Reduction In Fuel Consumption Research Articles

Predicting combustion, performance, and emission characteristics of a compression ignition engine utilising coconut oil methyl ester and MoO3 nanoparticles

The present study used a blend of biofuel and nanoparticles to examine the effectiveness and emission properties of a four-stroke, single-cylinder, direct-injection diesel engine with an injection pressure of 250 bar. The use of MoO3 in biodiesel enhances its environmental sustainability and dependability, making it a promising substitute for traditional diesel fuel. The study emphasises the ecological advantages of using a combination of coconut oil and MoO3 to accomplish the production of renewable energy. MoO3 nanoparticles ultimately function as an oxidation catalyst, enhancing combustion. A study with MoO3 nanoparticles at 50 and 100 ppm concentrations was carried out. Utilising a photo spectrometer, stability was determined. The CI engine demonstrated improved thermal efficiency and reduced Fuel Consumption compared to diesel, both at full load conditions and at 64.5 bar pressure. The performance, combustion, and emission parameters were precisely forecasted using Design Expert, exhibiting a robust connection with the testing results. The Rb values for Brake Specific Fuel Consumption (BSFC), Brake Thermal Efficiency (BTE), Peak Cylinder Pressure (P-CA), Net Heat Release Rate (NHRR), Crankcase Heat Release Rate (CHRR), Carbon Monoxide (CO), Unburned Hydrocarbons (UHC), Nitrogen Oxides (NOX), and Smoke Opacity are 0.997, 0.9998, 0.8898, 0.979, 0.9838, 0.9815, 0.9917, 0.9958, and 0.9261 respectively.

Adaptive fuzzy energy management strategy for range‐extended electric vehicles integrated with deep learning

AbstractA novel framework for adaptive energy management, rooted in deep learning principles, is proposed to minimize fuel consumption in extended‐range electric vehicles amidst intricate driving scenarios. This innovative approach integrates a long short‐term memory (LSTM) network for pattern recognition across three driving patterns and an adaptive fuzzy controller. To mitigate the impact of poor hyperparameter selection on recognition accuracy, Gray Wolf Optimization is employed to optimize the hidden layer nodes, training times, and learning rate of the LSTM. Simultaneously, a genetic algorithm is utilized to optimize the vertex coordinates of the fuzzy control membership function, enabling the adaptive adjustment of parameters in the fuzzy energy management strategy. The condition recognition model accurately identifies the vehicle's driving status and seamlessly transitions to an energy management strategy tailored to the present conditions. This ensures optimal operation, enhancing overall fuel efficiency and performance. The simulation results robustly validate the efficacy of this approach: the GWO‐LSTM network achieves an impressive 97.7% accuracy in recognizing working conditions, surpassing the 88.9% accuracy of the traditional LSTM network. Furthermore, the fuel consumption reduction achieved by the adaptive fuzzy energy management strategy amounts to 11.9% compared with the conventional fuzzy energy management approach. This outcome underscores the tangible enhancement in vehicle fuel economy resulting from the seamless integration of deep learning techniques.

A Hybrid Heavy Duty Diesel Power System for Off-Road Applications—Concept Validation

Abstract A multiyear power system R&D program was completed with the objective of developing an off-road hybrid heavy duty diesel engine with front end accessory drive-integrated energy storage. This system was validated to deliver 10.5–25.6% reduction in fuel consumption over current Tier 4 Final-based 18L diesel engines, over various off-road machine application cycles. The power system consisted of a downsized heavy-duty diesel 13L engine containing advanced combustion technologies, capable of elevated peak cylinder pressures and thermal efficiencies, thermal barrier coatings, exhaust waste heat recovery via SuperTurbo™ turbocompounding, and hybrid energy assisting and recovery through both mechanical and electrical systems. Following the concept definition, design, and analysis phases of the program, the final phase focused on building and validating the performance and efficiency in laboratory tests. While aspects of the system such as start/stop and reduced off-road cooling package energy losses were only analytically evaluated, the main 13L concept engine with full hybrid system was successfully built and tested in steady-state and in transient certification and real-world application cycles. Extensive simulations in Caterpillar's DYNASTY™ software environment utilized the validation test data to assess performance more fully and confidently over varied cycles and strategies. An average fuel consumption reduction of 17.9% was realized, and the majority (∼13%) of the benefit stemmed from the core concept 13L engine. To conclude, a total cost of ownership analysis provides context to commercial viability and where adoption focus should be placed.

