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英文原文Development of a Method for the Energy Efficiency Determination of Stacker Cranes in Automated High-Bay WarehousesAndreas Rcker Research Assistant;Technical University of Munich;Faculty of Mechanical Engineering;Chair of Materials Handling, Material Flow, Logistics (fml); Germany.Jona Rief Research Assistant;Technical University of Munich ;Faculty of Mechanical Engineering;Chair of Materials Handling,Material Flow, Logistics (fml); Germany。Johannes Fottner Professor;Technical University of Munich;Faculty of Mechanical Engineering;Chair of Materials Handling,Material Flow, Logistics (fml);GermanyModern intralogistic facilities fulfil important tasks within todays supply chains. Many different influences must be taken into account in their planning and construction. Current trends and circumstances show a strong tendency to build energy efficient and therefore environmentally friendly warehouses. This applies to the building technology of a warehouse and the intralogistic system used. Intralogistic facilities with an automated high-bay warehouse are very common and often operated by stacker cranes. This storage technology allows goods to be stored in a very volume and energy-efficient manner. The performance and energy demand of stacker cranes are influenced by a large number of parameters. To determine their energy efficiency is therefore complex. In this paper wepresent a method based on a simulation study which could be a possible solution for this problem.Keywords: Intralogistics; High-bay warehouse; Automated storage and retrieval system; Stacker crane; Energy efficiency.1. INTRODUCTIONIn times of global climate change, ecological aspects are very important in every part of supply chains. This results in an increasing need to reduce the energy consumption in the physical part of the supply chain. Besides transport systems, intralogistic facilities are an important part of the physical supply chain. These circumstances also affect high-bay warehouses, which are an important part of intralogistic facilites. The energy consumption of automated high-bay warehouses is therefore of bigger concern to manufacturers and operators. These high-bay warehouses are often operated by stacker cranes (SCs). SCs are used to store, relocate and retrieve small load carriers, pallets or special load carriers inside the storage racks. Their energy demand and performance is influenced by a large number of variable effects which can only be optimised systematically.Gnthner et al. proposed in 2009 that a system for managing sustainability measures via an evaluation system would increase the opportunities for improvement 12. They proposed to implement a control and measurement system before any improvements aremade to measure their impact. Their system is based on four steps: 1. definition of sustainability key figures, 2.establishing measurement systems, 3. assessment of the status quo, and 4. identification, implementation and evaluation of improvements. Based on these results, we suggest an implementation cycle for energy effiency measures pictured in Figure 1. The cycle consists offour steps which are: assessment of the status quo,identifying potentials for improvements, implementing improvements and evaluating improvements. For the assessment and the evaluation, a method for the determination of the energy efficiency is needed. Figure 1. Implementation cycle for energy efficiency measures (compare 23)The energy efficiency in public places is often linked to the EU energy efficiency label 8. These labels are used for the classification of white goods like dishwashers, washing machines and refrigerators. They are helpful for consumers to gain a