Fuel Consumption Optimization Research Articles

Optimization of Fuel Consumption by Controlling the Load Distribution between Engines in an LNG Ship Electric Propulsion Plant

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction.

Response Surface Optimization of Brake Thermal Efficiency and Specific Fuel Consumption of Spark-Ignition Engine Fueled with Gasoline–Pyrooil and Gasoline–Pyrooil–Ethanol Blends

<div>The present study explores the performance of high-density polyethylene (HDPE) pyrooil and ethanol blends with gasoline in SI engine using statistical modeling and analysis using response surface methodology (RSM) and the Anderson–Darling (AD) residual test. The pyrooil was extracted from HDPE through pyrolysis at 450°C and then distilled to separate the liquid fraction. Two blends were prepared by combining pyrooil and gasoline, and pyrooil–ethanol mixture (volume ratio of 9:1) and gasoline, both at volumetric concentrations ranging from 2% to 8% to evaluate brake thermal efficiency (BTE) and specific fuel consumption (SFC) in a SI engine. An experimental matrix containing speed, torque, and blend ratio as independent variables for both blends were designed, analyzed, and optimized using the RSM. The results show that a 4% blend of pyrooil with gasoline (P4) and a 6% blend of pyrooil–ethanol mixture with gasoline (P6E) were optimum for an SI engine. Also, the experimental findings show that the P6E blend exhibits 11% higher BTE and 11.82% lower SFC compared to base fuel (pure gasoline), and 7.55% higher BTE and 6% lower SFC than P4. From the AD test, the residuals for BTE and SFC follow a normal distribution. The results conclude that distilled HDPE pyrooil could be used in SI engines at concentrations of P4 and P6E without requiring engine modification.</div>

Technical evaluation of low-carbon fuels as a decarbonization pathway of the light-duty transport sector

The need to rapidly reduce greenhouse gas emissions from the transportation sector has renewed interest in low-carbon fuels, both from the production and utilization standpoint. The maturing of alternative processes to synthesize fuels from atmospheric CO2 and renewable non-edible feedstocks constitutes an interesting proposition, which needs to be matched by favorable drop-in properties and environmental performance once applied to internal combustion engines. To this end, the authors investigate the use of diesel-like high-reactivity and low-reactivity LCFs in a light-duty compression-ignition engine by experimentally assessing their implementation with existing hardware (drop-in) and discussing possible modifications to achieve improved results through calibration. The results revealed that high-reactivity fuels could operate under similar conditions to the standard diesel fuel, exhibiting similar efficiency and soot emissions combined with important reductions in NOx emissions. On the other hand, low-reactivity LCFs show advantages in promoting alternative combustion concepts because of the longer ignition delays, which make it possible to use lower injection pressures and have more stratified air–fuel mixtures, but at the same time, the results revealed that the implementation of these fuels might suffer from hardware limitations which worsens the combustion stability and the operation at low-load. Although the fuels tested exhibited variations in Tank-to-Wheel due to their carbon content and fuel economy, it was found that Well-to-Wheel CO2 intensity is eventually dominated by the renewable content of the fuel and the CO2 is greatly impacted by its production pathway, rather than the fuel consumption optimization. Finally, the study confirmed that, for the successful adoption of oxygenated low-carbon fuels (including alcohols), appropriate material selection in the engine fuel system and fuel additives are necessary to prevent components and seal performance degradation.

Development of a Genetic Algorithm-Based Control Strategy for Fuel Consumption Optimization in a Mild Hybrid Electrified Vehicle’s Electrified Propulsion System

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs’ development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development.

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control

Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model.

Optimization of Fuel Consumption for an Offshore Supply Tug Using a Backtracking Algorithm

This paper introduces a backtracking algorithm for the fuel optimization of an offshore supply tugboat. The aim is to determine the optimal cruising speed that minimizes fuel use under operational constraints. Many older vessels in local offshore regions face limitations in adopting new fuel efficient technologies due to financial constraints. Hence, alternative cost-effective methods are needed to improve energy use and reduce emissions from these older ships. We propose using a backtracking algorithm to systematically explore all potential speed solutions and find the optimal one. Operational constraints like time restrictions and weather factors are incorporated during the optimization. The algorithm branches out to potential solutions and backtracks when they violate constraints. This allows for the pruning of infeasible solutions to improve the computational efficiency. The study provides the basis for optimizing offshore voyages as a sustainable transportation activity. Further work could expand the technique by adding parameters and real-time data.

