Optimierung der vorausschauenden Antriebssteuerung bei einem Plug-In Hybrid
Bastian GINDROZ
Master of Science Thesis
at the Royal Institute of Technology, KTH Department of Vehicle Engineering
Stockholm, Sweden
Dates 16 September 2013 - 14 March 2014
Firm Audi AG Ingolstadt
I/EA-24
Local supervisor Boris BLASINSKI
Supervisor at KTH Daniel WANNER
Examiner at KTH Lars DRUGGE
Supervisor at ISAE-SUPAERO Joseph MORLIER
TRITA-AVE 2014:22
Over the past years, the regulations in terms of pollutants stimulated the industries’ innovation in almost all regions of the world, using directives such as the European emission standards have managed to limit the carbon dioxide emissions up to 82% between Euro 1 and Euro 4.
Between research towards new fuels and the improvement of internal combustion engines (ICE), one solution stands out: combining an electrical machine with a traditional ICE to reduce the environmental impact and the fuel consumption. The most advanced level of hybridization is the Plug-In Hybrid Vehicle (PHEV), which usually has a battery capacity allowing it to drive all electric on short ranges and can be recharged at a power socket. This technological solution opens to a wide range of possibilities and improvements in terms of fuel consumption, as well as commercial opportunities.
This master thesis, conducted at Audi AG in Ingolstadt (Germany) will focus on one development feature of Plug-In Hybrid Vehicles: the predictive drive strategy. Driving all electric everywhere would be the best solution in terms of fuel economy. However, a PHEV does not always allow it, especially on long rides. Mainly three drive modes are available: an electric mode that will use the ICE as seldom as possible, a hybrid mode that allows electrical experience at low speed and uses the ICE for higher needs in power, and a charge mode aiming at recharging the battery through the ICE. Some enhanced algorithms are also able to detect the different environments the vehicle is expected to drive through, and compute fuel and electrical consumption estimates for each modes available. Based on this information, an optimizer will determine the best drive strategy - i.e the best temporal combination of modes - in terms of fuel consumption, but also taking into account some external requirements, such as Zero Emission Zones that might come up along the route.
The method used to compute the best drive strategy relies on four main stages. A first step is to identify the different driving environments constituting the route, based on long-range power predictions. This can be done using statistical comparison methods based on the assumption that one driving environment can be characterised by a range of values for a single parameter. The interval the parameter belongs to will determine the driving environment. The Kolmogorov-Smirnov test function is the second method used, which compares the cumulative distributions of two series of values and assesses their similarity. Once the itinerary is properly split into segments, it is possible to compute the expected fuel and electrical consumptions by taking the drivetrain efficiency chain, the regenerative braking and E-boost characteristics into account. For each section, a high- priority drive mode can also be given in order to allow a more flexible optimization in terms of coding and algorithmic. Mainly two prioritization options have been studied in this thesis. One pertains to the use a finite number of identified driving environments, to which a prioritization of the driving mode has been performed offline beforehand. The second option, the online priority, uses realtime computed values for a given segment and compares them in order to find the mode to be prioritized. Finally, two optimizers have been developed to compute an efficient drive strategy. The tree-based global optimizer considers the drive mode combinations for the whole route and selects the most efficient one, while a simplified optimizer would only improve a given basic strategy to reduce the fuel consumption.
The results of the comparison methods show interesting perspectives for the Kolmogorov-Smirnov test func- tion, as it only tells two segments apart without trying to identify the environment they belong to, hence a faster implementation. The parametric method proved its efficiency when working with a dual-environment condition, although an extension to more than two environments would make the whole interval computation process much more complex. Then, the consumption computation processes have been precisely defined. Fi- nally, both optimizers gave interesting results. The global optimizer is very complex algorithm which requires considerable CPU resources. However, it provides absolute optima to the optimization problem that will be used to observe the trend, behaviour and accuracy of some drive strategies. The simplified optimizer is a suggestion of a faster algorithm that does not computes the best solution but rather a good one. If the quality of the resulting solution is very route-dependant, it can provide drive strategies very close to the best ones computes by the global optimizer.
Warm thanks to my engineering schools in Stockholm (KTH) and in Toulouse (ISAE-SUPAERO) and their great teachers, for giving me the knowledge and passion without which I would never have been able to conduct this work. A special thank to my examiner Lars Drugge and my supervisor Daniel Wanner at KTH, whose support and help were simply flawless.
I would like to thank all my colleagues from the department EA-242 at AUDI for always being friendly, caring and helpful. More particularly to my direct colleagues in the «Pr¨adiktive Betriebsstrategie» Team: Boris Blasinski, Markus Kaun, Florian M¨uhlfeld and Stefan Weinzierl. Finally, this master thesis would not even have existed without the devotion and strong support of my supervisor at Audi, Boris Blasinski. A big thank to you Boris, for our very interesting discussions and the role of advisor you perfectly played.
