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multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily

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ENERGY CONVERSION AND MANAGEMENT 2024年 314卷

作者： Cutore, E. Volpe, R. Alriols, M. Gonzalez Antxustegi, M. M. Fichera, A. Univ Catania Dept Elect Elect & Comp Engn Catania Italy Univ Basque Country Chem & Environm Engn Dept San Sebastian Spain

Europe's heavy reliance on diesel power for nearly half of its railway lines, for both goods and passengers, has significant implications for carbon emissions. To address this challenge, the European Union advocates for a shift towards hydrogen-based mobility, necessitating the development of robust and cost-effective hydrogen supply chains at regional and national levels. Leveraging renewable energy sources such as wind farms and solid biomass could foster the transition to sustainable hydrogen-based transportation. In this study, a mixed-integer linear programming approach integrated with an external heavy-duty refueling station analysis model is employed to address the optimal design of a new hydrogen supply chain. Through multi-objective optimization, this study aimed to minimize the overall daily costs and emissions of the supply chain. By applying the model to a case study in Sicily, different scenarios with varying supply chain configurations and wind curtailment factors were explored. The findings revealed that increasing the wind curtailment factor from 1% to 2% led to reductions of 12% and 15% in the total daily emission costs and network costs, respectively. Additionally, centralized biomass-based plants dominated hydrogen production, accounting for 96% and 94% of the total production under 1% and 2% wind curtailment factors, respectively. Furthermore, transporting gaseous hydrogen via tube trailers proved more cost effective than using tanker trucks for liquid hydrogen when compressed gaseous hydrogen is required at the dispenser of forecourt refueling stations. Finally, the breakdown of the levelized cost for the hydrogen refuelling station strongly depends on the form of hydrogen received at the gate, namely, liquid or gaseous. Specifically, for the former, the dispenser accounts for 60% of the total cost, whereas for the latter, the compressor is responsible for 58% of the total cost. This study highlights the importance of preliminary and quantitativ

关键词： Renewable hydrogen multi-objective optimization Design Mixed-integer linear programming Public transport Wind Biomass

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multi-objective optimization of exponential-cross-section piezoelectric cantilever under tire rotational excitation for self-powered tire pressure monitoring systems

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JOURNAL OF CLEANER PRODUCTION 2025年 500卷

作者： Momen, Mojtaba Ebrahimi-Nejad, Salman Mollajafari, Morteza Iran Univ Sci & Technol Sch Automot Engn Vehicle Dynam Syst Res Lab Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Vehicle Elect & Elect Res Lab Tehran Iran

In this study, a piezoelectric cantilever energy harvester model with two exponential cross-sections of width and thickness with a weighted block at the tip of the cantilever under tire rotational excitation is proposed for use in a tire pressure monitoring system (TPMS). The governing energy-harvesting equations were derived for an exponential cross-section beam under rotational tire excitation conditions for the first time. Few studies have considered both the thickness and width of the beam as variables, and no theoretical research has been conducted on this model under the rotational excitation of tires. After modeling the energy harvester, the governing mechanical equations were derived based on the Euler-Lagrange method and then coupled to the electrical relations. Moreover, suitable solutions for optimal energy harvesting from the proposed model have been proposed to increase the bandwidth of the optimal performance. To improve the results, multi-objective Particle Swarm optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGAII), two well-known multiobjective optimization algorithms with the objectives of increasing electric power and reducing weight, were used to find the optimal values for the geometric dimensions of the model. The results of this study confirm that using a beam with an exponential cross-section in width and thickness produces an energy harvester with more electrical power and less weight than other geometry types. To make the energy harvester model work near its resonance frequency at most of the vehicle movement speeds, i.e. from 1 to 151 km/h, and increasing the bandwidth of the optimal performance of the model, we found the ideal effective length of the cantilever as 5.997 mm along with the appropriate tension spring stiffness. The results show that using the ideal initial effective length, the average power of the energy harvester model at speeds between 1 and 151 km/h has increased from 3.51 mu W to 6.23 mu W, which

关键词： Tire energy harvester multi-objective optimization Piezoelectric cantilever Exponential cross-section Rotational excitation harvester Self-tuning resonant frequency

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multi-objective optimization of additive manufacturing process parameters of nitinol alloys for elastocaloric cooling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 154卷

作者： Wang, Shuyao Shi, Yongjun Cheng, Wenjia Fan, Kaijun Wang, Qin Li, Ying Li, Quan Sun, Huayang China Univ Petr East China Coll Mech & Elect Engn Qingdao 266580 Peoples R China China Oilfield Serv Ltd Tianjin Branch Tianjin 300459 Peoples R China