fast overview of the energy demand and to have the possibility to compare different machines. Labels like these are not common in the industrial environment. The rating for air filters 9 and the international efficiency code (IE-Code) for electric motors 4 are examples for the classification of individual machine components. The rating for elevators 27,28 is an example for the classification of a whole machine. The energy efficiency classification is often based on a reference machine and a reference cycle. It is not feasible to define a reference SC because these machines are individually designed for a specific application. Thus we only use a reference cycle in our method.In the field of intralogistics, there is currently only one new guideline for the energy consumption determination available 29. This guideline also contains a method to calculate the energy consumption of an SC.In a former paper 24, we presented an investigation of the mean energy demand and the performance of SCs.The simulation model which was used for the investigation in this paper is based on the FEM guideline. This paper uses the results of the conducted simulation study to develop a meta model for the determination of the mean energy demand and furthermore for the energy efficiency determination of SCs. Subsequently, we presented a possible method to develop classes for the energy efficiency labelling. We repeated our suggestion to use the FEM reference cycle for the mean energy demand determination. This work aims to be a contribution to improve the energy efficiency of SCs.2. LITERATUREThe change to green intralogistics was examined by Altintas et al. 1 in 2010. The research work especially shows potential for intralogistics by sampling and analysing literature of different optimisation potentials in intralogistics. They also conducted an expert survey to test the industrys views on green logistics. Their research work is based on the roadmap for sustainable intralogistics from Gnthner et al. 12. Optimising the energy efficiency in intralogistics was also part of the research work of Lottersberger and Hafner. They investigated the energy efficiency of material flow systems 13, 14 with a focus on continuous conveyors. A modern approach for optimising the investment expenses, the cycle times and the CO 2 footprint for automated storage and retrieval systems was presented by Rajkovic et al. 21. They presented a multi-objective optimisation model to simultaneously consider the different design criteria of automated storage and retrieval systems.The impact of the design options in intralogistics and building design on the overall energy demand and CO 2 emissions was part of the research work of Freis et al. 11. They investigated three reference intralogistics facilities with different degrees of automation. One of their conclusions was the significant increase of the energy demand with increasing automation.The last review paper presenting a literature survey on SCs was presented by Roodbergen 22, followed by a review paper in 2016 by Boysen and Stephan with a focus on scheduling tasks of SCs 2. The energy demand of SCs was part of various publications in recent years. Meneghetti et al. focused their research work on the influence of strategic parameters 1720. Lerher et al. presented a first method for calculating the energy efficiency of miniload stacker cranes (MSCs) 16. Ertl et al. also investigated energy demand and the energy efficiency of SCs and presented a method for energy efficiency classes for MSCs 5, 6. A method for the benchmarking of different types of automated storage and retrieval systems was presented by Sthr et al. 25. They proposed the use of the standard double cycle from VDI 3561 26 as a