MASTER’S THESIS2006:009 CIV

MATS BOHMAN

On Predicting Fuel Consumptionand Productivity of Wheel Loaders

MASTER OF SCIENCE PROGRAMMEEngineering Physics

Luleå University of TechnologyDepartment of Mathematics

2006:009 CIV • ISSN: 1402 - 1617 • ISRN: LTU - EX - - 06/9 - - SE

Abstract

Low fuel consumption is becoming more and more important as a selling argumentas fuel prices keeps rising. To be able to better show their advantage in this area,Volvo Wheel Loader need to develop the selling tool SiteSim in terms of fuel con-sumption predictions. This means modelling the actual work done by the wheelloaders.

A good physical model would produce the results wanted. This modelling is howeververy difficult due to the interaction between the hydraulic system and the drivetrain. To overcome this problem, a method of splitting up the work cycles of awheel loader into phases has been developed. The phases are defined by a specifictype of work, for example filling bucket or reversing from bank. This reduces theeffecting parameters and makes physical modelling possible in some phases. It alsomakes statistical modelling easier and more exact.

The statistical models yields good results if the user is experienced in the work thesimulated wheel loader are to perform. The physical models produce results with10-20% lower fuel consumption than tested value. Better models of energy losses indrive train would probably correct some of these errors.

Sammanfattning

Allt eftersom branslepriserna gar upp blir en lag bransleforbrukning ett allt viktigareforsaljningsargument. For att battre kunna visa sitt forsprang pa detta omradebehover Volvo Wheel Loader forbattra sitt forsaljningsverktyg SiteSim vad gallerbransleforbrukningsberakningar.

En bra fysikalisk modell skulle ge de onskade resultaten. Pa grund av kraftdelningenmellan hydraulik och drivlina ar denna modell svar att realisera. En metod som,genom att dela upp arbetscyklerna i mindre bitar, undviker detta problem har ut-vecklats. Dessa mindre delar kallas faser och defineras av en specific typ av arbete,till exemplel fylla skopa eller backa fran banken. Inom varje fas kommer pa dettasatt farre variabler paverka systemet och det blir i vissa faser mojligt att model-lera fysikaliskt. Ovriga faser blir genom uppdelningen ocksa lattare att modellerastatistiskt.

De statistiska modellerna ger goda resultat om anvandaren ar van arbetet somska utforas. Resultaten fran de fysikaliska modellerna visar pa en 10-20% lagrebransleforbrukning an resultat fran testkorningar. Battre modellering av forlusternai drivlinan kommer troligtvis minska dessa fel nagot.

Acknowledgements

I would like to express my sincerest gratitude towards all the people that havesupported me though this thesis. I would like to express special thanks to:

• My supervisor at Volvo Wheel Loader, Stefan Pettersson, for his guidance andsupport, it has been invaluable.

• Tomas Gunnarsson, my examiner at LTU.

• Sven-Ake Carlsson, for always finding time to answer questions and provideguidance in areas concerning power distribution.

• Reno Filla, for his support in building the model.

• Esko Bjurman and Pauli Hanssen for acting as operators during the testingweeks.

• Stefan Asplund, for installation and help with testing equipment.

• The whole department of UT, for their willingness to answer all possible ques-tions, interest in my work and for making me feel welcome at Volvo.

Finally, since this thesis ends my time at LTU, I would like to thank all the teachersand fellow students that has made my time in Lulea such a great time.

Goteborg, October 2005.

Mats Bohman

Contents

1 Background 11.1 Volvo Hauler Loader Business Line HLBL . . . . . . . . . . . . . . . 11.2 SiteSim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Reason for upgrading the SiteSim wheel loader model . . . . . . . . 21.4 Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Scope of master thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Basic wheel loader theory 42.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Main uses of large machines . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.1 Short cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2.2 Load and carry . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2.3 Timber handling . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Power distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3.1 Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3.2 Hydraulic system . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.3 BSS - Boom Suspension System . . . . . . . . . . . . . . . . 82.3.4 Drive train . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Hypothesis 113.1 The difficulties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.1 The operator . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.1.2 The dynamics of a wheel loader . . . . . . . . . . . . . . . . . 11

3.2 Purposed solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2.1 Phases and parameters . . . . . . . . . . . . . . . . . . . . . . 12

4 Gathering data 164.1 Pretest thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.2 Test vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.3 Sensor set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.4 Performed tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.5 Obtaining data from test logs . . . . . . . . . . . . . . . . . . . . . . 18

5 Resulting model 215.1 Filling bucket phase modelling . . . . . . . . . . . . . . . . . . . . . 215.2 Reverse from bank and reverse from receiver phases modelling . . . . 225.3 Emptying phase modelling . . . . . . . . . . . . . . . . . . . . . . . . 225.4 Transportation phases modelling . . . . . . . . . . . . . . . . . . . . 22

5.4.1 Statistical modelling . . . . . . . . . . . . . . . . . . . . . . . 235.4.2 Physical modelling . . . . . . . . . . . . . . . . . . . . . . . . 23

5.5 Summing up the phases . . . . . . . . . . . . . . . . . . . . . . . . . 245.6 Parameters in effect . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

6 Conclusions and future work 266.1 Model accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266.2 Requirements for good results . . . . . . . . . . . . . . . . . . . . . . 266.3 Effects of boom suspension system . . . . . . . . . . . . . . . . . . . 276.4 CAN-fuel signal verification . . . . . . . . . . . . . . . . . . . . . . . 276.5 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

A Statistical background 30

Chapter 1

Background

1.1 Volvo Hauler Loader Business Line HLBL

This thesis was ordered by Volvo Wheel Loader Business Line (WLBL) in Eskils-tuna. During the time the work was done it was decided that WLBL and ArticulatedHauler Business Line (AHBL) were to form the new Hauler Loader Business Line(HLBL).

