ENERGY EFFICIENCY ANALYSES OF HYBRID NON-ROAD MOBILE ...

Jarkko Nokka

ENERGY EFFICIENCY ANALYSES OF HYBRID NON-ROAD MOBILE MACHINERY BY REAL-TIME VIRTUAL PROTOTYPING

Acta Universitatis Lappeenrantaensis

785

Jarkko Nokka

ENERGY EFFICIENCY ANALYSES OF HYBRID NON-ROAD MOBILE MACHINERY BY REAL-TIME VIRTUAL PROTOTYPING

Acta Universitatis Lappeenrantaensis 785

Thesis for the degree of Doctor of Science (Technology) to be presented with due permission for public examination and criticism in the Auditorium 2310 at Lappeenranta University of Technology, Lappeenranta, Finland on the 12th of January, 2018, at noon.

Supervisor Professor Juha Pyrhönen

Electrical Engineering

LUT School of Energy Systems

Lappeenranta University of Technology

Finland

Reviewers Professor Kari Tammi

Mechatronics

Department of Mechanical Engineering

Aalto University

Finland

Dr. Veli-Matti Leppänen

Drives

ABB Oy

Finland

Opponents Professor Kari Tammi

Mechatronics

Department of Mechanical Engineering

Aalto University

Finland

Dr. Veli-Matti Leppänen

Drives

ABB Oy

Finland

ISBN 978-952-335-192-9

ISBN 978-952-335-193-6 (PDF)

ISSN-L 1456-4491

ISSN 1456-4491

Lappeenrannan teknillinen yliopisto

Yliopistopaino 2018

Abstract

Jarkko Nokka

Energy Efficiency Analyses of Hybrid Non-Road Mobile Machinery by Real-Time

Virtual Prototyping

Lappeenranta 2018

70 pages

Acta Universitatis Lappeenrantaensis 785

Diss. Lappeenranta University of Technology

ISBN 978-952-335-192-9, ISBN 978-952-335-193-6 (PDF),

ISSN-L 1456-4491 ISSN 1456-4491

Striving for energy efficiency and tightening emission regulations for diesel engines

drive the non-road mobile machinery towards new driveline solutions, diesel-electric

hybridization being one of them. Series hybridization has shown a positive impact on

machine fuel consumption by enabling the diesel engine to be operated according to the

machine average power, whereas with conventional diesel-mechanical or hydrostatic

driveline solutions, the diesel engine has to be dimensioned according to the peak power

demand of the machine, when it can suitably envelop the whole operating cycle power

demand. Typical characteristics of the non-road mobile machines include a high peak

power demand and a quite low average power (even 25% of the peak), which makes

hybridization an extremely attractive option.

However, penetrating the machine market with new solutions takes time and

prototyping. By applying virtual prototyping and cosimulation, a virtual multi-body

simulation model of a machine can be used to dimension and test various hybridization

concepts and iterate the driveline to a much further state before physical prototypes are

needed.

This doctoral dissertation introduces a hybrid non-road mobile machine cosimulation

platform, and by three separate case studies, demonstrates the fuel consumption saving

potential of hybridization. With all the cases, roughly a 50% fuel consumption reduction

is achieved without negative impacts on machine performance.

Keywords: non-road mobile machinery, diesel-electric hybridization, energy efficiency

Acknowledgements

This work was carried out at the LUT School of Energy Systems at Lappeenranta

University of Technology, Finland, between 2013 and 2017. The research was funded

by the Finnish Funding Agency for Technology and Innovation (TEKES).

I express my gratitude to Professor Juha Pyrhönen, the supervisor of this doctoral

dissertation, for all the guidance I’ve received throughout the writing process, as well as

all the questions asked and answered throughout the years. Without curiosity there are

no questions, and without questions there is no progress. I also want to thank my

supervisor in the Tubridi project, Associate Professor Lasse Laurila for all the

meticulous attention to all of the publications written. Further, I express my gratitude to

Dr. Paula Immonen, for the endeavors made in the modelling and simulation work.

My colleagues at both Lappeenranta University of Technology as well as in Mevea Ltd,

thank you for the support and interesting conversations throughout the years.

I want to express my deepest gratitude to my parents. You taught that everything is

achievable.

Special thanks go to my little princesses, Elisa and Veena; every day when I arrive

home and open the front door, I see you two running to greet me. Your endless energy

and curiosity towards, well, everything are such an inspiration.

Finally, and above all else, I want to express my heartfelt gratitude to my beloved wife

Minna. Your neverending love, support and encouragement truly makes this all worth it.

It’s a blessing to share my life with you, and without you my world would be a lot

emptier place.

Jarkko Nokka

December 2017

Lappeenranta, Finland

Before family comes nothing.

Contents

Abstract

Acknowledgements

Contents

List of publications 11

Nomenclature 13

1 Introduction 15 1.1 Hybrid Vehicles ....................................................................................... 19

1.1.1 Non-road mobile machinery ....................................................... 22 1.2 Scientific contribution ............................................................................. 22

2 Virtually assisted analysis method 25 2.1 Multi-body dynamics in machine simulation .......................................... 25 2.2 Diesel-electric hybrid driveline simulation ............................................. 26

2.2.1 Electrical drives ........................................................................... 26 2.2.2 Energy storages ........................................................................... 26 2.2.3 Diesel generator sets ................................................................... 27

2.3 Hardware setup ........................................................................................ 28 2.4 Boundary conditions of real-time virtual simulations ............................. 31

3 Hybridization of underground loaders 33 3.1 Case 1 Underground loader energy and working efficiency analyses .... 33

3.1.1 Testing the underground loader on a short route ........................ 34 3.1.2 Testing the underground loader on a long route ......................... 40

3.2 Case 2 Underground loader hybridization ............................................... 45 3.2.1 Diesel-powered machine – model verification ........................... 46 3.2.2 Hybridization .............................................................................. 53

4 Wheel loader 57 4.1 Initial test platform for electrical drives .................................................. 57

5 Conclusions 63 5.1 Future research ........................................................................................ 64

6 Discussion 65

7 References 67

Publications

11

List of publications

This doctoral dissertation is based on the following papers. The rights have been

granted by publishers to include the papers in the dissertation.

I. Nokka, J., Montonen, J.-H., Bin Baharudin, E., Immonen, P., Rouvinen, A.,

Laurila, L., Lindh, T., Mikkola, A., Sopanen, J., and Pyrhönen, J. (2015),

“Multi-Body Simulation based Development Environment for Hybrid Working

machines,” International Review on Modelling and Simulations (IREMOS), Vol.

8, No. 4, 2015.

II. Nokka, J., Laurila, L., and Pyrhönen, J. (2014), “Virtual simulation in energy

efficient hybrid powertrain design,” in Proceedings of EPE 2014 ECCE Europe,

16th European Conference on Power Electronics and Applications,

Lappeenranta, Finland, 26–28 August 2014.

III. Nokka, J., Laurila, L, and Pyrhönen, J. (2016), “Virtual simulation –based

underground loader hybridization study – comparative fuel efficiency and

working capability analysis,” International Review on Modelling and

Simulations (IREMOS), Vol. 10, No. 4, 2017.

IV. Montonen, J., Nokka, J., and Pyrhönen, J. (2016), “Virtual Wheel Loader

Simulation - Defining the Performance of Drive-Train Components,”

International Review on Modelling and Simulations (IREMOS), Vol. 9, No. 3,

2016.

V. Baharudin, E., Nokka, J., Montonen, J.-H., Immonen, P., Rouvinen, A., Laurila,

L., Lindh, T., Mikkola, A., Sopanen, J., Pyrhönen, J., “Simulation Environment

for the Real-Time Dynamic Analysis of Hybrid Mobile Machines,” in

Proceedings of ASME 2015 International Design Engineering Technical

Conferences & Computers and Information in Engineering Conference

(IDETC/CIE 2015), Boston, Massachusetts, August 2–5, 2015.

VI. Lindh, T., Montonen, J.-H., Niemela, M., Nokka, J., Laurila, L., and Pyrhönen,

J., "Dynamic performance of mechanical-level hardware-in-the-loop

simulation," in Proceedings of EPE 2014 ECCE Europe, 16th European

Conference on Power Electronics and Applications, Lappeenranta, Finland, 26–

28 August 2014.

Author’s contribution

In Publications I–III, Mr. Nokka was the corresponding author and the main contributor

responsible for the content of the papers, aside from the multi-body dynamics section,

where Mr. Baharudin was the main contributor. In Publication IV, Mr. Nokka was

responsible for the simulation of the wheel loader while Mr. Montonen was the electric

motor specialist and responsible for that area. In Publication V, Mr. Nokka was

responsible for the electric part of the simulation while Mr. Baharudin concentrated on

List of publications 12

the mechanical engineering simulation. In Publication VI, the role of Mr. Nokka was in

assisting in the cosimulation environment setup.

13

Nomenclature

Abbreviations

DoD Depth of Discharge

ECV Electric Commercial Vehicles (Programme of Tekes)

EPA The US Environmental Protection Agency

FL Front left (wheel)

FR Front right (wheel)

HC Hydrocarbon

LHD Underground loader (Load, Haul, Dump)

MBD Multi-Body Dynamics

NMHC Non-methane hydrocarbon

NRMM Non-Road Mobile Machinery

PM Particle matter

PN Particle number

PMSM Permanent Magnet Synchronous Machine

PRV Pressure Relief Valve

RL Rear left (wheel)

RR Rear right (wheel)

SoC State of Charge

15

1 Introduction

Virtual prototyping means that a mechanical system can be designed and tested in a

virtual world with computers before actually manufacturing a prototype. A virtual

prototype follows the laws of physics as accurately as possible. It has proven to be a

powerful digital simulation tool enabling fast product development at the very

beginning of an R&D process. Especially, the design of heavy non-road machinery

faces the problem that experienced machine designers have so far been accustomed to

traditional directly diesel-powered machines, but not to novel hybrid machines

including electrical powertrain components. When a hybrid system is to be designed,

new challenges arise. The first challenge is that the designer should know exactly how

the future machine will be used and what kind of work it will finally perform. If the

designer has an opportunity to drive the machine in a digital environment before the

natural one, he or she gets fast information of the future performance of the real

machine. Often even the load cycles of traditional machines are not accurately known,

and therefore, the first task in a new development project is to simulate the load cycles

of the machine in different probable cases.

