TVE 16 063

Examensarbete 15 hpJuni 2016

Simulation of Electrified Earthmoving

Marcus Kreku

Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student

Abstract

Simulation of Electrified Earthmoving

Marcus Kreku

Earlier studies have concluded that electric haulers would be a feasible substitute for diesel driven vehicles at a stone quarry. To decrease the total cost of ownership (TCO) different charging strategies can be used. This paper illustrates how to use discrete event simulation to calculate TCO for different charging strategies. By using vehicle simulations a DES module has been fed with energy and time requirements for an electric hauler. The DES module was modified to suit the different charging strategies and evaluation was done using a case study.

The charging strategy that gave the lowest TCO was to charge during dumping. Compared to using an external charging station the TCO could be reduced by 14 percent for the case study. It was also possible to reduce the TCO by 13 percent by concentrating the charging power to one vehicle.

The study showed that DES is a powerful method for simulating a quarry. The result is applicable on any arbitrary quarry but the percentage will vary depending on the specifications.

ISSN: 1401-5757, TVE 16 063Examinator: Jonas LindhÄmnesgranskare: Erik UhlinHandledare: Erik Uhlin

Acknowlegements

I wish to express my sincere thanks to Volvo CE Eskilstuna for making this projectpossible. Especially to Erik Uhlin, for letting me take part in his research and beingan excellent tutor.

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Populärvetenskaplig sammafattning

I dagsläget producerar fordonsindustrin huvudsakligen förbränningsmaskiner. Entydlig trend visar på att stora, så väl som mindre aktörer inom personbilar börjaröka sina investeringar inom eldriva fordon. Däremot är de eldrivna alternativen inomanläggningsmaskiner fortfarande väldigt få eller icke existerande, vilket öppnar uppför många nya forskningsområden.

Vid ett stenbrott transporteras sten från en lastplats till en stenkross. Dettaär en process som huvudsakligen utförs med hjälp av dieseldrivna dumprar. För attkunna designa lämpliga eldriva fordon måste ett flertal forskningsfrågor först besvaras,t.ex. nödvändig batteristorlek och optimal laddningseffekt. Detta kan göras genomatt analytiskt utföra beräkningar, men detta är en tidskrävande process med lågadaptivitet. Med hjälp av en simuleringsmiljö skulle man istället kunna testa framegenskaper genom att optimera med avseende på kostnad.

Det här arbetet syftar till att forma en sådan simuleringsmiljö. Simuleringsmiljönska användas för att ta fram vart i cykeln det är bäst lämpat för fordonen att laddabatteriet.

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Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Theory 32.1 Brief literature review . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Discrete event simulation and charge strategies . . . . . . . . . . . . . 32.3 TCO-calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 Method and case study 63.1 Electric hauler model . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.2 Case site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3 DES module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.3.1 Model A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.3.2 Model B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.3.3 Model C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.4 Specifications for DES simulations . . . . . . . . . . . . . . . . . . . . 10

4 Results 124.1 Results of electric hauler simulation . . . . . . . . . . . . . . . . . . . 124.2 Result of DES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.2.1 Model A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.2.2 Model B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.2.3 Model C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

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5 Discussion 155.1 Model Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155.2 Method evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

6 Conclusions 17

A Vehicle specifications 18

Bibliography 19

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Chapter 1

Introduction

1.1 Background

During the last decades scientists have warned about an increase of greenhouse gasesand global warming. Today the Swedish government gives economical support toselected green projects and tax subsides to products with a small ecological footprint.The transportation sector contributes by substituting combustion engines with elec-trical or hybrid solutions. With a greater interest for green alternatives the technologyimproves allowing other sectors to develop in an environmental aspect. One of thesesectors are construction equipment.

At a quarry aggregate is produced of stone from the bedrock. Stone is first blastedfrom the bedrock. The rocks are then loaded on to haulers (refereed as loading station)and transported to a stone crusher (refereed as dumping station), which crushes therocks into fine gravel. This activity can be done using wheel loaders and articulatedhaulers, usually driven by combustion engines. However, with emerging technologyelectric vehicles could be a feasible substitute.

