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Enhanced intelligent proportional-integral-like fuzzy knowledge-based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air-fuel ratio control system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第1期234卷 39-55页

作者： Li, Ziyang Zhou, Quan Zhang, Yunfan Li, Ji Xu, Hongming Univ Birmingham Sch Engn Birmingham B15 2TT W Midlands England

The self-adaptive and highly robust proportional-integral-like fuzzy knowledge-based controller has been developed to regulate air-fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge-based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge-based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air-fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge-based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge-based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm-based proportional-integral-like fuzzy knowledge-based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm-based proportional-int

关键词： Gasoline direct injection engine fuzzy control air-fuel ratio multiple-objective optimization particle swarm algorithm transient calibration

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Multi-objective Topology optimization for Curved Arm of Multifunctional Billet Tong Based on Characterization of Working Conditions

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JOURNAL OF ENGINEERING AND TECHNOLOGICAL SCIENCES 2022年第1期54卷 199-222页

作者： Liu, Jialiang Wang, Zhaohua Song, Dezhuang Wu, Fenghe Yanshan Univ Sch Mech Engn Qinhuangdao 066004 Hebei Peoples R China Shanghai Dianji Univ Mech Inst Shanghai 201306 Peoples R China Shanghai Large Scale Casting & Forging Mfg Techno Shanghai 201306 Peoples R China Heavy Duty Intelligent Mfg Equipment Innovat Ctr Qinhuangdao 066004 Hebei Peoples R China

A windlass driven heavy duty multifunctional billet tong was designed for large-scale forging and casting to reduce the number of auxiliary material handling devices in manufacturing workshops. To improve its mechanical performance and safety, a novel multi-objective topology optimization method for its curved arm is proposed in this paper. Firstly, the influence of different open angles and working frequencies for the curved arm was simplified to a multi-objective optimization problem. A comprehensive evaluation function was constructed using the compromise programming method, and a mathematical model of multi-objective topology optimization was established. Meanwhile, a radar chart was employed to portray the comparative measures of working conditions, the weight coefficient for each working condition was determined based on the corresponding enclosed areas, combining the stress indices, the displacement indices and the frequency indices of all working conditions. The optimization results showed that the stiffness and strength of the curved arm can be improved while its weight can be reduced by 10.77%, which shows that it is feasible and promising to achieve a lightweight design of the curved arm of a billet tong. The proposed method can be extended to other equipment with complex working conditions.

关键词： billet tong lightweight design multiple-objective optimization multiple working conditions topology optimization

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Comparative study of hybrid architectures integrated with dual-fuel intelligent charge compression ignition engine: A commercial powertrain solution towards carbon neutrality

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期292卷

作者： Zhang, Yaoyuan Wu, Haoqing Mi, Shijie Zhao, Wenbin He, Zhuoyao Qian, Yong Lu, Xingcai Shanghai Jiao Tong Univ Key Lab Power Machinery & Engn MOE Shanghai 200240 Peoples R China

The dual-fuel intelligent charge compression ignition (ICCI) engine, equipped with double direct injection systems, can simultaneously improve fuel consumption and gaseous emissions by substituting low-carbon fuels for conventional fuels. This work investigates the optimal application of the ICCI engine on hybrid powertrains under various standard driving conditions. The purpose is to evaluate the profit of combining the advantages of hybrid powertrains with the advanced engine. Dynamic programming (DP) is applied to the energy management control, realizing the global optimization in the design phase. Three typical hybrid configurations are compared, including series (SHEV), parallel (PHEV), and power-split (PSHEV) hybrid electric vehicles. Except for the charging sustaining stage, the optimal state of charge trajectories are also researched to verify the reasonable parameters in the charging stage. Considering multiple objectives, a non-dominated sorting genetic algorithm (NSGA-II) is coupled to optimize fuel consumption, gaseous emissions, and battery cost. The ICCI engine is mapped by the test bench results, while the involved hybrid configurations are modeled in Matlab/Simulink. The results illustrate that PSHEV achieves the best fuel economy and emission purification among the involved structures. With the ideal solution provided by DP, fuel consumption and gaseous emissions for PSHEV are reduced by 10% and 50%, and the low-carbon fuel utilization ratio has tripled, contributing to carbon neutrality. The engine was operated in several working conditions for SHEV, achieving the cleanest gaseous emissions. For PHEV, the engine performance was rarely improved compared with the engine without the assistance of hybrid systems. The optimization provided by NSGA-II indicates that the minimum values of multiple objectives cannot be reached simultaneously. A further 50% reduction in emissions would cause fuel consumption to double the original.

