Fuel Consumption Optimization Research Articles

Energy-efficient operation of diesel–electric locomotives using ahead path data

Diesel–electric locomotives have significant fuel consumption. In this study, fuzzy look-ahead control is considered as an online approach for fuel consumption optimization. A fuzzy controller will modify the desired speed profile by accounting for the gradient and speed limits of the path ahead. Journey time increment is used as an optimization constraint. The existing models for train motion simulation are calculating the fuel consumption by an indirect index. A new model for train-movement simulation is proposed to calculate fuel consumption more accurately. This model considers the locomotive subsystems and satisfies the experimental fuel consumption data specified in the locomotive's catalog. Simulation of a train with a GM Sd40-2 on three local tracks showed considerable reduction in fuel consumption along with an acceptable journey time increment. Simulation results also showed that fuzzy look-ahead controller has very faster calculations in comparison with the controller based on the dynamic programming method.

Combining artificial neural network and multi-objective optimization to reduce a heavy-duty diesel engine emissions and fuel consumption

Nondominated sorting genetic algorithm II (NSGA-II) is well known for engine optimization problem. Artificial neural networks (ANNs) followed by multi-objective optimization including a NSGA-II and strength pareto evolutionary algorithm (SPEA2) were used to optimize the operating parameters of a compression ignition (CI) heavy-duty diesel engine. First, a multi-layer perception (MLP) network was used for the ANN modeling and the back propagation algorithm was utilized as training algorithm. Then, two different multi-objective evolutionary algorithms were implemented to determine the optimal engine parameters. The objective of the present study is to decide which algorithm is preferable in terms of performance in engine emission and fuel consumption optimization problem.

Automatic Transmission Gear Ratio Optimization and Monte Carlo Simulation of Fuel Consumption with Parasitic Loss Uncertainty

Automatic Transmission Gear Ratio Optimization and Monte Carlo Simulation of Fuel Consumption with Parasitic Loss Uncertainty

Fuel consumption optimization in air transport: a review, classification, critique, simple meta-analysis, and future research implications

ObjectiveThis paper presents a review, classification schemes, critique, a simple meta-analysis and future research implication of fuel consumption optimization (FCO) literature in the air transport sector. This review is based on 277 articles published in various publication outlets between 1973 and 2014.MethodologyA review of 277 articles related to the FCO in air transport was carried out. It provides an academic database of literature between the periods of 1973– 2014 covering 69 journals and proposes a classification scheme to classify the articles. Twelve hundred of articles were identified and reviewed for their direct relevance to the FCO in air transport. Two hundred seventy seven articles were subsequently selected, reviewed and classified. Each of the 277 selected articles was categorized on four FCO dimensions (Aircraft technology & design, aviation operations & infrastructure, socioeconomic & policy measures, and alternate fuels & fuel properties). The articles were further classified into six categories of FCO research methodologies (analytical - conceptual, mathematical, statistical, and empirical- experimental, statistical, and case studies) and optimization techniques (linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms). In addition, a simple meta-analysis was also carried out to enhance understanding of the development and evolution of research in the FCO.Findings and conclusionsThis has resulted in the identification of 277 articles from 69 journals by year of publication, journal, and topic area based on the two classification schemes related to FCO research, published between, 1973 to December- 2014. In addition, the study has identified the 4 dimensions and 98 decision variables affecting the fuel consumption. Also, this study has explained the six categories of FCO research methodologies (analytical - conceptual, mathematical, statistical, and empirical-experimental, statistical, and case studies) and optimization techniques (linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms). The findings of this study indicate that the analytical-mathematical research methodologies represent the 47 % of FCO research. The results show that there is an increasing trend in research of the FCO. It is observed that the number of published articles between the period 1973 and 2000 is less (90 articles), so we can say that there are 187 articles which appeared in various journals and other publication sources in the area of FCO since 2000. Furthermore there is increased trend in research on FCO from 2000 onward. This is due to the fact that continuously new researchers are commencing their research activities in FCO research. This shows clearly that FCO research is a current research area among many research groups across the world. Lastly, the prices of jet fuel have significantly increased since the 2005. The aviation sector’s fuel efficiency improvements have slowed down since the 1970s–1980s due to the slower pace of technological development in engine and aerodynamic designs and airframe materials.We conclude that FCO models need to address the composite fuel consumption problem by extending models to include all the dimensions, i.e. aircraft technology & design, aviation operations & infrastructure, socioeconomic & policy measures, and alternative fuels & fuel properties. FCO models typically comprise all the four dimensions and this reality need to be taken into account in global FCO models. In addition, these models should have objectives or constraints to evaluate the aircraft sizes according to market structure, impact of various policy measures on fuel burn, and near term potential alternative fuel options in the global FCO problem. In the models reviewed, we evaluated that, only the few authors considered these factors. The literature identifies 98 decision variables affecting the fuel consumption related to various dimensions in air transport. So we can conclude that this analysis could represent the informational framework for FCO research in air transport.Future scopeOur analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of optimization techniques in fuel consumption of air transport. The addressed dimensions & decision variables could be of potential value to future researchers on the aviation fuel consumption optimization research and is also capable of further refinements. In future, for fuel consumption optimization the explored decision variables could be checked for their reliability and validity and a statistically significant model with minimum number of decision variable could be developed. Further, on the basis of this statistical significant model and with the best market requirement for transport aircraft, the researchers can frame the objective function for fuel consumption minimization problem & decide their dependent variables, independent variables, constant, and constraints. Furthermore, this study will also provide the base for fuel conservation, energy efficiency, and emission reduction in the aviation sector.