Technology of Fuel Consumption and Emission Reduction, and Enhanced Electricity Generation using Mid-Infrared Rays – A Laser Additive

Efficient utilization of available resources is a promising research direction. In-depth studies can provide a unique platform for reducing fuel consumption while simultaneously reducing pollution, thereby avoiding environmental pollution and health hazards for this purpose various fuel addictive are being used now. A laser additive for liquid and gaseous fuel is yet to be developed. In this context, we successfully used the 2-6 mid-infrared spectrum as a fuel additive. To generate mid-infrared we invented a hand-lit pocket-size mid-infrared generating automizer (MIRGA). The trial fuels were irradiated with this spectral range, which caused chemical changes in the fuels.

Ship Speed Optimization Method in Canal Environments Considering Waiting Times for Crossing Locks

The shipping industry faces a pressing challenge with carbon emissions, prompting a focus on speed optimization for energy conservation and emission reduction. While much research has centered on optimizing speeds in oceans and rivers, canals have received less attention, despite their unique challenges of narrow waterways and busy locks. This study fills this gap by establishing a fuel consumption prediction model integrating key environmental factors such as water depth, width, and flow velocity. Drawing upon established methodologies in speed optimization, this study augments these models with waiting time limits for each canal segment. To validate the efficacy of the model, three representative ships are selected as case studies. The findings reveal a high predictive capability of the fuel consumption model, as evidenced by R2 values exceeding 0.97 across all cases. Notably, the optimization approach yields a fuel consumption reduction ranging from 4% to 5% for short waiting times. Furthermore, compared to conventional methods, the proposed optimization strategy achieves an 8.19% enhancement in fuel consumption and carbon emission reduction for long waiting times, culminating in an overall optimization rate of 11.54%. These results underscore the significance of employing the proposed speed optimization methodology, particularly during peak periods of canal congestion.

Dynamic pricing and joint carbon reduction strategies at the production and consumption end in a dual-channel fuel vehicle supply chain

This paper investigates carbon reduction in a fuel automotive supply chain. It consists of a manufacturer responsible for carbon reduction and opening an online channel opportunely and a retailer who markets and sells the product through traditional offline channels. Consumers have a preference for low-carbon and low-fuel consumption. It first analyzes decentralized and centralized decision-making under dual channels as a benchmark for comparison. Then, compare the performance of single and dual channels under decentralized decision-making to explore the advantages and conditions of dual channels and link centralized decision-making to reveal the double marginal effect of the supply chain. Finally, it designs a marketing cost-sharing contract to coordinate the behavior of all parties in the supply chain. The results suggest that as long as consumer preference for online channels reaches a certain threshold, dual-channel operation leads to higher double-ended carbon reduction investment, lower prices, and higher total supply chain profits than single-channel, but instead decreases profits for the retailer. Besides, in contrast to decentralized decision-making, marketing cost-sharing contracts can improve profits for all parties in the supply chain, enhance carbon and fuel consumption reduction investment, and partially address the double-marginal effect of the supply chain.