The thesis was done at UT (Sales engineering & Marketing support) within WLBL.This department includes Sales engineers, Product specialists, Market communic-ation and Product administration & System development. The Sales engineerssupport dealers with technical expertise in sales and marketing situations, e.g. cus-tomer visits, sales training etc. The Product specialists support both dealers andWLBL with competence in machinery, applications and attachments. Work involvesboth product and sales development. Market communication produce marketingmaterial, develop sales tools and manages events. Product administration & Sys-tem development coordinates basic product information, administrates options andattachments and develop sales systems.

1.2 SiteSim

Both as a retailer and an entrepreneur it is good to be able to estimate how long timeand how many machines are needed to do a specific job for a potential customer.Traditionally, this was done using a performance manual for each of the machinetypes used. Using the performance manual and a map of the potential site youwere able to estimate these figures. The solution was crude and required hardwork. Though the performance manuals are still available a new tool has also beendeveloped. The new tool is SiteSim.

SiteSim is a computer program which can do all what an experienced user of theperformance manuals can do, and more. The user is able to build up the site,complete with dig phases and haul segments. It is also possible to set parameterssuch as fuel cost, interest cost, operator cost, work schedules, etc. Using theseparameters the program provides you with the optimal number of machines, the

1.3. Reason for upgrading the SiteSim wheel loader model 2

cost of production and the required time to reach a production target. The usermay also investigate the effects of using different equipments on the machines.

1.3 Reason for upgrading the SiteSim wheel loadermodel

Fuel consumption has been modelled with data collected over long times from cus-tomer sites using the D-series wheel loaders. When the E-series was introducedthe SiteSim-model was simply corrected with a constant with accordance to theperformance of the E-series opposed to the earlier D-series. This yields true resultsin most cases but might give a little better or worse performance depending on thetask.

Another problem is that the resolution of terrain quality is not correct. You havethe possibility to choose between good, bad or average. However, bad terrain can bemuddy, with wheels sinking several decimetres into the mud, making it very heavyto move the machine, resulting in low speed. It can also be very rocky, with stonessticking up several decimetres, forcing you to drive very slowly. Both cases effectsproduction in the same way, slow speed decreases the number of cycles per hour,which in turn decreases production. If we examine fuel consumption, the differencecomes into notice. In the muddy case, we force the engine to work heavily to propelthe loader, leading to a high fuel consumption. In the case with large rocks, theterrain forces us to drive slowly without forcing the engine to work hard which leadsto a lower fuel consumption. This difference is not modelled.

Also since the calculations are based on data from customer machines, any newmachines performance is only available when that specific model has been on themarket for quite some time. This means that you can not use SiteSim as a goodselling tool for new models.

1.4 Problem description

A new model of calculation is to be created. It shall be based on the current wheelloader series, it needs to be easily upgraded when a new wheel loader series reachesthe market. The new model should also have a finer resolution of parameters inground conditions in terms of ground structure and rolling resistance.

1.5 Scope of master thesis

This master thesis is limited in time to 20 weeks which rises the need for limitationsin project scope. When planning the project the following goals was set up:

• Core model

• Easy add functionality or upgrade to new model series

• Implementations in MATLAB

3 Chapter 1. Background

• One application

• Handle flat ground as well as slopes

• Examine the effect of Boom Suspension System (BSS) on fuel consumption

• Examine the effect of BSS on productivity?

Chapter 2

Basic wheel loader theory

2.1 Background

The wheel loader is a further development of the standard tractor. In 1954 Bolinder-Munktell AB realized that, by placing the bucket in the back over the large wheelaxle, a higher load could be lifted. The back-end loader was born (figure 2.1).

Bolinder-Munktell became BM Volvo, Volvo BM, VME (Volvo-Michigan-Euclid)and now Volvo CE. The back-end loader has evolved into the wheel loader withall-wheel drive and frame steering for manoeuvrability. The sizes range from 8 to52 metric tonnes which enables multiple uses from forklift loading and snow clearingto rock and timber handling.

2.2 Main uses of large machines

The large machines generally include all machines larger than 23 metric tonnes (theL150E, L180E, HL180E, L220E and the L330E). These are, depending on modeland equipment, mainly used for moving heavy loads of sand, gravel, rock or timber.In most cases the work follows a repeating cycle, starting at the dig phase where itfills the bucket etc., it then transports the material to the receiver (hauler, truck,crusher etc.), empties the bucket and returns to the dig phase.

2.2.1 Short cycle

A short cycle loading is when the receiver is placed adjacent to the dig site. Thetransporting distance should only be long enough to give the boom enough time tolift the bucket over the side of the receiver (see figure 2.3). A longer transportingdistance decreases productivity since a smaller fraction of working time is put intodigging.

5 Chapter 2. Basic wheel loader theory

Figure 2.1: The 1954 Bolinder-Munktell H10 back-end loader loading a hauler

2.2.2 Load and carry

In a load and carry cycle the receiver is not adjacent to the dig phase resultingin a longer transportation with the material in the bucket. The receiver can be ahauler, truck or pocket (crusher, hopper etc.). In the case of a pocket receiver, theunloading height will usually be quite low.