Optimization of Fuel Consumption in Compressor Stations

Natural gas passing through pipelines is transported by means of compressor stations that are installed in pipeline-network systems. These stations are usually installed at intervals greater than 60 miles to overcome pressure loss, and typically, they consume approximately 3 to 5% of the transported gas, making the problem of how to optimally operate the compressors driving the gas in a pipeline network important. The objective function of the optimization model is a nonlinear mathematical relationship. The model has been studied to minimize the fuel consumption in the compressor stations and to obtain suitable decision-making variables. Genetic algorithms are used as the optimization methodology, and the software program Lingo (Thompson 2011) is used to compare the optimization results. Two cases of centrifugal compressor stations with different performances are studied. The optimization model aims to improve the fuel consumption of the compressor stations in the pipeline network according to the conditions of the transmissions.

Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring

Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver.

Data-based speed-limit-respecting eco-driving system

The paper describes application of data-based Bayesian approach to model identification and control problems in the field of fuel consumption optimization for conventional vehicles. The main contributions of the presented approach are: (i) analysis of data measured on a driven vehicle; (ii) data-based model construction, its real-time estimation and adaptation; (iii) control criterion using simultaneously setpoints for fuel consumption and speed; and (iv) universal recursive Bayesian algorithms of estimation and control implemented as semi-automatic eco-driving system. Experiments with real data report reduction in fuel consumption.

Research on Fuel Optimization of the Formation Establishment Based on Double-Impulse

In view of fuel consumption problem of the formation establishment, starting from the fly around equation, using double-impulse, the optimization is simulated based on the Niche Genetic Algorithm (NGA). The simulation result shows that the NGA is a good way to solve the fuel consumption optimization problem of formation establishment, and the fuel consumption when the phase determined is greater than the fuel consumption when the phase not determined.

Research on the Fuel Consumption Optimization of Multi-Satellites Formation Establishment

In view of the multi-satellite formation establishment, the fuel consumption was optimized based on the Niche Genetic Algorithm. Some conclusions were drawn.