Based on load cycle information obtained by virtual testing of the machine, the designer

gets accurate information about the energy flows in the system, and can thus start

dimensioning of the power train components, especially the power plant and electric

energy storages. Such development work is complicated compared with traditional

system design; previously, if the performance of a traditional machine was not powerful

enough, a simple solution was to select a larger diesel engine. In the case of hybrid

machines, more precise dimensioning of the drive train components is required, and

virtual prototyping offers the designer a powerful tool to dimension the drive train

components in the most appropriate way.

Electrical drives offer superior controllability compared with all other means of

controlling movement. The torque of an electric motor can be controlled within a few

milliseconds from zero to the rated torque and beyond. The efficiency of systems can

also reach a new level compared with traditional solutions, where a lot of energy is lost

when producing motion.

Naturally, in non-road mobile machines, hydraulics will be needed also in the future

because forces that can be produced directly with electromagnetic actuators remain low

compared with hydraulic systems. However, electrical traction drives provide superior

efficiency and control opportunities compared for instance with hydrostatic

transmission systems. In addition to traction systems, electricity can be used in making

the hydraulic systems more energy efficient by, for example, utilizing variable speed

drives and constant displacement pumps as a replacement for adjustable pumps or

control valves, or by using the pump as a motor to regenerate energy from for example

lowering operations. At least the main boom of any non-road mobile machine should be

controlled by reversible hydraulic pumps directly driven by electrical machines. Such

arrangements allow the recovery of potential energy in a hydraulic system.

1 Introduction 16

There are examples of hybrid machines where hybridization enables 25–40% fuel

savings in typical load cycles, depending on the application [1], [2], and [3]. Komatsu

achieved such savings by only replacing the swing movement hydraulic drive in its

excavator with an electric drive recovering the high kinetic energy in azimuth swings.

This example shows how regenerating the kinetic energy from such a high-energy

motion can lead to 25–40% savings in fuel consumption when used again as supporting

power in the engine acceleration.

Caterpillar has used two alternative routes in hybridization, where the swing braking

energy in the 336E H excavator is accumulated in nitrogen gas accumulators, while the

D7E dozer has applied a diesel-electric hybrid driveline. In general, hybridization aims

at controlling the power flow and optimizing the diesel engine load point, thereby

increasing the fuel efficiency of the machine without compromising the machine output

performance.

The most traditional means of powering a non-road mobile machine is the diesel engine.

It represents practically the best technology for converting the chemical energy of

hydrocarbon into mechanical energy, and when driving an electric generator, into

electricity. Today, the practical efficiency maximum of large diesel engines is reaching

50%. However, diesel engines representing the power needed in typical non-road

vehicles may offer a practical maximum efficiency of only about 40%. Unfortunately,

there seems to be a trend towards slightly lower efficiencies because of the strong

demand for reduction in other than CO2 emissions.

At partial loads, the efficiency of a diesel engine is lower, especially if the operating

speed must be kept high. The main idea of hybridizing a non-road mobile machine is to

let its power plant – the engine and the generator – operate at its best efficiency or set it

to standby, subsequently leading to the downsizing of the diesel engine itself. Suitably

large electric energy storages mitigate all power variations of the power plant and let the

diesel operate at its best efficiency. Optimizing the diesel engine for one particular

speed and torque should also help in optimizing its efficiency and emissions at that

point. Unfortunately, present-day standards do not support this line of thinking, and

diesel engines must be designed to meet the emission standards in the whole operating

range of traditional drives. In the past, the storage of energy, providing this described

energy fluctuation buffer has been a problematic task, because the energy threshold

from the storage has to be quite large owing to the sub-optimal ratio of the high peak

power versus the low average power. Nowadays, advances in lithium battery

technologies [4] have improved both the power and energy densities of batteries, and

application of lithium-based battery technologies to hybrid or full electric non-road

machines offers many advantages.

However, hybridization enables using smaller engines, which have slightly less

stringent emission requirements at least in the present-day TIER 4 norm [5] and the

current and future EU stage III, IV, and V standards [6]. Saving both in the engine

prices and its emission control apparatus might be an interesting alternative for a

1.1 Hybrid Vehicles 17

machine builder, although still nonoptimal from the environmental point of view.

Nevertheless, the nonregulated CO2 emissions will stay much lower in the hybridized

machine compared with the traditional ones. The Tier 4 and current Stage III/IV

standards are presented in Table 1 and Table 2. The upcoming (2019–2020) EU stage V

standards are given in Table 3.

Table 1. EPA Tier 4 emission standard (2006–2014). Unmarked units in g/kWh.

Engine

Power

[kW]

CO NMHC1 NMHC+NOX NOX PM

< 8 8.0 - 7.5 - 0.4

8–19 6.6 - 7.5 - 0.4

19–37 5.5 - 7.5 (2008)

4.7 (2013)

- 0.3 (2008)

0.03 (2013)

37–56 5.0 - 4.7 - 0.3 (2008)

0.03 (2013)

56–130 5.0 0.19 - 0.40 0.02

130–560 3.5 0.19 - 0.40 0.02

1Non-Methane Hydrocarbon

Table 2. EU Stage III/IV emission standard (2006–2014), unmarked units in g/kWh

Engine

Power

[kW]

CO HC HC+NOX NOx PM

19–37

(III A)

5.5 - 7.5 - 0.6

37–56

(III B)

5.0 - 4.7 - 0.025

56–130

(IV)

5.0 0.19 - 0.4 0.025

130–560

(IV)

3.5 0.19 - 0.4 0.025

1 Introduction 18

Table 3. EU stage V emission standard (2019–2020), unmarked units in g/kWh

Engine

Power

[kW]

CO HC HC+NOX NOx PM PN

[count]

< 8 8.0 - 7.5 - 0.4

8-19 6.6 - 7.5 - 0.4

19–37 5.0 - 4.7 - 0.015 1*1012

37–56 5.0 - 4.7 - 0.015 1*1012

56–130 5.0 0.19 - 0.4 0.015 1*1012

130–560 3.5 0.19 - 0.4 0.015 1*1012

> 560 3.5 0.19 - 3.5 0.045 -

As presented in the tables above, the emission regulations, particularly in terms of PM,

are tightening rapidly when approaching the year 2020. The tightening regulations are

pushing the engine and mobile machinery manufacturers either to update the engine

base of the current machinery or to downsize them.

The fossil oil reservoirs are finite, and if used also in the future, should be replaced by

man-made hydrocarbons to mitigate the adverse effects of the increasing CO2

concentration in the atmosphere. Solar economics offers a sustainable way of using also

the diesel engines in the future without causing new CO2 emissions. Wind and direct

solar electric energy can be converted into hydrocarbons by different electrochemical

processes. Such a diesel fuel will have a higher price than the fossil one, and therefore,

in the future, there will be more and more interest in machines consuming low amounts

of fuel.

Today, we are still using fossil oil, whose prices have been volatile as the crude oil price

has more than halved from slightly over $100 to approximately $50 per barrel in the

latter half of 2014. Even though the oil price at the current level of $60 to $65 per barrel

can be considered affordable, since the past two years, the overall increase in price has

been over 100% [7]. The Brent spot price for the crude oil is presented in Figure 1.

Whatever the price of oil is, it always represents a high cost for the non-road mobile

machine operator. The machine offering the lowest consumption will most probably be

a competitive choice, and despite the higher cost of additional components (such as

batteries and electrical drives in hybrid drivelines), the investment pays itself back quite

fast.


Figure 1. Brent Crude oil daily prices from 2012 to the present.

1.1 Hybrid Vehicles

In the drivelines of hybrid vehicles, the two main hybrid arrangements are series hybrid

and parallel hybrid. In an ideal series hybrid non-road mobile machine, the power plant

of the machine operates independently producing electric energy efficiently to the

electric energy storage (batteries or supercapacitors) of the machine or directly to the

tractive or hydraulic drivelines at the average power of the system. This enables the

optimal (highest efficiency) use of the diesel engine powering the machine and lets the

diesel engine operate without transients. The energy storage acts as a low-pass filter

between the highly dynamic power demands in the tractive driveline, hydraulic system,

and the diesel engine, most optimally driven in a static operating point. The problem

related to the series hybrid system is that every mechanical action of the machine has to

be powered by an electrical drive system, and a lot of such systems may thus be needed.

Therefore, a new product must be developed when a series hybrid system is built. In

principle, the new machine is a fully electric machine, having, however, its own electric

power plant. If the electric energy storage is large enough and fully charged, the power

plant can be fully stopped during low-load operation of the machine. Overall, hybrid

vehicles have shown a positive impact on both fuel consumption and emissions levels

with similar performance capabilities as conventional vehicles [8]. The series hybrid

driveline topology is presented in Figure 2.

2012 2013 2014 2015 2016 2017 201820

40

60

80

100

120

140

Year

Do

llar

s p

er B

arre

l

1 Introduction 20

Internal Combustion Engine

Power Electronics

Battery/Supercapacitor

Power Electronics

Generator

Electrical Motor

Mechanical Load

Direction of Power

Figure 2. Series hybrid driveline flowchart.