The Vehicle Routing Problem with Simultaneous Pick-Up and Delivery under Fuzziness Considering Fuel Consumption

The increasing interest in environmental protection has propelled reverse logistics as a challenging field in supply chain optimization. This paper addresses the vehicle routing problem with simultaneous pick-up and delivery (VRPSDP) while considering fuzzy payloads, with the primary objective of minimizing fuzzy fuel consumption. The VRPSDP with fuzzy payloads poses a computationally intractable challenge, as it involves a fleet of vehicles departing from a central depot to both deliver and collect goods from a dispersed group of customers. To effectively tackle this problem, a genetic algorithm is applied that incorporates the concept of fuzziness. This problem diverges from the traditional VRPSDP by explicitly considering fuel consumption reduction towards environmental sustainability. To validate and assess the feasibility of the proposed approach, a series of test instances are utilized. The numerical results exhibit the efficiency of the proposed method and place emphasis on the influence of uncertainty in the quantities of goods collected and delivered by customers on the resulting solution.

Experimental investigation of fuel injection timing effects on a CRDI diesel engine running on hydrogen-enriched waste plastic oil

In response to fossil fuel concerns and the imperative of effective plastic waste management, this study investigates the impact of hydrogen-plastic oil (WPO) blends on a CRDI diesel engine. Six combinations (WPO10, WPO20, WPO30, WPO10 + H2, WPO20 + H2, WPO30 + H2) are examined across varying loads and fuel injection timings. Notably, WPO10 + H₂ (H2 energy share i.e., 8 lpm) exhibits a 15.1 % increase in brake thermal efficiency, an 11.2 % fuel consumption reduction, improved peak cylinder pressure (66 bar), and maximum heat release rate (40.16 J/°CA) compared to diesel. It also shows reduced emissions of CO, HC, CO2, and smoke opacity—34.8 %, 15.1 %, 10.7 %, and 28.5 %, respectively, relative to diesel. Elevated nitrogen oxide (NOx) emissions are observed across all blends. This study underscores the environmental benefits of blending waste plastic oil with diesel, promoting recycling, reducing reliance on traditional diesel, lowering greenhouse gas emissions, and supporting sustainability. Advanced fuel injection timing at higher loads slightly mitigates NOx emissions, while increasing the blend proportion leads to prolonged ignition delays, reduced thermal efficiency, and heightened emissions.

Can “dual credit” replace “subsidies” successfully? -based on analysis of vehicle supply chain decisions under the digital transformation of technology

Transportation is the second largest source of carbon emissions today, with more than 80% of the carbon emitted from automobile operations. Hence, reducing the fuel consumption per unit mileage while vigorously developing new energy vehicles has become an inevitable choice for carbon emission reduction. So, this paper applies the differential game approach to investigate the production trade-offs of fuel and new energy vehicles, fuel consumption reduction, and digital technology investment strategies in an automotive supply chain under a dynamic framework. In addition, it further analyzes whether the dual credit policy (DCP) can promote the healthy development of China's auto market after the withdrawal of the purchase subsidy policy for new energy vehicles (PSP). The results show that the implementation of DCP will reduce the sales of fuel vehicles with fuel consumption higher than the critical value (HFCV) but will not promote the willingness of manufacturers to invest in fuel consumption reduction of HFCV; the abolition of the PSP will not reduce the demand for fuel vehicles, nor will it affect the investment in fuel consumption reduction of fuel vehicles, but it will reduce the consumer demand for new energy vehicles. However, assuming that the government appropriately adjusts the intrinsic mechanism of the DCP, such as increasing the requirement for the proportion of new energy vehicles in the sales of cars by manufacturers and the value of the points per unit of new energy vehicles, it will be possible to promote the growth of new energy vehicles while discouraging the production of fuel-intensive vehicles. In addition, If the government abolishes PSP, the maximum rate of change in the profits of the manufacturer, retailer, and the entire supply chain in the parameter change intervals analyzed in our numerical example is only 1.6%, 0.04%, and 0.13%, respectively, which can be said to have minimal impact. The findings provide a theoretical basis for the government's choice of carbon regulation policy for the automotive supply chain.

Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving

This study proposes a predictive equivalent consumption minimization strategy (P-ECMS) that utilizes velocity prediction and considers various dynamic constraints to mitigate fuel cell degradation assessed using a dedicated sub-model. The objective is to reduce fuel consumption in real-world conditions without prior knowledge of the driving mission. The P-ECMS incorporates a velocity prediction layer into the Energy Management System. Comparative evaluations with a conventional adaptive-ECMS (A-ECMS), a standard ECMS with a well-tuned constant equivalence factor, and a rule-based strategy (RBS) are conducted across two driving cycles and three fuel cell dynamic restrictions (|di/dt|max≤ 0.1, 0.01, and 0.001 A/cm2s). The proposed strategy achieves H2 consumption reductions ranging from 1.4% to 3.0% compared to A-ECMS, and fuel consumption reductions of up to 6.1% when compared to RBS. Increasing dynamic limitations lead to increased H2 consumption and durability by up to 200% for all tested strategies.

Topology Generation of Naval Propulsion Architecture

Reducing shipping emissions at an affordable cost is critical and can be achieved through propulsion architecture optimization. The multiple choices of components and constraints to be fulfilled (required speed, fuel consumption, maintenance, ...) make architecture design increasingly complex. Some optimization methods have already been used, for example, to optimize diesel generators (number, type and load) for fuel consumption reduction. More complex architectures have been studied by including the absence or presence of some components in a superconfiguration but in the end the number of configurations remains limited. In this study, the ship architecture is not predetermined but is generated by a list of components associated with constraints and rules, making architecture creation more flexible. The algorithm written for this purpose follows the principles found in hybrid vehicle design but with adapted rules and components for naval applications. The main objective of this paper is to explain in detail the topology generation architecture algorithm rather than to find an optimal architecture for a specific ship. From this perspective, the test cases presented are general to demonstrate that the algorithm can be applied to various system configurations. The components are linked together based on their input and output energy type and the architecture is generated to comply with propulsion and hotel load requirements. Next, physical constraints are added to build realistic designs such as avoiding spurious redundant connections or defining the maximal occurrence for each component. All the constraints and the generation algorithm are written in Prolog. Two numerical applications are presented where the list of components covers different types of propulsion (mechanical and/or electrical with gas turbines and/or diesel engines) along with hotel load or heating requirements.

Investment and coordination strategies for fuel consumption reduction in a vehicle supply chain under government subsidies

Investment and coordination strategies for fuel consumption reduction in a vehicle supply chain under government subsidies

Telematics and machine learning system for estimating the load condition of a heavy-duty vehicle

This study employs machine learning techniques to determine whether trucks are loaded or empty based solely on data from the vehicle's communication network, especially from the engine module. We achieved an accuracy of over 85% for short hauls (0.5 to 5 km) and nearly 95% for long hauls (5 to 500 km). This approach not only streamlines fleet management, reducing the need for communication between managers and drivers but also plays a crucial role in research related to fuel consumption reduction and intelligent fault diagnostics. Minimizing dependence on imprecise and time-delayed human responses opens new avenues for effective studies and innovative solutions in the transportation industry.

A jam-absorption driving system for reducing multiple moving jams by estimating moving jam propagation

Jam-absorption driving (JAD) is a novel connected and automated vehicle-based (CAV-based) control strategy that uses either a single or several absorbing cars to clear moving jams. However, a practical control system has not yet been developed. This study presents a real-time control system for operating JAD against multiple moving jams on freeway sections under a partial CAVs and heterogeneous (mixture of cars and trucks) traffic environment. The system dynamically predicts absorbing end points by estimating the propagation of multiple moving jams without using a traffic model. Subsequently, it selects suitable CAVs as absorbing cars in traffic via vehicle-to-infrastructure communication to absorb moving jams. A control model embedded with complete/synergetic JAD strategy decision-making and moving jams–CAVs matching process was developed to guarantee moving jams can be properly absorbed as early as possible. Simulation results demonstrated that the proposed JAD system successfully reduced successive multiple moving jams in the toy model (with two artificial successive moving jams) and hypothetical bottleneck (with series moving jams generated by a speed limit zone) scenarios. Moreover, sensitivity analysis indicated that the JAD system effectively reduced moving jams and enhanced traffic performance (e.g., about 10% fuel consumption reduction) under approximately 1% to 10% CAV market penetration rates (MPRs). This implies that the proposed JAD system can be implemented at low CAV MPRs in the future.