Nitinol shape memory alloys are recognized as green and environmentally friendly solid-state refrigerants, and their preparation through additive manufacturing technology demonstrates significant application potential. However, minimizing energy consumption while enhancing material utilization and manufacturing efficiency remains a primary goal and challenge for researchers. This study proposes the application of intelligent optimization algorithms to optimize process parameters, thereby minimizing energy consumption, improving material utilization, and reducing dilution rates. The objective is to achieve full utilization of energy and materials, thus enhancing additive manufacturing efficiency. Compared to traditional experimental methods, this approach eliminates limitations related to test conditions and parameters, allowing for more effective optimization of additive manufacturing process parameters. Designing and conducting experiments on key process parameters-such as laser powers, scanning speeds, powder feeding rates, and shielding gas flow rates-over a broader range can provide accurate data for intelligent optimization algorithms. The results indicate that the established genetic algorithm-optimized back propagation neural network prediction model exhibits high prediction accuracy for energy consumption, powder utilization, and dilution rates. The optimal process parameters were determined using a Pareto front for a multi-objective optimization problem. This multi-objective optimization resulted in reduction of energy consumption and dilution rates by 34.1 % and 27.2 %, respectively, while increasing the metal powder utilization rate by 139.8 % compared to the average of the experimental results. This study contributes to the reduction of energy and material consumption in the additive manufacturing process of Nitinol shape memory alloys and promotes the development of green cooling technology utilizing these alloys as solid-state refrigerants.

关键词： multi-objective optimization Additive manufacturing Elastocaloric effect Intelligent algorithms

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multi-objective optimization for green logistics planning and operations management: From economic to environmental perspective

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 189卷

作者： Xie, Dongwei Qiu, Yunzhe Huang, Jingsi Peking Univ Dept Ind Engn & Management 5 Yiheyuan Rd Beijing 100871 Peoples R China Peking Univ Dept Informat Management 5 Yiheyuan Rd Beijing 100871 Peoples R China

In the energy crisis and climate change context, the trade-off between economic and environmental objectives of green supply chain management and operations in industrial parks has become increasingly prominent. This paper couples the management of the energy generation, the refrigeratory inventory, and the coldchain transportation systems of a green industrial park (GIP) for fresh goods that require low -temperature storage. We introduce the renewable energy microgrid (REM) to the energy chain and electric vehicles to the transportation system in the GIP to reduce carbon emissions. A two-phase multi -objective optimization model considering total expenditures and carbon emissions simultaneously is established to compare different renewable and conventional energy schemes. We exploit the real data to optimize scheduling strategies for energy, inventory, and fleet systems under a typical period based on different environmental conditions and time-sharing electricity prices. The empirical study shows that the adoption of REM and electrical vehicles can reduce carbon emissions in the supply chain by 13.6%, and GIP managers with different performance targets should choose different energy mixed schemes.

关键词： Supply chain management Green logistics Carbon reduction Renewable energy multi-objective optimization

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multi-objective optimization of an endoreversible closed Atkinson cycle

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JOURNAL OF NON-EQUILIBRIUM THERMODYNAMICS 2024年第1期49卷 73-88页

作者： Gong, Zheng Ge, Yanlin Chen, Lingen Feng, Huijun Wuhan Inst Technol Inst Thermal Sci & Power Engn Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China Res Ctr Green Chem Equipment Hubei Prov Engn Technol Wuhan 430205 Peoples R China

Based on finite-time-thermodynamic theory and the model established in previous literature, the multi-objective optimization analysis for an endoreversible closed Atkinson cycle is conducted through using the NSGA-II algorithm. With the final state point temperature (T-2) of cycle compression process as the optimization variable and the thermal efficiency (eta), the dimensionless efficient power (E-P), the dimensionless ecological function (E) and the dimensionless power (P) as the optimization objectives, the influences of T-2 on the four optimization objectives are analyzed, multi-objective optimization analyses of single-, two-, three-and four objective are conducted, and the optimal cycle optimization objective combination is chosen by using three decision-making methods which include LINMAP, TOPSIS, and Shannon Entropy. The result shows that when four-objective optimization is conducted, with the ascent of T-2, P descends, eta ascends, both E and E-P firstly ascend and then descend. In this situation, the deviation index is the smallest and equals to 0.2657 under the decision-making method of Shannon Entropy, so its optimization result is the optimal. The multi-objective optimization results are able to provide certain guidelines for the design of practical closed Atkinson cycle heat engine.

