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This article titled "Heavy-Duty Transportation in 10 Charts" by Ahmet Kusoglu discusses the significance of freight movement in the transportation industry and its economic implications. It emphasizes that heavy-duty trucking is responsible for the majority of cargo transportation in the United States, carrying 72% of freight in terms of value and two-thirds in terms of weight. However, heavy-duty trucks also contribute significantly to energy consumption, greenhouse gas emissions, and air pollution. The article highlights the importance of reducing the environmental impact of heavy-duty trucks to protect public health, especially in underserved communities. It discusses the disparities in PM2.5 concentration among different economies and the negative health effects of these emissions. The article stresses the need to eliminate harmful pollutants from vehicle combustion, particularly heavy-duty trucks, in order to decarbonize the transportation sector. It also acknowledges the challenge of reducing emissions in the trucking industry, given the higher per-vehicle emissions from heavy trucks compared to passenger cars. The article concludes by emphasizing the importance of decarbonization in reducing emissions and the need for further analysis of the transportation sector, specifically heavy-duty trucks and freight movement. [Extracted from the article]

A transition towards zero-emission fuels is required in the mobility sector in order to reach the climate goals. Here (green) renewable hydrogen for use in fuel cells will play an important role, especially for heavy duty applications such as trucks. However, there are still challenges to overcome regarding efficient storage, infrastructure integration and optimization of the refuelling process. A key aspect is to reduce the refuelling duration as much as possible, while staying below the maximum allowed temperature of 85 °C. Experimental tests for the refuelling of a 320 l type III tank were conducted at different operating conditions and the tank gas temperature measured at the front and back ends. The results indicate a strongly inhomogeneous temperature field, where measuring and verifying the actual maximum temperatures proves difficult. Furthermore, a simulation approach is provided to calculate the average tank gas temperature at the end of the refuelling process. • Refuelling tests with a 320 l heavy-duty tank were conducted. • Measurements suggest inhomogeneous temperature field. • Maximum and average temperature cannot be determined with OTV built-in sensors. • Parameter fit for heat transfer correlation. • Simulation model for average tank temperature at the end of refuelling. [ABSTRACT FROM AUTHOR]

Recently, the population density in cities has increased at a higher pace, so waste generation is on the rise in most societies due to population growth. Given this concern, it would be highly important to manage waste generation. Intelligent city planning is necessary to improve the quality of city life and make cities more livable. This paper presents an intelligent waste management system (IWMS) in smart cities based on Internet of Things components like sensors, detectors, and actuators. IWMS contains three main phases. The first phase of the system is to adapt the low energy adaptive clustering hierarchy approach as an optimization process to better balance the energy consumption of smart waste bins (SBs), thus leading to extending the life of the smart waste network. The second phase is handling the missing values which are retrieved from SBs using an improved version of the k-nearest neighbor algorithm based on artificial hummingbird optimization (AHA), while the third phase presents an optimal energy-efficient route process for the routing of waste trucks that improves fuel efficiency and reduces the time to get an appropriate SB. According to the experimental results, the proposed system has achieved energy savings of 34% for the smart waste bin network. Moreover, compared to other systems, it has a lower mean error rate when generating missing values, and the results related to convergence and running time validate its superiority compared with other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles' power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks. [ABSTRACT FROM AUTHOR]

To confront the lack of robust and accessible models predicting diesel truck fuel consumption, this study develops an Engine-based Correction Model (ECM) using a sample containing vehicle operation and road data from heavy-duty diesel trucks (HDTs) along an urban arterial in Sichuan Province, China. This model is compared with two popular methods, the VT-Micro model and the CMEM model, in terms of goodness-of-fit and out-of-sample prediction performance. The results show that the proposed ECM models explain on average 89.7% of the variation in fuel consumption across the four modes and produce the smallest error, measured by MAPE and RMSE, for predicting each mode. Specifically, the MAPE values of the new ECM models are 7.95% to 25.59% lower than those of the VT-Micro models and 5.74% to 13.27% lower than the CMEM models. The improvement of RMSE values ranges from 0.1780 to 2.0940 cc/s and from 0.0171 to 0.7259 cc/s, compared to the VT-Micro model and CMEM model, respectively. In sum, the new ECM outperforms the other two existing models in both goodness-of-fit and predictive power across all modes. The enhanced performance of ECM can be attributed to the correction component and a model-building process powered by the simulated annealing (SA) algorithm that reliably and quickly weed out ineffective model forms. The results can inform driver training to foster energy-efficient driving behavior and assess the impact on fuel consumption of roadway design policies. [ABSTRACT FROM AUTHOR]

Due to the increasing use of automobiles, the transportation industry is facing challenges of increased emissions, driver safety concerns, travel demand, etc. Hence, automotive industries are manufacturing vehicles that produce fewer emissions, are fuel-efficient, and provide safety for drivers. Artificial intelligence has taken a major leap recently and provides unprecedented opportunities to enhance performance, including in the automotive and transportation sectors. Artificial intelligence shows promising results in the trucking industry for increasing productivity, sustainability, reliability, and safety. Compared to passenger vehicles, heavy-duty vehicles present challenges due to their larger dimensions/weight and require attention to dynamics during operation. Data collected from vehicles can be used for emission and fuel consumption testing, as the drive cycle data represent real-world operating characteristics based on heavy-duty vehicles and their vocational use. Understanding the activity profiles of heavy-duty vehicles is important for freight companies to meet fuel consumption and emission standards, prevent unwanted downtime, and ensure the safety of drivers. Utilizing the large amount of data being collected these days and advanced computational methods such as artificial intelligence can help obtain insights in less time without on-road testing. However, the availability of data and the ability to apply data analysis/machine learning methods on heavy-duty vehicles have room for improvement in areas such as autonomous trucks, connected vehicles, predictive maintenance, fault diagnosis, etc. This paper presents a review of work on artificial intelligence, recent advancements, and research challenges in the trucking industry. Different applications of artificial intelligence in heavy-duty trucks, such as fuel consumption prediction, emissions estimation, self-driving technology, and predictive maintenance using various machine learning and deep learning methods, are discussed. [ABSTRACT FROM AUTHOR]

