Academic literature on the topic 'Fuel consumption minimisation'

The paper outlines a computationally efficient analytical method for evaluating the fuel consumption and the nitrogen oxide emissions during manoeuvres pertaining to the New European Driving Cycle. An integrated optimisation procedure is also included in the analyses with minimisation of the brake specific fuel consumption and minimisation of the nitrogen oxide emissions as objective functions. A set of optimum gear ratios are determined for a four-speed transmission, a five-speed transmission and six-speed transmission as the governing parameters in the optimisation process. The analysis highlights the determination of gear-shifting objective-driven strategies based on the minimisation of either of the declared objective functions. A reduction of 7.5% in the brake specific fuel consumption and a reduction of 6.75% in nitrogen oxide emissions are attainable in the best-case scenario for a six-speed transmission and a gear-shifting strategy based on the lowest brake specific fuel consumption for the case of an engine. The novel integrated analytical simulations and multi-objective optimisation have not been hitherto reported in literature. It provides the opportunity for an objective intelligent-based approach to the use of gear shift indicator technology. The results of this study also show that transmission optimisation can act as an effective and inexpensive mean to enhance the fuel efficiency and to reduce the emissions.

The paper addresses the impact of traffic light information availability in terms of fuel consumption and emissions by means of comparing three different scenarios that a driver of a diesel light-duty vehicle may face when trying to cover a particular route of 1 km with two traffic lights in between. The first scenario is that the driver does not know in advance the state of the traffic lights. The second scenario assumes that the driver knows the state of the traffic lights but has no modelling nor computation capabilities to solve the associated optimal control problem. In the third scenario, the driver knows in advance the state of the traffic lights and is also able to solve the corresponding optimal control problem that leads to fuel consumption or NOx emission minimisation. In this study, the vehicle-speed trajectories associated with the previously described three scenarios have been computed and then tested in a Euro 5 Diesel vehicle installed in a chassis dynamometer. The obtained results show that traffic light information is essential for fuel minimisation in urban conditions, promoting reductions of 7.5–12% and 13–32% for fuel consumption and NOx emissions in the studied case. In addition, differences in the engine-operating conditions for high efficiency and low NOx emissions may lead to extremely high fuel consumption when NOx minimisation is foreseen or viceversa.

Modelling and design of real-time energy management systems for optimising the operating costs of a fuel cell/battery electric vehicle are presented in this paper. The proposed energy management system consists of optimally sharing the propulsion power demand between the fuel cell and battery by enabling them to support each other for operating cost minimisation. The optimisation is achieved through real-time minimisation of a cost function, which accounts for fuel cell and battery degradation, hydrogen consumption and charge sustaining costs. A detailed analysis of each term of the overall cost function is performed and presented, which enables the development of a real-time, advanced energy management system for improving a previously presented simplified version using more accurate modelling and by considering cost function minimisation over a given time horizon. The performance of the proposed advanced energy management system are verified through numerical simulations over different driving cycles; particularly, simulations were performed in MATLAB-Simulink by considering a hysteresis-based energy management system and both simplified and advanced versions of the proposed energy management system for comparison.

An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.

The work sums up and briefly discusses solutions of soil tillage minimisation technologies and machinery, their agronomic, energetic, mechanic, economical and ecological aspects. Tillage minimisation is performed in directions: reducing number of passes, as well as tillage depth and intensity, joining operations, improving machine design and aggregation, using advanced more suitable technologies and machines, conducting tillage in optimal terms. Minimization of soil tillage is agronomically acceptable, energy, labour and cost saving action. Improvements in the machine design and use for the traditional soil tillage technologies allow to save 24- 36 % of energy (46-110 kWh/ha, which corresponds 12-27 kg/ha of fuel), to reduce labour consumption by 16- 22 %, as well as to cat tillage costs by 14-26 % (10-20 USD/ha). Soil tillage minimisation with ploughing reduces these indices up to two times, without ploughing – up to six times. Besides these actions there is preservation of soil and environment.

Improved fuel efficiency in hybrid electric vehicles requires a delicate balance between the internal combustion engine usage and battery energy, using a carefully designed energy management control algorithm. Numerous energy management strategies for hybrid electric vehicles have been proposed in literature, with many of these centered on the equivalent consumption minimisation strategy (ECMS) owing to its potential for online implementation. The key challenge with the equivalent consumption minimisation strategy lies in estimating or adapting the equivalence factor in real-time so that reasonable fuel savings are achieved without over-depleting the battery state of charge at the end of the defined driving cycle. To address the challenge, this paper proposes a novel state of charge feedback ECMS controller which simultaneously optimises and selects the adaption factors (proportional controller gain and initial equivalence factor) as single parameters which can be applied in real time, over any driving cycle. Unlike other existing state of charge feedback methods, this approach solves a conflicting multiple-objective optimisation control problem, thus ensuring that the obtained adaptation factors are optimised for robustness, charge sustenance and fuel reduction. The potential of the proposed approach was thoroughly explored over a number of legislative and real-world driving cycles with varying vehicle power requirements. The results showed that, whilst achieving fuel savings in the range of 8.40 −19.68% depending on the cycle, final battery state of charge can be optimally controlled to within ±5% of the target battery state of charge.