关键词： finite time thermodynamics closed Atkinson cycle multi-objective optimization NSGA-II algorithm ecological function efficient power

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multi-objective optimization with PCM integration and delayed cooling strategy for high-rate discharge applications

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2024年 218卷

作者： Qiu, Chenghui Kong, Benben Zou, Yitao Zheng, Rentong Shi, Hong Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212114 Jiangsu Peoples R China

The thermal safety of lithium-ion batteries has been a significant concern in the industry, prompting a pressing need for research to ensure efficient and energy-saving operation of energy storage battery's thermal management system. This study focuses on addressing the multi-objective optimization problem of heat dissipation coupling energy consumption in battery packs under high discharge rate with a maximum temperature (T-max), a maximum temperature difference (Delta T) and the pump energy consumption (P) as objectives. The Kriging interpolation and CRITIC weighting method are used for multi-objective optimization to determine the optimum solution objectively. The results indicate that increasing the diameter of the liquid cooling pipe (X) and the coolant inlet velocity (V-in) both contribute to reducing the maximum temperature of system. However, these changes have negative impacts on temperature uniformity within the battery pack and the energy consumption of the water pump. The above rule is reversed when considering the impact of initiation time of coolant flow (T), which in addition can lead to exhaustion of latent heat of CPCM and insufficient cooling capacity and thus potential overheating of the battery pack. According to the CRITIC weighting method analysis, the index weights of T-max, Delta T and P are 40.21%, 32.01% and 27.79%, respectively. Further analysis reveals that the optimal comprehensive performance of the battery pack thermal management system is achieved when X = 3.875 mm, V-in = 0.4 m/s, and T = 537.5 s, corresponding the P of 2.53 J, T-max of 309.32 K and Delta T of 2.41 K, both falling within the specified technical requirements at a discharge of 4 C . The latent heat of the phase change material in the battery pack has been recovered at this stage, providing sufficient cooling capacity for the next cycle of charging and discharging.

关键词： Energy storage battery Phase change material cooling coupled with liquid cooling CRITIC weighting method multi-objective optimization

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multi-objective optimization and theoretical analysis of re-entrant structure with enhanced mechanical properties

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THIN-WALLED STRUCTURES 2024年 199卷

作者： Ni, Xi Hai Teng, Xing Chi Jiang, Wei Zhang, Yi Ren, Xin Nanjing Tech Univ Coll Civil Engn Ctr Innovat Struct Nanjing 211816 Jiangsu Peoples R China Zhejiang Univ Key Lab CAD & CG Key Lab Soft Machines & Smart Devices Zhejiang Pro Hangzhou 310027 Peoples R China Zhejiang Univ Dept Engn Mech Hangzhou 310027 Peoples R China

In response to the challenge of low stiffness and energy absorption capacity, various designs of negative Poisson's ratio (NPR) structures have emerged. However, these designs often lack a comprehensive analysis of other mechanical properties, leading to subpar mechanical performance. Previous studies have explored the mechanical properties response of enhanced re-entrant honeycombs (ERH) under impact conditions, revealing limitations in optimizing the structure's performance with a single objective. Therefore, this study aims to enhance ERH structural parameters to achieve superior mechanical performance through theoretical derivations and geometric optimizations. The results demonstrate that the proposed theoretical model is consistent with finite element analysis and response surface (RS) predictions. Expressions for Young's modulus, specific energy absorption, and compressive strength are proposed, facilitating the identification of structural parameters that meet specific requirements during reverse design. Furthermore, a multi-objective optimization approach optimizes the geometric parameters based on maximum energy absorption and compressive strength. The mechanical behavior of the optimized ERH is investigated using the finite element method, revealing the energy absorption capacity of 13.78 J/g while maintaining Poisson's ratio at -1.06. Additionally, the deformation mode of the optimized structure showcases enhanced stability compared to traditional honeycomb structures. The theoretical model and RS method were used to guide the design of ERH and promote the application of NPR structures.