2.2.3 Timber handling

Timber handling does not usually show the same repeating pattern as materialmoving tasks. It involves a lot of transportations and quite a lot of work fixing thepiles and sorting the timber.

2.3 Power distribution

The Volvo wheel loaders are diesel engine driven. The way the power is distributedbetween hydraulics and drive train depends on how much power is requested fromthe different systems.

2.3.1 Engine

The engine in Volvo’s large wheel loaders is a turbocharged, electronically controlleddiesel engine. The electronic control handles fuel injection and tries to deliver the

2.3. Power distribution 6

Figure 2.2: A L180E in timber handling

7 Chapter 2. Basic wheel loader theory

Figure 2.3: Loading a hauler in a short cycle

2.3. Power distribution 8

torque requested by the machine. The requested torque depends on several factors,one is keeping the engine speed at a, by the operator, requested level. This isdifferent from a normal car engine and means that by pressing down the acceleratorpedal you request a higher engine speed, not necessarily a higher power output as isthe case in a car engine. On the other hand if a higher load is put on the engine thisdoes not necessarily effect the engine speed. The reason why the engine is speedcontrolled is that since the load on the engine can differ largely in a short time, dueto the hydraulics out-take, a power controlled engine would be very hard to operateand would probably come to a halt very often. As an operator you do not feel thisdifference when moving. The reason is that you react the same way. If you want toaccelerate you press the accelerator pedal. This allows the engine to reach a higherengine speed. A higher engine speed usually results in an acceleration (not alwaysdue to the torque converter 2.3.4).

Diesel engines have a governed speed, an engine speed where you limit the speedto go up too high by reducing the fuel injection. If this wasn’t the case the enginescould keep going up in engine speed and risk breakdown. When the acceleratorpedal is fully pressed this built in speed limit hinders the engine to over speed. Byonly pressing down the accelerator pedal some bit you change this limit rpm to alower value. There is also a run-out speed, a higher limit, at which injection islowered so that no usable torque is delivered.

The efficiency of today’s diesel engines is very high, with inter cooler and turbo itis about 43%. It could actually be a few percentage units higher if we could ignoretoday’s environmental regulations. The losses are exhaust (30%), cooling (20%) andfriction (the rest). It is the exhaust part that could be lessened if no regulationshad to be followed but this would result in higher levels of nitrogen oxides (NOx)in the exhaust.

2.3.2 Hydraulic system

The hydraulic system delivers power to the working hydraulics (lift, tilt and optionalfunctions if available), steering, servo, brake and to the hydraulic motor of thecooling fan.

The hydraulic pumps are axial piston pumps with variable flow. This means thatwhen neither flow nor pressure is needed the flow is reduced to a minimum andalmost all power can be used by the drive train. However, the system is always keptat a stand-by pressure that allows quick responses to operator commands. Thissystem provides high performance by minimizing power losses.

2.3.3 BSS - Boom Suspension System

The boom suspension system is an optional function on Volvo’s wheel loaders thatallows the hydraulic system operating the lifting cylinder to work as a suspensionsystem for the boom. This means that transportations over uneven ground can bedone in a more comfortable way.

9 Chapter 2. Basic wheel loader theory

Figure 2.4: L180E with part names

2.3.4 Drive train

The power from the engine is transferred and geared down in several steps. Closestto the engine is the torque converter followed by (in this order) gearbox, drop box,differential and finally hub reductions. Also affecting the driving force is the size oftires.

The torque converter

The torque converter is easily described as two propellers in a closed housing, filledwith oil. The engine is connected to one propeller and when turning this propellersets the water in motion. The other propeller picks up the water motion and deliversthis power to the rest of the drive train. Obviously this system can only delivera smaller or at best an equal torque as the one put into the system. However bydesigning the pool and the propellers in a special way we can make this constructiondeliver a higher torque (at a lower rotational speed) than has been put into thesystem, that is, make it work as a gear down. The torque converter, as opposedto a mechanical gear down, allows different ratios between speed out and speed in(nout/nin). This has some advantages. Since an engine has an optimal workingpoint you have to use the transmission to try to keep the engine speed close enoughto that point to sustain torque. If you tend to work too far from the optimal pointusing a fully mechanical transmission you need to introduce more gears. By usinga torque converter you extend the interval on each gear that is close enough to theoptimal engine working point, that is, a torque converter in a drive train meansthat fewer gears are needed. The torque converter also works as a shock absorberin the transmission.

Using a torque converter has a drawback. The efficiency is not constant. The ratiobetween speed out and speed in is denoted nout/nin = ν. The highest efficiency isusually found at νε[0.6, 0.9] and when ν = 0 and ν = 1 the efficiency is 0. Figure 2.5shows the efficiency of a torque converter at different ν:s. Depending on the machinethe torque converter is to be used in, the optimal efficiency interval might occur at

2.3. Power distribution 10

Figure 2.5: Converter efficiency at different ν:s

lower or higher ν:s.

The transmission

The transmission has four forward and four reverse gears. It is automatically con-trolled using several parameters, ν being the most important, for the most optimalgear shifting.

The drop box

The drop box delivers power to the front and rear wheel axles.

Differential gears

The differential gear distributes power from the drive shafts to the right and leftwheels. They are equipped with differential locks that distributes more power tothe wheel with the best traction.

