Results 1 - 10 of 97
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[en] This study presents a novel approach to solve the vehicle routing problem by focusing on greenhouse gas emissions and fuel consumption aiming to mitigate adverse environmental effects of transportation. A time-dependent model with time windows is developed to incorporate speed and schedule in transportation planning. The model considers speed limits for different times of the day in a realistic delivery context. Due to the complexity of solving the model, a graph transformation approach is proposed to reduce the complexity of the problem. By means of several steps, the problem is transformed into a vehicle routing problem without time windows. In this way, we can reduce the complexity of the problem. Our method can be used in practice to decrease fuel consumption and greenhouse gas emissions, while total cost is also controlled to some extent. Finally, future research directions and conclusion remarks are provided.
[en] Range extender is the core component of E-REV, its start-stop control determines the operation modes of vehicle. This paper based on a certain type of E-REV, researched constant power control strategy of range extender in extended-range model, to target range as constraint condition, combined with different driving cycle conditions, by correcting battery SOC for range extender start-stop moment, optimized the control strategy of range extender, and established the vehicle and range extender start-stop control simulation model. Selected NEDC and UDDS conditions simulation results show that: under certain target mileage, the range extender running time reduced by 37.2% and 28.2% in the NEDC condition, and running time UDDS conditions were reduced by 40.6% and 33.5% in the UDDS condition, reached the purpose of meeting the vehicle mileage and reducing consumption and emission. (paper)
[en] Highlights: • Different PHEV powertrain configurations are comparative analyzed. • Configuration-sizing-control integrated multi-objective optimization is developed. • PHEV powertrain configuration evaluation methodology is proposed. • Pareto optimal selection of PHEV powertrain configuration is provide. - Abstract: This study provides an optimal selection methodology for plug-in hybrid electric vehicle (PHEV) powertrain configuration by means of optimization and comprehensive evaluation of powertrain design schemes. The challenge of this study is to reveal each powertrain configuration performance potential in different situations of object trade-off and solve the control-physical integrated optimization problem of the PHEV powertrain design. To determine performance potential, a configuration-sizing-control strategy integrated multi-objective powertrain optimization design is proposed and applied to series, parallel pre-transmission (P2), output power-split, and multi-mode power-split powertrain configurations. Firstly, considering simultaneous optimization of fuel economy, electric energy consumption, and acceleration capacity, the parameters of the powertrain components and vehicle performance of each configuration are optimized based on global optimal control in different situations of object trade-off. Then, the Pareto optimal selection of powertrain configuration and its corresponding optimal component parameters are obtained by performance comparison and non-domination sorting. The results suggest that the P2 configuration and its optimal sizing can be selected when the goal is to optimize acceleration capacity, the multi-mode power-split configuration and its optimal sizing can be selected when the goal is to optimize electric energy efficiency, and the output power-split configuration and its optimal sizing can be selected when the fuel economy needs to be optimized.
[en] Highlights: • New method for the simulation of nonlinear dynamic operations of reheating furnaces. • Detailed radiation heat transfer in a time-varying computational domain. • Feedback and feedforward combined self-adapting predictive control scheme. • The model was verified by using radiometric imaging camera and SCADA data. • A fuel saving of about 6% can be achieved by the control scheme. - Abstract: Modern reheating furnaces are complex nonlinear dynamic systems having heat transfer performances which may be greatly influenced by operating conditions such as stock material properties, furnace scheduling and throughput rate. Commonly, each furnace is equipped with a tailored model predictive control system to ensure consistent heated product quality such as final discharge temperature and temperature uniformity within the stock pieces. Those furnace models normally perform well for a designed operating condition but cannot usually cope with a variety of transient furnace operations such as non-uniform batch scheduling and production delay from downstream processes. Under these conditions, manual interventions that rely on past experience are often used to assist the process until the next stable furnace operation has been attained. Therefore, more advanced furnace control systems are useful to meet the challenge of adapting to those circumstances whilst also being able to predict the dynamic thermal behaviour of the furnace. In view of the above, this paper describes in detail an episode of actual transient furnace operation, and demonstrates a nonlinear dynamic simulation of this furnace operation using a zone method based model with a self-adapting predictive control scheme. The proposed furnace model was found to be capable of dynamically responding to the changes that occurred in the furnace operation, achieving about ±10 °C discrepancies with respect to measured discharge temperature, and the self-adapting predictive control scheme is shown to outperform the existing scheme used for furnace control in terms of stability and fuel consumption (fuel saving of about 6%).
[en] Highlights: • The limit of APU power changing rate significantly affects the fuel consumption. • The dynamic characteristic of APU is better when engine speed keeps constant. • Two types strategies are proposed to track the demand power of APU. - Abstract: Range-extended electric vehicles (REEVs) are becoming a development trend of new vehicles. Energy management is one of the core problems in REEVs. The structure and control method of the auxiliary power unit (APU) is determined based on the configuration analysis in this paper. An energy management optimization problem is proposed to solve the power distributions of APUs and batteries in the charge-sustaining (CS) stage of REEVs, which are determined by dynamic programming and pseudo-spectral optimal control, respectively. The results show that different limits of the APU power changing rate significantly influence fuel consumption. To obtain the power changing rate of APUs and to evaluate the energy management optimization method of REEVs, a model of the APU control system is built and verified by a platform test; the dynamic response characteristics and control parameters of the APU are obtained by step-changing conditions. Two types of strategies for tracking APU power are proposed for different power changing rates, and the fuel consumption of REEVs is analyzed in four types of driving cycles. The effect on fuel consumption caused by the power changing rate of the APU is verified.