Then again, the parallel hybrid system offers, in practice, the smallest changes to an

already-existing machine. Only one electric machine attached to the diesel engine shaft

is needed. As a result, also only one power electronic converter and one electric energy

storage will be needed. The prime mover can be downsized and the power transients can

be taken care of by the electrical machine, but there remains a mechanical contact

between the diesel and the drive train of the system. Therefore, the diesel engine must

often be operated also in speed transients. In this basic parallel hybrid case, the diesel

engine must always run when the non-road machine is working. Naturally, a parallel

hybrid system can be built more complex, including a clutch allowing disconnection of

the diesel from the drive train and thereby full electric operation. The parallel hybrid

driveline topology is presented in Figure 3.


Internal Combustion Engine

Power ElectronicsBattery/

Supercapacitor

Electrical Motor

Mechanical Load

Direction of Power

Figure 3. Parallel hybrid driveline flowchart.

When implementing hybrid topologies into the non-road mobile machine industry, the

series hybrid option tends to be favored over the parallel hybrid one. This may be due to

the fact that the series hybrid is more straightforward in its structure and more robust in

component placing; as the diesel generator set is mechanically separated and oftentimes

downsized, there are more options for where and in which orientation it could be placed

in the machine. Individual electrical drives within the machine can also be better

dimensioned according to the demands of the specific mechanical load the drive is

connected to. The parallel hybrid topology, whilst offering potentially fewer energy

conversions, often requires the diesel engine to adapt to the rotational speed of the

mechanical driveline or react to the hydraulic system power demand, and thus, it can

operate the diesel engine in a suboptimal operational point.

Different combinations of series and parallel hybrid systems can be built, and the final

result of the development work depends on the case. However, in all design task cases,

virtual design, virtual prototyping, and virtual testing provide a strong assessment tool

for the systems.

1 Introduction 22

1.1.1 Non-road mobile machinery

The European Commission has established a Non-road mobile machinery NRMM

definition to cover various engine installations in machines that are used for other

purposes than transportation of passengers or goods [9]. The NRMM covers for

example forklifts, mobile cranes, bulldozers, construction machinery, and also smaller,

even handheld equipment such as chainsaws [10]. In the context of the research

presented here, the NRMM is used as a description of mobile, non-road machinery,

excluding handheld equipment.

1.2 Scientific contribution

1. Application of a real-time multi-body simulation engine in the development of a real-

time cosimulation environment for hybrid driveline dimensioning (Publications I, II,

and V). The cosimulation environment presented in this doctoral dissertation consists of

two parallel-running subsimulations. Synchronization and meeting the real-time

requirement between these two subsimulations are paramount in establishing a

cosimulation environment where hybrid driveline concepts can be tested efficiently and

in real time. The novelty of the cosimulation environment lies in the ability of being

able to dynamically load the hybrid driveline with complete, unrecorded load cycles

whilst running in real-time.

2. Analysis of the effects of hybridization on NRMMs using three machine cases

(Publications III and IV, Section 3.2). The research presented in this doctoral

dissertation demonstrates how series hybridization can greatly reduce the fuel

consumption of the NRMMs without compromising their working capabilities. The

three cases, two of which are underground loaders and one a wheel loader, showcase a

comparable fuel consumption reduction of roughly 50%, while the work done remains

the same and is conducted in the same time. The field of hybridized NRMMs is

constantly growing, but a method for rapid iteration and prototyping with a great

accuracy is required.

3. Discussion of the cosimulation boundary conditions (Publication III, Section 3.2). As

the presented environment does not use standardized load curves, as the hybridization

changes the dynamical behavior of the machine, new boundary conditions must be set

to ensure similarity between two separate drive cycles. The most significant change in

the machine dynamics, if predefined load curves were to be used, is the kinetic energy

recovery by electrical braking, only available to the hybrid NRMM. In the research

presented in this doctoral dissertation, where the energy efficiency and fuel

consumption are the main focus areas, the boundary conditions were set so that the

work done by the NRMM was the same and the cycles were of similar length.

Moreover, Section 3.2 presents a crude method of correcting the battery state of charge

levels. The target was to achieve the same state of charge in the hybrid driveline battery

at the end of each cycle as it was at the beginning. If and when any deviation occurs, the

SoC change has to be taken into account in the fuel consumption calculation.

1.2 Scientific contribution 23

4. Accuracy verification of the virtually simulated NRMM against its real-life

counterpart (Section 3.2). Even though the subsystems and modeling techniques used in

the presented research are scientifically accurate, verification of the virtual prototype

NRMM is required. In Section 3.2 of this doctoral dissertation, a virtual model of an

underground loader is verified by mimicking as closely as possible a recorded cycle

driven with a real-life machine. The powers of the hydraulic driveline and the diesel

engine are compared and the fuel consumption analyzed. This verification contributes to

verifying the overall credibility of virtual simulation as a powerful tool in the research

and development of new NRMM concepts.

25

2 Virtually assisted analysis method

Virtual prototyping and testing of a machine refers to accurate digital modeling and

testing of a product without building a real prototype. Here, accuracy means that the

mechanical stresses and the energy flows can be predicted with a high fidelity compared

with a real-world machine. Virtual prototyping has been shown to be a cost-effective

and fast method to establish and verify new machine concepts and iterate them to a

higher accuracy (compared with traditional R&D) before entering the prototype

manufacturing [11], [12].

2.1 Multi-body dynamics in machine simulation

Even though the Multi-Body Dynamic (MBD) modeling principle and MBD simulation

are outside the scope of the presented research, this section provides a brief introduction

to the field and its subareas. A more thorough description can be found in Publications I

and V. The MBD simulation is based on the commercial Mevea software [13].

MBD simulation is a commonly used method to calculate mechanical phenomena of a

system [14], [15], and it is typically used in different cosimulation or in-loop simulation

configurations [16], [17], [18], [19], and [20]. MBD simulation provides a reasonably

accurate and cost-effective method to dynamically estimate how the mechanics of a

system would react to software or hardware under test or development. Subsequently, if

the system under development fails, there is no danger associated with the simulated

mechanics, compared with physical prototyping.

The MBD simulation in the presented research consists of three subareas: mechanics,

hydraulics, and tire modeling. The mechanics of the virtual prototype can be simulated

by applying the multi-body system modeling principle. In this principle, the machine or

construction is divided into its functional components connected to each other with

joints. Each of these components is defined by its respective mass, center of mass, and

inertial matrix [21], [22]. The driveline components act as torque (or force) input

sources, and respectively, the MBD system feeds angular velocities (or location) data

back to the driveline components.

Another way of affecting the mechanical structure of the MBD simulation is by

hydraulics. The applied MBD simulation system combines kinematics with lumped

fluid-flow theory based hydraulic system actuator models, and a circuit modeling

principle where the circuit is divided into distinctive volumes, inside which the pressure

is considered to be homogeneous [23]. When these volumes are connected for example

to valves, whose parameters are known through iteration or catalogues, the flow rates of

the system can be solved.

The frictions acting between the machine operating environment and the tires are

calculated by using the LuGre friction model [24]. The LuGre model constructs the tire-

ground contact as microscopic bristles brushing each other. The bristles bend and

2 Virtually assisted analysis method 26

deflect at the contact point as a result of forces acting both tangentially and in friction. If

the deflection exceeds a certain threshold, the tire will start to slip [25].

2.2 Diesel-electric hybrid driveline simulation

Within the scope of the research presented in this doctoral dissertation, only series

hybrid drivelines are considered. Previous research indicates that in the non-road mobile

machine industry, a better fuel efficiency can be achieved with series hybrid diesel-

electric drivelines than with parallel hybrid constructions [26]. The simulations are

based on a quasi-static modeling principle where the electrical driveline is simulated as

powers, compared with the dynamic simulation principle where the voltages and

currents are separated. The quasi-static modeling principle provides an effective method

to solve larger, system-level driveline simulations without compromising the accuracy

too much [27], [28], and [29].

2.2.1 Electrical drives

The mechanical power of a hybrid driveline is partially (parallel or combined system) or

completely (series system) produced by an electrical drive. The electrical drive acts as a

torque source and is connected to either a diesel generator as a load torque or to the

mechanical and hydraulic drivelines of the MBD simulation. Depending on the case,

different approaches to the modeling of an electrical drive can be taken. If a more

accurate numerical presentation is needed, a two-axis model (presented in Publications

I, II, and III) of the motor can be applied alongside an inverter model. If general-case

studies are being made, efficiency maps for both the motor and the power electronic

converter can be used in conjunction with a suitable time constant presentation for the

dynamic output, and a simple control algorithm can operate the system. The latter

approach is more suitable also for more complex diesel-electric driveline models (such

as multi-motor drivelines), where computation of several micro-second-level two-axis

models in real time is not feasible. On the other hand, the efficiency-map-based model

offers more flexibility in the choice of the simulation time step.

2.2.2 Energy storages

There are two or more types of energy storages in a hybrid system. The fuel tank serves

as the main energy storage, but in a diesel-electric hybrid system, an electric energy

storage is crucial for both electric and hybrid drivelines. They enable the system,

especially the diesel generator set, to be driven in a smoother manner in terms of power,

eliminating most of the power fluctuation otherwise loading the diesel engine.

Hydraulic systems also use hydraulic accumulators to regenerate energy for example

from lowering a load to be reused in the next lift, and to balance the load the pump is

experiencing.

2.2 Diesel-electric hybrid driveline simulation 27

The battery pack is modeled with a constant internal resistance and a static source

voltage [30]. The power loss is calculated with the load current acting at the internal

resistance. The simplified electrical schematic of the battery is presented in Figure 4.

Source

Voltage

Internal resistance

Terminal current

Terminal PowerCharging Power

Loss

Power

Figure 4. Internal resistance battery model in charging mode.

Even though the battery model used in the research is rather simple by nature, it has

sufficient accuracy for system-level analysis. Because the model is simple and uses pre-

calculated data to solve terminal current for any given terminal power, the model runs

fast.

