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multi-objective component sizing of plug-in hybrid electric vehicle for optimal energy management

CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2016年第4期18卷 1189-1202页

作者： Madanipour, Vahid Montazeri-Gh, Morteza Mahmoodi-k, Mehdi Islamic Azad Univ Hashtgerd Branch Young Researchers & Elite Club Alborz Iran Iran Univ Sci & Technol Sch Mech Engn Syst Simulat & Control Lab Tehran Iran

This paper presents a methodology for component sizing optimization of a parallel plug-in hybrid electric vehicle by considering it as a multi-objective optimization problem. In this approach, two objective functions are defined to minimize the drivetrain cost, fuel consumption, and exhaust emissions simultaneously. Also, the driving performance requirements are considered as constraints. In addition, fuzzy logic controller including blended control strategy is developed for the PHEV. Finally, by means of multi-objective particle swarm optimization algorithm, the best choices of components are selected for 32 miles of the both TEH-CAR and UDDS driving cycles. Simulation results demonstrate the effectiveness and practicality of the approach, which prepare different optimal component sizes with various drivetrain costs, equivalent fuel consumption, and exhaust emissions.

关键词： Component sizing optimization Plug-in hybrid electric vehicle Fuzzy logic controller Blended control strategy multi-objective particle swarm optimization

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multi-objective optimization of a Hydrogen-Battery Hybrid Storage System for Offshore Wind Farm Using MOPSO

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第6期18卷 4091-4103页

作者： Tian, Tian Ma, Zetao Cui, Qiong Shu, Jie Tan, Lei Wang, Hao Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Guangzhou Inst Energy Convers Guangzhou Peoples R China Guangdong Lab Shanwei Branch Adv Energy Sci & Technol Shanwei Peoples R China

Recently, offshore wind farms (OWFs) are gaining more and more attention for its high efficiency and yearly energy production capacity. However, the power generated by OWFs has the drawbacks of intermittence and fluctuation, leading to the deterioration of electricity grid stability and wind curtailment. Energy storage is one of the most important solutions to smooth the wind power and capture its surplus. In this paper, we provide a multi-objective optimization approach that combines multi-objective particle swarm optimization and rule-based energy management strategy for an on-gird offshore wind-hydrogen-battery system to simultaneously address the economic (Eco), the qualified rate of smoothing offshore wind power fluctuations (QRS), and the rate of offshore wind power curtailment (ROC). Results revealed that ROC and Eco, QRS and Eco are negatively correlated, but ROC and QRS are positively correlated. The hybrid storage system is more conducive to improve QRS and reduce ROC. Comparing with other three systems, the improvement range for ROC is between 13.6 and 46% when QRS is 100%. In addition, battery storage improves QRS by 2.6%, hydrogen storage deteriorates Eco by 86.8% and improve ROC by 38.5%, the change of ROC and QRS brings by transmission project are close to 100% and 4.4%.

关键词： Energy management strategy multi-objective optimization multi-objective particle swarm optimization Offshore wind-hydrogen-battery storage system

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Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran

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INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT 2020年第3期25卷 620-632页

作者： Pishgar-Komleh, Seyyed Hassan Akram, Asadollah Keyhani, Alireza Sefeedpari, Paria Shine, Philip Brandao, Miguel Univ Tehran Fac Agr Engn & Technol Dept Agr Machinery POB 4111 Karaj Iran State Res Inst Inst Soil Sci & Plant Cultivat Dept Bioecon & Syst Anal Pulawy Poland Czech Univ Life Sci Fac Econ & Management Dept Econ Prague Czech Republic Cork Inst Technol Dept Proc Energy & Transport Engn Cork Ireland KTH Royal Inst Technol Dept Sustainable Dev Environm Sci & Engn Seed Div Sustainabil Assessment & Management S-10044 Stokholm Sweden

Purpose The main purpose of this study was to evaluate the use of an integrated life cycle assessment (LCA), artificial neural network, and metaheuristic optimization model to improve the sustainability of tomato-based cropping systems in Iran. The model outputs the combination of input usage in a tomato cropping system, which leads to the highest economic output and the least environmental impact. Methods The LCA inventory was created using data from 114 open-field tomato farms in the Alborz Province of Iran during one growing period in 2015. Among all management components, the main focus was on irrigation management systems. The optimization problem was designed by integrating three indicators: carbon footprint (CF), benefit-cost ratio (BCR), and energy use efficiency (EUE) as the objective of field tomato production. The functional unit was 1 kg of tomato aligned with the system boundary of the cradle to market life cycle. Three artificial neural networks (ANNs) were applied to model relationships between the inputs and three indices (CF, BCR, and EUE) as the objective functions. multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) were used to minimize the CF and maximize the BCR and EUE indicators. The abovementioned aims have been pursued by developing codes in MATLAB software. Results and discussion CF, BCR, and EUE were calculated to be 0.26 kg CO2-eq (kg tomato)(-1), 1.8, and 0.5, respectively. MOGA results envisage the possibility of an increase of 86% and 50% in the EUE and BCR and a 43% reduction in the CF of tomato production systems. Moreover, EUE and BCR increased by 83% and 49%, and CF was reduced by 39% from the optimum results obtained from the MOPSO algorithm. It was revealed that in order to optimize field tomato production with the target objectives of this study, a large additional use for irrigation pipes, plastic, and machinery in comparison to current situation is required, while a large reduction