Space exploration has recently been growing at an increasing pace and has caused a significant burden to the environment, in particular, during the launch of rockets, when a large amount of fuel is burned and the exhaust gases are released in the air. For this case study, we selected the SpaceX Falcon Heavy reusable heavy-lift launch vehicle, which is one of the most promising rockets for the low-cost lifting of heavy payloads into orbit and beyond. We evaluated several strategies for optimisation of fuel consumption and for minimisation of environmental impact during launch through the atmosphere for the case of its first launch on February 6, 2018, when the rocket carried a red Tesla Roadster with a “Starman” in the direction toward Mars. In addition to the flight plan and Newtonian equations of motion, we have taken into account the thermodynamic properties of the rocket engines. Results are similar but slightly different if one minimises the total fuel consumption for the desired flight plan or if one minimises the environmental pollution during the initial stage of the launch through the atmosphere. The same methodology can be extended for launches in other directions including the Earth orbit and the Moon.

Power systems contain four generic parts, including production, transmission, dispatch distribution, and consumption. Generally, dispatch distribution between powerhouses modelled with the goal of minimisation in utilisation cost. However, Environmental concerns were given more attention to powerhouse emissions such as SO2, CO2 and NO cause to investigate modelling in recent researches. However consideration of the objectives of fuel cost and emission value in the dispatch distribution problems known as the eco-environmental dispatch distribution. Although Due to the paradox between the reduction of utility costs and external costs, different methods used.

Le réchauffement climatique, la pollution de l'environnement et l'épuisement des énergies pétrolières ont attiré l'attention de l'humanité dans le monde entier. Les véhicules électriques hybrides à pile à combustible (FCHEV), utilisant l’hydrogène comme carburant et n’émettant aucune émission, sont considérés par les organismes publics et privés comme l’un des meilleurs moyens de résoudre ces problèmes. Cette thèse de doctorat considère un FCHEV avec trois sources d'énergie: pile à combustible, batterie et supercondensateur, ce qui complique l'élaboration d'une stratégie de gestion de l'énergie (EMS) pour répartir la puissance entre différentes sources d'alimentation. Parmi les méthodes de gestion de l'énergie de la littérature actuelle, la stratégie de minimisation de la consommation équivalente (ECMS) a été sélectionnée car elle permet une optimisation locale sans connaissance préalable des conditions de conduite et cela en donnant des résultats optimaux.En raison de la faible densité énergétique du supercondensateur, sa consommation équivalente d'hydrogène est négligée dans la plupart des références bibliographiques, ce qui va non seulement à l'encontre de l'objectif de minimiser la consommation totale d'hydrogène, mais accroît également la complexité du système EMS en raison du besoin d'un système EMS supplémentaire pour calculer la demande en puissance du supercondensateur. Ainsi, une stratégie ECMS à programmation quadratique séquentielle (SECMS) est proposée pour prendre en compte le coût énergétique des trois sources d’énergie dans la fonction objectif. Une stratégie de contrôle basée sur des règles (RBCS) et une stratégie hybride (HEOS) a été également conçues pour être comparée à SECMS. La dégradation des sources d'énergie représente un défi majeur pour la stabilité du système SECMS développé. Basé sur l'estimation en ligne de l'état de santé de la pile à combustible et de la batterie, le système ECMS adaptatif (AECMS) a été implémenté en ajustant le facteur équivalent et le taux de changement dynamique de la pile à combustible. Les résultats de la simulation montrent que l’AECMS peut assurer le maintien de la charge de la batterie et l’augmentation de la durabilité de la pile à combustible.Pour valider les algorithmes de gestion de l'énergie et les modèles numériques proposés, un banc d'essai expérimental a été construit autour de l'interface temps réel DSPACE. La comparaison des résultats de la simulation numérique et des résultats expérimentaux a montré que le système SECMS proposé fonctionne à un rendement maximal, que le supercondensateur fournit la puissance de pointe et que la batterie fonctionne comme un tampon d’énergie. Il a été prouvé que la négligence de la consommation d'hydrogène équivalente au supercondensateur dans l'ECMS conduit à un fonctionnement non optimal. Comparé à RBCS et HEOS, la SECMS a le moins d'hydrogène consommé et le courant de pile à combustible le plus stable
Global warming, environment pollution and exhaustion of petroleum energies have risen their attention of the humanity over the world. Fuel cell hybrid electric vehicle (FCHEV) taking hydrogen as fuel and have zero emission, is thought by public and private organisms as one of the best ways to solve these problems. This PhD dissertation consider a FCHEV with three power sources: fuel cell, battery and supercapacitor, which increases the difficult to design an energy management strategy (EMS) to split the power between the different power sources.Among the EMS available in the current literature, the Equivalent consumption minimization strategy (ECMS) was selected because it allows a local optimization without rely on prior knowledge of driving condition while giving optimal results.Due to low energy density of supercapacitor, its equivalent hydrogen consumption is neglected in most bibliographic references, which not only counter to the aim of minimizing whole hydrogen consumption but also increase the complication of EMS due to the need of an additional EMS to calculate supercapacitor power demand. Thus, a sequential quadratic programming ECMS (SECMS) strategy is proposed to consider energy cost of all three power sources into the objective function. A rule based control strategy (RBCS) and hybrid strategy (HEOS) are also designed in order to to be compared with SECMS. Degradation of energy sources represents a major challenge for the stability of the developed SECMS system. So, based on online estimating state of heath of fuel cell and battery, an adaptive ECMS (AECMS) has been designed through adjusting the equivalent factor and dynamical change rate of fuel cell. The simulation results show that the AECMS can ensure the charge sustenance of battery and the increase of fuel cell durability.To validate the proposed energy management algorithms and the numerical models an exerimental test bench has been built around the real time interface DSPACE. The comparison of the simulation and experimental results showed that the proposed SECMS is operated at around maximum efficiency, supercapacitor supplies peak power, battery works as the energy buffer. It has been proved that the neglect of supercapacitor equivalent hydrogen consumption in ECMS leads to not optimal operation. Compared with RBCS and HEOS, SECMS has least hydrogen consumption and most stable fuel cell current