关键词： Negative Poisson's ratio Energy absorption multi-objective optimization Theoretical analysis Mechanical property

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multi-objective optimization for Noise Reduction of IPMSM Based on Non-Uniform Air Gap

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IEEE ACCESS 2024年 12卷 48819-48829页

作者： Zhang, Youguo Yang, Yanbo Gu, Z. Y. Li, Liangzi Ma, Tiancai Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China SAIC Volkswagen Automot Co Elect Antrieb Dept Shanghai 201804 Peoples R China FR Motor Co Shanghai 201804 Peoples R China Tongji Univ Inst Carbon Neutral Shanghai 200092 Peoples R China

Owing to the high speed of the interior permanent magnet synchronous motor (IPMSM), the high-frequency howling noise of electromagnetic vibration is disruptive to passengers. The harmonics of the electromagnetic force serve as the primary source of vibration noise. In this work, a non-uniform air gap with a microgroove surface of the motor was proposed for optimizing the harmonics of the electromagnetic force. The research was based on a developing project of a 150 kW IPMSM for a battery electric vehicle (BEV). To find the optimal microgroove surface of the rotor, six variables were defined for the microgrooves through 9900 calculations using the Ansys Workbench. By finite element analysis (FEA) comparisons, the non-uniform air gap with microgroove surface significantly reduced the no-load back Electromotive Force (EMF) harmonics, the total distortion rate (THD) from 2.57% to 1.52%, and the torque ripple from 10.84% to 4.06%. A motor prototype with a non-uniform air gap was manufactured to prove the accuracy of the simulation. Under the whole open throttle (WOT) condition, the vibration acceleration of the 24th and 48th order was approximately 10 dB lower than the target vibration noise limit.

关键词： Air gaps Motors Harmonic analysis Vibrations Rotors Electromagnetic forces Magnetic flux Permanent magnet motors Vibration measurement Interior permanent magnet synchronous motor non-uniform air gap with microgroove surface multi-objective optimization vibration noise

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multi-objective optimization Analysis of Electromagnetic Performance of Permanent Magnet Synchronous Motors Based on the PSO Algorithm

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ENERGIES 2024年第18期17卷 4637页

作者： Cen, Yufei Shen, Haoyu Wang, Xiaoyuan Wu, Yongming Du, Jingjuan Changan Univ Sch Construct Machinery Xian 710064 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Leadr Technol Liuzhou Co Ltd Liuzhou 545000 Peoples R China Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China

In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable and the peak cogging torque and no-load back electromotive force (EMF) amplitude of the motor as the optimization objectives. A multi-objective optimization method based on the particle swarm optimization (PSO) algorithm is adopted to obtain a structural parameter combination that minimizes the peak cogging torque and no-load back EMF amplitude while meeting the reasonable range requirements of magnetic flux density amplitude. The optimized motor structure design prototype is experimentally verified. The results show that through multi-objective optimization based on the PSO algorithm, the electromagnetic performance of the motor has been improved, with a reduction of 36.33% in peak cogging torque and 2.65% in peak no-load back EMF, indicating a reasonable magnetic flux density amplitude. The experimental results of the optimized prototype show that the difference between the theoretical simulation values and the experimental values is within a reasonable range, which verifies the effectiveness of the multi-objective optimization method.

关键词： permanent magnet synchronous motor particle swarm optimization algorithm multi-objective optimization electromagnetic performance

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multi-objective optimization method of product service systems configuration based on customer demand constraint mechanism

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2024年第6期18卷 1-18页

作者： Su, Zhongji Hua, Zexi Yang, Wenquan Southwest Jiaotong Univ Sch Informat Sci & Technol 999 Xian Rd Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence 999 Xian Rd Chengdu 611756 Peoples R China

This article proposes a multi-objective optimization method for product service configuration based on customer demand constraint mechanism, targeting the diverse customer needs of product service systems (PSS). This method first forms a utility matrix based on the response of product service instances to customer needs and establishes constraints based on product attributes and enterprise resources. The PSS configuration module is divided into product basic modules and optional modules based on customer demand constraint mechanisms, aiming to express the combination of product and service attributes. Through the utility matrix, multiple optional objectives such as safety, energy efficiency, and economy of PSS configuration are established to fully express the multi-dimensional needs of customer satisfaction. Then a new MOEA/D algorithm was developed for multi- objective optimization of PSS configuration. The improved MOEA/D algorithm adopts a weight vector adjustment strategy based on correlation sparsity, which improves the convergence and diversity of optimization results in high-dimensional objective space and solves the problem of exponential growth in solution space caused by the increase in customer demand types. This method was applied to practical cases of home appliance PSS configuration and compared with the performance of five advanced methods. The experimental results showed that the model established in this paper and the improved algorithm obtained configuration schemes had significant advantages in convergence and diversity.

关键词： Service-oriented manufacturing Product service system(PSS) PSS configuration optimization multi-objective optimization

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