Route-Based Energy Analysis of Hybrid Rail Consists

Abstract Adding battery locomotives to traditional diesel locomotives to form a hybrid consist has the potential to reduce fuel consumption and emissions in freight rail operations. This paper provides route-based estimates of diesel (hpd/ton) and battery (hpb/ton) power requirements and diesel (gal/ton) and battery (hpbhr/ton) energy requirements for hybrid consists. Given a route-specific power profile, the power split between the battery pack and the diesel engine is optimized to minimize fuel consumption by running the diesel engine at maximum efficiency during the entire route. Simulations for 200-mile round trips between Chicago and Harrisburg show that the diesel power is maximum in low-gradient regions at 1.05 (hpd/ton) and battery power is maximum in hilly regions at 2 (hpb/ton) for discharge and 6.5 (hpb/ton) for charge. Maximum fuel efficiency gains of approximately 60% are seen in the mountains, where 2.09 (hpbhr/ton) of battery energy is needed. Minimum fuel economy gains are observed in flat regions, with 20% fuel consumption reduction. With battery current and voltage limits, the battery pack size increases, and fuel savings decrease.

Retrofitting the Bow of a General Cargo Vessel Andevaluating Energy Efficiency Operational Index

Abstract This report examines the feasibility and impact of retrofitting the bulbous bow on a general cargo ship, in terms of the energy efficiency operational index (EEOI), in the areas of Western Europe and the Eastern Mediterranean. Three ship forms were developed and analysed: with a bulbous bow, without a bulbous bow, and with a modified bulbous bow. The goal in developing the ship forms and conducting the analysis was to achieve minimal differences in the ship’s characteristics with the same volumetric displacement, aided by PolyCAD software. A route was selected between two ports: Varna and Rotterdam. The labour intensity of the bulbous bow retrofitting process was evaluated and approximate values of labours costs and cost for the task were determined. The results obtained for resistance during ship motion, EEOI, and fuel consumption reductions, or increases, were compared against the retrofitting values. The return cost of retrofitting is evaluated and measured in terms of fuel saved.

Determination of the efficiency of a plowing unit with an electro-hydraulic control system of working bodies

BACKGROUND: When performing plowing operations with modern tractor units equipped with automatic control systems for hitches, various methods of regulating the depth of soil cultivation are used. The main attention should be paid to compliance with agricultural requirements and fuel consumption reduction. However, there is no integrated numerical criterion for joint assessment of these technological process parameters, helping to justify a reasonable regulation method. AIMS: Development the integral criterion for assessment of the efficiency of plowing unit operation and selection of a reasonable method for adjusting the depth of cultivation for specific soil conditions. METHODS: Mathematical modeling of a plowing unit including the Belarus 1523 tractor with an electro-hydraulic system for controlling the hitch and with the PLN-4-35 four-furrow mounted plow. RESULTS: When using the positional method of adjusting the soil cultivation depth, the total fuel consumption per shift is mp = 28.18 kg with a variation of the cultivation depth Va = 9.99%, when using the power method — mp = 29.74 kg and Va = 5.25%, when using the height method — mp = 29.78 kg and Va = 3.23%, and when using combined methods of adjustment, such as positional-power or height-power, the fuel consumption is mp = 28.94 kg, Va = 5.01% and mp = 29.58 kg, Va = 2.63% respectively. CONCLUSIONS: The comparative analysis of the research results shows that, given the force and kinematic disturbances from the soil on the plowing unit, the preferred method of adjusting the soil cultivation depth is the height-force method with a mixing coefficient of α = 0.5. With this method, the value of the integral criterion is respectively 1.4 and 1.2 times lower than with force and height adjustment methods.

Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle

Energy management strategies play an essential role in improving fuel economy and extending battery lifetime for fuel cell hybrid electric vehicles. However, the traditional energy management strategy ignores the lifetime of the battery for good fuel economy. To overcome this drawback, a battery longevity-conscious energy management predictive control strategy is proposed based on the deep reinforcement learning algorithm predictive equivalent consumption minimization strategy (DRL-PECMS) in this study. To begin with, the back-propagation neural network is devised for predicting demand power, and the predictive equivalent consumption minimum strategy (PECMS) is proposed to improve the hydrogen consumption. Then, in order to improve the battery durability performance, the deep reinforcement learning algorithm is utilized to optimize the battery power and improve battery lifetime. Finally, numerical verification and hard-ware in the loop experiments are conducted to validate hydrogen consumption and battery durability performance of the proposed strategy. The validation results show that, compared with CD/CS and SQP(Sequential Quadratic Programming), the PECMS combined can achieve better fuel economy with the fuel consumption reduction by 55.6 % and 5.27 %, which effectively improves the fuel economy. The DRL-PECMS can reduce the effective Ah-throughput by 3.1 % compared with the PECMS. The numerous validations and comparisons demonstrate that the proposed strategy effectively accomplishes the trade-off optimization between energy consumption and battery durability performance.

Potential of applying the thermochemical recuperation in combined cooling, heating and power generation: Optimized recuperation regulation with syngas storage

In the traditional combined cooling, heating, and power (CCHP) systems, the frequently changed user loads result in unfavorable supply-demand matching characteristics, which weakens the practical operational regulation performance. In this work, the thermochemical recuperation (TCR) with syngas storage method is proposed to optimize the system regulation characteristics and enhance the waste heat recovery. Various building energy load scenarios are also adopted to comprehensively evaluate the modified CCHP system operation characterizes. Meanwhile, with the consideration of reasonable operation regulation of syngas storage and multi-energy demands supplementary between different buildings, the coordinated system energy scheduling capacity are deeply explored to investigate the practical system application feasibility. The results indicate that under the designated operating conditions, the cooling/heating-to-power output ratios have been adjusted from 2.45 and 2.73 to 0.24–2.55 and 0.64–2.85, respectively. Additionally, with the efficient operation regulation process of TCR and syngas storage, the annual average energy utilization efficiency of the hotel, hospital, office and shopping center will be increased by 3.88%, 3.85%, 4.50% and 3.98%. Besides, through the synergistic complementary of syngas storage and adjustment between shopping center and hospital, the annual average syngas storage utilization rate of the shopping center can be increased from 16.99% to 97.41%, with the annual fuel consumption reduction of 5.78% in hospital scenario. This paper provides a combined TCR and syngas storage regulation method for the CCHP system, which enhances the system energy output flexibility and waste heat recovery capabilities in the distributed energy system application.

Revolutionizing Aviation: Pressure Assisted Electromagnetic Braking System (PAEBS) for Sustainable Air Travel

Air transportation is integral to modern society, facilitating global connectivity and access to remote regions. Nevertheless, challenges such as runway length requirements, landing/take-off accidents, airport congestion, and fuel inefficiencies necessitate innovative solutions. This research paper introduces the Pressure Assisted Electromagnetic Braking System (PAEBS), a self-sustained braking and accelerating system designed to address these challenges and enhance air travel safety, accessibility, and sustainability. The integration of the PAEBS is a seamless process, necessitating no significant alterations to the existing hardware of commercial aircraft, as its operation occurs beneath the runway surface. This innovation has the remarkable capacity to reduce the required runway length for aircraft landing and take-off by up to half. Traditionally, a runway extends to approximately 3000 meters, but with the implementation of PAEBS, this length can be halved to a mere 1500 meters. Preliminary calculations indicate the potential for substantial fuel consumption reductions, ranging from 3% to 4.5% (Rowland, n.d.) [1], contingent upon the size of the airliner. The adoption of this system holds the promise of reducing worldwide commercial jet fuel consumption by a significant 9 billion litres annually (stat, n.d.) [2], which would result in substantial cost savings for airliners, estimated at approximately $4.2 billion per year.