With significantly increasing concerns about greenhouse effects and sustainable economy, the marine industry presents great potential for reducing its environmental impact. Recent developments in power electronics and hybridisation technologies create new opportunities for innovative marine power plants which utilize both traditional diesel generators and energy storage like batteries and/or supercapacitors as the power sources. However, power management of such complex systems in order to achieve the best efficiency becomes one of the major challenges. Acknowledging this importance, this research aims to develop an optimal control strategy (OCS) for hybrid marine power plants. First, architecture of the researched marine power plant is briefly discussed and a simple plant model is presented. The generator can be used to charge the batteries when the ship works with low power demands. Conversely, this battery energy can be used as an additional power source to drive the propulsion or assist the generators when necessary. In addition, energy losses through braking can be recuperated and stored in the battery for later use. Second, the OCS is developed based on equivalent fuel consumption minimisation (EFCM) approach to manage efficiently the power flow between the power sources. This helps the generators to work at the optimal operating conditions, conserving fuel and lowering emissions. In principle, the EFCM is based on the simple concept that discharging the battery at present is equivalent to a fuel burn in the future and vice-versa and, is suitable for real-time implementation. However, instantaneously regulating the power sources’ demands could affect the system stability as well as the lifetime of the components. To overcome this drawback and to achieve smooth energy management, the OCS is designed with a number of penalty factors by considering carefully the system states, such as generators’ fuel consumption and dynamics (stop/start and cranking behaviour), battery state of charge and power demands. Moreover, adaptive energy conversion factors are designed using artificial intelligence and integrated in the OCS design to improve the management performance. The system therefore is capable of operating in the highest fuel economy zone and without sacrificing the overall performance. Furthermore, a real-time simulation platform has been developed for the future investigation of the control logic. The effectiveness of the proposed OCS is then verified through numerical simulations with a number of test cases.

Representative simulation of gas turbine operation has to be performed by engine manufacturers during engine development programs for a number of reasons, including technical risk mitigation, safety analysis and cycle optimisation. Minimisation of Specific Fuel Consumption (SFC) is the main challenge faced by the WR-21 engine, an intercooled and recuperated gas turbine which is at the forefront of future naval propulsion systems. Steady state simulation tools need to be developed to accurately predict engine performance under any set of environmental conditions. The methodology used involves an integrated “analysis-synthesis” simulation technique where extensive test data analysis is built into a full thermodynamic model of the engine. Simulation of the gas turbine behaviour under transient conditions is also required to ensure maximum operability through optimised control, and gas turbine stability under any circumstances. For the WR-21 engine this simulation capability has been developed in both Real Time (RT) and Non-Real Time (NRT) modes. This process is particularly challenging for such an advanced cycle gas turbine where the number of components, and their thermodynamic interaction, is far greater than for a simple cycle prime mover. This paper examines how these challenges have been successfully overcome by the WR-21 project. It describes the tools and techniques that have been developed in the project, and exemplifies their application through demonstration of significant technical achievements. It also expands on the strategic issue of model flexibility and connectivity, and elaborates on future developments in simulation techniques, that will drive the naval gas turbine industry towards continual process improvement, ever closer to its customers.