Controller Design for Optimizing Fuel Consumption of Truck Platoon on Hilly Roads

Platoons consisting of automated and connected vehicles show great potential in reducing fuel or energy consumption. However, the fuel consumption optimization problem for truck platoons traveling on hilly roads has not been investigated thoroughly. To address that problem, a hierarchical control framework is proposed in this paper as follows: (1) The supervising layer is responsible for generating the fuel-oriented optimal speed profile based on the terrain information; (2) The distributed layer consists of an LQR feedback controller, a DMPC feedforward controller and a tube integration method to integrate the two controllers; it receives the optimal speed profile from the supervising layer and yields the control input to the individual vehicle. In this paper, a novel optimal speed profile generation method is proposed, a novel integration of tube method is applied, and the stability performance is analyzed rigorously. Simulations based on a real hilly road are conducted, and the performance of the proposed controller is evaluated regarding the platoon stability, fuel consumption and computation efficiency. The results of the simulation show that the controller is capable of maintaining the string stability of the truck platoon and reducing fuel consumed on hilly roads while improving computation efficiency.

Toward the optimization of fuel consumption on container-on-barge operations in river transportation

The national policy for fuel prices in Colombia and the management of the fluvial route of the Magdalena River has led to effects on productivity and benefits for companies and maritime ports due to contracts for dredging that guarantee drafts for six months. Moreover, increases in fuel prices have generated higher operating costs than those of previous years. Data analysis is a crucial decision-making tool for operational-level decision-makers in companies, supported by the measures of indicators, competitiveness, and productivity in terminal services. The transportation and shipping of cargo entail significant costs in terms of fuel, labor, and time, as well as other costs that must be met for meeting customer commitments.This paper presents a methodology for fuel consumption optimization in the specific case of transportation by a Colombian river transportation company, using regression models and response-surface methodology as optimization tools. This approach yields significant results, reducing fuel consumption by approximately 25% through a combination of various factors involved in barge transportation operations, including labor, fuel, velocity, weight, and other operational aspects. The primary contribution of this research lies in its promotion of sustainable operations through cost reduction and the reduction of fuel consumption reduction. The use of statistical optimization techniques makes this methodology applicable to similar cases of shipping and transportation. It additionally addresses national policy changes related to emissions, fuel costs, and sustainability, thereby aligning with a focus on energy transition to promote cost reduction.

Fuel Rate Prediction for Heavy-Duty Trucks

Fuel cost contributes significantly to the high operation cost of heavy-duty trucks. Developing fuel rate prediction models is the cornerstone of fuel consumption optimization approaches for heavy-duty trucks. However, limited by accurate features directly related to the truck’s fuel consumption, state-of-the-art models show poor performance and are rarely deployed in practice. In this paper, we use the truck’s engine management system (EMS) and Instant Fuel Meter (IFM) to collect a three-month dataset during the period of December 2019 to June 2020. Seven prediction models, including linear regression, polynomial regression, MLP, CNN, LSTM, CNN-LSTM, and AutoML, are investigated and evaluated to predict real-time fuel rate. The evaluation results show that the EMS and IFM dataset help to improve the coefficient of determination of traditional linear/polynomial models from 0.87 to 0.96, while learning-based approach AutoML improves the coefficient of determination to attain 0.99. Besides, we explore the actual deployment of fuel rate prediction with transfer learning and path planning for autonomous driving.