关键词： Low-temperature combustion engine Hybrid electric vehicle Dynamic programming multiple-objective optimization

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Performance modeling and optimization of multiple-objective cross-layer design in multi-flow ad-hoc networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第3期32卷 e3861-e3861页

作者： Mehta, Ridhima Lobiyal, Daya Krishan Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

In ad-hoc wireless networks, to achieve good performance, multiple parameters need to be optimized jointly. However, existing literature lacks a design framework that investigates the synchronic impact of several parameters on overall system performance. Among several design parameters, energy conservation, end-to-end delay minimization, and improved throughput are considered most important for efficient operation of these networks. In this paper, we propose a novel scheme for multiple-objective cross-layer optimization capable of optimizing all these performance objectives simultaneously for reliable, energy-efficient, and timely transmission of continuous media information across the network. The three global criteria considered for optimization are incorporated in a single programming problem via linear scalarization. Besides, we employ standard convex optimization method and Lagrangian technique to solve the proposed problem to seek optimality. Extensive simulation results are generated accounting for several topologies with multiple concurrent flows in the network. These results are used to validate the analytical results and demonstrate the efficiency of the proposed optimization model. Efficiency of the model is verified by finding the set of Pareto-optimal solutions plotted in three-dimensional objective space. These solution points constituting the Pareto front are used as the best possible balance points among maximum throughput, maximum residual energy, and least network delay. Finally, to emphasize the effectiveness and supremacy of our proposed multiple-objective cross-layer design scheme, we compare it with the conventional multiple-objective genetic algorithm. Simulation results demonstrate that our method provides significant performance gain over the genetic algorithm approach in terms of the above specified three objectives.

关键词： ad-hoc wireless network delay multiple-objective optimization pareto-optimality residual energy throughput

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Congestion-Aware and Energy-Aware Virtual Network Embedding

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第1期28卷 210-223页

作者： Pham, Minh Hoang, Doan B. Chaczko, Zenon Univ Technol Sydney Fac Engn & IT Ultimo NSW 2007 Australia

Network virtualization is an inherent component of future internet architectures. Network resources are virtualized from the underlying substrate and elastically provisioned and offered to customers on-demand. Optimal allocation of network resources in terms of utilization, quality of service, and energy consumption has been a challenge. Existing solutions consider congestion control in a single-objective virtual network embedding (VNE) problem. This paper defines a multiple-objective VNE problem called the congestion-aware, energy-aware VNE (CEVNE). The aim is to seek a solution that saves cost, saves energy and avoids network congestion simultaneously. CEVNE modelling techniques and solution approaches apply both the weighting method and the constraint method to search for pareto-optimal solutions that produce the best compromised solutions for all three objectives. Solving VNE problem is, however, NP-hard. A heuristic solution is proposed involving a two-stage coordinated CEVNE. The node-mapping algorithm searches for the sub-optimal solutions for three objectives. The link mapping process is an SDN-based heuristic algorithm that deploys a path service and a resource monitoring application on an SDN controller. The solution is realized using SDN, Segment Routing, and open network operating system platform (ONOS) technologies. The energy minimization is implemented with a registry that keeps track of active nodes and sets inactive nodes to sleep mode. The evaluation results showed that the multiple-objective CEVNE approach is feasible and achieves its goals of optimizing the resource allocation, improving the runtime, saving the energy consumption and controlling the network congestion.

关键词： Virtual network embedding network virtualization software-defined networks segment routing energy saving congestion control path services multiple-objective optimization

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Pareto frontier for multi-objective wind farm layout optimization balancing power production and turbine fatigue life

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Renewable Energy 2025年 252卷

作者： Kun Yang Mingming Zhang Shanghui Yang Yuwei Song Xinhui Dong Yanfei Deng Xiaowei Deng Department of Civil Engineering The University of Hong Kong Hong Kong China Institute of Green-Low Carbon Energy Innovative Technology Harbin Institute of Technology (Shenzhen) Shenzhen China

Layout optimization is a promising design process that will enhance the power efficiency of wind farms in the design stage. Single optimization targeting maximizing power production for wind farms has been studied for decades. Recently, multiple-objective layout optimization, considering more practical factors, has become urgently required with the rapid growth of wind energy. In this paper, we established a multiple-objective layout optimization framework that considers power performance and turbine fatigue life. Efficient power and fatigue life assessment approaches are adopted. Different scales of wind farms and typical wind roses are investigated. Pareto frontiers are drawn for each case to show the optimal solutions for multiple-objective optimizations. Notably, the deduction of wind speed and increase of turbulence intensity in the wake region have contrasting influences on the fatigue life. The turbines of optimal layouts are distant for higher overall power output but prefer to be closer for longer fatigue lives. Characteristic spacing describing the turbine density and the utilized area is suggested to be larger than 3.65 D for any case and no less than 4.85 D if the wind farm cannot afford the 85% power performance of the theoretical upper limit without wake loss.

关键词： Wind farm layout optimization multiple-objective optimization Fatigue life assessment Energy efficiency Pareto frontier

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Intelligent transient calibration of a dual-loop EGR diesel engine using chaos-enhanced accelerated particle swarm optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第7期233卷 1698-1711页

作者： Zhang, Yunfan Zhou, Quan Li, Ziyang Li, Ji Xu, Hongming Univ Birmingham Sch Engn Dept Mech Engn Birmingham W Midlands England

This article proposes an intelligent transient calibration method for the air-path controller of a light-duty diesel engine. This method is developed based on the chaos-enhanced accelerated particle swarm optimization algorithm. The target is to reduce the engine's fuel consumption during transient scenarios by optimizing the controller parameters. The advanced dual-loop exhaust gas recirculation system is first introduced. Then, it formulates the transient calibration process as a multiple-objective optimization problem with constraints. Different from steady state calibration, the proposed method designs a new cost-function to evaluate the controller's transient performance. The intelligent transient calibration module is programmed in MATLAB code. Interface between the calibration module and a physical engine plant is established via ETAS INCA. The optimization result of the proposal method is discussed by comparing it with the result of existing calibration methods. The engine performance with the calibrated controller is evaluated based on engine tests.