reference cycle for the comparison between MSCs, shuttle systems and horizontal carousel systems.The energy demand of pallet stacker cranes (PSCs) has only been investigated by Braun and Furmans 3.Their work was focused on the the calibration of a simulation model to anticipate the energy demand of a specific SC. In their work, they did not investigate superordinate relationships for a large number of PSCs. With our work, we try to close this research gap.3. MATERIALS AND METHODSA typical scheme of an SC and the nomenclature used in the paper is shown in Figure 2. Because of their fundamentally different mass ratios, we distinguish between MSCs and PSCs in the analyses in this work. Figure 2. Scheme of a stacker crane (compare 23)In addition, we symbolically represent the energy efficiency classes from A to G, which will later be proposed for classification. The main components are the base frame, the mast, the head, the lifting device with load handling device and the drives. SCs have two main drives for the horizontal and vertical movement of the payload. The power supplies of the main drives are electrically connected via a DC link 24.In the paper we present an approach for the energy efficiency determination of an SC. Before we specify the method, we present a simple meta model of the energy demand of an SC using the FEM cycle. Both methods are based on the results of the simulation study performed in 24. The list of varied parameters which specify the SC configurations in the simulation study is shown in Table 1.Table 1. List of the varied parameters and their maximum and minimum values for MSC and PSC 24ParameterMSC minMSC maxPSC minPSC maxNumber of columns ncol30802060Number of levels nlev2040818Acceleration drive unitax2 m/s5 m/s0.5 m/s1.5m/sAcceleration lift unit ay1 m/s3 m/s0.2 m/s1 m/sVelocity drive unit vx4 m /s6 m /s1.5 m/s3.5 m/sVelocity lift unitvy in m/s1.5 m/s3 m/s0.5 m/s2 m/sEfficiency drive and liftunit x,y0.40.70.40.7Mass base frame mbase1.2 t2 t8 t20 tParameterMSC minMSC maxPSC minPSC maxMass lift unit mLu250 kg450 kg2.5 t4.5 tMass payload mload25 kg75 kg0.5 t1.5 tStorage occupancy OCC0.40.90.40.9Use of a refeed unit RU0101In between these minimum and maximum values of each parameter, we used 20,000 different SC configurations selected by a Latin hypercube sampling algorithm for the simulation study. We used 5,000 different SC configurations for MSC and PSC, either with (RU1) or without (RU0) a refeed unit. The refeed unit can feed electrical energy back into the grid, thus reducing the energy demand. We evaluated the mean energy demand and the throughput for each configuration during a large number of randomly created operation cycles. Subsequently, the data was used to find a suitable reference cycle. We developed two different ways for calculating a reference cycle and compared it with the FEM 9.851 10 reference cycle. In our comparison, the FEM cycle was the best possible cycle for our purposes. It allowed the determination of the mean energy demand of an SC using just one double cycle and minimum error.3.1 Meta modelling the energy demand of a stacker craneFigures 3 and 4 show the results of the simulation study with the correlations of the energy demand and the throughput (TP) and the varied parameters. Figure 3 shows the correlations for the MSC double cycleconfigurations without a refeed unit (RU0).Furthermore, Figure 4 shows the correlations for the PSC double cycle configuratio- ns with a refeed unit (RU1).Based on these results, we showed in 24 that the mean energy demand could be assumed by using thereference double cycle from FEM 9.851 10. It is also very well approved as a reference cycle for the mean cycle time. The FEM double cycle is pictured in