The European transportation sector employs 10 million people and accounts for 4.6% of the European Union GDP. Due to climate change, this workforce is increasingly affected by high temperatures and radiant loads, particularly during summer. They rely on air conditioning (AC) to minimize heat inside the truck cabins, increasing fuel consumption and tailpipe emissions. Because sustainable transportation is crucial for climate change mitigation, we developed a numerical investigation on the dynamic thermal exchanges of cabins of heavy-duty trucks in realistic conditions of a summer workday, to quantify the potential impact of interventions in the glazing and paint optical properties, over the truck AC loads. We observed that the changes in air temperature and solar irradiation throughout the workday imply substantial variations in the truck's AC loads and, consequently, in its fuel consumption and tailpipe emissions. Furthermore, windshields and side windows with transmissivity of 0.33 instead of typical 0.79 and 0.84, respectively, can reduce AC loads by up to 16%. External paints with reflectivity of 0.70 instead of 0.04 can reduce the AC loads by up to 30%, whereas cumulative changes to glazing and paint can reduce the AC load by up to 40%. These interventions can lower fuel consumption and emissions by up to 0.4%. These results show that important improvements in fuel efficiency and tailpipe emissions are possible, if the research community, policy makers and industry stakeholders successfully promote the adaptation of the European transportation fleet. [ABSTRACT FROM AUTHOR]

A new report reveals that the rise in used heavy-duty vehicles (HDVs) is a major contributor to pollution, with CO2 emissions associated with these vehicles increasing by over 30% since 2000. The report recommends reducing sector emissions through standardizing, monitoring, and greening freight strategies. However, regulations and enforcement on the quality of used HDVs imported are often weak or non-existent, amplifying their negative impacts. It is a shared responsibility of importing and exporting countries to ensure cleaner and safer used vehicles are on the roads, and regional cooperation is needed to introduce and enforce minimum standards. [Extracted from the article]

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations. [ABSTRACT FROM AUTHOR]

Heavy-duty trucks, as important players in pollution and greenhouse gas emissions, need to undertake critical responsibility in road freight. By estimating the well-to-wheel emissions and total cost of ownership for internal combustion engine heavy-duty trucks, this study proposes the model of carbon-abatement benefit to conducting an environment-economic comparison of traditional diesel and potential alternative fuel. The results show that liquefied natural gas has better economic performance, with a cost 14.9% lower than diesel, and biodiesel reduces greenhouse gas emissions by more than 40% compared to diesel. Compared to other alternative fuels, liquefied natural gas has better carbon reduction benefits, with a carbon abatement value of 0.51 CNY/km. By 2040, all alternative fuels have positive carbon abatement values, ranging from 0.26 to 1.28 CNY/km. The progress of replacing diesel by alternative fuels for heavy duty trucks depending on the incentive policies, the clean production of fuels, and the competition from electric trucks. [ABSTRACT FROM AUTHOR]

Excessive fuel consumption of heavy duty trucks aggravates environmental pollution and leads to reduced economic benefits of road transportation enterprises. In order to study the influencing factors of fuel consumption of heavy duty trucks, based on real-time vehicle operating parameter data, this paper establishes a segment fuel consumption prediction model based on LightGBM algorithm. The fitting ability of the model is improved by feature selection and Optuna hyperparameter search. The performance is compared with other models. The results show that the prediction effect of LightGBM model is optimal. Finally, based on the SHAP framework, the prediction results of the optimal fragmented fuel consumption model are visually explained. The conclusion that average speed and average throttle pedal value have a significant impact on segmented fuel consumption. Ensuring higher and more stable speeds, reducing the frequency of acceleration and deceleration can reduce the fuel consumption appropriately. • Establishing a LightGBM model to predict fuel consumption of heavy duty trucks. • Improving the model prediction performance through feature selection and parameter optimization. • The LightGBM model shows good predictive performance with R2 of 0.8725. • Visual interpretation of prediction results based on the SHAP framework. [ABSTRACT FROM AUTHOR]

• Analyzed over 4 million field operating and fuel consumption data from 162 HDTs. • Developed a novel LSTM-Conv model integrating vehicle weight for fuel estimation. • Observed significant, non-linear impacts of vehicle weight on fuel consumption rate. • LSTM-Conv achieved MAPEs of 9.81% for fuel rate and 1.49% for trip fuel estimation. Insufficient consideration of vehicle weight dynamics during real-world driving could lead to inaccurate fuel consumption estimates. This study examined the impact of vehicle weight on fuel consumption rate (FCR) by analyzing extensive, high-resolution operating data obtained from 162 heavy-duty trucks (HDTs). An engine output power-based (EOP) model, an artificial neural network (ANN) model, and a long short-term memory-convolutional (LSTM-Conv) model were developed and contrasted with conventional vehicle specific power (VSP) and Virginia Tech Microscopic (VT-Micro) models. The results indicated a significant, non-linear relationship between weight and FCR. Compared to 5-ton trucks, FCR for trucks weighing 15–25 tons and 45–55 tons increased by 290% and 755%, respectively, under low-speed and positive acceleration conditions. The LSTM-Conv model outperformed the VSP, VT-Micro, and EOP models, achieving MAPEs of 9.81% for FCR and 1.49% for trip fuel economy estimation. The deep-learning models exhibited enhanced stability across varying speeds, accelerations, and vehicle weights. [ABSTRACT FROM AUTHOR]