IT-Assisted Optimisation of Fuel Consumption in Air Transport

Die Luftfahrt ist nach wie vor ein wichtiges Transportmittelfür Passagiere und Fracht. Im Jahr 2022 war Europa wiederauf etwa 85 % des Niveaus von 2019 gestiegen. Allerdings sind TreibstoffTreibstoffeinsparung und Emissionsreduzierung habenin den letzten Jahren immer wichtiger geworden.Im Rahmen einer Doktorarbeit wurden Möglichkeiten zur Reduzierung des KraftstoffKraftstoffverbrauchs durch Verringerung der Kraftstoffreserveuntersucht. Das Hauptaugenmerk dieses Papiers liegt auf den Chancen und Herausforderungen, diedie sich mit der Einführung neuer Treibstoffvorschriften in dereuropäischen Luftfahrtvorschriften. Betreiber miteinem angemessenen Sicherheitsniveau können maßgeschneiderteBestimmungen anwenden. Dies erfordert den Nachweis des Sicherheitsniveausniveaus. Dies wird durch die Festlegung spezifischer SicherheitsLeistungsindikatoren (SPI), deren Einhaltungdann während des Betriebs kontinuierlich überwacht und bewertetBetrieb. Dies erfordert die Sammlung und Auswertung vonentsprechend großer Datenmengen. Dies ist nurnur mit entsprechenden IT-Anwendungen möglich. Andererseits ermöglichen leistungsbezogene Regelungeneine individuellere Umsetzung auf und durch diejeweiligen Unternehmen durch den Nachweis eines entsprechendenentsprechenden Sicherheitsniveaus. Dazu werden Sicherheitsindikatoren verwendetverwendet, die aus verschiedenen vorhandenen Daten gewonnen undaus verschiedenen vorhandenen Daten gewonnen und ausgewertet werden müssen. Eine große Menge an Daten,die nur mit Hilfe verschiedener IT-Anwendungen gesammelt und verarbeitet werden könnenIT-Anwendungen gesammelt und verarbeitet werden können, stellt eine Herausforderung dar.Unternehmen können jedoch von denentsprechende Vorteile, wenn sie diese bewältigen können.

Fuel Consumption Prediction and Optimization Model for Pure Car/Truck Transport Ships

Predicting and optimizing ship fuel use is a crucial technology for lowering greenhouse gas emissions. Unfortunately, existing research is rarely capable of developing fuel consumption forecasts and optimization models for a particular transport system. This study develops a fuel consumption prediction model based on machine learning and a fuel consumption optimization model based on particle swarm optimization for ships. We studied nearly ten years of big data from a large Korean pure car and truck shipping company (PCTC), which contained 16,189 observations from 2012 to 2021. Results indicate that the XGBoost deep learning model outperforms conventional prediction models at the stage of fuel consumption prediction, with an R2 of 0.97. Furthermore, in the fuel consumption optimization stage, the particle swarm optimization method can effectively reduce fuel consumption. This study helps PCTC companies control shipping costs and save energy. Insights for shipping businesses to meet environmental demands are provided as well.

Investigation of Kalina Cycle for Power Generation from Heat Dissipation of Tarasht Power Plant

Fuel consumption optimization in thermal power plants is one of the essential topics in the saving energy field in the world. Then; it is necessary to review and provide solutions to increase efficiency. The present manuscript has recovered the heat dissipation from power plant stacks. The Kalina cycle was studied to use exhaust gases to generate power in the Tarasht power plant. Net power output is calculated at about 2080 kW, which increases the total power production of the plant by about 5%. According to environmental analysis, this cycle will cause less damage to the environment due to lowering the temperature of the exhaust gases in the atmosphere and not using additional fossil fuels. Therefore; it is a good solution for using heat dissipation from power plants. The only thing to consider in this solution (based on economic analysis) is the high construction cost compared to other power plant units.

Fuel consumption optimization for a plug-in hybrid electric bus during the vehicle-following scenario

In this paper, a robust SOC (state of charge of battery) trajectory planning method is proposed for a plug-in hybrid electric bus (PHEB) in the vehicle-following scenario. Firstly, the trajectory areas of SOC are planned based on the Taguchi robust design (TRD) considering the noises of the vehicle mass and driving cycles. Moreover, the reliability of the result of TRD is verified based on the Monte Carlo simulation (MCS). Then, the longitudinal driving control of PHEB is transformed into a multi-objective optimal problem that incorporates driving safety, fuel economy and ride comfort in the vehicle-following scenario. Finally, based on a novel nonlinear model predictive control (NMPC), the electric motor, engine and brake system are collaboratively optimized to comprehensively adjust the dynamic and economy of PHEB. The results show that the planning trajectory areas of SOC effectively improved the fuel economy compared with the pre-planning. Furthermore, based on the novel NMPC, the fuel consumption of the proposed method is lower than the method that just adds an energy management strategy to adaptive cruise control (ACC + EMS). The hardware-in-loop test results show that the proposed method can be implemented into the vehicle controller, and the results between the HIL and offline simulations are largely coincident.