Figure 5. Figure 3. Correlations for the MSC double cycle configurations without a refeed unit and with TP in moved items per hour and E in kJ 24 Figure 4. Correlations for the PSC double cycle configurations without a refeed unit and with TP in moved items per hour and E in MJ 24 Figure 5. Double cycle from FEM 9.851 10For the FEM cycle the store and retrieve position are defined in (1) with as the rack length and as the rack height.Store position:Pr(1/5.L,2/3.H) Retrieve position:Pr(2/3.L,1/5.H) (1)Firstly a store cycle from the I/O point to the store position, secondly a drive from the store to the retrieve point and thirdly a retrieve cycle from the retrieve position back to the I/O point are performed. We focused our investigations on the reference double cycle.The simulation results of the 20,000 configurations for the correlation between the overall mean energy demand and the energy demand for one FEM double cycle are shown in Figures 6 and 7. The results are split into MSC and PSC and are displayed with thecorresponding error distribution. We discovered a strong indication for a linear correlation in both cases. Figure 6. Correlation for the overall mean energy demand and the energy demand of an FEM double cycle with the corresponding relative error for MSC.The error distribution is somewhat narrower for MSC and has a lower mean error than for PSC. Figure 7. Correlation for the overall mean energy demand and the energy demand of an FEM double cycle with the corresponding relative error for PSCBased on the simulation results, the FEM double cycle leads to an overestimation of the mean energy demand of approximately 10%. Because of this relation, we propose a si- mple linear model to calculate the overall mean energy demand EMwith the energy dem- and of an FEM double cycle E FEM in equation (2).EM ( EFEM ) = cFEM EFEM (2)For the double cycles of MSC and PSC in all configurations, we evaluated a correction factor c FEM = 0.891. The results for the residual energy demand values and the relative error for different configurations are presented in section 4.1.3.2 Describing the energy efficiency of stacker cranesIn general the energy efficiency is defined in the EU directive 2012/27/EU as the ratio of output of performance, service, goods or energy to input energy 7. The main definition of energy efficiency can be stated with (3) with the performance and the electrical energy demand E el to achieve this performance. The nomenclature is based on the definition from Ertl 5. He used the reciprocal value of kEE for his work. kEE =F/Eel (3)The performance definition of the SC is based on mass flow and volume capacity of the storage rack. Equation (4) states the definition for the performance . It contains throughput , the mean transported payload mass mload , the number of columns ncol and levels nlev and the volume of a storage compartment VUnit. The throughput represents the number of load units moved per time unit. （4）Lerher used also the warehouse volume as a performance measure for a shuttle based storage and retrieval system 15. The combination of the selected parameters should be suitable for the consideration of the performance. The energy demand Eel can also beexpressed through the mean electrical power Pel , the throughput and the number of transported load units in the review period nload. （5）Combining the equations (3), (4) and (5), we get the full definition for the energy efficiency key figure with either energy demand or mean electrical power (6)。