[en] Highlights: • Novel instantaneous energy management strategy for series hybrid electric vehicles. • Finds a range for optimal equivalent factor of equivalent consumption minimization strategy. • The strategy needs less calibration in comparison with existing instantaneous strategies. • Simulation Model is developed based on experimental setup of powertrain components. - Abstract: This paper introduces a new energy management (EM) strategy for series hybrid electric vehicles (HEVs). Series HEVs operate in charge-depletion mode and then switch to the charge-sustaining mode in which the battery state of charge (SOC) is maintained within a certain range. The proposed EM strategy in this paper is a form of adaptive equivalent consumption minimization strategy (ECMS) that is designed for the charge-sustaining mode. The EM strategy defines soft bounds on the battery SOC and is penalized for exceeding these bounds. But, to catch energy-saving opportunities (CESOs), the EM strategy allows SOC to exceed the soft bounds. Thus, the introduced EM strategy is named ECMS-CESO. In addition, a range for the ECMS optimal equivalent factor is proposed for series HEVs. The proposed range is used in deriving the formula for calculating the adaptive equivalent factor. The main advantage of the proposed EM strategy is that ECMS-CESO can achieve close to optimal fuel economy without the need for predicting future driver demand. Since there is no need for prediction, the intensive calculations for finding the optimal control over the prediction horizon can be eliminated. Therefore, implementation of ECMS-CESO is easily feasible for real-time applications. Experimental powertrain data is collected to develop a powertrain model for a series HEV in this study. Simulation results on several drivecycles show that, on average, the fuel economy achieved by ECMS-CESO is within 6% of the maximum fuel economy. In addition, comparing ECMS-CESO with two existing adaptive ECMSs shows up to 5% improvement in fuel economy, on average.
[en] Highlights: • Velocity is predicted by multi scale single step method with post-processing. • A state reconstitution method is proposed to tackle reference state deficiencies. • SMPC-based strategies with variable horizons are built to improve energy management for practical cycle. • HIL experiments with practical driving cycles are conducted to verify the strategy. - Abstract: Model predictive control (MPC) can effectively solve online optimization issues, even with various constraints, when maintained at high robustness. Considering the energy management issue of plug-in hybrid electric bus (PHEB) as a constrained nonlinear optimization problem, a strategy based on stochastic model predictive control (SMPC) is put forward and verified in this paper. Firstly, Markov Chain Monte Carlo Method (MCMC) is adopted to forecast velocity sequences at every current state, in the form of multi scale single step (MSSS), with post-processing algorithms to moderate fluctuations of the prediction results like average filtering, quadratic fitting, and the like. The offline simulation results show that the optimization can effectively improve the predictive accuracy, make the following energy management feasible and reduce the fuel consumption by 1.9%. Then the SMPC-based energy management strategy is proposed. In order to prevent the driving cycle state deficiencies from interrupting the prediction for practical application, a state reconstitution method is constructed accordingly. Besides, the predictive steps are made time-varying by an online accuracy estimation method and a corresponding threshold to maintain the accuracy of forecast. Finally, the hardware-in-the-loop (HIL) experiments are conducted and the results show that the SMPC-based strategy is reasonable and the fuel consumption decreases by 3.9% further with variable predictive steps than that of fixed ones. In summary, this paper illustrates an effective SMPC-based methodology for energy management for PHEB, and techniques like MSSS prediction with post-processing, state reconstitution method, online accuracy estimation can be adopted to solve similar problems.
[en] Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm. (paper)
[en] This work was commissioned by ADEME and GrDF to help design the tools and interventions that could take place during the pilot phase of the roll-out of the French gas smart meter GAZPAR. The gas smart meter will provide households with more precise and frequent consumption data. The objective of this bibliographical study was to explore how the provision of this data, and related public or private action, could be made as efficient as possible as far as energy savings are concerned. More specifically, the aim of this study was to examine how energy consumption data is adopted by households in order to draw recommendations as to what information services or support-to-households measures should come with the meter roll-out. This report can be useful for any organisation willing to develop services based on information feedbacks or support-to-households approaches. In particular, it can be useful to local stakeholders (including communities, local authorities and social landlords) as well as to energy providers. Recommendations apply to gas-consumption data as well as to joint gas and electricity approach. The report focusses on four questions: - 1: What information-based services should be offered to households to encourage them to adopt low energy gas-related practices in the long term? - 2: What support measures should local stakeholders develop to complement information-based services? - 3: How can we identify the various user profiles? How can we communicate to each of them? - 4: How should we capture impact?
[en] Modern Telecom Sector is eventually facing exceptionally tough challenges because of continuous and unexpected increase in power density requirement for the communicating machinery and equipment. To fulfil the power requirements for the equipment, a significant architecture and an optimal technique must be introduced. In this paper, a microcontroller-based optimization use of power-density has been carried out. Meeting above requirements, various equipment and electronic devices are employed. We have designed a microcontroller-based system via PROTEUS Virtual System Modeling to acquire efficient and effective results. The main focus of our work is to supply the power to Telecom equipment in meantime. The power is feeding on batteries and DG (Diesel Generator) set, depending on the condition of the power requirements. The changeover operations are performed by different relays, which are dully programmed via a microcontroller in Keil software. The power capacity of Telecom ((Telecommunication) equipment is ranged from 39-48 Volts DC. The rectification process is done by switch mode rectifiers instead of linear rectifiers. Because the switch-mode rectifier technology has brought fabulous improvements in power density as compared to linear rectifiers. This is done via simulation of the smart switch in PROTEUS software. The outcomes of the proposed system are cost effective in terms of fuel consumption of DG. (author)