关键词： Diesel engine multiple-objective optimization particle swarm algorithm transient calibration test bench validation

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GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization

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COMPUTERS & OPERATIONS RESEARCH 2012年第9期39卷 1951-1968页

作者： Zinflou, Arnaud Gagne, Caroline Gravel, Marc Inst Rech Hydro Quebec IREQ Varennes PQ J3X 1S1 Canada Univ Quebec Chicoutimi Dept Sci Econ & Adm Chicoutimi PQ G7H 2B1 Canada Univ Quebec Chicoutimi Dept Informat & Math Chicoutimi PQ G7H 2B1 Canada

In this paper, we propose a new Pareto generic algorithm, called GISMOO, which hybridizes genetic algorithm and artificial immune systems. GISMOO algorithm is generic in the sense that it can be used to solve both combinatorial and continuous optimization problems. The proposed approach offers an original iterative process in two phases: a Genetic Phase and an Immune Phase. The Immune Phase is used to identify and to emphasize the solutions located in less crowded regions found during the iterative process of the algorithm. Simulation results on difficult test problems, both in combinatorial and continuous optimization, show that the proposed approach, in most problems, is able to obtain better results than state of the art algorithms. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Evolutionary algorithm Artificial immune systems multiple-objective optimization Hybridization MOKP ZDT

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Optimizing spatial layout of afforestation to realize the maximum benefit of water resources in arid regions: A case study of Alxa, China

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JOURNAL OF CLEANER PRODUCTION 2021年 320卷 128827-128827页

作者： Zhang, Xiaoyu Xu, Duanyang Wang, Ziyu Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Land Surface Pattern & Simulat Beijing 100101 Peoples R China Beijing Normal Univ Fac Geog Sci Beijing 100088 Peoples R China Beijing Forestry Univ Coll Forestry Beijing 100083 Peoples R China

Water shortage, ecological protection, and economic development are long-standing challenges faced by arid regions. Scientific afforestation project provides an effective way to realize the maximum benefit of water resources in the perspective of ecology and economy "win-win". However, how to plan and shape afforestation to meet different objectives and overcome different obstacles is an essential problem in policy-making. In this study, a method of optimizing the spatial layout of afforestation by considering ecological and economic goals, priority areas, and water resources capacity is developed. A case study of spatial layout planning of afforestation (represented by H. ammodendron) in Alxa, China is proposed. Results show that the scale of afforestation in Alxa would increase from 2019 to 2030 by considering the maximum benefit of water resources in ecology and economy. The priority areas for afforestation are mainly located in the west side of Helan Mountain, the east and south sides of the Badain Jaran Desert, and the central part of Ejin Banner. The spatial heterogeneity of precipitation makes the afforestation density increase from northwest to southeast, and the density would be changed under different climate scenarios. The method and results of this study provide a practical reference for optimizing afforestation and water resources allocation to achieve win-win pattern for ecology and economy in arid regions.

关键词： Ecological restoration Water balance multiple-objective optimization Spatial planning Arid region

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An Optimal ZigBee Wireless Sensor Network Design for Energy Storage System 29

An Optimal ZigBee Wireless Sensor Network Design for Energy ...

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IEEE 29th International Symposium on Industrial Electronics (ISIE)

作者： Liu, Yucheng Wei, Yang Wang, Hao Tsang, Kim Fung Zhu, Hongxu Chow, Yuk Tak City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781728156354

Energy Storage System (ESS) has become a vital development for smart buildings and smart grids. Centralized intelligent ESS controller requires intelligent sensing with accurate real-time sensing data, which demands high latency and packet abnormal rate (PAR) performance of the communications between sensors and the control centralizer. Large scale indoor public spaces (IPS), generally characterized by large volumes, require robust connections among deployed sensors for ESS management. Wireless sensor network (WSN) has been proven to be one of the best candidates for data transmission in large scale ESS systems. ZigBee, attributed to its excellent performance on scalability, low power consumption and relatively sufficient data rate, is a crucial wireless protocol for WSNs in building applications. In this paper, a ZigBee based optimal WSN for ESS is proposed. The design considers multiple parameters for ESS which includes power efficiency, throughput, and robustness (i.e. packet loss, packet error). The parameters are optimized by designing a multi-objective optimization formulation on ESS. The optimization achieves a low latency and a low packet abnormal rate (PAR) and yet provides a real-time accurate data transmission. Experimental results reveal that the proposed system achieves a low PAR of 0.08%. The optimal design provides generic guidance for large scale WSNs design for ESS using ZigBee.

关键词： Large-scale Energy Storage System multiple-objective optimization Wireless Sensor Networks

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