kEE=F/ Eel (6)The calculation method executed for all configu- rations leads to the resulting distribution of for MSC in Figure 8 and for PSC in Figure 9. Figure 8. Distribution of the energy efficiency index kEE for MSCThe figures for PSC are higher than for MSC by a factor of ten. Both distributions indicate a moved standard distribution of the energy efficiency index. The whole range is based on the parameter space from Table1. The distributions are used to apply different classi- fication methods. The results are shown in Section 4.2. igure 9. DFistribution of the energy efficiency index kEE for PSC4.RESULTSThe results section is split into two different parts. First, we show the results from our metamodelling and error correction of the energy demand with the FEM cycle. Second, we state the results for three different distribution methods of energy efficiency classes.4.1 Metamodelling the mean energy demand with the FEM cycleUsing the described correction factor from Section 3.1, we get the following results for different configurations. To have a more detailed insight, we split the results for MSC and PSC in the 5,000 configurations without (RU0) and with (RU1) a refeed unit. The resulting residuals (difference between overall mean energy demand and estimated energy demand) for the different configurations are shown in Figure 10. The residuals are mainly evenly distributed, except for individual outliers. Figure 10. Residuals for the overall mean energy demand and the energy demand of an FEM double cycleThe resulting relative error distributions in Figure 11 indicate small relative errors with mostly an overes- timation of the mean energy demand. Only the PSC configura- tions without a refeed unit are mostly underestimated. Figure 11. Relative error of an FEM double cycle related to the overall mean energy demandTable 2 lists the values for the error distributions in Figure 11. To keep the absolute value of the mean error at a lower level, we accepted the overestimation in some of the configurations.Table 2. Mean, minimum and maximum error values and the share of configurations with an absolute error smaller or equal to 10 % for the double cycle configurations in %.ConfigurationmeanminmaxP(| 10%|)MSC, RU02.3-3.811.899.94MSC, RU13.2-0.79.1100PSC, RU01.6-6.110.299.98PSC, RU1-2.8-12.04.899.74.2 Energy efficiency class determinationIn this section, we introduce three different possibilities to build seven efficiency classes from A to G. The stated definition of the energy efficiency index seems to lead to a moved normal distribution of kEE over the simulated configurations (see Figures 8 and 9). To split these ranges into different efficiency classes, we used five symmetric intervals with an interval range of one standard deviation in the range of 2.5, an additional interval over +2.5 and below -2.5. The corresponding percentile, the share and the limit values for the seven intervals are displayed in Table 3.Table 3. Energy efficiency class limit values based on a standard distributionClassPer- centileShareMSCkEE,minkg m3inJ hPSCkEE,minkg m3inJ hA0.62 %0.62 %19.38185.81B6.7 %6.1 %15.04138.41C30.9 %24.2 %10.8197.10D69.1 %38.3 %7.2463.37E93.3 %24.2 %4.7740.27F99.4 %6.1 %3.5125.74G100 %0.62 %00 Using these values, we can split our four different basic configurations into the seven classes. The resulting class distributions show that naturally the share of higher efficient SCs is higher with the use of a refeed unit (see Figure 12). Combining the results for RU0 and RU1 would lead to the normal distribution with the shares listed in Table 3.In addition to the normal distribution-based split and for a better assessment, we tried out two other distribution methods. The first is based on a linear series and the second is based on a geometric series. The linear series starts with the limit value of class A from the standard distribution kEE,1 and is built with the distance between two classes dkEE,lin using equation (7).kEE,i,lin = kEE,1 - dkEE,lin i (7)Splitting the classes with a linear series leads to a less uniform distribution compared to the normal distribution. The results in Figure 13 show a not necessarily desired shift of the main class towards E.The geometric series is based on a logarithmic distribution. Ertl also used a geometric series to develop his classification 5. The distribution with the logarithmic factor di kEE,log is built using equation (8).kEE,i,log = kEE,1 di kEE,log (8) Figure 12. Classification of the energy efficiency based on a normal distribution Figure 13. Classification of the energy efficiency based on a linear series Figure 14. Classification of the energy efficiency based on a geometric seriesApplying the logarithmic distribution results in a more uniform distribution compared to the linear distribution. The resulting class distributions (see Figure14) are similar compared to the standard distribution ones.In the comparison between the classification on the basis of normal distribution and on the basis of the geometric row, we cannot find a clear advantage for either method. The calculation parameters for the linear and geometric distribution are listed in Table 4.Table 4. Parameters for calculating the limit values for linear and logarithmic distributionTypekEE,1inkg m3 J hdkEE,linkg m3inJ hkkEE,logMSC19.383.1730.711PSC185.8132.0140.673 5.CONCLUSIONBased on the results of a simulation study, we developed a simplified method to calculate the overall mean energy demand with the help of the FEM reference cycle. To achieve this, we introduced a correction factor cFEM to reduce the error for the estimated mean energy demand.We also proposed a method for calculating an energy efficiency index. The energy efficiency index takes the throughput and the storage volume into acco- unt and correlates it to the energy demand. We used this energy efficiency index and developed three different methods to build efficiency classes. Because of the even class distribution, the best way to build the efficiency classes is the classification based on a normal distribution. It is based on the normal distribution of the energy efficiency index and should therefore best match the physical conditions.Our further research is focused on the investigation of strategic parameters in the operation of stacker cranes. These strategic parameters, for instance the storage bin allocation, are very important for the application of SCs. Finally, we will deal with the practical applicability of the method for energy efficiency classification.ACKNOWLEDGEMENTSThe research has been conducted on the basis of the industrielle Gemeinschaftsforschung collective indus- trial research (iGF) project “Entwicklung einer Bewer- tungsmethodik fr die Energieeffizienz eines Regalbediengertes” (iGF project number 18839N). This project has been funded by the Federal Ministry for Economic Affairs and Energy on the basis of a decision of the German Bundestag Federal Parliament.REFERENCES1 Altintas, O., Avsar, C., Klumpp, M., Ag, D.: Change to Green in Intralogistics, Conference Proceedings, pp. 373377, 2010.2 Boysen, N., Stephan, K.: A survey on single crane scheduling in automated storage/retrieval systems, European Journal of Operational Research, Vol. 254, No. 3, pp. 691704, 20163 Braun, M., Furmans, K.: Evaluation of simulation models for forecasting the energy demand of storage and retrieval machines with empirically collected measurement data (German), Logistics Journal Proceedings, 2016.4 German Institute for Standardization: Adjustable speed electrical power drive systems - Part 9-1: Energy efficiency of power systems, motor starters, power electronics and their driven equipment (German), DIN EN 61800, 