Evolving base for the fuel consumption optimization in Indian air transport: application of structural equation modeling

The article aims to evolve the base for fuel consumption optimization (FCO) in Indian air transport industry. The objective of this paper is to design the methodology and to develop five facet model of fuel consumption optimization (FCO). Limited researches have been conducted to explore influencing factors for FCO in air transport industry. To fill this gap, this study proposes the model of FCO, and investigates key factors affecting FCO. The research steps included exploratory factor analysis, confirmatory factor analysis, and testing of structural model. In the first stage exploratory factor analysis (EFA) was used to provide the grouping of variables underline the complete set of item based upon the strong correlation. In the second stage, a confirmatory factor analysis (CFA) was used to specify and estimate of one or more hypothetical models of factor structure, each of which propose a set of latent variables to account for covariance within a set of observed variables. In the third stage, we used the Structural Equation Modeling (SEM) technique and empirically tested the relationships between fuel consumption optimization and aircraft operations (AO), aircraft technology & design (ATD), social-economic & political (SEP), aviation infrastructural (AI), and alternate fuels & fuel properties (AFP). The results and applications of structural equation modeling (SEM) evolve variety of findings. (1) Aircraft operations (AO), aircraft technology & design (ATD), socio-economic & political issues (SEP), aviation infrastructure (AI), and alternative fuels & fuel properties (AFP) are proved to be the five key influence factors with respect to the Indian context and have positive effect on FCO. (2) Among the five influence factors for FCO, aircraft technology & design exhibits the strongest effect on FCO, followed by aircraft operation, alternative fuels & fuel properties, socio-economic & political, and aviation infrastructure. (3) The highest squared correlation was observed between aircraft technology & design and aircraft operations. This study has provided empirical justification for the proposed research framework which describes the relationships between FCO and its dimensions. This has developed an integrated model of FCO, with the purposes of identifying the key factors affecting the FCO. The knowledge of relationship among variables can lead to frame objective function, constraints, and set of equations pertaining situations with regard to Indian scenario. To constitute the equations the data of identified critical factors with regard to Indian scenario can be utilized which will lead to develop optimization based model for fuel consumption that leaves the scope for further study. This study produces the results which represent the base for optimum solution of fuel consumption on which future researchers can target.

Fuel Consumption Optimization and Noise Reduction in a Spark-Ignition Turbocharged VVA Engine

Fuel Consumption Optimization and Noise Reduction in a Spark-Ignition Turbocharged VVA Engine

Double Optimization of Fuel Consumption and Speed Tracking

This paper presents automatic fuel consumption optimization with simultaneous keeping the recommended vehicle's speed. These tasks are closely related since a simple minimization of fuel consumption leads to stopping a vehicle. The proposed “double” optimization is performed online using combination of two controllers. The first of them is based on fully probabilistic design (FPD) under Bayesian methodology. It optimizes the “driver-vehicle” closed loop with the aim to save fuel and keep the recommended speed, using externally given setpoints. Optimized values serve as setpoints for PID controller, which provides necessary setpoint tracking. Research is performed in collaboration with Škoda auto ( www.skoda-auto.com ).

Fuel Consumption Optimization: Early Experiments

The paper deals with a problem of fuel consumption optimization. Solutions existing in this field are mainly based on the various conceptual approaches such as hybrid and electric vehicles. However, it leads to high initial cost of a vehicle. The approach presented in this paper aims at conventional vehicles and is based on recursive algorithms of system identification and adaptive quadratic optimal control under Bayesian methodology. Experiments with real data measured on a driven vehicle are provided.

Using Genetic Algorithm for Fuel Consumption Optimization of a Natural Gas Transmission Compressor Station

In this study the goal is to optimize the compressors’ fuel consumption through manipulating the compressors’ affecting parameters as well as the operating condition parameters of the turbines and the air coolers within a gas compression station unit in operation phase by using Genetic Algorithm. The simulation part is written in a custom-built computer program and is checked by known software and optimization is through the use of Genetic Algorithm. By considering the complexity of these systems, GA is used to do the optimization part in a proper manner. The results show that in compressor stations with same turbo compressor packages which are designed in recent years, despite nonlinear relations, linear load sharing will be the most optimized choice (the effect of air coolers and line pack has been studied too). And also Genetic Algorithm optimization method is a good one for optimization of gas transmission systems.