Hub reduction

The hub reduction is located at each end of the wheel axles, closest to the wheels.They gear down the shaft speeds one last time. This means reduced stress onpropeller shafts and axle shafts.

Chapter 3

Hypothesis

3.1 The difficulties

We wish to describe a wheel loader given a specific task. The difficulties in thishas two reasons. First of all, the performance of the loader is closely linked tothe operators performance, and second, the dynamics of the wheel loader is verycomplex.

3.1.1 The operator

Given two experienced operators doing a specific task, a wheel loader can showtwo completely different results when it comes to fuel consumption and production.The reasons are several. An operator can, for instance, be more or less aggressivein using the accelerator pedal, that is, be a faster regulator. This results in higherfuel consumption as well as a slightly higher productivity. Another problem comesfrom the fact that the Volvo wheel loaders are built to be used at low engine speeds.An experienced driver used to other machines might not realize this and thereforeoperate the machine at a far to high engine speed and thus have a much higher fuelconsumption.

The resulting model will probably not need to be able to show the performanceof a driver using a loader in a bad way. However it must be able to simulate theperformance of both inexperienced and experienced, aggressive and calm operatorsand whatever other difference with impact we can find.

3.1.2 The dynamics of a wheel loader

In Volvo CE:s wheel loaders the engine delivers torque to both the transmission andto one or more hydraulic pumps (read section 2.3 for a more complete description).The dynamics of this is difficult to describe since, when filling bucket etc., therequired torque and the required hydraulic pressure is by no means constant and atany given moment it is not possible to know if more or less power will be divertedto the hydraulics.

3.2. Purposed solution 12

However, during transport the bucket is more or less locked in position and thereforealmost all engine torque is transferred to the transmission. This makes transporta-tion far easier to model physically.

3.2 Purposed solution

The resulting model should be able to give an approximate value of the fuel volumeconsumed during a specific period of time doing a specific task. Due to the repeatingpattern of most tasks for heavy machines, all that is need to do is to find the meantime and fuel consumption of a cycle of the specific task. The fact that the workwill be done over a long time means that a large number of cycles will be performed.The differences that exists between different cycles will have little effect on the totalwork since

V (n∑

i=1

ciXi) = nσ2 (3.1)

D(n∑

i=1

Xi) =√

nσ, (3.2)

where V (x) and D(x) is variance and standard deviation of x and σ is the standarddeviation of all Xi. This means that determining the variances of the stochasticvariables will not be needed (Theorem 1 in appendix A). However, the same theoremtells us that

E(n∑

i=1

Xi) = n ∗m, (3.3)

where E(x) is the expectancy value of x and m is the expectancy value of all Xi.This means that an error in expectancy value will result in an error in the result ofthe same magnitude. This, in turn, means that the expectancy values need to bedetermined with a high precision for the calculation model to yield good results.

Since different parts of a working cycle has different properties it is desirable to trydo divide every cycle into smaller parts with similar properties. These parts arecalled phases and are linearly combinable.

3.2.1 Phases and parameters

The identification of phases have been done by looking at both physical reality aswell as the operators intention. This means that if the operator feels that a newphase in the cycle is about to start, then it is. More on this in the phase descriptionsbelow. The phases should also be as long as possible without loosing the advantagewe gain by splitting up the cycles.

The phases that were identified are filling bucket, reversing from bank, transport toreceiver, emptying bucket, reversing from receiver and transport to bank. They aredescribed below and in table 3.1 is a summary of possible parameters connected toeach phase.

The duration time and fuel consumption are of unknown distribution. The sumis however, in accordance to Theorem 2 in appendix A, of approximately normaldistribution. Mean values will be used as expectation values.

13 Chapter 3. Hypothesis

Filling bucket

This phase starts as the bucket hits the bank. As a result, the pressure on the plusside of the lifting cylinder goes up. It ends as a reversing gear is requested. Thisphase should be close to independent of the task since the time needed to fill abucket is independent of where the material is going. However, the operator mightbe more inclined to overfill the bucket if he knows that transporting the materialwill take longer time. If this is the fact it need to be modelled.

The size of the bucket as well as material and bank hardness are obvious parametersto take into account. The operators experience and driving style should also affecttime and fuel consumption.

Due to the heavy work of the hydraulics as well as the drive train, the difficultiesmentioned in section 3.1.2 will make this phase very hard do model physically andis thus probably best modelled statistically.

Reversing from bank

This phase begins as the operator requests a reverse gear and ends as a forwardgear is requested. This means that the machine will be rolling backwards when thephase ends since the drive train is used for retardations. The operator however hasmore or less decided that this phase is over and has started to focus on the nextphase.

Parameters should include mass since a heavy machine requires more work to move.Also rolling resistance and the length of reversation should be taken into account.The operators experience might, and driving style should affect time and fuel con-sumption.

During most of this phase the hydraulics as well as drive train should be working.The hydraulic work however should be fairly constant and this phase should bepossible to model physically. An easier statistical model might however producebetter results.

Transport to receiver

This phase starts as forward gear is requested and ends as the bucket starts toempty, that is, as the pressure on the plus side in the lifting hydraulics cylinderstarts to fall.

If the transportation length is short this phase is about the same as reversing frombank since the bucket will constantly be going up.

In a longer transportation however, the work of the hydraulics will have less effectand might in some cases be considered as not doing any work. If this is true we haveeliminated one of the big difficulties in physical modelling of a working wheel loaderand should therefore be able to construct a good physical model of this phase. Ina longer transportation, ground structure also affects time and fuel consumptionsince it has a large effect on maximum speed.