1.2 Problem description

Volvo CE initiated a project called Electric Site, aiming to offer a construction sitewith improved energy efficiency and reduced CO2 emissions before 2019. The haulersused to transport the stone is electrical and needs to charge during the active hoursof the quarry. The charge strategy should be designed to decrease the total costper tonne transported material (TCO). The available power at the quarry is limited,which means that the design has to include allocation of the charging power. Theabove discussion motivates following research questions:

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Which charge strategy would result in the lowest TCO per tonne delivered material?

How should the available power be allocated for an optimal charging strategy?

1.3 Objective

The objective is to find the optimal charging strategy using computer simulations. Fuconcludes that discrete event simulation (DES) is an effective method for simulatingearthmoving (jiali Fu, 2012). The method is suitable due to the repetitiveness ofthe process and Fu could validate the functionality. The DES module has to be fedwith energy and time requirements for each segment. This will be done using vehiclesimulations like previous studies by Uhlin (Erik Uhlin, 2011).

After designing and implementing different charge strategies they will be evaluatedusing a case site. To enable a useful comparison some variable has to be constant, inthis case the amount of delivered material. The objective can be broken into followingparts:

1. Find a model of an electrified hauler. The model has to be able to simulate anarbitrary path between the different stations at a quarry.

2. Find a DES model to simulate a fleet of haulers at a quarry.

3. Produce different charge strategies and implement them in the DES model.

4. Design a cost model to calculate TCO.

5. Use system to simulate for a case quarry.

When the simulations are done the results can be compared to conclude whichcharge strategy is better in terms of TCO.

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Chapter 2

Theory

2.1 Brief literature review

Fu presents a DES model (Jiali Fu, 2012) designed to simulate a quarry with a singlecapacity loading- and dumping station. The DES module is illustrated in figure 2:1.The module consists of 11 discrete events. The loader first enters queue (event 1)to fill the first bucket of the loading cycle and creates a new "fill bucket" event (2).When the bucket is full it enters a new queue waiting for a hauler to arrive (3). Whena hauler arrives (4) loading begins (5) and thereafter the hauler starts transportingthe material (6). At the dumping station (7) the hauler waits until the station gotenough space for the material (8) and it can begin to dump (9). When dumping isdone the hauler returns back to the loading station (10) counting the number of cyclesdone (11). This continues for 7.5 effective hours with 2 minor breaks of 20 minutesand a longer lunch break of 40 minutes.

Fu concludes that DES is an effective method for this type of application andillustrates how to perform TCO calculations. Uhlin applies Fu’s DES-module tooptimize fleet size (Erik Uhlin, 2011). In another article Uhlin & Unnebäck evaluatethe possibilities of electrified earthmoving by modifying Fu’s DES module to suitelectric haulers (Erik Uhlin & Joacim Unnebäck, 2012). The haulers charge at thedumping station which is incorporated in the DES schematics shown in figure 2:1.They conclude that two electric haulers with 20 tonne payload could maintain 500tonnes per hour productivity at a typical quarry with a charging power of 150 kW.

2.2 Discrete event simulation and charge strategies

The design of the DES-module depends on the charge strategy. In this work thecharge placement will be divided into two types; Those who requires the haulers to

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Figure 2.1: DES modelcreated by Fu. DSstands for dumping sta-tion and LS stands forloading station. Fig-ure created and modi-fied by writer.

charge while carrying material and those who do not. One drawback with chargingwhile carrying material is the increased risk of destroying the charging equipment.With a higher mass the difficulties to dock increases and there is a risk that materialwill fall of the vehicle. The benefit is that the haulers dont have to follow a fixed pathand can eliminate one phase by charging and dumping simultaneously.