Polyoxymethylene dimethyl ethers (PODE), as the most potential oxygenated alternative fuel for diesel engines, is widely investigated. Considering the importance of research on real driving emissions (RDE) and the few studies focus on the emission characteristics of the PODE/diesel blended fuels under real driving conditions, a portable emission measurement system (PEMS) was applied to measure the RDE of a heavy-duty tractor fueled with diesel or PODE/diesel blends. The tests were carried out in accordance with the relevant regulations of the CHINA VI emission standards. The second-by-second data from PEMS and the OBD system were utilized to construct engine transient operating maps. The results indicated that the addition of PODE can still decrease CO and PN emissions significantly under real driving conditions, although the low load conditions are still the areas of high brake specific CO and brake specific PN emissions. The NOx emissions, however, were not reduced as the results of the steady-state experiment of the same model of the engine. Fuel mass consumption raised when PODE was added, while the overall brake thermal efficiency improved, especially for the blending ratio of 30%, up to 40.3%, which is higher than 38.4% of pure diesel operation. [ABSTRACT FROM AUTHOR]

With the growing energy requirements, it is important to understand the impacts of driving patterns on fuel-consumption. This study is aimed at investigating the quantification effects of driving patterns such as distance travelled, instantaneous speed, hard braking, hard accelerating, and engine idle timings on fuel-consumption of heavy-duty vehicles. The telematics data from a leading logistics firm in Thailand was collected for three different types of vehicles (i.e., 6-W NGV, 18-W NGV, and 18-W-Diesel) over a period of two months. Stepwise regression modeling approach was employed to predict fuel-consumption. The results showed that average error between the predicted and measured fuel-consumption was 12.02%, 7.07% and 12.85% for 6-W NGV, 12-W NGV, and 12-W Diesel vehicles, respectively. This research study shows that regression modelling can be used to observe the quantification impacts of real-world driving patterns in heavy-duty vehicle on fuelconsumption. [ABSTRACT FROM AUTHOR]

Achieving global food security and ensuring sustainable agriculture, the dual objectives of the second Sustainable Development Goal (SDG 2), necessitate immediate and collaborative efforts from developing and developed nations. The adverse effects of ozone on crop yields have the potential to significantly undermine the United Nations' ambitious target of attaining food security and ending hunger by 2030. This review examines the causes of growing tropospheric ozone, especially in India and China which lead to a substantial reduction in crop yield and forest biomass. The findings show that a nexus of high population, rapid urbanization and regional pollution sources aggravates the problem in these countries. It elucidates that when plants are exposed to ozone, specific cellular pathways are triggered, resulting in changes in the expression of genes related to hormone production, antioxidant metabolism, respiration, and photosynthesis. Assessing the risks associated with ozone exposure involves using response functions that link exposure-based and flux-based measurements to variables like crop yield. Precisely quantifying the losses in yield and economic value in food crops due to current ozone levels is of utmost importance in comprehending the risks ozone poses to global food security. We conclude that policymakers should focus on implementing measures to decrease the emissions of ozone precursors, such as enhancing vehicle fuel efficiency standards and promoting the use of cleaner energy sources. Additionally, efforts should be directed toward mapping or developing crop varieties that can tolerate ozone, applying protective measures at critical stages of plant growth and establishing ozone-related vegetation protection standards. [Display omitted] • Tropospheric ozone jeopardises food security. • Use of OTC and FACE systems to evaluate O 3 -crop response. • Exposure-based index AOT40 widely used in studies. • Use of emission control, chemical protectants, and bioengineering techniques to prevent yield losses. [ABSTRACT FROM AUTHOR]

Road transport has grown significantly in the last decade. The continuous increase in the scale of production has led to the introduction of new technologies in the various systems of the machine. The mass use of cars for various purposes is the reason for the great development in the automotive industry. The consumption of liquid fuels represents a significant part of the total costs when using mobile and stationary machines running on gasoline, diesel fuel, industrial gas oil and others. Therefore, the question of accurately determining the amount of fuel not consumed will become increasingly relevant and important in everyday practice. Prices are constantly rising and about 40% of the costs in transport companies are for fuel. In this context, reducing the fuel consumption of cars reveals one of the possibilities for reducing air pollution from road transport. [ABSTRACT FROM AUTHOR]

Amongst all the working elements of single grip harvesters, the working elements of felling and processing play an important role within the cut-to-length (CTL) harvester's working phases. This includes the felling cutting and cross-cutting of stems within the felling and processing operation. The detailed investigation of such individual machine activities may help to analyse and improve the performance of forest machines. The objective of this study was to investigate the cutting duration and other performance parameters, including the fuel consumption, of a CTL harvester's sawing unit under real working conditions. Detailed information on the felling cutting and cross-cutting performance was collected at short intervals using CAN bus data of two single grip harvesters in final felling from two different sites in Eastern Finland. As a result, models for effective time consumption in the work phase of cutting as a function of stem size were developed, both for felling cutting and cross-cutting. Felling cutting and cross-cutting durations were somewhat identical until the cutting diameters of 400–450 mm, depending on the site. Thereafter, the cutting time difference increased and was higher in felling cutting. At the site with large diameters of 550–650 mm, the difference varied between 15% and 28%, between the comparisons of formulae. In addition, other performance parameters, including the respective fuel consumption of this working phase, were part of the study. The study revealed a higher hourly based fuel consumption for the entire guide bar movement time compared to the pure cutting time in cross-cutting with stem diameters below 400 mm. Detailed knowledge of the performance of the sawing unit's activities might help the planning process of future studies, as well as support the future development of efficient and intelligent machinery. [ABSTRACT FROM AUTHOR]

Trucks became essential for the freight transportation systems' functioning and the enterprises considered several variables in their operations. One variable was the mileage, where fluctuations were observed with assignable causes to the enterprises in several occasions and it decreased over the time. This paper's objective was to forecast one North Benz truck's possible monthly mileage in a 33 truck fleet for the 2021 year. The truck's possible monthly mileage variation was attenuated using the moving average technique. It was obtained a mathematical model of the monthly mileage's moving average according to the truck age, the mileage's typical deviation and the mileage's relative error. The model was applied to forecast the North Benz truck fleet's performance at the enterprise. It contributed to facilitate the enterprise's decision taking on the transportation offer and incomeses. [ABSTRACT FROM AUTHOR]