2018.5 Ertl, R.: Energy demand determination and energy efficiency assessment of storage and retrieval machines in automatic small parts warehouses, (German), PhD Thesis, Technical University of Munich, 2016.6 Ertl, R., Willibald A., G.: Meta-model for calculating the mean energy demand of automated storage and retrieval systems, Logistics Journal, 2016.7 European Parliament: Directive 2012/27 of the Euro- pean Parliament and of the Council of 25 October 2012 on energy efficiency, 2012/27/EU, 2012.8 European Parliament: Regulation 2017/1369 setting a framework for energy labelling, 2017/1369/EU, 2017.9 Eurovent Certita Certification: Rating Standard tor the Certification of Air Filters, RS4/C/001-2015, 2015.10 European Materials Handling Federation: Performance record for storage and retrieval machines Cycle times, FEM 9.851, 2003.11 Freis, J., Vohlidka, P., Gnthner, W.: Low-Carbon Warehousing: Examining Impacts of Building and Intra-Logistics Design Options on Energy Demand and the CO2 Emissions of Logistics Centers, Sustainability, Vol. 8, No. 448, pp. 136 2016.12 Gnthner, W.A., Galka, S., Tenerowicz-Wirth, P.: Roadmap for sustainable intralogistics (German), In: Ziems, D., Inderfurth, K., Schenk, M., Wscher, G., Zadek, H. (eds.), Sustainable Logistics, 14. Scientific symposium Magdeburg, pp. 205220, 2009.13 Hafner, N., Lottersberger, F.: Energy Efficiency in Material Flow Systems (effMFS). FME Transaction, Vol. 40, No. 4 , pp. 181186, 2012.14 Hafner, N., Lottersberger, F.: Intralogistics systems- optimization of energy efficiency. FME Tran- sactions, Vol. 44, No. 3, pp. 256262, 2016.15 Lerher, T.: Design of Experiments for Identifying the Throughput Performance of Shuttle-Based Storage and Retrieval Systems, Procedia Engineering, Vol. 187, pp. 324334, 2017.16 Lerher, T., Edl, M., Rosi, B.: Energy efficiency model for the mini-load automated storage and retrieval systems, International Journal of Advanced Manufacturing Technology, Vol. 70, pp. 97114, 2014.17 Meneghetti, A., Monti, L.: Energy Efficient Dual Command Cycles in Automated Storage and Retrieval Systems, Proceedings World Renewable Energy Congress Sweden, pp. 16681675, 2011.18 Meneghetti, A., Monti, L.: Multiple-weight unit load storage assignment strategies for energy efficient automated warehouses, International Journal of Logistics Research and Applications, Vol. 17, No. 4, pp. 304322, 2013.19 Meneghetti, A., Monti, L.: Sustainable storage assignment and dwell-point policies for automated storage and retrieval systems, Production Planning & Control: The Management of Operations, Vol. 24, No. 6, pp. 511520, 2013.20 Meneghetti, A., Dal Borgo, E., Monti, L.: Rack shape and energy efficient operations in automated storage and retrieval systems, International journal of production research, Vol. 53, No. 23/24, pp. 70907103, 2015.21 Rajkovi, M., Zrni, N., Kosani, N., Borovinek, M., Lerher, T.L.: A multi-objective optimization model for minimizing investment expenses, cycle times and CO2 footprint of an automated storage and retrieval systems, Transport, Vol. 34, No. 3, pp. 275286, 2019.22 Roodbergen, K.J., Vis, I.F.A.: A survey of literature on automated storage and retrieval systems, European Journal of Operational Research, Vol. 194, No. 2, pp. 343362, 2009.23 Rcker, A., Fottner, J.: Development of a multi- component evaluation methodology for the energy efficiency of stacker cranes (German), Logistics Journal Proceedings, pp. 115 125, 2018.24 Rcker, A., Rief, J., Fottner, J.: An investigation of mean energy demand, performance and reference cycles for stacker cranes, FME Transactions, Vol. 48, No. 2, pp. 307312, 2020.25 Sthr, T., Schadler, M. Hafner, N.: Benchmarking the energy efficiency of diverse Automated Storage and Retrieval Systems, FME Transactions, Vol. 46, No. 3, pp. 330335, 2018.26 Association of German Engineers: Test games for performance comparison and approval of storage and retrieval machines (German), VDI 3561,1973.27 Association of German Engineers: Lifts - Energy efficiency (German), VDI 4707-1, 2009.28 Association of German Engineers: Lifts - Energy efficiency of components (German), VDI 4707-2, 2013.29 European Materials Handling Federation: Energy consumption determination methods (ECoDe- MISE), FEM 9.865, 2017.