关键词： Artificial neural networks Benefit-cost ratio Carbon footprint Energy use efficiency Field tomato Iran Life cycle assessment multi-objective genetic algorithm multi-objective particle swarm optimization

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Numerical study and parameter optimization of partial journal bearing using MOPSO algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY 2020年第1期234卷 145-158页

作者： Mikaeeli, S. Z. Aghanajafi, C. Akbarzadeh, P. KN Toosi Univ Technol Sch Mech Engn Tehran Iran Shahrood Univ Technol Fac Mech & Mechatron Engn Shahrood Iran

In this paper, multi-objective particle swarm optimization method is developed for optimizing thermo-hydrodynamic journal bearings. This paper focuses on the use of multi-objective particle swarm optimization algorithm with a combination of the thermal hydrodynamic governing equations of the fluid film (i.e. momentum and energy equations) to optimize hydrodynamic partial pad journal bearings and compare with other articles. The governing equations are solved by the central difference method with a successive over-relaxation scheme and the backward difference with an iterative technique. In the paper, the lubricant viscosity changes with the temperature variation in whole fluid film. In this optimization, the bearing power loss, the minimum oil film thickness, and the maximum oil temperature are considered as objective functions and the radial clearance and length to diameter ratio are selected as design variables. The results of the objective functions are compared to other articles. Also, this study discusses the entropy and availability of two concentric cylinders with low curvature and constant wall temperature. Calculations showed that by increasing the Eckert number, the availability increases.

关键词： multi-objective particle swarm optimization availability entropy journal bearing film thickness hydrodynamic lubrication optimization fluid mechanics

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Comprehensive performance analysis of a shearer drum in a complicated seam based on discrete element method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第5期236卷 2462-2477页

作者： Zhao, Lijuan Zhang, Meichen Shi, Baisheng Liu, Xionghao Wang, Yadong Liaoning Tech Univ Sch Mech Engn Fuxing Peoples R China

Drum of Shearer undertakes the main function of coal falling and loading, and its performance directly affects the working efficiency of the shearer. Therefore, in order to realize the analysis of the performance of the shearer drum, the MG2 x 55/250-BW shearer drum was the engineered object. Combining the physical and mechanical properties experiment results of coal samples, the coupling model of the drum cutting in complex coal seam was established using discrete element method. The falling-coal characteristics of the spiral drum were studied under different working conditions, and the falling-coal trajectories of the coal and rock particles were fitted. Based on a virtual prototype, the variations of the coal loading rate and lump coal rate with different design parameters were determined by studying the falling-coal effect and loading performance of the drum. Considering the drum performance, multi-objective optimization theory was used to determine the optimal operating and structural parameters. The results indicate that, in the process of drum cutting, the cutting depth has the most significant effect on the coal loading rate, while, the blade spiral angle has the least significant. Moreover, with the increase of the cutting depth of drum and the traction speed, the lump coal rate increases. While, with the increase of the drum rotation speed and the blade spiral angle, the lump coal rate decreases. It is found that when the cutting depth of the drum is 597 mm, the traction speed is 5.4 m/min, the drum rotation speed is 104.8 r/min, and the blade spiral angle is 12 degrees the performance of the drum is optimal. Compared with the falling-coal trajectories before optimization, the displacements of the coal and rock particles ejected along the optimal falling-coal trajectories increase in the coal loading direction. The loading rate and lump coal rate of the drum increase by 6.05% and 12.27%, respectively. The load fluctuation of the drum decreases, and the pro

关键词： Shearer drum falling-coal trajectory coal loading performance lump coal rate multi-objective particle swarm optimization

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A Systematic Approach for Online Minimizing Volume Difference of multiple Chambers in Machining Processes Based on High-Definition Metrology

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2017年第8期139卷 081003页

作者： Huang, De-Lin Du, Shi-Chang Li, Gui-Long Wu, Zhuo-Qi Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

The volume variation of multiple chambers of a workpiece is one of the most important factors that can directly influence the performance of the final product. This paper presents a novel systematic approach for online minimizing the volume difference of multiple chambers of a workpiece based on high-definition metrology (HDM). First, the datum of high-density points is transformed by a random sample consensus (RANSAC) algorithm due to its good robustness in fitting. Second, a procedure containing reconstruction of interior curved surfaces of chambers, boundary extraction, and projection is developed to calculate the accurate volumes of the multiple chambers. Third, a model for obtaining an optimized machining parameter for depth of chambers is explored to minimize the volume difference of any two ones of all the chambers. The model is formulated as a multi-objective optimization (MOO) problem, and a new procedure of multi-objective particle swarm optimization (MOPSO) algorithm is developed to solve this problem. Finally, a milling depth is output as the optimal milling parameter for controlling the volume variation of multiple chambers. The results of a case study show that the proposed approach can minimize the volume difference of four combustion chambers of a cylinder head and it can be well applied online in volume variation control of multiple chambers in machining processes.