In the case of short as well as long transportation the main work done by the

3.2. Purposed solution 14

hydraulics is to lift the bucket to unloading height. This is a factor that might needto be taken into account.

Emptying bucket

This phase starts as the bucket starts to empty and ends as the wheel loader startsto reverse, that is, the wheels rotates backwards. This means that the operator mayuse the drive train to retard without initiating a new phase.

Parameters include bucket size and how easy it is to empty the bucket, that is, ifthe operator needs to be careful when emptying or just need to drop the load.

This phase includes very little work which means that fuel consumption will be verylow and the model more or less only has to return time. Due to the difficulties ofquantifying the difficulty to empty the bucket a statistical model will probably yieldthe best results.

Reversing from receiver

Negative velocity starts the phase and forward gear request ends it.

This phase is very similar to reversing from bank. The difference is that almost nowork is done by the hydraulics since the empty bucket is going down.

Transport to bank

The phase starts as forward gear is requested and ends as the bucket hits the bank,i.e. the pressure on the plus side in the lifting cylinder goes up.

Similar to transport to receiver with one difference, no lifting of the bucket meanslittle hydraulic work.

Modelling should follow the same pattern as transport to receiver.

15 Chapter 3. Hypothesis

Phase Parameters

Filling bucket Material, bank hardness, bucket,operator experience, operator style,length of transport phase

Reversing from bank Total mass, rolling resistance, length of phaseoperator experience, operator style

Transport to receiver Total mass, ground structure, rolling resistance,unloading height, operator experience,operator style

Emptying Bucket Bucket size, how hard is it to empty

Reversing from receiver Total mass, rolling resistance, length of phaseoperator experience, operator style

Transport to Bank Total mass, ground structure, rolling resistance,operator experience, operator style

Table 3.1: Phases and possible variables

Chapter 4

Gathering data

4.1 Pretest thoughts

The tests should be done using a machine working in standard cycles. Interestingparameters are the effects of different operators, materials and ground conditions.

As described in section 3.2, the phases in the purposed solution should be delimitedby gear changes, pressure changes and directional changes. These parameters shouldtherefore be logged. For easy identification of where in a working cycle we are, theangle of the lifting boom is also useful. To be able to look at work load andoperator performance, engine speed, accelerator pedal and some value of deliveredtorque should also be logged. Also, since this thesis concerns fuel consumption,this value should not be forgotten. Since the cooling fan requires quite some torquewhen in use the work of the fan could be useful.

4.2 Test vehicle

For testing, a L180E was used. It was standard issue apart from these specialfeatures:

• Tires: 800/65R29 XLD-65-1

• Swing out mud guards

• Attachment brackets

• Logging counterweight

This resulted in a slightly heavier machine which should mean a slightly higher fuelconsumption than a standard machine.

17 Chapter 4. Gathering data

Parameter Source UnitGear control signal CAN -Gear value Calculated -Engine speed CAN rpmAccelerator pedal position CAN %Fuel consumption CAN (requested) mg/strFuel consumption Calculated kg/hTotal fuel consumption Calculated kgLifting boom position External sensor %Lifting cylinder pressure External sensor MPaBucket accelerometer External sensor m/s2

Cooling fan speed CAN rpmSigned drive shaft speed External sensor rpmTrip Calculated m

Table 4.1: Logged parameters in tests

4.3 Sensor set up

The on-board computer gather data from a lot of built in sensors on a wheel loader.These can be accessed through the CAN bus. Some parameters of interest arehowever not measured. This meant that extra sensors needed to be installed. Theseextra sensors where an angel sensor for the boom, a pressure sensor for the liftingcylinder, an accelerometer on the bucket and a new sensor for drive shaft speed.The last sensor was needed because of the need of signed velocity and the standardsensor only measures the number of revolutions. Fuel consumption is controlled bythe on-board computer. The signal is however not usually accessible through CANbus, though it is possible to request it to the bus. This needs to be done each timethe on-board computer is restarted.

Apart from logging computer data the wheel loader was fitted with a fuel meas-urement tube for reference purposes since the accuracy in using the CAN-bus forfuel consumption measurements had not been tested earlier. A fuel measurementtube is an approximately 2m long tube with a known diameter that can be usedinstead of the standard fuel tank. By placing the tube vertically the amount of fuelconsumed can be read off with quite good precision. The dimensions of the tubewere such that 56mm = 1l.

To log the data from different sources a specialized computer, the eDaq, was used. Itis basically a Linux computer with both digital and analogue interfaces for logging.The eDaq can be accessed through a TCP/IP interface. Since the eDaq is a com-puter it is also possible to let it do some calculations right away. The calculationsthe eDaq was set up to do were to transform fuel consumption from mg/stroke tokg/h as well as total fuel consumed, gear control signal transformed to actual gearand the drive shaft speed was integrated to total distance travelled. A summary ofparameters logged is shown in table 4.1.

4.4. Performed tests 18

4.4 Performed tests

The tests were performed at at Volvo CE:s testing site in Eskilstuna. Three dif-ferent operators performed a total of 21 different tests. Apart from several signalverification tests and transportation tests, 9 were short cycle hauler loading, 7 longcycle hauler loading, 6 were feeding a pocket and 1 were timber handling. Thematerials handled where timber, gravel, macadam, clay and blasted rock. Table 4.2shows the tests done.