Optimized energy management control for the Toyota Hybrid System using dynamic programming on a predicted route with short computation time

Among the general problematic of the HEV power trains, the most critical point is the determination of the power-split ratio between the mechanical and the electrical paths, known as the energy management strategy (EMS). Many EMS are proposed in the literature, and can be grouped in two categories: the local optimization EMS and the global optimization EMS. The local optimization category corresponds to the EMS based on human expertise and the knowledge of the power train components efficiency maps. Thus, the local optimization EMS manages the power train operations by referring to predefined rules. The drawback of such strategies is that it brings an instantaneous fuel consumption optimization, and does not fully optimize the fuel consumption over the whole trip. Therefore, additional fuel savings are still possible. This paper presents an overall optimized predictive EMS for the Toyota Hybrid System (THS-II) power train of the Prius. The proposed EMS is based on Dynamic Programming (DP), where the prior knowledge of the route is required in order to predetermine the power-split ratio and optimize the fuel consumption for the whole predicted route. The DP EMS proposed for the THS-II power train is designed with a very short computation time, intended to be implemented in real-time applications. The potential of this DP-controller in reducing fuel consumption on regulatory cycles are computed and compared to a rule-based controller and to the Prius published fuel consumption results. Finally, the fuel reduction enhancements of the DP-controller are computed for real road tests achieved on a MY06 Prius in Ile-de-France, by comparing to the associated observed consumption measurements.

An Iterated Local Search Algorithm for Fuel Consumption Optimization of Vehicle Routing Problems

The iterated local search algorithm has been widely used in combinatorial optimization problems. A new fuel consumption objective for the vehicle routing problems was presented in this paper. A fuel consumption modal of the vehicle load is introduced and an improved iterated local search algorithm is used for the problem. An initial solution is generated by the Solomon I1 algorithm, and then the iterated local search algorithm is proposed for the fuel consumption optimization.

Fuel Consumption Optimization for Vehicle Routing Problem with Time Windows

Fuel Consumption Optimization for Vehicle Routing Problem with Time Windows

Design and Fuel Consumption Optimization for a Bio-Inspired Semi-floating Hybrid Vehicle

Based on a bionic concept and combing air-cushion techniques and track driving mechanisms, a novel semi-floating hybrid concept vehicle is proposed to meet the transportation requirements on soft terrain. First, the vehicle scheme and its improved duel-spring flexible suspension design are described. Then, its fuel consumption model is proposed accordingly with respect to two vehicle operating parameters. Aiming at minimizing the fuel consumption, two Genetic Algorithms (GAs) are designed and implemented. For the initial one (GA-1), despite getting an acceptable result, there still existed some problems in its optimization process. Based on an analysis of the defects of GA-1, an improved algorithm GA-2 was developed whose effectiveness and stability were embodied in the optimization process and results. The proposed design scheme and optimization approaches can provide valuable references for this new kind of vehicle with promising applications in the areas of agriculture, petroleum industry, military or scientific exploitations, etc.

Development of a fuel consumption optimization model for the capacitated vehicle routing problem

Fuel consumption accounts for a large and increasing part of transportation costs. In this paper, the Fuel Consumption Rate (FCR), a factor considered as a load dependant function, is added to the classical capacitated vehicle routing problem (CVRP) to extend traditional studies on CVRP with the objective of minimizing fuel consumption. We present a mathematical optimization model to formally characterize the FCR considered CVRP (FCVRP) as well as a string based version for calculation. A simulated annealing algorithm with a hybrid exchange rule is developed to solve FCVRP and shows good performance on both the traditional CVRP and the FCVRP in substantial computation experiments. The results of the experiments show that the FCVRP model can reduce fuel consumption by 5% on average compared to the CVRP model. Factors causing the variation in fuel consumption are also identified and discussed in this study.

Optimization of Fuel Consumption in Micro-Turbines

Distributed generation (DG), and especially microturbine generation (MTG) system, plays a significant role in Smart Grids. One of the most important variables of a MTG system is the fuel consumption. This paper defines a problem on fuel consumption of MTs for a sample load change that can be used for a 24-hour load change pattern. The aim is to optimize the speed governer parameters in order to minimize the fuel consumption of MTG. Differential Evolutionary Algorithm (DEA) is employed to solve the problem. The stability issue of the MTG system is also discussed in the paper. The simulation results show the effectiveness of a fine-tuned speed governer by using the proposed method.