Three different charge strategies are considered to be interesting to analyze. Onewhere charging only occurs at the dumping station, another where charging onlyoccurs at an external charging station and one cycle that combines charging at thedumping station with an external charging station.

2.3 TCO-calculations

TCO is often used to evaluate the efficiency of the facilities at a construction site. Thisstudy will be restricted to the transportation of the material and therefore TCO willonly include cost connected to the electric haulers. TCO will be calculated by addingoperating cost and owning cost, divided by the amount of transported material, asshown in equation 2.1.

TCO =Operating cost+Owning cost

Delivered material(2.1)

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Operating cost consists of electricity cost, drivetrain cost and battery cost. Own-ing cost is depreciation, interest, insurance etc. The costs will be per hour exceptfrom electricity cost that depends on the distance traveled by the haulers.

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Chapter 3

Method and case study

The DES module created by Fu and later modified by Uhlin to suit electric haulers wasthe starting point for the computer simulations. The study was limited to the threedifferent charge strategies mentioned in section two. Specifications for the quarry andits facilities was contributed by Volvo CE in Eskilstuna.

3.1 Electric hauler model

The electric hauler was designed to match current technology. Table one shows anextract of the specifications, for an extended list see appendix

Power 150 kWWeight 6 tonneMax load 15 tonneBattery capacity 6 kWh

Table 3.1: Extract of hauler specifications

A computer model of an electric hauler was used to simulate the paths betweenthe different stations. The model was made by Uhlin in the Matlab module Simulink.After inserting the parameters for the specific hauler, energy need and duration foreach segment was extracted from the simulations and inserted into the DES module.

3.2 Case site

The specifications of the site was given by Volvo CE to match a typical quarry. Theroad between the loading and dumping station was broken into three parts:

1. 150 meters horizontal, 0 meters vertical

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2. 300 meters horizontal, 30 meters vertical

3. 150 meters horizontal, 0 meters vertical

The same road but backwards was used for vehicles returning to the loadingstation. Note that this leads to a negative altitude difference. The road between thedumping and charging station was 20 meters, without altitude difference. The quarryis illustrated in figure 3:1.

Figure 3.1: Quarryused in case study.Y-coordinate corre-sponds to the altitude.Observe that axles arenot in scale.

The available charging power was limited to 200 kW. By knowing the capacity ofthe loader it is possible to determine the loading time. With 900 tonnes per hourproductivity for the loader and a maximal load of 15 tonnes per hauler, the requiredtime to load was 60 seconds.

The dumping time only depends on the hauler and was estimated to be 50 seconds.The longer loading time restricts the capacity of the quarry as incoming haulers tothe dumping station will be less frequent than haulers leaving. This is only true ifthe haulers don’t have to spend additional time waiting on the crusher to processmaterial in order to make space for the hauler load. This is possible if the crusherhas a higher productivity then the loader and/or a way to pile the material. For thiscase study the latter of the two were assumed.

Without regarding the productivity of the haulers the maximum capacity of thequarry is the 900 tonnes per hour achieved by the loader. This was the target capacity

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when evaluating the transportation. The quarry specifications are summarized intable 3:2.

Loading time 60 sDumping time 50 sTarget capacity 900 tonnes/hourPower limitation 200 kW

Table 3.2: Case specifications for quarry

3.3 DES module

The DES module contributed by Uhlin and Fu was written in MATLAB and modifiedfor each model. The modifications are described below.

3.3.1 Model A

Model A has the same events as the DES module used by Uhlin & Unnebäck, de-scribed in chapter two. The external charging station is unused and charging onlyoccur at the dumping station. The haulers charge until the battery is full which canincrease the required time at the dumping station. The module was modified so itwas possible for more than one hauler to dump/charge simultaneously. Figure 2:1 isstill a correct representation.

The Following parameters were adjustable:- Number of haulers- Dumping station hauler capacity- Charging power at dumping station

3.3.2 Model B

Model B has an external charging station with a total charging power of 200 kW. Af-ter leaving the dumping station the haulers travel to the charging station and chargeuntil the battery is full. In contrary to model A no charging is done at the dumpingstation. Figure 3:2 illustrates the DES module.