4.5 Obtaining data from test logs

The eDaq logs are in a raw data format and to access these Somat EASE was needed.Somat EASE is a program built for the after work of an experiment with viewingand some calculation tools. These tools where however slow in the version availableat Volvo but EASE also offered the possibility to save the data in a MATLAB.mat-file. This means that by opening the files and storing them as MATLAB files,analysis in MATLAB was enabled.

The test-logs where about 30 minutes long. Splitting these into cycles and phaseswas made semi automatically by a MATLAB function that was constructed. Thisuses the phase delimiters defined in chapter 3 to find the approximate phase shiftpositions and allows the user to correct this if needed. This correction is usuallyneeded in phase shifts defined by hydraulic pressures.

Because of different reasons not all tests and cycles were complete and thereforeneeded to be excluded from the analysis. These cycles are shown in table 4.3.

19 Chapter 4. Gathering data

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Table 4.2: The tests run

4.5. Obtaining data from test logs 20

Test run Faulty cycle Reason05 SC1 1-3 Faulty fuel signal05 SC1 12 Incomplete cycle06 SC1 Last Incomplete cycle07 LC1 1-3 Wrong unloading height07 SC1 4 last Unloading without receiver08 SC1 12,15,18 Incomplete cycles08 SC2 3,6,9 Incomplete cycles19 CLAY 4,7 Faulty cycles19 KR1 all Faulty fuel signal19 KR2 1,2 Problem with transporter resulted in faulty cycles

Table 4.3: Discarded cycles with the reasons to discard them

Chapter 5

Resulting model

The resulting model splits the task into phases and uses statistical or physicalmodelling depending on phase. Physical modelling has been tried when possiblebut if an easier statistical model has delivered better results this has been chosen.

5.1 Filling bucket phase modelling

The filling bucket phase is characterized by high work intensity, i.e. high fuel con-sumption. However when entering the filling bucket phase, intensity is low since toohigh torque when entering the bank might cause the bucket to get wedged in thebank. The work intensity is also low in the end since this phase shift is defined bya gear change and low engine speed is required in gear changes for comfort driving.

For short, fuel consumption is initially low but is after some time at maximum. Itgoes however down just before the phase ends. This behaviour can be approximatedwith three strait lines such that

mfill = m(0 ≤ t < τ1) + m(τ1 ≤ t < τ2) +m(τ2 ≤ t ≤ Tfill) (5.1)

m(0 ≤ t < τ1) = (mmin +mmax − mmin

2) ∗ τ1 (5.2)

m(τ1 ≤ t < τ2) = (τ2 − τ1) ∗ mmax (5.3)

m(τ2 ≤ t ≤ Tfill) = (mmin +mmax − mmin

2) ∗ (Tfill − τ2) (5.4)

where mfill is total fuel consumed in filling bucket phase, Tfill is total time offilling bucket phase and τ1 and τ2 is the time when full intensity is reached and leftrespectively. mmax and mmin are the fuel consumptions at maximum intensitiesand idling respectively. With a little work this transforms to

mfill = τ ∗ mmax + mmin

2+ (Tfill − τ) ∗ (mmax) (5.5)

whereτ = τ1 + (Tfill − τ2), (5.6)

that is, the time the loader does not work at maximum intensity. How long τand Tfill are depends on the operator as well as material and bank hardness. An

5.2. Reverse from bank and reverse from receiver phases modelling 22

aggressive operator will have a shorter τ and a slightly shorter Tfill. Materialsharder to excavate will have larger Tfill independent of operator.

5.2 Reverse from bank and reverse from receiverphases modelling

The mean times of these phases depend mostly on how long the driver need toreverse. In this model the user are given a statistical value corresponding to thechosen operator doing a 90◦ turn (45◦ in reversing from phases). In the case of alarger turn the user may change this as he or she see fit.

The fuel consumptions, mfromBank and mfromReceiver,are calculated as

mfromBank = TfromBank ∗ mmax ∗ i (5.7)mfromReceiver = TfromReceiver ∗ mmax ∗ i (5.8)

where TfromBank and TfromReceiver are mean times of the phases, mmax is fuelconsumption at maximum intensity and i is the mean intensity during the phase. idepends on the operator as well as the rolling resistance. The user is given a valueof i corresponding to the chosen operator and a 3% rolling resistance. In the caseof a larger resistance the user may change this as he or she see fit.

5.3 Emptying phase modelling

The emptying bucket phase is characterized by a very low fuel consumption due tothe low amount of work that needs to be done. The duration time of the phasevaries a lot depending on the receiver and, in the case with a hauler as receiver,how well the match is between the bucket and the receiver. If the bucket is wellmatched the difference in time between the first and the last bucket into the haulerwill differ much. This is since the last bucket will have to be slowly dropped ontothe hauler to avoid spill.

Fuel consumption, mempty, is calculated as it is in the reverse from phases, that is

mempty = Tempty ∗ mmax ∗ i. (5.9)

Values of Tempty and i are given corresponding to the chosen user and a well matchedhauler taking 3 buckets. If the user see so fit he or she may change these values.

5.4 Transportation phases modelling

The modelling of the transport to receiver and transport to bank phases is made intwo different ways. If the transportations are shorter than 10 meters a statisticalmodel is used. If they are longer than 10 meters each, they are modelled physically.The reason for this division is that the precision of the physical modelling is notgood enough on short transport legs. More on this in chapter 6.

