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Aero-Engine Blade Cryogenic Cooling Milling Deformation Simulation and Process Parameter Optimization

MATERIALS 2023年第11期16卷 4072-4072页

作者： Chen, Ting Xu, Yun Huang, Bo Shi, Yan Zhang, Jiahu Li, Lei Meng, Yaozhi Li, Xuqing Sichuan Univ Sci & Engn Sch Mech Engn Zigong 643000 Peoples R China

For the machining of aero-engine blades, factors such as machining residual stress, milling force, and heat deformation can result in poor blade profile accuracy. To address this issue, simulations of blade milling were completed using DEFORM11.0 and ABAQUS2020 software to analyze blade deformation under heat-force fields. Process parameters such as spindle speed, feed per tooth, depth of cut, and jet temperature are used to design both a single-factor control and BBD test scheme to study the influence of jet temperature and multiple changes in process parameters on blade deformation. The multiple quadratic regression method was applied to establish a mathematical model correlating blade deformation with process parameters, and a preferred set of process parameters was obtained through the particle swarm algorithm. Results from the single-factor test indicated that blade deformation rates were reduced by more than 31.36% in low-temperature milling (-190 degrees C to -10 degrees C) compared with dry milling (10 degrees C to 20 degrees C). However, the margin of the blade profile exceeded the permissible range (similar to 50 mu m;therefore, the particle swarm optimization algorithm was used to optimize machining process parameters, resulting in a maximum deformation of 0.0396 mm when the blade temperature was -160 degrees C similar to -180 degrees C, meeting the allowable blade profile deformation error.

关键词： aero-engine blade titanium cryogenic machining simulations particle swarm algorithm

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Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries

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ENERGIES 2020年第10期13卷 2543页

作者： Chen, Zeyu Lu, Jiahuan Liu, Bo Zhou, Nan Li, Shijie Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

The performance of lithium-ion batteries will inevitably degrade during the high frequently charging/discharging load applied in electric vehicles. For hybrid electric vehicles, battery aging not only declines the performance and reliability of the battery itself, but it also affects the whole energy efficiency of the vehicle since the engine has to participate more. Therefore, the energy management strategy is required to be adjusted during the entire lifespan of lithium-ion batteries to maintain the optimality of energy economy. In this study, tests of the battery performances under thirteen different aging stages are involved and a parameters-varying battery model that represents the battery degradation is established. The influences of battery aging on energy consumption of a given plug-in hybrid electric vehicle (PHEV) are analyzed quantitatively. The results indicate that the variations of capacity and internal resistance are the main factors while the polarization and open circuit voltage (OCV) have a minor effect on the energy consumption. Based on the above efforts, the optimal energy management strategy is proposed for optimizing the energy efficiency concerning both the fresh and aging batteries in PHEV. The presented strategy is evaluated by a simulation study with different driving cycles, illustrating that it can balance out some of the harmful effects that battery aging can have on energy efficiency. The energy consumption is reduced by up to 2.24% compared with that under the optimal strategy without considering the battery aging.

关键词： battery aging plug-in electric vehicles energy management global optimization state of health particle swarm algorithm genetic algorithm

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On a multiple priorities matching system with heterogeneous delay sensitive individuals

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APPLIED MATHEMATICS AND COMPUTATION 2021年 395卷 125873-125873页

作者： Chai, Xudong Jiang, Tao Chang, Baoxian Liu, Liwei Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China Shandong Univ Sci & Technol Coll Econ & Management Qingdao 266590 Shandong Peoples R China

In this paper, we study the equilibrium strategies of heterogeneous delay sensitive individuals in matching systems. Take customers as the research object. The priorities hinge on the magnitude of customers' admission fee. Before entering the system where there are no servers, customers will make a two-stage strategy. First, customers should determine an optimal additional fee to pursue the optimal utilities, i.e., the so-called additional payment strategy. Then, they decide whether to join or not. We primarily give some necessary conditions for the equilibrium. As regards the heterogeneity of customers in delay sensitivity, both of the non-atomic continuous and atomic discrete types of customers are discussed, respectively. Applying the sequential approach, we capture the unique equilibrium for both cases. Specially, for the non-atomic continuous type of customers, the incoming ones adopt the pure additional payment strategy at equilibrium. While for the atomic discrete type of customers, the equilibrium additional payment strategies for incoming ones become mixed-type. It is found that the equilibrium additional fee and flexible cost for each incoming customer are both decreasing in fundamental payment. Finally, we use the particle swarm algorithm to consider the optimal social revenue when the incoming ones from both sides are strategic. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Matching queueing system Heterogeneity in delay sensitivity Two-stage strategy Non-atomic continuous Atomic discrete type particle swarm algorithm

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Real-world data-driven charging strategies for incorporating health awareness in electric buses

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JOURNAL OF ENERGY STORAGE 2024年 92卷