Finally, I cannot forget to thank warmly my two families. My German host family, Angelika, Antonia, Joachim and Sugar for accommodating me in Ingolstadt during these six months and making me feel like home!
And of course, thanks to my mother, father and brother for supporting me through all of my studies.
1 Introduction 1
1.1 Hybrid cars as an answer to a global trend . . . 1
1.1.1 Environmental context and global trends . . . 1
1.1.2 Regulations and standards . . . 2
1.1.2.1 European regulations . . . 2
1.1.2.2 Pollutant formation . . . 4
1.1.3 Hybrid cars . . . 5
1.1.3.1 Electricity generation . . . 6
1.1.3.2 Hybrid technology . . . 6
1.2 Hybrid technology at Audi . . . 14
1.2.1 Audi AG . . . 14
1.2.2 Hybrid technology . . . 16
1.2.2.1 Electric Drivetrain Development in the R&D . . . 16
1.2.2.2 Audi hybrid models and technologies . . . 17
2 Towards the optimal PDS: introduction 21 2.1 Principle . . . 21
2.2 State of the art . . . 22
2.2.1 Research studies . . . 22
2.2.2 In the industry . . . 23
2.2.2.1 BMW: Predictive Power Management . . . 23
2.2.2.2 BMW: Anticipatory Energy Management . . . 24
2.2.2.3 Daimler: predictive drive strategy for E-trucks . . . 24
3 Towards the optimal PDS: methods 25 3.1 Measurements and datasets . . . 25
3.1.1 Test car: Audi A6 Hybrid . . . 25
3.1.2 Measured parameters . . . 25
3.2 Simulink model . . . 27
3.2.1 Input: road profiles . . . 27
3.2.2 Power calculation . . . 29
3.2.3 Gearbox . . . 30
3.2.4 Energy Management System . . . 31
3.2.5 Consumption and emissions . . . 31
3.3 Energy vs Power distribution . . . 32
3.4 Segmentation . . . 33
3.4.1 Kolmogorov-Smirnov test . . . 34
3.4.2 Statistical parametric comparison . . . 35
3.5 Fuel and electrical consumptions . . . 36
3.6 Priorities . . . 39
CONTENTS CONTENTS
3.6.1 Offline prioritization . . . 39
3.6.2 Online prioritization . . . 41
3.7 Optimizer . . . 41
3.7.1 Global Optimizer . . . 42
3.7.2 Simplified Optimizer . . . 44
4 Towards the optimal PDS: results 47 4.1 Segmentation . . . 47
4.2 Fuel and electrical consumptions . . . 54
4.2.1 Index i0 and i1 . . . 54
4.2.2 Conversion values . . . 56
4.3 Optimal drive strategy . . . 57
4.3.1 Global optimum . . . 57
4.3.2 Enhanced basic strategy . . . 61
4.4 Resulting fuel savings . . . 63
5 Conclusion 65 5.1 Predictive drive strategy implementation . . . 65
5.2 Future work . . . 66
6 Bibliography and tables 68 7 Appendix 77 7.1 Complementary Information about Audi . . . 77
7.1.1 The car fleet . . . 77
7.2 Algorithm implementations . . . 79
7.2.1 Global optimizer: C-code . . . 79
7.2.1.1 Tree construction: TreeBuilding.h and TreeBuilding.c . . . 79
7.2.1.2 Tree analysis: TreeAnalysis.h and TreeAnalysis.c . . . 81
7.2.1.3 Main: main.c . . . 82
7.2.2 Global optimizer: ASCET . . . 84
Introduction
1.1 Hybrid cars as an answer to a global trend
1.1.1 Environmental context and global trends
Over the past decades, mobility has become a central concept for the society. Being able to move independently, quickly and almost anywhere is today more than just a possibility. It is a necessity. Even if several solutions exist to transport people from one place to another, ground vehicles, such as trains, cars and buses, are today the most affordable and flexible option that is offered for short- and mid-range travels. However, public transportation allows less flexibility and freedom than individual transportation. Individualism is also slowly taking over social bounds, so that travelling alone or with a restricted group of acquaintances has become a common and logical luxury. The consequence of these attributes of a modern society is that private means of transportation are today more popular than ever.