More detailed battery models naturally yield more accurate battery behavior

characteristics, but are more cumbersome to solve and often contain differential

equations that are required to be solved in real time [31].

2.2.3 Diesel generator sets

A diesel process is very complicated to model accurately if all the features related to

fluid flows, thermodynamics of burning, and mechanics are to be taken into account. In

hybrid system virtual testing, however, it suffices to describe the dynamics and energy

consumption of the system well enough. In this work, the diesel generator sets are

modeled as first-order torque followers (1/(1+Ds)), which describes the diesel

dynamics with only one time constant and an efficiency map, with which it is possible

to analyze the energy efficiency of the engine at different working points. The

efficiency map transfers the mechanical shaft power into power that is consumed in

diesel oil, and when the density and energy content of the oil are known, the fuel

consumption can be derived. An example efficiency map is presented in Figure 5. The

implementation of transient fuel consumption calculations [32], [33] was attempted in

the early stages of the research (Publications I, II, and V), but the calculations were left

out from the rest of the research because of the unrealistic torque and angular velocity

transients experienced by the diesel engine model. This was due to the lack of damping

in the mechanical driveline within the MBD simulation. The presented model, without

the transient fuel consumption model, does not affect the series hybrid driveline


practically at all, and also the effect on the unhybridized models is small. The fuel

consumption figures presented in Section 3.1 were verified by the machine

manufacturer to be adequately accurate.

Figure 5. Example of a diesel engine efficiency map.

2.3 Hardware setup

The hardware setup of the cosimulation platform used in the research presented in this

doctoral dissertation combines a MBD simulation environment with diesel-electric

hybrid driveline simulations and a human interface. With a motion platform and a six-

screen cabin, the operator has the feel and view resembling real-life scenarios, and with

the industrial-grade I/O interface pedals and joysticks [34], also the control of the

machine is realistic.

100

200

300

400

500

0

100

200

3000

10

20

30

40

50

Rotational speed [rpm]Torque [Nm]

Eff

icie

ncy

[%

]

10

15

20

25

30

35

40

2.3 Hardware setup 29

Cabin, visualization, control

interfaces (3 computers)

Drive

pedal

Brake

pedal

Joysticks

MBD, hydraulics and

mechanical driveline

simulation environment

(1 computer)Inputs

Screens, speakersPicture

and

sound

Motion platform

Movement

Hybrid driveline simulation

(1 computer)

Reference input

Torque

Angular velocity

Figure 6. Simplified signal structure of the cosimulation setup, demonstrating one motor signal

loop (black) between the subsimulations.

The MBD simulation environment consists of four separate computers, one of which is

responsible for the calculations and three for visualization by the cabin screens.

The diesel-electric driveline simulation model runs on a fifth computer in a Matlab

Simulink environment. The connection between it and the MBD simulation is

established by a TCP IP socket connection. The socket IP connection enables the two

simulation models to run in two separate time steps, with the boundary condition of the

time step of the MBD simulation to be higher than the Simulink time step and an integer

multiple of it. This is a highly beneficial feature as the computational power can be

allocated to where it is needed; rapid flux density equations of electrical drives can be

solved for example in 50 µs in Simulink, whilst the MBD simulation can be run in 1–2

ms time steps, being more suitable for the mechanical phenomena. The network delays

can be considered negligible.


The achievement of the real-time requirement can be evaluated using the calculation

loop time. The loop time takes into account all of the calculations made and can be

compared with the main time step of the whole simulation system. If the loop time is

lower than the main time step (for example the next 2 ms is solved in 1.5 ms), the

simulation stays in the real-time execution, and even has some resources to spare. On

the other hand, if the loop time grows larger than the main time step, the simulation

system drops off of real-time, resulting in sluggish, “slow-motion-like” execution, and

modifications to the system must be made, either in the models or in the physical

hardware. As the models increase in complexity, the computational effort to solve them

also grows, and to meet the real-time requirement, either the subsections of the model

must be simplified or the model time step increased. The computational performance

can also be enhanced by optimizing the computer hardware itself.

The timing and synchronization of the two subsimulations are performed by the MBD

simulation. The inherent ability of the IP Socket to halt code execution until the receive

buffer is full acts as a synchronization element:

MBD simulation sends and reads its output and input vectors.

Simulink receives a full input buffer and continues simulating on its own (faster)

time step until the simulation time reaches the value where the next input values

are required and halts. The Simulink output values are ready to be sent.

Simultaneously, the MBD simulation continues to calculate mechanics,

hydraulics, and driveline equations with the input values it has received from the

Simulink until the next input/output refresh.

MBD simulation sends and reads its output and input vectors.

Repeat.

2.4 Boundary conditions of real-time virtual simulations 31

2.4 Boundary conditions of real-time virtual simulations

The traditional approach to iterative simulations, such as hybridization analyses, has

been to base the machine operation around predefined and often standardized power or

load curve recording [35], [36], and [37]. However, when introducing the electrical

drives into the hydraulic and tractive drivelines, the machine dynamics change. The

main differences with the series hybrid driveline compared with the diesel-engine-

powered one are:

Electrical drives reach their maximum torque production capabilities already at

standstill, while a diesel engine cannot produce torque at or below idling speed

and at low speeds the torque production is limited.

Electrical drives respond faster, typically in milliseconds, while diesel engines

respond in tenths of a second to seconds.

Electrical drives have an inherent ability to operate in both generator and motor

modes, enabling regenerative braking.

The presented research requires a different set of boundary conditions to ensure

similarity and repeatability of two separate simulation cycles. The boundary conditions

were selected as follows:

The work done must be equal.

The route driven must be the same.

The time span for the load cycles must be similar.

Even though the boundaries presented are flexible, and chosen according to the research

focus, such conditions are needed when using the presented simulation environment in

research scenarios. Because each of the cycles is driven by a human operator, there are

inherent variations in the cycles, shown either as instantaneous velocities of the

machines, irregularities in steering, or otherwise different behavior. The presented

boundary conditions ensure that even though any instantaneous moment in the cycle

may be different, in the end, the cycle is comparable with other cycles driven with the

same boundary conditions.

On the other hand, the alteration of the driveline components requires that the operator

must be driving the machine at each iteration, since even slightest alterations for

example in the electrical drive characteristics produce different output performance.

This variation in the output performance would lead the recordings-based machine to a

different route than intended, possibly banging against other objects. One option would

be to implement some kind of a location-based controller to the machine inputs, but that

would make the machine inputs inhuman and skew the final results, even though the

similarity on the above-described boundary condition scale would be more accurate.

33

3 Hybridization of underground loaders

This section presents the two main research cases related to the virtual prototyping

cosimulation environment and the NRMM hybridization research. The framework of

these cases lies within the ECV/Tubridi project [38], where the case machine was

required to be a Load, Haul, Dump machine (LHD) intended for underground mining.

The cases were built around two distinct Sandvik LHD models; 26.2 ton LH410 and 13

ton EJC90 (LH204).

To demonstrate and verify the simulation method developed in the Tubridi research

project, a comparison between a real LHD and a virtual one was made. While the real

machine was acquired and hybridized by Aalto University in Helsinki, Finland, the

virtual-model-based research was conducted at Lappeenranta University of Technology.

The real machine case was Sandvik’s LH204 (also known as EJC90, the product name

given by the previous manufacturer of the machine, and dubbed as such in the context

of this doctoral dissertation). When the Tubridi project started, LUT kick-started its

research by acquiring a ready-made LHD model of Sandvik LH410, which had been

used for research purposes and could thus be considered an accurate estimation of the

real-life LHD. Because the infrastructure of the cosimulation loop remained the same

regardless of the case machine, the project could be started faster as parallel research

could be done before and while the modeling and iteration of the EJC90 was in

progress. The LHD cases were as follows:

Case 1 – LH410

o Virtual models of both hybridized and unhybridized LH410

o Professional driver

o Fuel consumption, energy efficiency, and working efficiency analyses

Case 2 – EJC90

o Modelling of virtual unhybridized EJC90 based on the design data

Validation by comparing with the measured load cycle of a real

machine

o Hybridization of the virtual EJC90

o Fuel consumption analysis

Battery state of charge correction algorithm

3.1 Case 1 Underground loader energy and working efficiency

analyses

A professional driver conducted a series of test drives using both unhybridized and

series hybridized LH410 LHD models. The tests were also divided into two different

driving distances, the shorter being closer to a typical hauling distance and the longer

resembling the measured data used as a verification data source in the EJC90 case

3 Hybridization of underground loaders 34

presented in Section 3.2. The fuel consumption, working efficiency (tons per hour), and

energy efficiency (tons per liter of diesel fuel) of both variants were analyzed and

compared. The main parameters of LH410 are listed in Table 4.

Table 4. LH410 Main parameters

Length 10.01 m

Width 2.67 m

Height 2.38 m

Mass 26.2 t

Engine power 220 kW

Hauling capacity 10 t

3.1.1 Testing the underground loader on a short route

This short route is a model of a real test mine route, which is closer to a typical haul

length of LHDs, compared with the longer route presented in Section 3.1.2. The short

route cycles are a repetition of two separate subcycles, driven in a relatively flat, 1%

downhill towards the dumping point, spanning about 100 m one-way. The operator

drove the machine with an empty bucket first from the start point to the pick-up point,

filled the bucket, and backed up to the dump area. This cycle was driven twice per

recording. The short route top-down view is presented in Figure 7.

3.1 Case 1 Underground loader energy and working efficiency analyses 35

Figure 7. Top-down view of the short route. The distance between the pick-up point and the

dumping point is 100 m, and the slope rate is 1%.

Six cycles were recorded with both the hybridized and unhybridized LHD. The total

power consumption of the un-hybridized LHD is shown in Figure 8, and the hybridized

LHD power and SoC in Figure 9 and Figure 10. The combined tonnages of the two sub-

cycles in each main cycle are presented in Figure 11. The unhybridized and hybridized

cycle indices are not related to each other, but each cycle is unique, and the cycle index

number given to any recorded cycle is used merely as a reference to that specific cycle.