关键词： volume variation control high-definition metrology multiple chambers optimization model multi-objective particle swarm optimization

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multi-objective Dynamic Construction Site Layout Planning in Fuzzy Random Environment

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AUTOMATION IN CONSTRUCTION 2012年 27卷 155-169页

作者： Xu, Jiuping Li, Zongmin Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610064 Peoples R China Sichuan Univ Uncertainty Decis Making Lab Chengdu 610064 Peoples R China

Construction site layout planning is a fundamental task for any project undertaking. In practice, most of construction site layout planning problems are dynamic, multi-objective and uncertain in nature. To enhance the general practice of dynamic construction site layout planning, this paper proposes a fuzzy random multi-objective decision making model. In this model, two objectives are considered: (1) minimizing the total cost of site layout: and (2) maximizing the distance between the 'high-risk' facilities and the 'high-protection' facilities to reduce the possibility of safety or environmental accidents. After stating the problem and presenting the mathematical formulation, this paper deals with fuzzy random variables to propose an equivalent crisp model. According to the characteristics of the proposed model, a multi-objective particle swarm optimization algorithm (MOPSO) with permutation-based representation is proposed to solve the problem. The approach is applied to the Longtan hydropower construction project to illustrate the effectiveness of the proposed model and algorithm. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Dynamic construction site layout planning Fuzzy random environment Uncertainty multi-objective particle swarm optimization

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Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第10期38卷 12051-12057页

作者： Moslemi, H. Zandieh, M. Shahid Beheshti Univ Dept Ind Management Management & Accounting Fac Tehran Iran Qazvin Islamic Azad Univ Dept Mech & Ind Engn Qazvin Iran

This paper presents comparisons of some recent improving strategies on multi-objective particle swarm optimization (MOPSO) algorithm which is based on Pareto dominance for handling multiple objective in continuous review stochastic inventory control system. The complexity of considering conflict objectives such as cost minimization and service level maximization in the real-world inventory control problem needs to employ more exact optimizers generating more diverse and better non-dominated solutions of a reorder point and order size system. At first, we apply the original MOPSO employed for the multi-objective inventory control problem. Then we incorporate the mutation operator to maintain diversity in the swarm and explore all the search space into the MOPSO. Next we change the leader selection strategy used that called geographically-based system (Grids) and instead of that, crowding distance factor is also applied to select the global optimal particle as a leader. Also we use epsilon-dominance concept to bound archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. Finally, the MOPSO algorithms created using these strategies are evaluated and compared with each other in terms of some performance metrics taken from the literature. The results indicate that these strategies have significant influences on computational time, convergence, and diversity of generated Pareto optimal solutions. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： multi-objective particle swarm optimization Inventory planning and control Crowding distance epsilon-Dominance

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multi-objective optimization-based prediction of excavation-induced tunnel displacement

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Underground Space 2022年第5期7卷 735-747页

作者： Yuanqin Tao Wei He Honglei Sun Yuanqiang Cai Junqiang Chen Research Center of Coastal and Urban Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058China Institute of Geotechnical Engineering College of Civil EngineeringZhejiang University of TechnologyHangzhou 310000China Institute of Rock and Soil Mechanics Chinese Academy of SciencesWuhan 430061China

This paper proposes an inverse method for improving the prediction of tunnel displacements during adjacent *** this framework,staged data assimilation and parameter identification are conducted using the multi-objective particle swarm optimization *** monitoring data are assumed to be more informative and assigned more weights in the multi-objective optimization to improve the prediction ***,an empirical formula is applied to correct the time effect of tunnel *** Kriging method is introduced to surrogate the finite element model to reduce computational *** proposed framework is verified using a typical staged“excavation nearing tunnel”*** predictions using the updated parameters are in good agreement with the *** identified values of underlying soil parameters are within the typical ranges for the unloading *** updated time effect indicates that tunnel displacements may develop excessively in the three months after the region S1-B is excavated to the *** maximum vertical tunnel displacement may increase from the currently measured 12 mm to the calculated 26 mm if the later construction is suspended long *** constructions need to be timely conducted to restrain the time effect and control tunnel displacements.

关键词： Tunnel displacement Excavation Time effect multi-objective particle swarm optimization Parameter identification

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multi-objective preventive maintenance strategy and optimization considering unavailability and cost: A case study on VOBC

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ALEXANDRIA ENGINEERING JOURNAL 2024年 105卷 567-577页

作者： Peng, Cong Wei, Shangguan Peng, Jiali Wang, Zongyao Chai, Linguo Cai, Baigen Xing, Yulong Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China China Acad Railway Sci Corp Ltd Stand & Metrol Res Inst Beijing 100081 Peoples R China China Railway Test & Certificat Ctr Beijing 100081 Peoples R China

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway's VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC.

关键词： Preventive maintenance multi-objective maintenance strategy System unavailability Maintenance costs multi-objective particle swarm optimization and genetic algorithm Vehicle on-board controller

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