The "Car of the Future" project converted a 2015 rear-wheel drive (RWD) Subaru BRZ into a hybrid electric vehicle (HEV) with an intermediate milestone of a battery electric vehicle (BEV). BEV architecture required removal of the conventional powertrain components, such as internal combustion engine, transmission and differential, introduced an electric axle and battery. This intermediate BEV step provided a point at which the vehicle could be evaluated in its all electric operation with the absence of what was once critical components including its original powertrain and powertrain electronics. This step also ensures the electric components are working properly before more complexity is added to the system in building HEV. In our previous work, BEV Vehicle Technical Specifications (VTS) or requirements were developed and an electric axle was appropriately sized and selected to meet these requirements. After selecting the electrical axle with independent rear motors that will meet BEV performance requirements, Environmental Protection Agency (EPA) fuel economy rating of the BEV should be assessed. This paper presents a drive cycle analysis of the BEV vehicle using the EPA Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) drive cycles by means of dynamic modeling and simulation. In this study, the power required at the wheels, the efficiency of each motor and the energy required at the selected electrical axle were determined. In addition, the city, highway and combined miles per gallon equivalent (MPGe) fuel economy were determined. [ABSTRACT FROM AUTHOR]

If connected and autonomous vehicles use highways under current lane management policy, only partial benefit can be reaped from the platooning of advanced vehicles. Agencies may have to update the existing policy in the near future because of the regular presence of connected and autonomous vehicles on the highways. The probable management approaches are likely to affect highway construction, rehabilitation, and fuel consumption cost. This paper studied a range of lane management approaches in terms of truck platooning and estimated construction and rehabilitation costs using existing models. It also developed a probabilistic approach of generating varying degrees of vehicle platooning to estimate fuel consumption in a simulated environment of highway segment. The results showed that, compared with a no-platoon approach, most of the approaches considered reduce construction cost but increase rehabilitation cost for a unit length of a highway. Fuel consumption cost decreases due to truck platooning with all possible approaches. If truck platooning is achieved by adding a separate truck-only lane, the fuel consumption benefits are considerably higher. [ABSTRACT FROM AUTHOR]

Using the laboratory-based fuel consumption models for predicting real-world fuel consumption requires the measurement of data under certain conditions to obtain high accuracy of predicted result. Therefore, it is necessary to develop a logging device for measuring the real-time fuel consumption and speed of vehicle on the road. This article presents a study on developing the on-board data logging device to collect real-world data of fuel consumption and speed for motorcycles with the update rate of 1 Hz. The instantaneous speed of the motorcycle was determined based on the rotational speed of the wheel and the wheel radius. Another module was used to determine the instantaneous fuel consumption rate (FR) though measuring the duration injection pulse. The relationship between the duration injection pulse and the injected amount of fuel was established with high correlation coefficient of 0.997. In addition, a filter was designed to remove noise in the dataset collected using the data logging device. The random errors in the speed and the FR profiles were detected and replaced, the percentage of these errors is 1.8% and 2.4%, respectively. The developed method is a precise one for transient fuel consumption and speed measurement. In chassis dynamometer test, the average deviation between steady speed measured by the chassis and the data logging device is only approximately 0.35%. At transient state, the biggest deviation between these two datasets is less than 3.5%. The average FR at steady speed measured by the developed method is slightly different from the one measured by the carbon balance method. The difference is 0.9%, 2.5%, and 0.25% at the speeds of 30 km/h, 50 km/h, and 70 km/h, respectively. Following the transient test cycle, the fuel consumption measured by the developed method is 4.35% lower than that determined by the carbon balance method. Implications: A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed was developed for motorcycles. The proposed method can record well the real-world driving data for motorcycles, including the fuel consumption and speed, with the update rate of 1 Hz. The filter was designed to minimize noise while maintaining data integrity of the collected dataset, the percentage of errors in the the speed and the FR profiles is 1.8% and 2.4%, respectively. The proposed method, therefore, can be used as effective tools for future studies relating to the fuel consumption and emission of motorcycles on the road. [ABSTRACT FROM AUTHOR]

• Design a virtual power plant with electric waste truck and waste incineration power. • The relationship between electric waste truck and virtual power plant was studied. • Explore electric waste truck transport and virtual power plant scheduling. • Bi-level model ensures the benefits of electric waste truck and virtual power plant. Aggregating waste incineration plants, electric waste trucks, and renewable energy sources into virtual power plants is important to promote renewable energy utilization and sustainable waste disposal. However, electric waste trucks must perform daily waste transportation tasks while participating in virtual power plant scheduling. Therefore, coordinating the relationship between the two has become a key to the stable operation of the virtual power plant systems. This study constructed a novel virtual power plant system incorporating waste incineration plants and electric waste trucks. The transportation strategy of the electric waste truck and the scheduling strategy of the virtual power plant were simultaneously considered through a bi-level programming model. The simulation results show that: (1) the proposed virtual power plant's revenue increased by 19.59%, and electric waste truck operating costs decreased by 45.91% compared to independent operations; (2) the of revenue plant increased by only 5.81% when electric waste trucks fully adhered to the virtual power plant schedule before performing waste transportation tasks, but electric waste truck costs increased by 27.14% compared to the proposed bi-level programming model. In conclusion, this bi-level programming model effectively balances the interests of various stakeholders, promotes efficient waste resource utilization, and enhances municipal solid waste removal efficiency. The proposed virtual power plant planning scheme fully utilizes renewable energy and waste resources, improves waste utilization efficiency, and provides innovative ideas for renewable energy consumption. [ABSTRACT FROM AUTHOR]