Hence, for example hybrid cycle number 3 presented in Figure 11 is unrelated to the

diesel cycle number 3. The most straightforward way of interpreting figures in Section

3.1, in general, is to divide the hybrid and diesel values into two separate datasets.

Start and dump point

Pick-up point


Figure 8. Short-route diesel engine mechanical power of the unhybridized LHD.

Figure 9. Short-route powers of the hybridized LHD. The produced generator set (Genset)

power (green) is consumed in the traction and hydraulics.

0 50 100 150 200 250 300 3500

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

5

Pow

er [

W]

Time [s]

0 50 100 150 200 250 300-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10

5

Time [s]

Pow

er [

W]

Traction

Genset

Hydraulics


Figure 10. Short-route battery state of charge of the hybridized LHD.

Figure 11. Combined tonnages of the short-route cycles. Each bar represents the combined

tonnage hauled in total during the two back-and-forth trips to the pick-up point. The average

values throughout the six cycles, separated into hybridized and unhybridized cases, are

presented as horizontal lines.

It can be seen in Figure 11, where six cycles of both the hybridized and original diesel-

powered machine are depicted, that the average combined hauled tonnages remain the

same regardless of the powertrain type and the inaccuracy caused by the human driver.

Even though there were some individual variations, for example when the bucket fill

0 50 100 150 200 250 30089.4

89.6

89.8

90

90.2

90.4

Sta

te o

f C

har

ge

[%]

Time [s]

1 2 3 4 5 60

1

2

3

4

5

6

7

8

Cycle index

Tonnag

e [

t ]

5.59

6.28

7.42

5.69

4.81

7.41

6.20

6.14

7.18

4.49

6.87

5.00

7.27

6.16

Diesel

Hybrid


was unsatisfactory, the average value difference over the whole six-cycle study was

only 40 kg, being about 0.1% of the total mass hauled. The total fuel consumptions are

presented in Figure 12 and as time-average values in Figure 13.

Figure 12. Total cumulative fuel consumptions of the short-route hauling cycles.

Figure 13. Average fuel consumptions of the short-route hauling cycles.

1 2 3 4 5 60

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Cycle index

Tota

l fu

el c

onsu

mpti

on [

l ]

1.69 1.70

1.57

1.68

1.49

1.701.64

0.70 0.70 0.74

0.850.80 0.77

0.76

Diesel

Hybrid

1 2 3 4 5 60

2

4

6

8

10

12

14

16

18

20

22

Cycle index

Aver

age

fuel

consu

mpti

on [

l/h

]

18.36

20.06

18.93

21.00

19.33

20.8019.75

9.29 9.29 9.30 9.33 9.31 9.31

9.31

Diesel

Hybrid


Although Figure 11 shows that the overall end efficiency, that is, the capability of

hauling rocks, is not hampered by hybridization, it does not show how the actual

hauling is done. With the information shown in Figure 12 and Figure 13, the time and

fuel aspects are introduced. It can be seen that in terms of total consumption, the

hybridized LHD uses roughly half of the amount of diesel fuel compared with the

original LHD, and because the average fuel consumption is also halved, the duration of

the cycle is roughly the same regardless of the hybridization.

The operational efficiencies of the machine, both with respect to the work done (tons

hauled per hour) and the energy consumed (tons hauled per liter of diesel oil) are

presented in Figure 14 and Figure 15.

Figure 14. Work efficiencies, expressed as tons hauled per hour, of the short-route cycles.

Figure 14 shows that in addition to the LHD being able to haul the same payload (as

seen in Figure 11), the time that it takes to do so is also the same. A conclusion can be

drawn that the hybridization has little or no effect on the LHD hauling capabilities, as it

is still able to complete the same task in the same time.

1 2 3 4 5 60

10

20

30

40

50

60

70

80

90

100

Cycle index

Work

Eff

icie

ncy

[ t

/h ]

60.56

74.01

89.32

71.13

62.44

90.50

74.66

81.65

94.77

56.55

75.06

58.59

87.61

75.71

Diesel

Hybrid


Figure 15. Energy efficiencies, expressed as tons hauled per consumed liter of diesel oil, of the

short-route cycles.

3.1.2 Testing the underground loader on a long route

The long route starts and ends at the same point as the short route, but it is almost a

quadruple of the distance, spanning one-way roughly 380 m and containing a steep, 13-

15% downhill gradient from 100 m to 278 m, measured from the start point. To keep

the cycle lengths manageable, the operator was instructed to drive only one cycle per

recording (start, drive to pick-up point, filling of the bucket, reversing back to the dump

point, dump). The long route top-down view is presented in Figure 16. The number of

cycles was also reduced to four. An example of the unhybridized cycle is presented in

Figure 17, and an example of the hybridized cycle in Figure 18 and Figure 19.

1 2 3 4 5 60

2

4

6

8

10

12

Cycle index

Ener

gy E

ffic

iency

[ t

/l ]

8.79

10.20

6.08

8.05

6.29

9.41

8.14

3.303.69

4.72

3.39 3.23

4.353.78

Diesel

Hybrid


Figure 16. Top-down view of the long route. The total length of the route is 378 m one-way.

The downhill section of the route is indicated by red on the route line.

Figure 17. Long-route diesel engine mechanical power of the unhybridized LHD.

0 50 100 150 200 250 300 350 4000

2

4

6

8

10

12

14

16

18x 10

4

Pow

er [

W]

Time [s]

Start and dump point

Pick-up point


Figure 18. Long-route powers of the hybridized LHD. The produced generator set (Genset)

power (green) is consumed in the traction and hydraulics.

Figure 19. Long-route battery state of charge of the hybridized LHD.

The same analyses were conducted with the long-route cycles as with the short route in

Section 3.1.1. The tonnages are presented in Figure 20, and the fuel consumptions in

Figure 21 (cumulative) and Figure 22 (average).

0 50 100 150 200 250 300 350

-1

-0.5

0

0.5

1

1.5x 10

5

Time [s]

Pow

er [

W]

Traction

Genset

Hydraulics

0 50 100 150 200 250 300 35088

89

90

91

92

93

94

Sta

te o

f C

har

ge

[%]

Time [s]


Figure 20. Combined tonnages of the long-route cycles.

Figure 21. Total cumulative fuel consumptions of the long-route cycles.

.

1 2 3 40

0.5

1

1.5

2

2.5

3

3.5

4

Cycle index

Tonnag

e [

t ]

3.62

3.19

2.47

3.88

3.29

3.18

3.40

3.18 3.123.22

Diesel

Hybrid

1 2 3 40

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Cycle index

Tota

l fu

el c

onsu

mpti

on [

l ]

1.95

1.841.88

1.941.90

0.89

0.800.71

0.900.83

Diesel

Hybrid


Figure 22. Average fuel consumptions of the long-route cycles.

The same conclusions can be drawn from the above three figures as with the respective

short-route ones in Section 3.1.1. It can be seen that the hybridized LHD uses roughly

50% of the diesel fuel to complete the same amount of work. Figure 20 shows how, as

only one load–haul–dump cycle is driven per recording, the tonnages are roughly halved

when compared with Figure 11, where the total tonnages two subcycles are depicted.

The operational efficiencies are presented in Figure 23 and Figure 24.

Figure 23. Work efficiencies, expressed as tons hauled per hour, of the long-route cycles.

1 2 3 40

2

4

6

8

10

12

14

16

18

20

22

Cycle index

Aver

age

fuel

consu

mpti

on [

l/h

]

19.16

21.21

18.8619.60

19.71

9.33 9.32 9.30 9.339.32

Diesel

Hybrid

1 2 3 40

5

10

15

20

25

30

35

40

45

Cycle index

Work

Eff

icie

ncy

[ t

/h ]

35.6436.72

24.80

39.11

34.07 33.26

39.56

41.58

32.36

36.69

Diesel

Hybrid

3.2 Case 2 Underground loader hybridization 45

Figure 24. Energy efficiencies, expressed as tons hauled per consumed liter of diesel oil, of the

long-route cycles.

The above two figures confirm the conclusions made in Section 2.1.1: the work

efficiency of the machine is virtually unaffected by the hybridization, whereas the

energy efficiency is roughly doubled.

3.2 Case 2 Underground loader hybridization

EJC90 is relatively small when it comes to LHDs [39], at least compared with the

LH410 studied in the previous sections. The operating mass of the EJC90 is less than

half of that of the LH410, being slightly over 13 t (versus 28.5 t), the hauling capacity

being 4 t (versus 10 t). A photograph of the EJC90 is presented in Figure 25 and its

main parameters in Table 5.

1 2 3 40

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Cycle index

Ener

gy E

ffic

iency

[ t

/l ]

3.56

4.254.47

3.47

3.94

1.861.73

1.31

2.001.73

Diesel

Hybrid


Figure 25. EJC90 underground loader.

Table 5. EJC90 main parameters

Length 7.77 m

Width 1.60 m

Height 2.13 m

Mass 13.300 t

Turning radii (inside/outside) 2.89/5.16 m

Engine power 93 kW

Hauling capacity 4 t

3.2.1 Diesel-powered machine – model verification

The EJC90 employs a hydrostatic driveline to provide traction. This means that the

machine is operated by hydraulic motors instead of being directly driven by a diesel

engine. The machine has one motor in the front and one in the back, both driving two

wheels. In addition, there are two separate hydraulic systems, one controlling the

steering through one cylinder located on the right side of the central joint and another

controlling the lift and tilt motions of the bucket.