作者： Li, Taotao Zhang, Wencan Huang, Guangshan He, Hancheng Xie, Yi Zhu, Taoxi Liu, Guote Foshan Univ Sch Mechatron Engn Foshan 528200 Peoples R China Guangdong Tianshu New Energy Technol Co Ltd Foshan 528300 Peoples R China

Electric buses are progressively supplanting conventional fuel-powered vehicles in urban public transportation. However, owing to the extensive daily mileage and frequent charging-discharging cycles, power batteries accelerate aging, augmenting operation and maintenance costs for electric buses. To tackle this issue, the present study initially develops a thermo-electric-health coupling model using actual charge-discharge data obtained from power batteries to cover its aging loss, state of charge, and temperature effects. Then, by employing the particle swarm Optimization and Model Predictive Control methods, the fast charging strategy, maximum health charging strategy, and balanced charging strategy are formulated to manage lithium-ion battery aging effectively, enhance charging efficiency, and ensure thermal safety. The practical application and simulation results demonstrate that, under the proposed fast charging strategy, the maximum health charging strategy, and the balanced charging strategy, it takes 1809 s, 4282 s, and 2523 s for the power battery State of Charge to increase from 50 % to 95 %. Moreover, after undergoing 60 charge cycles, the power battery experiences a health degradation of only 0.47 %, 0.21 %, and 0.3 %, respectively. Compared to the conventional charging mode, the proposed charging strategy effectively mitigates power battery aging across various charging scenarios, ensuring safety and high charging efficiency.

关键词： Real -world data Battery health Charging strategy Model predictive control particle swarm algorithm

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Optimal charging of lithium-ion batteries based on lithium precipitation suppression

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JOURNAL OF ENERGY STORAGE 2024年 82卷

作者： Yu, Changzhou Huang, Siqi Xu, Haizhen Yan, Jiale Rong, Kang Sun, Meimei Hefei Univ Sch Adv Mfg Engn Hefei Anhui Peoples R China Naval Aviat Univ Zhifu Rd Yantai Peoples R China

With the development of the times, lithium-ion batteries have gradually become the main power source for new energy electric vehicles. The development of new energy electric vehicle charging technology cannot be separated from the progress of lithium-ion battery charging technology. Due to the secondary reaction of lithium precipitation during the lithium-ion battery charging process, there is a higher demand for lithium-ion battery charging strategies. This paper introduces an optimized charging method for suppressing lithium precipitation based on the second-order RC equivalent circuit model of lithium-ion batteries. An adaptability function is established to determine the lithium precipitation suppression value and temperature increase value, and the particle swarm algorithm is used to optimize the current value for each charging stage. The effectiveness of this charging method based on the particle swarm algorithm is verified through contrasted simulations with the method of suppressing lithium precipitation at 20 C in MATLAB/Simulink, which proves its efficiency and superiority.

关键词： Lithium -ion battery particle swarm algorithm Suppress lithium precipitation Optimized charging

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Logistics Pure Electric Vehicle Routing Based on GA-PSO algorithm

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MECHANIKA 2023年第3期29卷 235-242页

作者： Wang, Mengqin Xie, Qiyue Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Peoples R China

In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging fa-cility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution cen-ter electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.

关键词： electric vehicle path planning particle swarm algorithm genetic algorithm optimization

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Correlation analysis of runway icing parameters and improved PSO-LSSVM icing prediction

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COLD REGIONS SCIENCE AND TECHNOLOGY 2022年 193卷 103415-103415页

作者： Chen, Bin Zhou, Chong Liu, Yue Liu, Jianhua Civil Aviat Univ China Inst Elect Informat & Automat Tianjin 300300 Peoples R China Civil Aviat Univ China Ground Support Equipment Res Base Tianjin 300300 Peoples R China

The runway icing prediction is an important guarantee for the safe and efficient operation of the airport in winter. Aiming at the complex problem of airport runway icing with many factors, the grey correlation analysis method is used to study the sensitivity of runway icing to multiple parameters, and the main influencing factors of ice thickness are obtained. The kernel function and penalty function of the least squares support vector machine (LSSVM) are optimized by the improved particle swarm optimization (IPSO) algorithm to realize the nonlinear mapping of runway ice thickness and multi-parameter. The prediction sample set of runway icing is formed by the integrated processing of three kinds of data, namely the low-temperature experimental data, the outdoor icing experiment data and the actual airport data. After sample training, testing and error analysis, a prediction model of LSSVM based on grey correlation analysis is established. The experimental result shows that the average relative error of the prediction model of the LSSVM optimized by the IPSO algorithm is kept within 5%, which is better than other prediction methods such as BP. The above research results provide a scientific basis for the deployment of airport deicing equipment in snow and ice.