Impacts of biofouling were evaluated in-real conditions as a decision support system for industrial purse seiners and trawlers operating in the Mediterranean and the Black Sea (GFCM) in a fishing season. Initially, face-to-face interviews determined the most commonly used antifouling coatings among fishermen, resulting in the selection of five self-polishing copolymer (SPC) coatings. These SPC coatings, along with an uncoated reference panel, were immersed in the Black Sea's natural seawater environment for periodic fouling accumulation observations (SPC immersion tests). Additionally, selected SPC coating patches were applied on a fishing vessel to compare performances of the SPC coatings under the same conditions after a year of operation (namely ship tests). These ship test results were then compared with the outcomes from the static immersion tests. Finally, case studies were employed with the data generated from the static immersion tests for three fishing vessels operating in two different locations equipped with either trawl or purse seine. In addition to SPC coatings, foul release (FR) coatings were tested to compare their performance. Results indicated that applying an SPC coating to an industrial fishing vessel can yield fuel savings of 6%–10.26%, while a basic antifouling coating choice can save up to 3.11% in total fuel consumption over a fishing season in the GFCM area. Regardless of fishing gear used (purse seiner or trawler) or location of fishing activities conducted (Mediterranean or Black Sea), antifouling coating selection decision can reduce fuel consumption by up to 146.1M litres, fuel costs by £112.5M, and CO 2 emissions by 0.42M tonnes for the regional GFCM fleet. These numbers are extrapolated to global fleet, estimating that fuel consumption can be decreased by up to 660.37M litres, fuel costs by £508.48M, and CO 2 emissions by 1.9M tonnes based on the antifouling selection. • Impacts of biofouling on industrial fishing vessels revealed. • A simple antifouling coating selection plays notable role for reducing fuel costs. • SPC and FR antifouling coatings were compared under idle conditions. • Local antifouling coating companies are more successful at preventing biofouling. • Coating an industrial fishing vessel saves up to 10.26% in fuel consumption. [ABSTRACT FROM AUTHOR]

The mining sector is currently confronted with an energy market and evolving regulatory landscape that exerts pressure on the industry to consistently enhance its strategies and decision-making in support of sustainable practices. Traditionally, the ore is transported using heavy-duty trucks powered by diesel engines which have been identified as a primary contributor to environmental degradation. An emerging and technologically advanced solution of considerable interest is the implementation of battery-electric trucks, which still generates some uncertainty about its actual impact on the environment. The primary hypothesis under investigation is that battery-electric vehicles have significantly lower environmental impacts than conventional combustion vehicles. Thorough research is needed to bridge this knowledge gap and robustly understand the environmental effects of these technologies in the mining context. This study uses Life Cycle Assessment to evaluate the potential environmental impacts of battery-electric trucks compared to diesel trucks in a theoretical underground mine. The study analyses the environmental impacts around three main stages: the raw material supply for the manufacture of the vehicles, the energy supply (electricity or diesel fuel), and other indirect impacts associated with the operation of both types of trucks. The characterization methodology used is the ReCiPe2016 Midpoint (Hierarchical) using open-access software OpenLCA v1.10.3 for the impact assessment. The functional unit selected is one ton of material transported over 1 km. Our results for the baseline Chilean scenario indicate that diesel trucks have a considerably greater environmental impact in two of the five categories studied: global warming potential and stratospheric ozone depletion. The energy supply is the 'hotspot' for the electric truck life cycle, while the diesel truck life cycle has the operation and the energy supply as the main environmental impact sources due to the upstream and direct emissions associated with diesel supply and combustion, respectively. However, when considering particulate matter formation and water consumption, electric trucks exhibit higher impacts due to the technologies employed for generating the electricity used to charge their batteries. Furthermore, the environmental profile of the electric truck improves considerably when renewable sources of electricity substantially substitute coal in the electricity mix, as projected in the Chilean policy context. The main novelty of this study is that it is the first initiative comparing a battery-electric truck with a diesel truck considering an approach to the underground operation reality for the mining sector. • Diesel truck has a greater global warming impact than electric truck. • The main impacts of electric trucks are due to raw materials and energy supply. • The energy supply stage causes the greatest impact on the life cycle of both trucks. • Renewable electricity projections boost Chilean mining's sustainability. [ABSTRACT FROM AUTHOR]

Potential carbon neutrality of the global trucking, shipping and aviation sectors by 2050 could be achieved by substituting fossil fuels with renewable hydrogen and synthetic fuels. To investigate the economic impact of fuel substitution over time, a holistic cost model is developed and applied to three case studies in Norway, an early adopter of carbon-neutral freight transport. The model covers the value chains from local electricity and fuel production (hydrogen, ammonia, Fischer–Tropsch e-fuel) to fuel consumption for long-haul trucking, short-sea shipping and mid-haul aviation. The estimates are internally consistent and allow cross-mode and cross-fuel comparisons that set this work apart from previous studies more narrowly focused on a given transport mode or fuel. The model contains 150 techno-economic parameters to identify which components along the value chains drive levelized costs. This paper finds a cost reduction potential for renewable fuels of 41% to 68% until 2050, but carbon-neutral transport will suffer asymmetric cost disadvantages. Fuel substitution is most expensive in short-sea shipping, followed by mid-haul aviation and long-haul trucking. Cost developments of electricity, direct air capture of carbon, vehicle expenses, and fuel-related payload losses are significant drivers. [Display omitted] • Levelized costs of renewable fuels in road, sea and air freight are estimated. • A new holistic cost model allows cross-mode and cross-fuel comparison. • Renewable fuels have a cost reduction potential of 41% to 68% until 2050. • Fuel substitution is most expensive in shipping, followed by aviation and trucking. • Reducing fuel-related payload losses is a significant cost lever. [ABSTRACT FROM AUTHOR]

This paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making decisions regarding setting the proper commanded outputs to is extraordinarily difficult. To support such decisions, we have developed a decision support system. Its main elements are the ANN models enabling ship fuel consumption and speed prediction. To collect data needed for building ANN models, sea trials were conducted. In this paper, the decision support system concept, input and variables of the ship driveline system models, and data acquisition methods are presented. Based on them, we developed appropriate ANN models. Subsequently, we performed a quality assessment of the collected data set, data normalization and division of the data set, selection of an ANN model architecture and assessment of their quality. [ABSTRACT FROM AUTHOR]