The Following parameters were adjustable:- Number of haulers

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- Charging station hauler capacity- Charge power at charging station

Figure 3.2: DESschematics for modelB. CS stands forcharging station.

3.3.3 Model C

Model C combines the external charging station from model B with charging at thedumping station. Due to the limitation of 200 kW for the whole quarry, the chargingstation and dumping station only has 100 kW for disposal each. Prior results by theauthor show that it is unwise to lower the charging power to less than 100 kW. With100 kW available power and 100 kW charging power the dumping station and thecharging station are restricted to a single hauler capacity. This means that the onlyadjustable parameter is the number of haulers.

The external charging station makes it possible to include more advanced algo-rithms. The haulers do not have to charge at a fixed time, on the contrary they cancharge in a order that maximizes the productivity. The algorithm chosen for thisstudy is based up on following three statements:

1) Haulers should always use the dumping station if there is an empty space.2) If there is a queue at the dumping station, haulers should charge at the chargingstation.

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3) State of charge for the battery is not allowed to be lower than 20 percent.

This resulted in the DES module shown in figure 3:3. The haulers do not have afixed path and can charge before, during and after dumping. This creates new de-mands on the information that the haulers have to carry. They need to know theirbattery’s current energy level and if they are carrying material or not.

Figure 3.3: DES schematics for model C.

3.4 Specifications for DES simulations

The specifications for the performed simulations are summarized in table 3:2. Thenumber of haulers was adjusted to meet the target capacity. Unfortunate combina-tions of haulers, loaders, etc. can result in a capacity very close to the target, withoutmeeting the requirement. To prevent misleading the reader two simulations have beendone for every combination of parameters; one that meets the requirement and onesimulation with one hauler less. Charging power at dumping station and chargingstation was determined so that it uses all of the available 200 kW.

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Model CS spaces CS power (kW) DS spaces DS power (kW) Number of HaulersA - - 1 200 5 and 6A - - 2 100 5, 6B 1 200 1 0 6,7B 2 100 1 0 6,7C 1 100 1 100 6,7

Table 3.3: DES simulations

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Chapter 4

Results

4.1 Results of electric hauler simulation

Table 4:1 summarizes the results of the hauler simulation. Note that the road betweencharging station and dumping station is identical regardless of direction. The roadback to the loading station gave a higher energy input than output (due to chargingthe battery downhill) resulting in a higher energy level for the battery.

Segment / hauler load Energy req. (kWh) Time (s)LS to DS / loaded -2.9809 107.1DS to CS / loaded -0.0540 10.19CS to DS / loaded -0.0540 10.19DS to CS / empty -0.0213 8.61CS to DS / empty -0.0213 8.61DS to LS / empty 0.1793 92.39

Table 4.1: Road simulations done in Simulink

4.2 Result of DES

Each model produced 892 tonnes per hour when the number of haulers was set ac-cording to table 3:3. The deviation from 900 tonnes per hour is due to time lossesfor the loader at the end of each period. For the presentation of the result the targetcapacity will be the practical capacity of 892 tonnes per hour.

4.2.1 Model A

With the results from part 4.1 it is possible to conclude that each cycle requires 2.8kWh electric energy. Since the haulers need to be energy neutral each cycle requires100.8 seconds for charging with 100 kW. With space for two haulers at the dumping

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station the average time for a hauler to pass the dumping station is 50.4 seconds.This is less than the loading time and will not result in a bottleneck. The same logicapplies for the case with a single hauler capacity, but with 200 kW charging power.The charging time will be shorter than the loading time making it possible to achievethe target production. This is observable in table 4.2 which concludes the results ofmodel A.