中文译文自动化高架仓库堆垛机能效测定方法的研究 安德烈亚斯洛克 研究助理慕尼黑工业大学机械工程学院材料处理，材料流动，物流(fml) 德国约翰Rief 研究助理慕尼黑工业大学机械工程学院材料处理，材料流动，物流(fml) 德国约翰Fottner 教授慕尼黑工业大学机械工程学院材料处理，材料流动，物流(fml) 德国 现代的内部逻辑设备在当今的供应链中完成了重要的任务。在规划和建设中必须考虑到许多不同的影响因素。目前的趋势和情况显示出一种强烈的趋势，即建造节能、因此对环境友好的仓库。这适用于仓库的建筑技术和所使用的内部系统。带有自动化高架仓库的内部设备非常常见，通常由堆垛起重机操作。这种存储技术允许货物以非常大的体积和节能的方式存储。堆垛机的性能和能量需求受到大量参数的影响。因此，要确定它们的能源效率是很复杂的。本文提出了一种基于仿真研究的方法来解决这一问题。关键词:内部物流;高架仓库业;自动存取系统;塔式起重机;能源效率。1.介绍在全球气候变化时期，生态因素在供应链的每个部分都非常重要。这导致越来越多的人需要减少供应链物理部分的能源消耗。除了运输系统，内部物流设施是实体供应链的重要组成部分。这些情况也影响到作为内部逻辑设施的重要组成部分的高架仓库。因此，高隔间自动仓库的能源消耗是制造商和运营商更关心的问题。这些高架仓库通常由堆垛机操作。SCs可用于存放、搬运和回收货架内的小型载物架、托盘或特殊载物架。它们的能源需求和性能受到大量变量影响，这些变量只能系统地进行优化。Gnthner等人在2009年提出，通过评价体系管理可持续性措施的体系将增加改进12的机会。他们建议在进行任何改进以衡量其影响之前，实施一个控制和测量系统。他们的系统基于四个步骤:1.可持续发展关键数字的定义，2.建立测量系统;3.评估现状;4.识别、实施和评估改进。基于这些结果，我们提出了如图1所示的能效措施的实施周期。这个周期包括这四个步骤是:评估现状，确定改进的潜力，实施改进和评估改进。为了进行评估和评价，需要一种确定能源效率的方法。 图1.能源效益措施的实施周期(比较23)公共场所的能源效率通常与欧盟能源效率标签8联系在一起。这些标签用于分类白色家电，如洗碗机，洗衣机和冰箱。它们有助于消费者获得能源需求的快速概览，并有可能比较不同的机器。像这样的标签在工业环境中并不常见。空气过滤器的等级9和电动马达的国际效率代码4是单个机器部件分类的例子。电梯的评级27,28是整台机器分类的一个例子。能效分类通常是基于一个参考机器和一个参考周期。它是定义参考SC是不可行的，因为这些机器是单独为特定应用程序设计的。因此，我们在方法中只使用一个引用循环。在内部逻辑学领域，目前只有一个新的确定能源消耗的指南29。该指南还包含了一种计算碳纳米管能源消耗的方法。在前一篇论文24中，我们对碳纳米管的平均能源需求和性能进行了调查。本文所建立的仿真模型是基于有限元法准则建立的。本文利用所进行的模拟研究的结果来开发一个元模型，以确定平均能源需求，进而确定SCs的能源效率。随后，我们提出了一种可能的方法来开发能源效率标签课程。我们重申了我们的建议，使用FEM参考周期来确定平均能量需求。这项工作旨在为提高SCs的能源效率做出贡献。2.著作分析Altintas等人1在2010年研究了绿色内部逻辑的变化。研究工作特别显示了内在逻辑的潜力，通过抽样和分析文献的不同优化潜力在内在逻辑。他们还进行了一项专家调查，以测试业界对绿色物流的看法。他们的研究工作基于Gnthner等人的“可持续内部物流路线图”。在内部物流中优化能源效率也是Lottersberger和Hafner研究工作的一部分。他们研究了物料流动系统的能源效率13,14，重点放在连续输送机上。一种优化投资费用、周期和收益的现代方法2 自动化存储和检索系统的足迹由Rajkovic等人介绍。他们提出了一个多目标优化模型，同时考虑自动存储和检索系统的不同设计标准。内部逻辑和建筑设计中的设计选择对整体能源需求和CO的影响2 辐射是Freis等人11研究工作的一部分。他们调查了三种不同自动化程度的参考内部物流设施。他们的结论之一是，随着自动化程度的提高，能源需求显著增加。上一篇关于SCs的文献调查的综述论文是由Roodbergen22发表的，随后是Boysen和Stephan在2016年发表的一篇关于SCs2的调度任务的综述论文。SCs的能源需求是近年来各种出版物的一部分。Meneghetti等人的研究工作主要集中在战略参数的影响上17-20。Lerher等人提出了一种计算小型装载堆垛起重机(MSCs)16能量效率的第一种方法。Ertl等人也研究了msc的能源需求和能源效率，并提出了msc能源效率等级的方法5,6。Sthr等人提出了一种不同类型的自动存储和检索系统的基准测试方法。他们建议使用VDI 356126的标准双循环作为比较MSCs、穿梭系统和水平旋转系统的参考循环。只有Braun和Furmans3研究过托盘堆垛机(PSCs)的能量需求。他们的工作重点是校准一个模拟模型，以预测特定SC的能量需求。在他们的工作中，他们没有调查大量PSCs的上级关系。通过我们的工作，我们试图缩小这一研究差距。3.材料和方法图2显示了SC的典型方案和本文中使用的命名法。由于它们的质量比根本不同，我们在本工作的分析中区分了msc和PSCs。图2.堆垛机方案(比较23)此外，我们用符号表示了从A到G的能效类别，稍后将提出分类。主要部件有基架、井架、机头、带装卸装置的起重装置和传动装置。SCs有两个主要驱动载荷的水平和垂直运动。主驱动器的电源通过直流连接24在本文中，我们提出了一种确定碳纳米管能源效率的方法。在详细说明该方法之前，我们提出了一个简单的碳纳米管能源需求元模型。这两种方法都是基于在24中进行的仿真研究结果。表1列出了在模拟研究中指定SC配置的各种参数。表1.MSC和PSC24的各种参数及其最大值和最小值的列表参数MSC minMSC maxPSC minPSC max列数ncol30802060层数 nlev2040818加速驱动装置ax2 m/s5 m/s0.5 m/s1.5m/s加速提升单位 ay1 m/s3 m/s0.2 m/s1 m/s速度驱动装置 vx4 m /s6 m /s1.5 m/s3.5 m/s速度提升单位vy in m/s1.5 m/s3 m/s0.5 m/s2 m/s高效驱动和提升装置 x,y0.40.70.40.7 质量基础框架mbase1.2 t2 t8 t20 t参数MSC minMSC maxPSC minPSC max质量提升单位 mLu250 kg450 kg2.5 t4.5 t质量的有效载荷 mload25 kg75 kg0.5 t1.5 t存储占用OCC0.40.90.40.9使用补给装置RU0101在每个参数的最小值和最大值之间，我们使用拉丁超立方采样算法选择20,000种不同的SC配置进行仿真研究。我们为MSC和PSC使用了5000种不同的SC配置，有(RU1)或没有(RU0)再馈电单元。再馈电装置可以将电能馈入电网，从而减少能源需求。我们评估了在大量随机创建的运行周期中，每个配置的平均能量需求和吞吐量。随后，数据被用来寻找一个合适的参考循环。我们开发了两种不同的计算参考循环的方法，并与FEM 9.85110参考循环进行了比较。在我们的比较中，FEM周期是我们的目的可能的最佳周期。它允许使用一个双循环和最小误差确定SC的平均能量需求。3.1堆垛机能量需求元建模图3和图4显示了模拟研究的结果与能源需求和吞吐量(TP)的相关性和不同的参数。图3显示了没有再馈电单元(RU0)的MSC双循环配置的相关性。 图3.MSC双循环配置的相关性，没有再馈单