The "Car of the Future" project converted a 2015 rear-wheel drive (RWD) Subaru BRZ into a hybrid electric vehicle (HEV) with an intermediate milestone of a battery electric vehicle (BEV). BEV architecture required removal of the conventional powertrain components, such as internal combustion engine, transmission and differential, introduced an electric axle and battery. This intermediate BEV step provided a point at which the vehicle could be evaluated in its all electric operation with the absence of what was once critical components including its original powertrain and powertrain electronics. This step also ensures the electric components are working properly before more complexity is added to the system in building HEV. In our previous work, BEV Vehicle Technical Specifications (VTS) or requirements were developed and an electric axle was appropriately sized and selected to meet these requirements. After selecting the electrical axle with independent rear motors that will meet BEV performance requirements, Environmental Protection Agency (EPA) fuel economy rating of the BEV should be assessed. This paper presents a drive cycle analysis of the BEV vehicle using the EPA Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) drive cycles by means of dynamic modeling and simulation. In this study, the power required at the wheels, the efficiency of each motor and the energy required at the selected electrical axle were determined. In addition, the city, highway and combined miles per gallon equivalent (MPGe) fuel economy were determined. [ABSTRACT FROM AUTHOR]

Eco-driving has been linked to considerable reductions in negative externalities and costs for transportation companies, employees and communities (including fuel consumption, safety and emission benefits). Nevertheless, some of the biggest challenges to its implementation are related to promoting behavioural change among drivers. This paper presents the results of three behavioural field interventions that were successful to improve fuel efficiency in heavy freight transportation. The interventions brought further improvement even though the target company already had strong training, incentive, control and feedback procedures in place. The Installation Theory framework and the Subjective Evidence-Based Ethnography (SEBE) technique were used to systematically analyse determinants of driving behaviours, and to design cost-effective behavioural interventions based on social norms. The effects of three interventions were then tested using a pre-test post-test control group design among 211 drivers of the company. Results show significant decreases in average monthly fuel consumption of up to 4% in month 1 and up to 4.5% in month 3. Our findings show (with certain qualifications), that the Installation Theory framework and social norm interventions can be a cost-effective method to improve fuel efficiency in road freight transport companies, even when strong training, incentive, control and feedback procedures are already in place. [ABSTRACT FROM AUTHOR]

• The systematic adoption of safety distance was not the best car-following strategy. • Driving to keep Distance (DD) promoted higher speed variability. • Driving to keep Inertia (DI) required greater car-following distance. • Better health parameters (less crashes, distress, pollution) alienate with DI. • A platoon of 8 virtual DD followers required less road space following a DI driver. Nature offers numerous examples of animal species exhibiting harmonious collective movement. Unfortunately, the motorized Homo sapiens sapiens is not included and pays a price for it. Too often, drivers who simply follow other drivers are caught in the worst road threat after a crash: congestions. In the past, the solution to this problem has gone hand in hand with infrastructure investment. However, approaches such as the Nagoya Paradigm propose now to see congestion as the consequence of multiple interacting particles whose disturbances are transmitted in a waveform. This view clashes with a longlasting assumption ordering traffic flows, the rational driver postulate (i.e., drivers' alleged propensity to maintain a safe distance). Rather than a mere coincidence, the worldwide adoption of the safety-distance tenet and the worldwide presence of congestion emerge now as cause and effect. Nevertheless, nothing in the drivers' endowment impedes the adoption of other car-following (CF) strategies. The present study questions the a priori of safety-distance, comparing two elementary CF strategies, Driving to keep Distance (DD), that still prevails worldwide, and Driving to keep Inertia (DI), a complementary CF technique that offsets traffic waves disturbances, ensuring uninterrupted traffic flows. By asking drivers to drive DD and DI, we aim to characterize both CF strategies, comparing their effects on the individual driver (how he drives, how he feels, what he pays attention to) and also on the road space occupied by a platoon of DD robot-followers. Thirty drivers (50% women) were invited to adopt DD/DI in a driving simulator following a swinging leader. The design was a repeated measures model controlling for order. The CF technique, DD or DI, was the within-subject factor. Order (DD-DI / DI-DD) was the between-subjects factor. There were four blocks of dependent measures: individual driving performance (accelerations, decelerations, crashes, distance to lead vehicle, speed and fuel consumption), emotional dimensions (measures of skin conductance and self-reports of affective states concerning valence, arousal, and dominance), and visual behavior (fixations count and average duration, dwell times, and revisits) concerning three regions of the driving scene (the Top Rear Car –TRC- or the Bottom Rear Car –BRC- of the leading vehicle and the surrounding White Space Area -WSA). The final block concerned the road space occupied by a platoon of 8 virtual DD followers. Drivers easily understood and applied DD/DI as required, switching back and forth between the two. Average speeds for DD/DI were similar, but DD drivers exhibited a greater number of accelerations, decelerations, speed variability, and crashes. Conversely, DI required greater CF distance, that was dynamically adjusted, and spent less fuel. Valence was similar, but DI drivers felt less aroused and more dominant. When driving DD visual scan was centered on the leader's BRC, whereas DI elicited more attention to WSA (i.e., adopting wider vision angles). In spite of DI requiring more CF distance, the resulting road space occupied between the leader and the 8th DD robot was greater when driving DD. [ABSTRACT FROM AUTHOR]