Sim. Haulers DS spaces Prod. (tonnes/h) Deviation (%) TCO (SEK/tonne)A1 5 1 868 2.7 1.57A2 6 1 892 0 1.83A3 5 2 748 16 1.82A4 6 2 892 0 1.83

Table 4.2: DES results for model A. Deviation is the loss in productivity comparedto the practical target production of 892 tonnes per hour

Lowest TCO was achieved when the charging power was concentrated to onehauler. This enabled five haulers to maintain 97 percent of the target capacity. How-ever, to achieve the maximum 892 tonnes per hour 6 haulers were needed, resultingin a 16 percent higher TCO. The increased TCO is a result of longer queue times forthe haulers at the loading station. This can be seen in table 4:3 that shows queuetimes for all stations at the quarry. Note that the loader has zero waiting time whenthe maximum capacity is reached.

Simulation AH Queue LS (%) AH Queue DS (%) Loader idle (%)A1 1.66 0 2.96A2 14.31 0 0A3 1.66 0 15.08A4 2.08 0 0.08

Table 4.3: Queues for model A. AH stands for articulated hauler

4.2.2 Model B

The results for model B resemble that of model A. Lowest TCO was 1.84 SEK pertonne, which was achieved with 200 kW charging power and a single hauler capacity atthe charging station. In addition it could maintain 99 percent of the target production.With 7 haulers 892 tonne per hour was produced, but TCO increased by 15 percent.The results for model B are summarized in table 4:4.

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Sim. Haulers DS spaces Prod. (tonnes/h) Deviation (%) TCO (SEK/tonne)B1 6 1 884 0.9 1.8436B2 7 1 892 0 2.1222B3 6 2 776 13 2.0958B4 7 2 892 0 2.1222

Table 4.4: DES results for model B

As for model A the queue times depended on the number of haulers. Whenthe haulers maintain the target capacity the loader always has an available hauler,resulting in zero waiting time for the loader but increased queue time for the haulers.All queue times are summarized in table 4:5.

Simulation AH Queue LS (%) AH Queue DS (%) Loader idle (%)B1 2.0833 0 1.5391B2 13.6640 0 0B3 2.0833 0 12.6596B4 3.1286 0 0

Table 4.5: Queues for model B

4.2.3 Model C

Model C required 7 haulers to reach the target capacity, resulting in the highest TCO.The results are summarized in table 4:6.

Sim. Haulers DS spaces Prod. (tonnes/h) Deviation (%) TCO (SEK/tonne)C1 6 1 772 13 2.1068C2 7 1 892 0 2.1224

Table 4.6: DES results for model C

In contrary to model A and B, lowering the number of haulers didn’t do a sig-nificant difference to the TCO. This means that 7 haulers was a suitable match formodel C, not resulting in longer queues at the loading station. This can be seen intable 4:7 that shows queue times for all stations.

Sim. AH Queue LS (%) AH Queue DS (%) AH Queue CS (%) Loader idle (%)C1 6.3327 0 0 12.5724C2 7.5581 0 0 0.0760

Table 4.7: Queues for model A

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Chapter 5

Discussion

5.1 Model Comparison

Compared to the other models, model A required one hauler less to maintain targetproductivity, which resulted in a lower TCO. The lower requirement of haulers arisewhen dumping and charging is combined into one step. Each cycle is reduced by thetime period of the process that requires the shortest time, in this case dumping. Inaddition the haulers do not have to travel to an external charging station. The twofactors reduce the cycle time by approximately 66 seconds or 15.3 percent. With aloading time of 60 second this corresponds to one less hauler.

This result depends strictly on the time needed for dumping and charging. Withshorter dumping or charging time the difference between the models would decrease.However, the TCO for model B and model C will never be able to become less thanthe TCO for model A making it the optimal charging cycle.

There are situations where model A is not applicable. If the dumping station onlyhas space for one hauler and the charging power is restricted, the charging time wouldincrease rapidly. In this case it would be more suitable to use Model C. If the risk todestroy the charging equipment is high model B should be used.