关键词： Runway icing Grey correlation analysis Least squares support vector machine particle swarm algorithm Icing prediction

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Optimization of a Three-Phase Coaxial 10 kV/1.5 kA HTS Power Cable for Balancing 3-Phase Current Distribution

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2022年第6期32卷

作者： Yang, Qifan Zhu, Jiahui Chen, Panpan Yang, Yanfang Ma, Shizhuo Cao, Jingying Liu, Jiaxin Wei, Defu China Elect Power Res Inst Beijing 100192 Peoples R China State Grid Jiangsu Elect Power Co Nanjing 211103 Peoples R China State Grid Liaoning Elect Power Co Shenyang 110006 Peoples R China

With the increasing power load of urban power grid, the upgrading of conventional cable is facing with the problem of space resource shortage. Three-phase coaxial high temperature superconducting cable has the advantages of compact structure, low loss, and nearly 2/3 reduction in the consumption of HTS tape, which provides a new prospective solution for urban expansion. However, due to the different radius of each phase of the three-phase coaxial HTS cable, the symmetry of electromagnetic parameters of each phase are affected, resulting in the imbalance of the three-phase current, which will reduce the transmission efficiency. Based on the multi-loop method, we establish the differential equation of three-phase coaxial superconducting cable in this paper, by calculating the parameters of three-phase inductance and capacitance by a numerical method, and obtain the instantaneous variation rule of voltage and current in the three-phase coaxial HTS cable. We propose a novel two-section particle swarm Optimization to solve the problem of three-phase current imbalance in HTS cable. The algorithm divides the C-phase conductor and the copper shield layer into two segments with the same radius but different pitches and twist directions, which increases the variable by one-half. By changing the pitches and winding directions, considering the thickness of the insulation, optimization of current distribution of three-phase coaxial HTS cable is achieved. Finally, we designed a 10 kV/1.5 kA three-phase coaxial HTS cable. The results show that the current imbalance ratio of the superconducting cable is reduced from 41% to 1% after the two-section particle swarm Optimization. The mathematical model and the imbalance optimization algorithm of three-phase coaxial HTS cable both are verified. The optimization results provide important reference for the structural design and experimental research of three-phase coaxial HTS cable for urban power grid.

关键词： Superconducting cables Coaxial cables High-temperature superconductors Power cables Cable shielding Voltage Optimization HTS cable design particle swarm algorithm three-phase coaxial cable three-phase current balance

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Two-Stage, Three-Layer Stochastic Robust Model and Solution for Multi-Energy Access System Based on Hybrid Game Theory

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PROCESSES 2024年第12期12卷 2656页

作者： Wu, Guodong Li, Xiaohu Wang, Jianhui Zhang, Ruixiao Bao, Guangqing Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Grid Gansu Elect Power Co Lanzhou 730030 Peoples R China State Grid Gansu Elect Power Co Elect Power Sci Res Inst Lanzhou 730030 Peoples R China SouthWest Petr Univ Sch Elect & Informat Engn Chengdu 610500 Peoples R China

This paper proposes a two-stage, three-layer stochastic robust model and its solution method for a multi-energy access system (MEAS) considering different weather scenarios which are described through scenario probabilities and output uncertainties. In the first stage, based on the principle of the master-slave game, the master-slave relationship between the grid dispatch department (GDD) and the MEAS is constructed and the master-slave game transaction mechanism is analyzed. The GDD establishes a stochastic pricing model that takes into account the uncertainty of wind power scenario probabilities. In the second stage, considering the impacts of wind power and photovoltaic scenario probability uncertainties and output uncertainties, a max-max-min three-layer structured stochastic robust model for the MEAS is established and its cooperation model is constructed based on the Nash bargaining principle. A variable alternating iteration algorithm combining Karush-Kuhn-Tucker conditions (KKT) is proposed to solve the stochastic robust model of the MEAS. The alternating direction method of multipliers (ADMM) is used to solve the cooperation model of the MEAS and a particle swarm algorithm (PSO) is employed to solve the non-convex two-stage model. Finally, the effectiveness of the proposed model and method is verified through case studies.

关键词： weather scenarios hybrid game stochastic robust variable alternating iteration particle swarm algorithm

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Analysis and optimization of the performance parameters of non cooperative target location detection system

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OPTIK 2021年 227卷 166100-166100页

作者： Ning, Shuang Zhu, Yang Lv, Xueying Song, Hongzhen Zhang, Ruoxi Zhang, Guanyu Zhang, Liu Jilin Univ Coll Instrumentat & Elect Engn Changchun 130061 Peoples R China Jilin Univ Natl Engn Res Ctr Geophys Explorat Instruments Changchun 130061 Peoples R China

This study applies an improved particle swarm optimization (IEPSO) algorithm to optimize the structural design parameters of a binocular detection system. First, the mathematical model of the performance analysis of the binocular detection system is established, and the influencing factors of index parameters are analyzed. Second, the IEPSO algorithm is proposed to overcome the shortcomings of common PSO. Finally, taking a model of the binocular system as an example, the effective field of view and resolution are used as constraints, and the IEPSO algorithm is used to optimize the binocular system's parameters. Optimization result can enable the detection error of the system to reach 0.01 mm.

关键词： particle swarm algorithm Binocular system structure Error analysis

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