A microscopic fuel consumption model with respect to comprehensive fuel consumption key impact factors that can reflect dynamic engine operations and various real-world driving conditions is essential for fuel efficient model-based vehicle and powertrain control (MB-VPC). Existing microscopic fuel consumption models are mostly steady state models with limited vehicle parameters as dynamic variables. Data-driven solutions can make the model comprehensive by introducing broad impact factors. However, without the required accessible online computational capability and fast model adaptation mechanisms, those data-driven solutions cannot quickly adapt to unseen driving conditions and engine condition changes based on real-world vehicle data to support MB-VPC. In this paper, a connected vehicle-based data mining (CV-DM) framework is proposed to achieve online adaptive dynamic fuel consumption modeling through knowledge sharing over CVs and the CV remote data center. Based on the CV-DM framework, CV-supported Distributed Meta-regression (CV-DMR) algorithms are developed to realize a fast few-shot adaptation with limited training data. Extensive proof-of-concept experiments are conducted with steady-state and transient vehicle engine data. Compared to the baseline physical model and the existing non-adaptive grey-box model, prediction accuracy is improved by 47%–85% and 38%–80% respectively with only limited training data needed for the subject vehicle. Accordingly, a fuel savings of up to 9.4% can be achieved owing to the improvement of prediction accuracy. [ABSTRACT FROM AUTHOR]

In this study, we investigated the RDE (real driving emissions) test with a diesel passenger vehicle and two RDE driving routes. We then experimentally analyzed the implications for fuel consumption of a 2.2L diesel-fueled vehicle. First, we found that the weighting factor, which is the product of the carbon dioxide average value from the MAW (moving average window) analysis, results in real-road fuel consumption measurement that are closer to the WLTC (worldwide harmonized light vehicles test cycles) measurement results. Therefore, we ignored the weighting factor of the WLTC and used a new weighting factor of distance instead. In order to determine the influence of each factor on the fuel efficiency, we analyzed the carbon-dioxide emission for the operation of additional equipment. And OBD values were used to develop correction equations to compensate for vehicle driving regions where GPS data was not measured in the test route. When using the weighting factor based on the WLTC standard, the weighting factor based on distance was introduced to calculate the fuel consumption in this study. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to minimize aircraft fuel consumption during the cruise phase when the flight is subjected to a specific time of arrival for different weights and distances. Design/methodology/approach: The approach adopted herein uses sequential quadratic programming algorithm from MATLAB optimization toolbox, which includes a mathematical model of a jet airliner based on the Base of Aircraft Data as a function evaluator, to find out the impact of meet-time of arrival constraints on fuel consumption. The cruising speeds at predefined segments and the altitude are defined as the design variables. Findings: The algorithm determines the optimum cruise altitudes and speeds for minimum fuel consumption in the case of no time constraints, also, for different time constraints where the flight time shall be reduced by increasing speed and lowering the altitude in most of the investigated cases. Practical implications: The algorithm computes the optimum speed and the altitude according to different flight scenarios with the meet-time of arrival constraints for minimum fuel consumption which affects the direct operating cost of the flight. The algorithm might greatly help in decision-making for the meet-time of arrival operations. Originality/value: Developing an algorithm to optimize the speed and the altitude of an aircraft based on weight and range for minimization of fuel consumption. It is a pioneer study in the literature that deals with the effect of meet-time constraints on fuel consumption. [ABSTRACT FROM AUTHOR]

The time-dependent vehicle routing problems have lately received great attention for logistics companies due to their crucial roles in reducing the time and economic costs, as well as fuel consumption and carbon emissions. However, the dynamic routing environment and traffic congestions have made it challenging to make the actual travelling trajectory optimal during the delivery process. To overcome this challenge, this study proposed an unconventional path optimization approach, fissile ripple spreading algorithm (FRSA), which is based on the advanced structure of coevolutionary path optimization (CEPO). The objective of the proposed model is to minimize the travelling time and path length of the vehicle, which are the popular indicators in path optimization. Some significant factors usually ignored in other research are considered in this study, such as congestion evolution, routing environment dynamics, signal control, and the complicated correlation between delivery sequence and the shortest path. The effectiveness of the proposed approach was demonstrated well in two sets of simulated experiments. The results prove that the proposed FRSA can scientifically find out the optimal delivery trajectory in a single run via global research, effectively avoid traffic congestion, and decrease the total delivery costs. This finding paves a new way to explore a promising methodology for addressing the delivery sequence and the shortest path problems at the same time. This study can provide theoretical support for the practical application in logistics delivery. [ABSTRACT FROM AUTHOR]

Study on fresh food safety reliability and temperature control has being a research focus in the fresh food cold distribution optimization study field. On this basis, optimization of transportation routing problem with time windows for fresh food in time-varying road network is studied by considering both economic cost and fresh food safety loss. A calculation method for path division strategy is designed. A food safety value loss measurement function, a metric function of energy and heat conversion a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, cost of temperature control, the loss of food safety reliability and carbon emissions from transportation and temperature control. The fresh food cold chain distribution vehicle routing problem model with time windows in time-varying road network is formulated based on the objective of the distribution cost and food safety value loss minimization. According to the characteristics of the model, an adaptive improved ant colony algorithm is designed. Finally, the experimental data show that the model can effectively avoid the congestion period, reasonably control the refrigeration temperature, reduce the distribution cost, and improve food safety. [ABSTRACT FROM AUTHOR]

The uncertain climatic changes-related issues in the recent times has led an apprehension for all automotive makers to develop innovative composite materials to reduce vehicle's fuel consumption. Hence, this review article has given special attention toward the factors that motivated several automakers to undergo research on biocomposites as it plays an important role for biodegradability and sustainability across the globe. In particular, cellulose nanofibers-reinforced composites are the main research focus in the current scenario which can be applied for different automotive applications with numerous advantages and abundant accessibility. Polypropylene (PP) is the emerging polymer in the automotive industry, and many researchers are now mainly focusing their research on PP-based composites which can serve as a boon for different environmental problems faced by the current scenario as depicted by Figure 1. It also reported the recent advancement on the development of innovative composite materials by different automakers with regard to finding suitable actions for reduction of fuel consumption to the recommended confines. [ABSTRACT FROM AUTHOR]