The model was verified by measurements done with an actual machine, and comparing

the energies consumed in the hydrostatic driveline, the respective powers, and the diesel

engine fuel consumption and power. The operating environment of the machine was

mimicked in the virtual simulation, and thus, the load cycle could be driven in the same


way as it was recorded. The cycle data could then be compared and the model fine-

tuned and verified. The initial requirement of the verification accuracy was to get within

a 10% error in the fuel consumption. The main objective of the model verification was

to match the output performance (by tractive driveline and working hydraulics) and the

fuel consumption, while giving more freedom for variation in the intermediate

components.

The hydraulic systems of the simulated EJC90 are simplified when compared with the

respective system in the real machine to meet the real-time requirement of the whole

system. The reduced system, however, represents the operation of the original driveline

with sufficient accuracy. The hydrostatic driveline schematic used in the simulated

EJC90 is presented in Figure 26, and the working hydraulic schematic in Figure 27.

Figure 26. EJC90 Hydrostatic driveline hydraulic schematic used in the verification simulation.


Figure 27. EJC90 working hydraulics schematic used in the verification simulation.

The virtual model of the case NRMM was verified by comparing the virtual model

operation and various metrics against a real-life recorded load cycle. Recording of the

500 s load cycle consisted of idling at the beginning and at the end of the cycle, and four

different work operations. The following list divides the total cycle into its main parts:

0–50 s idling

50–170 s driving down from the dumping area to the loading area

170–200 s loading rocks (2 300 kg)

200–420 s reversing back from the loading area to the dumping area

420–450 s dumping rocks

450–500 s idling

Actual machine design drawings were acquired from Sandvik to get accurate inertial

and mass data of the functional components of the EJC90.


The following figures from Figure 29 to Figure 32 present comparisons between the

measured cycle and the virtually simulated one, and Figure 28 the measurement points

where the curves are acquired. Comparisons are made at the power level from the front

and back shaft motors (Figure 29 and Figure 30, respectively), the drive pump (Figure

31), and the diesel engine of the machine (Figure 32). Noteworthy in these figures is

that the curves are basically similar, but in practice, there are two different operators

completing the same task. The simulated curves are aimed to be replicas of the

measured one. The diesel power curves presented in Figure 32 are different by that the

measured curve does not contain certain loss components, whereas the simulated one

does. This is due to the diesel engine model itself, where its rather simple nature

compels these losses to be calculated as part of the engine output power in order for the

fuel consumption estimate to take these losses into account.

M

Diesel

power

Pump

power

Motor

powers

Figure 28. Hydrostatic driveline measurement points. The diesel power is measured from the

mechanical output power, the pump power from the hydraulic line, and the hydraulic motors

from their output mechanical powers.


Figure 29. Front hydraulic motor power comparison between the measured and simulated

cycles.

Figure 30. Back hydraulic motor power comparison between the measured and simulated

cycles.

0 50 100 150 200 250 300 350 400 450 500-40

-20

0

20

40

60

80

Time [s]

P [

kW

]

Measured

Simulated

0 50 100 150 200 250 300 350 400 450 500-40

-20

0

20

40

60

80

Time [s]

P [

kW

]

Measured

Simulated


Figure 31. Drive pump power comparison between the measured and simulated cycles.

Figure 32. Diesel engine power comparison between the measured and simulated cycles.

It can be seen that the simulated power levels in the hydraulic motors and the drive

pump follow the measured data quite well, the only differences being in the regions

where the loading and unloading takes place. The simulated loading took place slightly

later than the measured one and was also faster, resulting in a more spike-like load,

while the measured one was more spread out (Figure 29 and Figure 30 at the time 180–

210 s).

The simulated diesel engine power in Figure 32 does not fully replicate the measured

data. However, the simulated model does not include any higher-level machine control

0 50 100 150 200 250 300 350 400 450 500-80

-60

-40

-20

0

20

40

60

80

100

120

Time [s]

P [

kW

]

Measured

Simulated

0 50 100 150 200 250 300 350 400 450 500-40

-20

0

20

40

60

80

100

120

Time [s]

Po

wer

[k

W]

Measured

Simulated


system, typically present in a real-life machine, which may explain the difference. This

is partially compensated with the idling loss estimations (which can be seen in Figure

32 as a static load), but some iterations of simulating the higher-level control of the

machine should be considered.

The energy-consumption-based accuracy estimations for the hydraulic motors are

presented in Table 6. The integrals for positive and negative powers are separated to

distinguish the behavior of the motors both in the motoring and generating modes.

Table 6. Energy consumptions of the hydraulic motors, separated into positive (+) and negative

energies () of the traction motors on the front (F) and rear (R) shafts. The values are integrated

from the power data above.

As it can be seen in Table 6, the positive-side energy consumptions are well matched at

a 2% difference. The negative-side energies, however, are somewhat different. This is

due to the low energy content, and even small irregularities result in a large relative

difference. On the positive axis, the simulated driveline results lie well within the initial

10% accuracy requirement. The same phenomenon can also be observed in Table 7,

where the energy consumption of the traction circuit hydraulic pump are presented.

Table 7. Energy consumption of the hydraulic pump, separated into positive and negative

energies.

Measured [MJ] Simulated [MJ] Difference [%]

EF+ 6.3 6.2 −2 %

EF- −0.7 −0.5 28 %

ER+ 6.0 6.1 2 %

ER- −0.6 −0.5 17 %

Measured

[MJ]

Simulated

[MJ]

Difference

[%]

EP+ 13 13.6 5%

EP- −1.2 −1.1 8%


Table 8. Energy consumption of the diesel engine, separated into positive and negative energies.

Table 9. Fuel consumption comparison of the measured and simulated cycles.

Measurement Simulation Relative

difference

Fuel consumption [l] 2.0 1.9 5%

Average fuel consumption

[l/h]

14.4 13.7 5%

According to Table 8 and Table 9, both the fuel consumption and overall energy

consumption of the diesel engine fall under the initial boundary of the 10% error. Even

though the driveline has some intermediate points where the errors are greater than

10%, the points where the impact is most significant, that is, the hydraulic motors and

the fuel consumption, the accuracy is equal to or under 5%, with the exception of the

negative powers of the hydraulic motors.

3.2.2 Hybridization

After the virtual model of the diesel-powered original LHD had been validated, the

machine hybridization potential could be analyzed. The original hydrostatic driveline

was stripped from the virtual model, along with the diesel engine. The hydraulic

systems operating the turning and bucket operations were left unchanged, and the

pumps previously connected to the diesel engine were driven by one electrical drive.

The energy efficiency of the hybridized LHD was analyzed by a total of fifteen separate

cycles, each of which was a repetition of the cycle used to verify the model in Section

3.2.1. As the driveline was now significantly modified compared with the original, the

similarity boundary conditions presented in Section 2.4 were adopted.

The hybrid cycles were recorded with the objective that the energy storage SoC was the

same at the start and end of the cycle. To achieve this along with the requirement of the

cycle length to be approximately the same with the recorded original cycle, some

iteration to the generator set operating point was made. The hybrid driveline generator

set was iterated to run in a stable 35.6 kW operating point, close to the 2-liter diesel

engine optimal efficiency point. The load sources for the hybrid driveline were the two

Measured [MJ] Simulated

[MJ]

Difference [%]

ED 22.9 21 8%


electrical drives located in the LHD front and rear shafts, the hydraulic pump drive

powering the steering and working hydraulics, and constant estimation of the idling

losses of the engine evaluated at approximately 13% of the engine maximum power. A

flowchart of the hybrid driveline is presented in Figure 33.

Diesel

Generator Set

Battery

Front Shaft

Electrical Drive

Rear Shaft

Electrical Drive

Hydraulic

System

Electrical Drive

Hydraulic

Pumps

Hydraulic

circuits (Bucket,

Lift, Steering)

Belt Gear Gearbox

Belt Gear

Mechanical

Rear Shaft and

Wheels

Front Shaft and

Wheels

Electrical

Figure 33. EJC90 hybrid driveline flowchart

Because each cycle was human-driven and thus slightly different from one another,

some SoC variations were observed during the hybrid cycle recordings. This difference

was compensated for by adjusting the fuel consumption according to the SoC

difference. The adjustment calculates for how much more (or less) time the generator

set should have been running in the selected operating point to achieve the SoC

equilibrium. As the static operating point efficiency is known, the corrected fuel amount

can be calculated from that time difference. A one percent difference in the SoC

correlates to approximately 38 milliliters of diesel oil. The adjustment of fuel

consumption and final results of the hybrid cycles are presented in Table 10 and Figure

34.


Table 10. Fuel consumption analysis of the hybrid EJC90 load cycles. The column ‘relative fuel

consumption’ gives the relative fuel consumption to the simulated fuel consumption of the

unhybridized LHD, presented in Table 9.

Time [s]

SoC

Difference

[%]

Fuel

Consumption

[l]

Fuel

consumption

adjustment

[l]

Adjusted fuel

consumption

[l]

Relative fuel

consumption

[%]

1 399 0.46% 0.987 −0.017 0.969 51.0%

2 386 −1.38% 0.954 0.052 1.006 52.9%

3 395 0.97% 0.977 −0.037 0.941 49.5%

4 392 −0.22% 0.969 0.008 0.977 51.4%

5 396 −0.56% 0.979 0.021 1.000 52.6%

6 401 0.46% 0.992 −0.017 0.975 51.3%

7 399 0.56% 0.986 −0.021 0.965 50.8%

8 392 0.00% 0.970 0.000 0.970 51.0%

9 391 0.18% 0.965 −0.007 0.958 50.4%

10 393 0.31% 0.972 −0.012 0.960 50.5%

11 402 0.83% 0.995 −0.032 0.964 50.7%

12 393 0.71% 0.972 −0.027 0.945 49.7%

13 406 1.56% 1.005 −0.059 0.946 49.8%

14 394 0.52% 0.973 −0.020 0.953 50.1%

15 397 0.90% 0.983 −0.034 0.949 49.9%

The hybridization was carried out according to the hybridization of the actual test

platform of the Tubridi project. The driveline was operated with two induction

machines, coupled to the front and back shafts through belt-driven gears. The back shaft

driveline also included a four-gear gearbox.


