longitudinal deceleration control process, the commercial vehicles face not only challenges such as large inertia mass and random road gradient resistance of the vehicle layer, but also non-linear characteristics of the EBS actuator layer. In order to solve these problems, this paper proposes a commercial vehicle’s longitudinal deceleration precise control strategy considering vehicle-actuator dynamic characteristics. First, longitudinal dynamics of commercial vehicle is analyzed, and so is the EBS’ non-linear response hysteresis characteristics. Then, we design the dual layer deceleration control strategy. In vehicle layer, the recursive least squares with forgetting factor and Kalman filtering are comprehensively applied to dynamically estimate the vehicle mass and driving road slope. Based on the vehicle dynamics feedforward module, we have successfully converted target deceleration request of commercial vehicle into the EBS’ target braking pressure. At the same time, the Proportional-Integral-Derivative (PID) is used as feedback control to improving deceleration following accuracy. In the actuator layer, the EBS’ boosting, holding, and reducing pressure coordination control logic is designed. In order to achieve brake pressure following, we also utilize the fuzzy theory to establish the mapping relationship between the EBS’ non-linear response characteristics and the parameters of pressure closed-loop PID controller. Finally, a joint simulation platform is built based on MATLAB/Simulink, TruckSim, and AMESim. The simulation results demonstrate the designed deceleration control algorithm could help commercial vehicle achieve precise braking deceleration follow-up effects in various experimental conditions.
Strategies to Reduce Higher Unburned Hydrocarbon and Carbon Monoxide Emissions in Reactivity Controlled Compression Ignition
Authors: Tripathi Saurabh , Krishnasamy Anand
Published: April 2024

Reactivity Controlled Compression Ignition (RCCI) is a promising, high-efficiency, clean combustion mode for diesel engines. One of the significant limitations of RCCI is its higher unburned hydrocarbon (HC) and carbon monoxide (CO) emissions compared to conventional diesel combustion. After-treatment control of HC and CO emissions is difficult to achieve in RCCI because of lower exhaust gas temperatures associated with the low-temperature combustion (LTC) mode of operation. The present study involves combined experimental and computational fluid dynamic (CFD) investigations to develop the most effective HC and CO control strategy for RCCI. A production light-duty diesel engine is modified to run in RCCI mode by introducing electronic port fuel injection with the replacement of mechanical injectors by the CRDI system. Experimental data were obtained using diesel as HRF (High reactive fuel) and gasoline as LRF (low reactive fuel). The combustion simulation was performed using the CONVERGE 3D CFD tool. A reduced PRF mechanism was used where iso-octane represents gasoline and n-heptane as diesel. After validation of engine combustion, performance, and emission parameters, parametric investigations were carried out to investigate the effects of HRF's start of injection timing, premixed energy share, and intake charge temperature on combustion and exhaust emissions. The results obtained from both CFD and experiment show that the start of injection and intake charge temperature significantly influence combustion phasing, while the premixed ratio controls mixture reactivity and combustion quality. The blending ratio of high HRF to LRF governs reactivity stratification, which controls the magnitude of low and high-temperature heat release, combustion phasing and combustion duration. Controlling the amount of LRF and HRF in direct injection (DI) allows for shifting the heat release rate, which modifies combustion phasing and rate of pressure rise. Multiple injection strategies using double pulse helped reduce CO formation and achieve better control over combustion parameters with improved efficiency. By varying IVC temperature, optimizing SOI timing using a double injection strategy up to 18.57%, 25.5% reduction in CO and 93.68% drop in HC emissions, 3.7% reduction in soot are obtained in RCCI compared to the baseline case.