Research overseas has found that vehicles on the road consume more fuel and produce higher exhaust emissions under real-world driving conditions than the values to which they are type-approved when manufactured. New Zealand vehicles enter the fleet either as new vehicles or as imported used vehicles sourced from a range of countries as we do not manufacture vehicles here. While exhaust emissions standards are regulated at the border, New Zealand does not currently regulate fuel economy. The NZ Transport Agency’s Vehicle Emission Prediction Model (VEPM) is used to model emissions and fuel consumption of New Zealand vehicles for assessing environmental impacts and fleet trends. However, the diversity of standards in use and the subsequent real-world performance can introduce considerable uncertainty into the prediction. A portable emissions monitoring system (PEMS) was developed to measure real-world emissions from a range of typical New Zealand vehicles on a route typical of New Zealand conditions. Testing was undertaken in Auckland between January and May 2018 on six light duty petrol vehicles, 20 light duty diesel vehicles and six heavy duty diesel vehicles, including New Zealand-new and Japanese-used imported vehicles manufactured between 1996 and 2014. As expected, our testing found that real-world emissions of most pollutants were higher than regulated standards (up to eight times). The real-world nitrogen oxides (NOX) results were comparable to those measured for similar vehicles in Europe and Australia. Although based on real-world factors, VEPM emissions factors were found to be different from those derived from the PEMS results in this study for all vehicles tested and all speed bins analysed. Recommendations have been made to improve and update VEPM utilising the results of this study and other international and New Zealand evidence. Additional testing of heavy duty diesel vehicles is also recommended. . [ABSTRACT FROM AUTHOR]

In this paper the observability of bearing-only tracking is analyzed and the trajectory optimization algorithms to enhance the observability during the rendezvous are presented. Firstly, the relative dynamic model and measurement model are established, and the necessary and sufficient condition for observability when maneuvering is determined using the pseudo linear method. Then, the observability indices, such as the filter covariance matrix trace and the Fisher information matrix (FIM) determinant, are combined with the fuel consumption index to optimize the approaching trajectory. In order to keep the line-of-sight (LOS) aligned with the target and to ensure continuous observation, the attitude of the observer is adjusted synchronously during the rendezvous. Finally, the coordinated control of attitude and orbit for the observer is achieved by optimizing the thrust distribution of the onboard propulsion system. Numerical results demonstrate that the combined optimization of the observability and fuel consumption can generate an optimal control trajectory with high tracking accuracy. The simulation also presents how to choose the optimal relative weight of the two optimization terms, optimal rendezvous time and optimal docking point according to the practical requirements to get the best comprehensive performance of observability and fuel consumption. • Necessary and sufficient condition for observability when maneuvering is determined. • Combined optimization of observability and fuel consumption is proposed. • Coordinated control of attitude and orbit ensures the continuity of observation. • Numerical results validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

For an unmanned aerial vehicle (UAV) carrying out a maritime radar surveillance mission, there is a tradeoff between maximizing information obtained from the search area and minimizing fuel consumption. This article presents an approach for the optimization of a UAV's trajectory for maritime radar wide area persistent surveillance to simultaneously minimize fuel consumption, maximize mean probability of detection, and minimize mean revisit time. Quintic polynomials are used to generate UAV trajectories due to their ability to provide complete and complex solutions while requiring few inputs. Furthermore, the UAV dynamics and surveillance mission requirements are used to ensure that a trajectory is realistic and mission compatible. A wide area search radar model is used within this article in conjunction with a discretized grid in order to determine the search area's mean probability of detection and mean revisit time. The trajectory generation method is then used in conjunction with a multiobjective particle swarm optimization algorithm to obtain a global optimum in terms of path, airspeed (and thus time), and altitude. The performance of the approach is then tested over two common maritime surveillance scenarios and compared to an industry recommended baseline. [ABSTRACT FROM AUTHOR]

• The pavement-related challenges of truck platooning are highlighted. • V2I based optimization framework for truck platooning is proposed. • The results showed that pavement life cycle cost can be reduced up to 50%. With the introduction of connected and autonomous trucks (CATs), truck platooning is expected to be more feasible and prevalent. The reported benefits of the truck platooning include regularizing traffic, reducing congestion, increasing highway safety, and decreasing fuel consumption and emission. Truck platooning may, however, decrease pavement longevity because it would cause channelized load application and hinder the healing properties of asphalt concrete. This study proposes a centralized control strategy that converts the pavement-related challenges of truck platooning into opportunities. This strategy leverages the auto-pilot technologies in CATs by optimizing the lateral position of each platoon or group of platoons. The efficiency of the proposed control strategy was demonstrated in a case study. Results showed that pavement life-cycle costs could be reduced up to 50% by controlling the lateral position of the platoons for each day. [ABSTRACT FROM AUTHOR]

This research systematically presents the feasibility of allocating reusable cargoes in a quasi-periodic p : q resonant cycler orbit to construct a cislunar in-orbit infrastructure. First, p : q resonant cycler orbits are generated by resonance conditions in the context of a planar circular restricted three-body problem through differential correction and then are numerically refined using a multiple shooting method in the bi-circular model. The trajectories from/to the 167 km Earth and 100 km Moon parking orbits are constructed via modified Lambert transfers. Furthermore, aiming at a specific 2:1 resonant cycler orbit, the launch window and rendezvous window from/to the Earth and the Moon are obtained and analysed numerically. Finally, a new concept, i.e., a cislunar in-orbit infrastructure for supply and data transmission, rescue missions and spacecraft maintenance, is proposed, and its feasibility and reusability are verified. In this transportation system, several cargoes are evenly allocated in this quasi-periodic orbit. Once switched from quiescent mode to operational mode, they depart from the 2:1 cycler orbit, rendezvous with the crew vehicle in the 167 km Earth or the 100 km Moon parking orbit, and return to the cycler orbit after the mission. The feasibility of this architecture is demonstrated based on the total fuel consumption and the corresponding time of flight. • A cislunar in-orbit infrastructure is systematically constructed based on cycler orbits. • The launch/rendezvous windows for the cislunar in-orbit infrastructure is presented. • The multiple shooting algorithm is developed and applied to refine cycler orbits to BCM model. [ABSTRACT FROM AUTHOR]