Figure 34 Simulated fuel consumptions of the EJC90 underground loader. A comparison of the

iterated unhybridized model and repetition of 15 cycles with the hybridized one.

Figure 34 shows a 49% fuel consumption reduction for the EJC90 underground loader,

when implementing the diesel-electric series hybrid driveline. This result is well in line

with the previous case of the LH410, with the respective value of 53% for both the

short- and long-route analyses.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Lit

ers

1.90

0.971.01

0.940.98 1.00 0.97 0.96 0.97 0.96 0.96 0.96 0.95 0.95 0.95 0.950.97

Diesel

Hybrid

57

4 Wheel loader

The wheel loader case demonstrates many similarities when compared with the

previously presented LHDs, mainly in terms of machine structure and its general

operation. The wheel loader comprises a rear and a front frame connected with a joint,

and it traverses in its operating environment on four wheels. The lift arms and the

bucket are attached to the front frame. The operation of the wheel loader in the selected

case resembles that of the LHD in the previous cases in the sense that it also moves

around in its operating area and uses its bucket to load and dump rocks. However, the

main difference in the cycles produced with a wheel loader is that the haul phase is

absent, and the loading and dumping take place at the same location and last for a

longer time because several buckets are loaded. The wheel loader under, which is not of

any particular brand but constructed more to be a generic testing and training model,

study is presented in Figure 35. The case is discussed in more detail in Publication IV.

Figure 35. Virtually simulated wheel loader.

4.1 Initial test platform for electrical drives

The wheel loader case study was conducted to test an early design of a PMSM with an

embedded 2-speed planetary gear, designed for wheel hub operation on non-road mobile

machinery [40] [41]. The main objective of the study was to analyze the PMSM

performance in the mechanical driveline of the wheel loader in a four-motor-drive

configuration, where each wheel was driven with one such machine. Since the machine

design and the hub motor research project strongly related to non-road mobile

machinery hybridization, also hybridization and fuel economy analyses were conducted

and collaborative research between separate projects achieved.

4 Wheel loader 58

The integrated planetary gear in the electrical drive was developed to minimize the

overall driveline volume in the hybridized or electrical machinery, without

compromising the performance of the machine. Because of the inherent and wide-range

torque production characteristics of the electrical drives in the angular velocity plane,

the planetary gear contains only two gears, one for high-torque operation and one for

high-speed operation. No additional gearboxes or clutches are required, only the final

reduction gear in the wheel hubs. An overview of the drive is presented in Figure 36.

Figure 36. Integrated planetary gear inside an electrical motor.

With the assistance of the presented virtual prototyping cosimulation environment, the

wheel hub reduction gear ratio could be iterated and the overall performance evaluated.

A flowchart of the driveline structure is presented in Figure 37. The analysis is based on

a simulated cycle consisting of uphill-downhill driving sections at the start and at the

end, and loading six buckets of rocks in the middle.

4.1 Initial test platform for electrical drives 59

PMSM(s)Hydraulic Pump

Drive(s)

Battery

MBD simulation environment

(Mechanics of the machine)

Socket IP

External Interface

Torque

Angular velocity

Reference (Control)

Variable(s)Angular Velocity

Load Torque

Diesel genset

+

Power

Power

Power

Mechanical

Driveline

Hydraulic

System

Figure 37. Wheel loader diesel-electric hybrid driveline structure.

With the proposed electrical drives and adopting the series hybrid driveline, the diesel

engine was statically operating at an optimal operating point, and the fuel consumption

was reduced by more than 50%. The two-gear construction of the electrical drives was

found to be sufficient for the machine operation. The test cycle power distribution is

presented in Figure 38.

4 Wheel loader 60

Figure 38. Wheel loader power distribution.

Figure 38 shows that the diesel generator set is again operated in a static operating

point. Owing to the nature of the cycle, being more burst-like in terms of the output

power requirement, the power output of the diesel generator set is as low as 27 kW. The

SoC curve for the same cycle is presented in Figure 39.

Figure 39. Wheel loader test cycle SoC.

0 50 100 150 200 250 300 350 400 450-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5x 10

5

Pow

er

[w]

T ime [s]

Traction power

Diesel genset

Hydraulics

0 50 100 150 200 250 300 350 400 45088

88.5

89

89.5

90

90.5

Time [s]

SoC

[%

]

4.1 Initial test platform for electrical drives 61

Figure 39 shows that the SoC of the battery experiences rapid power bursts from the

tractive driveline during the test cycle, but in general, the battery remains within less

than 2% DoD with respect to the initial value. The SoC difference at the end of the

cycle corresponds to roughly 45 ml of diesel fuel being consumed in the generator set.

The correction calculation is done similarly to the correction calculations presented in

Section 3.2.2.

63

5 Conclusions

Present-day batteries can store about 100 Wh/kg of electric energy while diesel fuel

stores about 10 kWh/kg. In a hybrid system, the chemical energy of the fuel can be used

more effectively than in a directly diesel-operated system. If the diesel engine generator

set can convert almost 40% of this chemical energy into electric energy, we get a

comparable value of 4 kWh/kg. Naturally, the electrical generator system slightly

reduces the value, yet the fuel storage is 40 times as efficient as present-day batteries in

the case of an optimally running diesel engine. Therefore, the fuel tank is still a superior

energy storage for a real non-road mobile machine, which must be capable of operating

long hours on a daily basis. Against this background, driveline diesel-electric

hybridization has been a rising trend in the mobile machine industry over the past few

years. The reduction in the operating costs of NRMMs, coming mainly from fuel

consumption and collateral costs, seems to outweigh the unit cost increase when

implementing for example diesel-electric hybrid drivelines to them.

The challenges with series hybridization, that is, the energy storage size compared with

the energy and power content, affect the popularity and hinder its breakthrough. On the

other hand, prototyping and testing these new prototypes are long processes and take

resources both in terms of time and money, with no guarantee that the prototypes will

actually work as desired.

Virtual simulations and virtual prototyping in general have proven to be powerful tools

in reducing the R&D process time and monetary costs. However, the accuracy and

verification of models have raised questions.

The research presented in this doctoral dissertation demonstrates both the effectiveness

of the virtual simulation as part of a cosimulation loop and the potential benefits of

hybridizing NRMMs. In three separate cases reported in this study, the fuel

consumption figures constantly showed roughly a 50% reduction in the consumed diesel

fuel without any reduction in the NRMM working capabilities.

5 Conclusions 64

5.1 Future research

The MBD environment has been shown to be a powerful instrument acting as a

dynamic load cycle source and a hybrid driveline iteration tool. With three different

case studies, the presented research demonstrates the effects of hybridization on the

energy efficiency of the machine, leaving the possible improvements in the machine

operation untouched.

Future research prospects in the area:

Further study of the implications of the dynamic load cycles. The boundary

conditions of the presented cases are extremely restrictive in terms of working

efficiency; thus, a further analysis of the similarity aspects of the cycles is

required to fully understand the effects of hybridization.

The presented equipment can be applied as a dynamic load in hardware-in-loop

simulations and used to drive and control prototype electrical drives. Even

whole diesel-electric drivelines could be constructed for example in the

laboratory and loaded through an MBD simulation environment. The advantages

and disadvantages of such capabilities could be studied.

65

6 Discussion

The objective of the research presented in this doctoral dissertation was mainly to

analyze how the presented cosimulation environment could be incorporated into hybrid

non-road mobile machine development and to give estimates with this environment of

the effectiveness of hybrid drivelines on the fuel efficiency of the machines. The system

simulated the given models in real time and, in general, suited well for its purpose. The

method was validated by modeling an underground loader according to a real-life

counterpart and comparing the model performance against a recording of the real

machine. Based on the results, the cosimulation environment replicated the behavior of

the original machine well, and the objective concerning the usability was thus achieved.

The presented cases of hybridization showed a roughly 50% fuel consumption reduction

without degrading the machine performance. The machine type used in the cases

seemed to adapt well to hybridization, and it can thus be concluded that machinery that

operates in a relatively stable environment with low, close to idling diesel operation but

still requires a substantial amount of peak power would benefit greatly from series

hybridization. This kind of machinery can utilize regenerative braking both in flat

ground operation and in longer downhills. The energy buffer that the battery packs offer

allows the diesel engine to be downsized to the optimal operation in the average power

demand point of the machine, which eliminates the load fluctuations and ensures a high

fuel efficiency of the machine. Machine cases where the environment is softer or

otherwise more resistant, such as harvesters or forwarders, could achieve enhanced fuel

economy, yet not to the same degree. In the forestry machine cases, the tractive power

demand is still highly alternating and pulse-like, and using an electrical drive to handle

the power variations would benefit the machine fuel economy, but the machine

operation cannot utilize kinetic energy recovery and the diesel downsizing aspects of

hybridization that well. Some individual machine movements can be electrified and

energy from those movements regenerated, but in this machinery group the optimal

operation constitutes the majority of the fuel consumption reduction.

For some machine types, diesel-electric hybridization is not a preferable option. These

machines already incorporate some form of load balancing, for example through a

hydraulic accumulator, and have movements in which energy recovery is not possible.

One example of this machine type is a piling machine. While the machine block can

have a mass of over 15 tons, the potential energy it initially has is transferred into the

pile. While in operation, the hydraulic circuit of the hammer contains a load-balancing

accumulator, stabilizing the load towards the diesel engine. When operating, the

machine itself does not move aside from minute micromovements and the piling.

When considering hybridization, its cost must naturally be taken into account. Even

though the cost of the hybridized machine or the cost of converting it into a hybrid one

is outside the scope of this study, Publication IV demonstrated a crude estimation of

conversion hybrid assembly cost as well as the break-even point. When the machine is

highly suitable for hybridization, such as the wheel loader in Publication IV, the break-

Discussion 66

even point of three to four years can be achieved based on the fuel cost only. The

simulations were made with arguably oversized battery packs to counteract the

degradation of the cells, thereby increasing the initial cost but basically eliminating the

need for battery replacements due to degradation.