23 Chapter 5. Resulting model

5.4.1 Statistical modelling

The statistical modelling is made using linear regression with rolling resistance asdependant variable. The differences in mass, transportation length and operator ex-perience and operator style have too little effect on both time and fuel consumptionto be possible to model in a good way. The resulting model becomes

Ttransports = 2 ∗ (3.9 + 0.2 ∗ µrolling) (5.10)mtransports = 2 ∗ (0.0255 + 0.0015 ∗ µrolling) (5.11)

where Ttransports and mtransports is total transport time per cycle and fuel consump-tion respectively, both to receiver and to bank, and µrolling is rolling resistance.

5.4.2 Physical modelling

The physical modelling of time uses engine and converter performance data tocalculate traction force. This is used to calculate acceleration and thus velocity.This way the transport times Ttransporti i = 1, 2, . . . is calculated for each leg of atransport. Fuel consumption is calculated using the engine control data backwards.A delivered torque at a specific engine speed means that a specific mass fuel wasinjected.

To do a little more thorough description lets go through the functions doing thework.

calcPower

This function handles calculations involving the drive train. It uses model specificcomponent data to calculate delivered torque and power at different converter-ν:s.Depending on how the function is called it can also return the traction force, thefuel consumption or required engine speed.

The main part of this function is the calculation of delivered torque at differentconverter-ν:s. As described in section 2.3.1 Volvo’s wheel loaders are engine speedcontrolled and by changing position of the accelerator pedal you change the max-imum engine speed allowed. Denote this engine speed ωmax. Since maximum torqueis not always delivered at ωmax, the engine speed where maximum torque is deliveredneeds to be calculated. This is also done at different converter-ν:s since the effi-ciency of the torque converter differs greatly between different ν:s (more on this insection 2.3.4). We denote the wanted engine speeds ωνi

First of all, the vector ω is constructed. Its lowest value is the lowest value of theengine performance data and its highest values is ωmax. Include also the referenceengine speed for converter data (ωconverterRef ). Values of engine torque (Mengine),hydraulics deduction (Mhydraulics) and cooling fan deduction (Mcooling) are inter-polated to fit ω using tabulated data. By deducing charge pump torque (Mcharge)and interpolated torques from Mengine, the usable propulsive torque (Mnet) is ac-quired.

At a specific νi, the tabulated converter data can be scaled using

Mνi= ω2

0 ∗MconverterRef

ω2converterRef

, (5.12)

5.5. Summing up the phases 24

where Mνiis scaled torque at the engine speed ω0 and MconverterRef is the tabulated

converter data. ωνiis the ω0 at which the line defined by 5.12 intersects Mnet. By

repeating this for all tabulated νi the required results are achieved. From theseresults it is easy to calculate traction forces etc.

fuelFromTorqueRPM

The engine performance is controlled by an on-board computer. Depending on therequired torque a specific fuel mass is injected and that torque is delivered. Thisfact can be used the other way as well. Given a specific delivered torque a specificmass fuel must have been injected and hence the fuel consumption is given.

The torque delivered from the engine is actually the acquired torque from the com-bustion minus internal engine friction. This means that when we do these cal-culations we first of all need to add engine friction to the delivered torque. Thecalculated torque is then used to interpolate the injected fuel mass per stroke. Usingthe known engine speed this is the easily converted to [kg/s]

transport

This method simulates the operator. It is presented with information about thetransportation, divided into shorter legs, and what operator model to use, and usesthis to model the operators usage of the accelerator pedal.

The information required about the transport legs is initial engine speed, initialvelocity, maximum velocity, rolling resistance, incline or decline, and mass. Theoperator is assumed to accelerate as fast as possible up to a point defined by operatortype. At this point acceleration is lessened and the model tries to simulate anoperator trying to keep the current speed. Also defined by the operator style is howfast he or she retards. This combined with final velocity decides when the operatorreleases the accelerator pedal and presses the brake.

5.5 Summing up the phases

There is a simple Graphical User Interface (GUI) that ties all these functions to-gether. In this the user may change operator model, phase times and intensities ifneeded and set up the transportations. The GUI executes the calculations giventhe parameters set and the phase times and fuel consumptions are added up andthe answers are returned to the user.

5.6 Parameters in effect

In chapter 3 a table of possible effecting parameters was set up (table 4.1). Thesehave been considered and if the effect has been measurable and the parameterquantifiable the parameter has been used in the model. In the statistical models theeffect comes from different tabulated values of parameters and in the physical model

25 Chapter 5. Resulting model

the parameters act as parameters in either operator model or machine performancecalculations.

Chapter 6

Conclusions and future work

6.1 Model accuracy

No tests has been run apart from the ones used to build the model. This meansthat the precision of the model has only been tested on the data it was built from.These tests indicates that an experienced user will have no problem in setting thetime parameters of the statistical calculations in such a way that the errors in theresults are less than 5% of the measured values in both time and fuel consumption.

As for the physical model of transportations, the error in time estimates are small(less than 5%). The estimated fuel consumption however is approximately 85% ofthe measured values (estimated values range from 80-90% of the measured values).One reason for this is believed to be that the efficiency of the drive train in the modelis independent of engine speed. The truth is however that at higher engine speedthe efficiency gets lower and thus more energy is needed to do the work needed. Inspite of these errors the model is interesting since it should be independent of whichwheel loader model is used. It is based on component data and machine weightand is easily changed when needed. Any statistical model needs new tests to beupgraded.