Investigation of Heavy-Duty Vehicle Chassis Dynamometer Fuel Consumption and CO2 Emissions Based on a Binning-Reconstruction Model Using Real-Road Data

Global warming is directly related to heavy-duty vehicle fuel consumption and greenhouse gas (CO2 mainly) emissions, which, in China, are certified on the vehicle chassis dynamometer. Currently, vast amounts of vehicle real-road data from the portable emission measurement system (PEMS) and remote monitoring are being collected worldwide. In this study, a binning-reconstruction calculation model is proposed, to predict the chassis dynamometer fuel consumption and CO2 emissions with real-road data, regardless of operating conditions. The model is validated against chassis dynamometer and PEMS test results, and remote monitoring data. Furthermore, based on the proposed model, the fuel consumption levels of 1408 heavy-duty vehicles in China are analyzed, to evaluate the challenge to meet the upcoming China fourth stage fuel consumption limits. For accumulated fuel consumption based on the on-board diagnostic (OBD) data stream, a predictive relative error less than 5% is expected for the present model. For bag sampling results, the proposed model’s accuracy is expected to be within 10%. The average relative errors between the average fuel consumption and the China fourth stage limits are about 3%, 8%, and 0.7%, for current trucks, tractors, and dump trucks, respectively. The urban operating condition, with lower vehicle speeds, is the main challenge for fuel consumption optimization.

Ecological Cooperative Adaptive Cruise Control for Heterogenous Vehicle Platoons Subject to Time Delays and Input Saturations

Public concerns about energy crisis and environmental issues lead to higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles, and ecological cooperative adaptive cruise control (eco-CACC) is considered to be effective in reducing fuel consumption and greenhouse gas emissions of vehicle platoons. In this paper, an eco-CACC strategy is presented for heterogeneous platoons with time delays and input saturations to achieve platoon stability, fuel economy, riding comfort and driving efficiency. The proposed eco-CACC strategy includes distributed linear feedback control (DLFC) for following vehicles and model predictive control (MPC) for the leading one. To obtain platoon stability, the DLFC protocol is designed using state errors between an ego vehicle and its preceding one, and the frequency domain method is employed to derive the sufficient conditions of platoon stability for the DLFC protocol. Then, to achieve fuel economy, passenger comfort and driving efficiency without violating the different input saturations of vehicles, the constrained fuel consumption optimization problem of the leading vehicle (CFCO-LV) is formulated based on delay MPC, where the weighted sum of the overall fuel consumption and velocity band-stop functions of vehicle platoon is minimized. To quickly obtain the MPC controller, an improved particle swarm optimization (PSO) algorithm is used to solve the CFCO-LV problem. Simulations are implemented to validate the effectiveness of the proposed eco-CACC strategy, and the simulation results demonstrate that, compared with benchmark, the proposed strategy can save 2.19%~8.24% of fuel for the heterogeneous platoon under different velocity ranges.

Real-Time Optimization of Fuel-Consumption and Travel-Time of CAVs for Cooperative Intersection Crossing

Connected and Autonomous Vehicles (CAVs) have the potential to revolutionize road transportation in terms of safety and efficiency, offering important societal, economic, and environmental benefits. In this work, we harness CAV capabilities, such as seamless connectivity and fine-grained control to safely and efficiently coordinate a set of CAVs crossing an unsignalized intersection. Coordinated control is achieved by generating an acceleration profile for each CAV to simultaneously optimize fuel consumption and travel time. As the resulting problem is non-convex and challenging to solve, we design a novel centralized solution approach. First, we construct a relaxed reformulation of the problem by ignoring certain safety constraints to eliminate interdependence between the trajectories of CAVs traveling in the same lane. Because the relaxed problem is still non-convex, we develop a custom convex-concave procedure that yields the travel time of each CAV to traverse the intersection without lateral collisions. Finally, the derived travel times are utilized to construct collision-free trajectories for all CAVs using convex optimization. Elaborating on principles from the centralized approach, we also introduce a decentralized scheme that solves the problem on a vehicle-by-vehicle basis. Extensive simulation results substantiate the effectiveness of the proposed solution approaches in terms of solution quality and execution speed. Simulation results also highlight the importance of optimizing the trade-off between travel time and fuel consumption as small sacrifices in travel time lead to substantial fuel savings. Finally, simulation results indicate that the simultaneous coordination of a set of CAVs yields significant performance benefits compared to decentralized control.