5.2 Method evaluation

The DES model makes it possible to do exact simulations as long as the data input iscorrect. If the energy needs are incorrect this will effect the charging time and increaseor decrease the TCO. Variations in the time for every segment will in a similar wayeffect the TCO. This means that the accuracy of the DES-method is restricted to theprecision of the vehicle simulations.

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However, as a tool for relative comparison it is very useful. The errors for modelA will also effect the simulations for model B which makes the relative difference lesssensitive to poor vehicle simulations. The DES simulation could be fed with data froman actual quarry where the paths are driven with the actual vehicle(s). This wouldremove the uncertainty from the vehicle simulations, but demand high accuracy onthe practical measurements.

A weakness of the method is the case restriction that is discussed in previoussection. There are general conclusions that are applicable on any quarry, but themagnitude of the differences strictly depend on the parameters in the case study.

Except from the electricity cost the TCO only depends on time variables. This isnot entirely realistic as the most ware should occur due to movement. With a costmodel that is distance dependent the TCO differences between the models shoulddecrease as they travel the same amount of cycles.

5.3 Future work

Currently the DES model is strictly deterministic. However, it is often more suitableto describe our environment as stochastic. By adding a randomness to loads, energyneeds and travel times the model becomes more realistic. Using a Monte Carlomethod and repeatedly doing simulations the result will approach an expected valuethat should be more accurate.

The case restriction discussed in method evaluation could be decreased by doinga Monte Carlo simulation for each variable. By inserting a normal distribution forthe expected battery capacity the next 10 to 20 years it is possible to evaluate whichcharge cycle that would be optimal in the future. Same can be done for chargingpower and other parameters that are expected to improve in the close future.

Model C is currently designed with a primitive algorithm. By making it moreintelligent it would become a great complement to model A if the charging powerand capacity is limited at the dumping station. The updated algorithm should avoidan empty dumping station when there are available haulers charging at the chargingstation. This have to be done with real application in mind. A too complex algorithmmay work for computer simulations but be impossible to implement.

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Chapter 6

Conclusions

The charge cycle that produce the lowest TCO per tonne for a specific capacity ismodel A. Charging while dumping eliminates the time requirement of the shortestprocess. The advantage of the model depends on the dumping and charging time.The difference in TCO will be larger the longer the shortest of the two times are.With the parameters used in the case study the number of haulers could be reducedby one, decreasing the TCO with 14 percent compared to the lowest TCO for modelB and C.

Concentrating the charging power to one hauler, instead of two, reduced the TCOby a maximum of 13 percent. This was achieved when using 5 vehicles and modelA. Other simulations gave less differences but the higher power was always to preferover a higher capacity.

To summarize, the DES module was an effective tool to compare different chargecycles. The optimal charge cycle is to charge while dumping and the available chargingpower should be maximized to one vehicle.

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Appendix A

Vehicle specifications

Engine power 150 kWTotal weight 6 tonnesMax load 15 tonnesNumber of wheels 4Electric engine efficiency 93 %Mechanical efficiency 97 %Battery efficiency 95 %Inverter efficiency 98 %Gear ratio 49Number of batteries 1Battery capacity 6 kWh

Table A.1: Hauler specifications

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Bibliography

[1] Erik Uhlin, A model for battery wear. Volvo CE, Eskilstuna, Sweden, 2012.

[2] Eric Uhlin1,2 & Joacim Unnebäck1. On electrification of mass excavation.Volvo CE1, Eskilstuna, Sweden, KTH2, Royal Institute of Technology - Depart-ment of Traffic and Logistics. Stockholm, Sweden, 2012.

[3] Jiali Fu A Microscopic Simulation Model for Earthmoving Operations. Interna-tional Journal of Civil, Environmental, Structural, Construction and ArchitecturalEngineering Vol:6, No:7, 2012.

[4] Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol, Discrete-EventSystem Simulation, Pearson, 5th edition, New Jersey, USA, 2010

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