6.2 Requirements for good results

To get good results in modelling, the user is required to have good knowledge infill times of different materials and bank hardnesses since these variables have sucha large effect on the fill time and thus on the fuel consumption. The differencesin time in different bank hardnesses of gravel can go from 6 seconds in loose bankall the way up to 10 seconds in a virgin bank. Since the time the machine is notworking at maximum load will be approximately the same in both cases, the 4 extraseconds will have a large effect on fuel consumption (doubled in some cases) as wellas cycle time (and thus productivity).

Knowledge of times in the from bank and from receiver phases is also good but notrequired. The differences in these phases have shown to be small given a specificterrain and task.

27 Chapter 6. Conclusions and future work

6.3 Effects of boom suspension system

To examine the effect of BSS on production and fuel consumption two sets of runswere made with and without BSS. The sets where composed of one 30 minutes longload and carry run on even ground, and one 30 minutes long run with four 10 cmhigh bumps put out approximately 10 meters apart.

No clear results could be drawn from the results. Visually however, no BSS onbumps meant that a fifth bump started building up after the last due to smallamounts of macadam falling from the bucket at every run. This should result inhigher need of maintenance on the transport leg.

It is possible that, since the runs where only 30 minutes long, the operator couldmaintain a higher speed than possible in a regular work site. In a normal site theuncomfortable operating of no BSS might result in lower speeds over the bumpsand accelerations when past them.

6.4 CAN-fuel signal verification

The fuel tube was used in several runs. When the machines auto throttle was setto a specific value the logged data were 99-101% of the actual value of the tube.In a pocket feeding task the value was 99.2% of the tube value. This leads usto the conclusion that logging fuel consumption by use of the CAN-bus results insufficiently good values.

6.5 Future work

Wanted in the final model of calculations is that it is easily upgraded to be able tohandle any change made to the wheel loaders. To upgrade the model of calculationsas it is now, new tests needs to be done at any new release. This means that thenew model needs to be fitted with eDaq and sensors and sent into work for a week.This is costly both in money and resources. However some further developmentwould probably lessen these costs. Two paths are possible. Try to produce a betterphysical model or try to find some correlation between different model sizes.

The physical model does not produce good enough results as it is now. A bettermodel of losses (primarily in transmission) might change this. One other thing tolook into is the operator model which is crude as it is now. Operator style in loadand carry applications does not affect transportation time very much, however itaffects fuel consumption. Needed for improvements is to better quantify differentoperator styles and how they affect acceleration, retardation and maximum speedin different conditions. It is possible that these steps might bring the model to thepoint at which it can be used in the reverse from bank and reverse from receiverphases as well as the transport to receiver and transport to bank phases. If thisis achieved the project goal of an easily upgraded model is reached. The modelwill simulate filling bucket and emptying bucket statistically and everything elsephysically. The statistical models will be easy to keep upgraded since changes inexpectation values of time and consumption in these phases is easy to measure.The physical models will be upgraded through new component data and will only

6.5. Future work 28

require some verification testing.

The other path will result in a cruder model that is harder to upgrade. It willhowever not require any further theoretical work, only extensive testing.

No matter which path is chosen, more tests in different material and bank hardnesseswith different operator styles should be done. This would produce a more completematerial knowledge in the model and would lessen the need of user knowledge inphase times. It would also help in quantifying operator styles.

Bibliography

[1] Malmberg, Carl Einar et al. Terrangmaskinen 1. Gummessons Tryckeri AB,Falkoping 1993. ISBN 91-07614-083-0.

[2] Vannman, Kerstin Matematisk statistik. 2nd edition. Studentliteratur, Lund2002. ISBN 91-44-01690-5.

[3] Blom, Gunnar. Sannolikhetsteori med tillampningar A. 2nd edition. Student-litteratur, Lund 1984. ISBN 91-44-04372-4.

[4] Montgomery, Douglas C et al. Applied statistics and probability for engineers2nd edition. John Wiley & Sons, Inc., New York 1999 ISBN 0-471-17027-5

[5] Filla, Reno Operator and Machine Models for Dynamic Simulation of Con-struction Machinery. Licentiate thesis, Department of Mechanical Engineering,Linkopings universitet, Linkoping, Sweden, September 16, 2005. Permanentlink: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4092

Appendix A

Statistical background

Theorem 1 (Linear combination of locatives variables). For all stochasticvariables X1, X2, ..., Xn we have

E(n∑

i=1

ciXi) =n∑

i=1

ciE(Xi) (A.1)

V (n∑

i=1

ciXi) =n∑

i=1

ciV (Xi) + 2∑i<j

cicjC(Xi, Xj). (A.2)

If E(X1) = E(X2) = ... = E(Xn) = m then

(A.1) = nm. (A.3)

It also follows that if X1, X2, ..., Xn are independent then

(A.2) =n∑

i=1

c2i V (Xi), (A.4)

and if V (X1) = V (X2) = ... = V (Xn) = σ2

(A.4) = nσ2 => (A.5)

D(n∑

i=1

Xi) =√

nσ. (A.6)

Theorem 2 (Central Limit Theorem). If Xi i = 1, 2, .. is an infinite seriesof equally distributed stochastic variables with an expected value of m and astandard deviation of σ > 0 and if Yn = X1 + X2 + ... + Xn then

P (a <Yn − nm

σ√

n< b)→ Φ(b)− Φ(a) as n→∞ (A.7)