Optimization of Aircraft Fuel Consumption During the Climb Phase

Optimization of Aircraft Fuel Consumption During the Climb Phase

Delayed Fuel Consumption Optimization System

In this work, we considered the problem of optimizing the crowd during aircraft delays before landing. Having examined the statistics, we can conclude that there was a constant increase in passenger traffic from 2013 to 2018. But also due to the constant growth of passenger traffic, the percentage of flights with delays is increasing. The problem is that that most of the flights accounted for a small percentage of the most "popular" airports (In Ukraine, 75 percent of all flights accounted for the airports of Boryspil and Zhuliany). Although since 2019 the number of air travel around the world has dropped sharply over time, the number of air travel will return to the previous number and even continue to grow. Also, a new problem has now arisen for air carriers - a significant increase in aviation fuel prices. According to the Mundi Index, since December 2021, there has been a constant increase in the price of fuel for aircraft. This is an additional incentive for airlines to optimize their routes in order to avoid unnecessary fuel costs. In this work, on the example of a flight for a Boeing 737 from Riga to Odessa, two parameters were considered, when changing which it is possible to minimize fuel costs during a delay. An algorithm was also developed to optimize crowd costs on flights with delays.

Economic analysis and investment in fuel industry considering risk management by analytical hierarchy process

Natural Gas production and distribution network plans are one of the most common ways to increase energy consumption efficiency. Due to the uncertainty of the economic parameters, the economic justification of this system is very difficult. In this research, the economic risk of investing in urban gas distribution systems has been done with the analytical hierarchy process (AHP). With the development and condensation of urban gas supply networks, accidents caused by gas leaks and other external factors have also increased. Therefore, in recent years, the issue of safety and risk analysis of urban gas networks has received special attention from responsible companies and other urban and safety experts. Gas distribution lines often pass in the vicinity of crowded areas and buildings, so any accident for these lines will lead to a lot of human and financial losses. In the present study, in order to improve the gas supply situation and reduce the risk of failure in Indonesia, urban gas pipelines have been studied to reduce the costs. In this paper, multi-criteria decision making method analysis is used to identify and evaluate the hazards of urban gas pipelines and the risk of occurrence of each hazard is calculated. There was a meaningful relationship based on economic-financial theories between economic factors and investment risk in the gas industry, which emphasizes the greater and more significant effect of exchange rate, failures and inflation rate variables in comparison with other research variables in the matter of controlling investment risk in the gas industry. The effects of changes in energy prices and the economic environment in explaining the behavior of investment risk in this industry have placed the next level of importance. The results showed that with the increase in energy prices, the economics of gas production and distribution risk management plans have improved and the regular trend of energy price increases can guarantee the profitability of fuel consumption optimization plans.

A predictive controller for real-time energy management of plug-in hybrid electric vehicles

Battery aging can degrade the energy efficiency of plug-in hybrid electric vehicles (PHEVs) significantly. This paper presents a novel intelligent real-time energy management strategy (EMS) for PHEVs, integrating battery life and fuel consumption optimization. The two-objective offline optimization problem is solved by the dynamic programming (DP) approach to obtain the globally optimal solutions. For the real-time implementation, a model predictive control (MPC) scheme is combined with an adaptive neuro-fuzzy inference system (ANFIS) model. DP carries out a receding-horizon optimization using future traffic information. Level-set functions are exploited within the DP algorithm to reduce numerical errors and decrease the computational effort of the baseline DP approach. Contrary to the real-time EMSs with a pre-determined state-of-charge (SOC) reference, an ANFIS model provides the SOC reference online. The proposed method is evaluated in simulation over multiple real-time driving cycles and compared with the DP results and two other real-time approaches. The effect of prediction horizon length is also studied. The simulation results demonstrate that the developed method can optimize battery life and fuel consumption. The results indicate 93%–97% matching to those of optimal controller, that is much better compared to the two other tested approaches.