Even though the fuel consumption reduction is consistent between the cases under

study, and even though the models are accurately modeled, the machine fuel

consumption reduction is somewhat lower in real life. When setting up virtual models

of the machines, the tire-environment interaction plays a great role in defining the

power required in the tractive driveline to move the machine. When modeling the

working environment digitally, there are naturally some limitations on how uneven the

surfaces can be, especially in contact graphics. This tends to lead to more evened-out

surfaces for the machine to drive on, the overall feel of the surface being harder (in the

sense of having a lower rolling resistance) than in real life. In the presented simulations

this has been counteracted in the tire model to provide sufficient resistance and to more

correctly load the driveline connected to the tire, but this simplification evens out the

variations throughout the operational environment to one level.

Nowadays, instead of using contact stiffness and single contact surface, there are

methods to make the tire-ground interaction more sophisticated by using heightfield-

based modification of the ground. This means that the ground will yield and transform

under the machine tires and provide more realistic interaction and loads to the tires in

order to increase the accuracy of the fuel consumption calculations. Naturally,

deforming ground requires a lot more computational power, which must be taken into

account for example in reaching the real-time constraint. In the time of the writing of

this dissertation, the calculation power of high-end computers has reached a level where

this is no longer that much of an issue, and heightfield-based environment interaction

can quite freely be implemented.

The presented methodology relies on virtual simulation and is thus aimed for separate,

operator-driven load cycles. The absence of the traditional method of using prerecorded

curves as a load source instead of a dynamic environment calls for new sets of

boundaries to ensure simulation similarity between any two cycles, in order for the

results to be comparable with each other. In a nutshell, these boundary conditions

require two comparable cycles with the same time span, the same work done, and the

same route driven. The presented approach lowers the repeatability to some extent, but

the accuracy of the results by letting the machine operate in a natural way while

controlling the operation from a higher level is greatly increased and the digital

prototyping can be applied further. The presented boundary conditions are specifically

chosen for the needs of the energy efficiency and verification studies, but naturally,

these boundaries vary depending on the focus of the virtual prototype usage.

67

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Publication I

Nokka, J., Montonen, J.-H., Bin Baharudin, E., Immonen, P., Rouvinen, A., Laurila, L., Lindh, T., Mikkola, A., Sopanen, J., and Pyrhönen, J.

Multi-Body Simulation based Development Environment for Hybrid Working machines

Reprinted with permission from International Review on Modelling and Simulations (IREMOS)

Vol. 8, No. 4pp. 466–476, 2015 © 2015, Praise Worthy Prize

Publication II

Nokka, J., Laurila, L., and Pyrhönen, J. Virtual simulation in energy efficient hybrid powertrain design

Reprinted with permission from Proceedings of EPE 2014 ECCE Europe, 16th European Conference on Power

Electronics and Applications © 2014, IEEE

Publication III

Nokka, J., Laurila, L, and Pyrhönen, J. Virtual simulation –based underground loader hybridization study – comparative

fuel efficiency and working capability analysis

Reprinted with permission from International Review on Modelling and Simulations (IREMOS)

Vol. 10, No. 4, pp. 222–231, 2017 © 2017, Praise Worthy Prize

Publication IV

Montonen, J., Nokka, J., and Pyrhönen, J. Virtual Wheel Loader Simulation - Defining the Performance of Drive-Train

Components

Reprinted with permission from International Review on Modelling and Simulations (IREMOS)l

Vol. 9, No. 3, pp. 208–216, 2016 © 2016, Praise Worthy Prize

Publication V

Baharudin, E., Nokka, J., Montonen, J.-H., Immonen, P., Rouvinen, A., Laurila, L., Lindh, T., Mikkola, A., Sopanen, J., Pyrhönen, J.

Simulation Environment for the Real-Time Dynamic Analysis of Hybrid Mobile Machines

Reprinted with permission from Proceedings of ASME 2015 International Design Engineering Technical Conferences &

Computers and Information in Engineering Conference (IDETC/CIE 2015) © 2015, ASME

Publication VI

Lindh, T., Montonen, J.-H., Niemelä, M., Nokka, J., Laurila, L., and Pyrhönen, J. Dynamic performance of mechanical-level hardware-in-the-loop simulation

Reprinted with permission from Proceedings of EPE 2014 ECCE Europe, 16th European Conference on Power

Electronics and Applications © 2014, IEEE

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749. KASHANI, HAMED TASALLOTI. On dissimilar welding: a new approach for enhanced decision-making. 2017. Diss.

750. MVOLA BELINGA, ERIC MARTIAL. Effects of adaptive GMAW processes: performanceand dissimilar weld quality. 2017. Diss.

751. KARTTUNEN, JUSSI. Current harmonic compensation in dual three-phase permanentmagnet synchronous machines. 2017. Diss.

752. SHI, SHANSHUANG. Development of the EAST articulated maintenance arm and an algorithm study of deflection prediction and error compensation. 2017. Diss.

753. CHEN, JIE. Institutions, social entrepreneurship, and internationalization. 2017. Diss.

754. HUOTARI, PONTUS. Strategic interaction in platform-based markets: An agent-based simulation approach. 2017. Diss.

755. QU, BIN. Water chemistry and greenhouse gases emissions in the rivers of the "ThirdPole" / Water Tower of Asia". 2017. Diss.

756. KARHU, PÄIVI. Cognitive ambidexterity: Examination of the cognitive dimension indecision-making dualities. 2017. Diss.

757. AGAFONOVA, OXANA. A numerical study of forest influences on the atmospheric boundary layer and wind turbines. 2017. Diss.

758. AZAM, RAHAMATHUNNISA MUHAMMAD. The study of chromium nitride coating by asymmetric bipolar pulsed DC reactive magnetron sputtering. 2017. Diss.

759. AHI, MOHAMADALI. Foreign market entry mode decision-making: Insights from real options reasoning. 2017. Diss.

760. AL HAMDI, ABDULLAH. Synthesis and comparison of the photocatalytic activities of antimony, iodide and rare earth metals on SnO2 for the photodegradation of phenol and its intermediates under UV, solar and visible light irradiations. 2017. Diss.

761. KAUTTO, JESSE. Evaluation of two pulping-based biorefinery concepts. 2017. Diss.

762. AFZALIFAR, ALI. Modelling nucleating flows of steam. 2017. Diss.

763. VANNINEN, HEINI. Micromultinationals - antecedents, processes and outcomes of themultinationalization of small- and medium-sized firms. 2017. Diss.

764. DEVIATKIN, IVAN. The role of waste pretreatment on the environmental sustainability of waste management. 2017. Diss.

765. TOGHYANI, AMIR. Effect of temperature on the shaping process of an extruded wood-plastic composite (WPC) profile in a novel post-production process. 2017. Diss.

766. LAAKKONEN, JUSSI. An approach for distinct information privacy risk assessment.2017. Diss.

767. KASURINEN, HELI. Identifying the opportunities to develop holistically sustainablebioenergy business. 2017. Diss.

768. KESKISAARI, ANNA. The impact of recycled raw materials on the properties of wood-plastic composites. 2017. Diss.

769. JUKKA, MINNA. Perceptions of international buyer-supplier relational exchange. 2017.Diss.

770. BAYGILDINA, ELVIRA. Thermal load analysis and monitoring of doubly-fed wind powerconverters in low wind speed conditions. 2017. Diss.

771. STADE, SAM. Examination of the compaction of ultrafiltration membranes withultrasonic time-domain reflectometry. 2017. Diss.

772. KOZLOVA, MARIIA. Analyzing the effects of a renewable energy support mechanism on investments under uncertainty: case of Russia. 2017. Diss.

773. KURAMA, ONESFOLE. Similarity based classification methods with differentaggregation operators. 2017. Diss.

774. LYYTIKÄINEN, KATJA. Removal of xylan from birch kraft pulps and the effect of its removal on fiber properties, colloidal interactions and retention in papermaking. 2017.Diss.

775. GAFUROV, SALIMZHAN. Theoretical and experimental analysis of dynamic loading of a two-stage aircraft engine fuel pump and methods for its decreasing. 2017. Diss.

776. KULESHOV, DMITRII. Modelling the operation of short-term electricity market inRussia. 2017. Diss.

777. SAARI, JUSSI. Improving the effectiveness and profitability of thermal conversion of biomass. 2017. Diss.

778. ZHAO, FEIPING. Cross-linked chitosan and β-cyclodextrin as functional adsorbents inwater treatment. 2017. Diss.

779. KORHONEN, ILKKA. Mobile sensor for measurements inside combustion chamber – preliminary study. 2017. Diss.

780. SIKIÖ, PÄIVI. Dynamical tree models for high Reynolds number turbulence applied in fluid-solid systems of 1D-space and time. 2017. Diss.

781. ROMANENKO, ALEKSEI. Study of inverter-induced bearing damage monitoring invariable-speed-driven motor systems. 2017. Diss.

782. SIPILÄ, JENNI. The many faces of ambivalence in the decision-making process. 2017.Diss.

783. HAN, MEI. Hydrodynamics and mass transfer in airlift bioreactors; experimental and numerical simulation analysis. 2017. Diss.

784. ESCALANTE, JOHN BRUZZO. Dynamic simulation of cross-country skiing. 2017. Diss.

Acta Universitatis Lappeenrantaensis

785

ISBN 978-952-335-192-9 ISBN 978-952-335-193-6 (PDF) ISSN-L 1456-4491ISSN 1456-4491 Lappeenranta 2018

ENERGY EFFICIENCY ANALYSES OF HYBRID NON-ROAD MOBILE ...

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