Cars represent an appropriate answer to these desires. According to recent figures [1], the world’s vehicle population exceeded one billion units already in 2010. Even though this number also includes trucks and buses, cars would represent 70% of this amount. The emerging countries are, to this extent, a quite interesting indicator of the world dynamic. A widely used example is of course China, with its 240 million vehicles1in 2013. If this only represents some 86 vehicles per thousand inhabitants, which is way below USA’s 800 vehicles per 1000 persons [2], some projections show that the Chinese level will top today’s American one by 2030 [3]. Most of automakers aim at taking advantage of this trend, whose effects are already visible on their sales records. For instance, Audi sold 21% more vehicles in China in 2013 than in the previous year [4], breaking its all time record in this region of the world with almost 500 000 deliveries.
A large majority of the vehicles on the roads is equipped with traditional internal combustion engines. The increasing petrol demand will eventually use up all of the existing oil resources. The only question is about when it will occur. Exact figures regarding the world remaining oil resources do not exist, although some estimates reckon that the reserves are expected to deplete within 40 to 120 years. Transportation is in the front line and accounts for more than 61% of the yearly petroleum consumption [5], 85% of which for land transportation.
During the conversion of gasoline or diesel to mechanical energy through the combustion process, greenhouse gases (GHG), toxic chemicals and irritating particles are released. According to the European Commission, 12%
of Europe’s carbon dioxide emissions stem from cars, which is somewhat problematic as CO2 has one of the biggest contributions to the greenhouse effect among all GHG. In comparison, road transport has the second largest share in Europe, a few percents behind power generation.
The combustion from a chemical point of view will be dealt with while discussing the emission regulations and standards in subsection 1.1.2. Hybrid cars, which are vehicles powered by two types of energies, come often as an environmental solution. A description of the electrical hybrid technologies will follow in subsection 1.1.3, with a special focus on Audi and its hybrid developments in section 1.2.
1Statement of the Chinese Ministry of Public Security, 30/01/2013
CHAPTER 1. INTRODUCTION 1.1. HYBRID CARS AS AN ANSWER TO A GLOBAL TREND
Chapter 2 will first present the principle of a predictive drive strategy in section 2.1 and then a state of the art in the research publications and the industry in section 2.2. The methods used to compute a predictive drive strategy will be presented in 3, followed by the results in chapter 4. Finally, the conclusion chapter 5 will wrap up the thesis while suggesting directions for future works in the area of predictive drive strategies.
1.1.2 Regulations and standards
Considering the extent of these environmental and economic challenges, regulations have been introduced.
However, such changes in the industry and the people’s mindset are not instantaneous, and always present an economic risk for the automakers. If the innovation will be driven by the research and development departments of each car company, nothing will ever happen without a strong regulatory context ruling and monitoring this evolution. Therefore, regulations and standards aim at controlling, stabilizing and possibly decreasing the scale of these challenges.
1.1.2.1 European regulations
As far as emissions are concerned, two tools are usually used by the relevant institutions:
• Regulation targets, applied to automakers’ strategies. They set a goal to reach and the time extent allowed for fulfilling the target.
• Regulation standards focus more specifically on the emission performance of each vehicle. They have a defined date as of which they take effect and a set of emission levels that each car has to follow.
Even if an international emission regulation rule does not exist today, almost all vehicles of the developed countries obey to some emission regulation sets.
European emission standards are among the most widespread regulations in the world. Elaborated and set by the European Union by means of directives since 1991 [6], the Euro regulation standards are not limited to the old continent anymore. Many countries model on this set of directives to implement their own, especially developing countries such as China [7] and India [8].
Table 1.1: European emission standards for a passenger car (g/km), Diesel.
Tier Date CO THC NMHC NOx HC+NOx PM
Euro 1 July 1992 2.72 - - - 0.97 0.14
Euro 2 January 1996 1.0 - - - 0.7 0.08
Euro 3 January 2000 0.64 - - 0.50 0.56 0.05
Euro 4 January 2005 0.50 - - 0.25 0.30 0.025
Euro 5 September 2009 0.50 - - 0.180 0.230 0.005
Euro 6 September 2014 0.50 - - 0.080 0.170 0.005
Table 1.2: European emission standards for a passenger car (g/km), Gasoline.
Tier Date CO THC NMHC NOx HC+NOx PM
Euro 1 July 1992 2.72 - - - 0.97 -
Euro 2 January 1996 2.2 - - - 0.5 -
Euro 3 January 2000 2.3 0.20 - 0.15 - -
Euro 4 January 2005 1.0 0.10 - 0.08 - -
Euro 5 September 2009 1.0 0.10 0.068 0.060 - 0.005
Euro 6 September 2014 1.0 0.10 0.068 0.060 - 0.005
As shown on Tables 1.1 and 1.2, the regulations have become stricter since the introduction of Euro 1 in 1992, stressing the growing necessity to reduce the ecological footprint of vehicles.
The pace of emission reductions and worldwide long-term plans enabled the European Union to set regulation targets. These directives aim at setting a framework for the automotive industry and the coming passenger car fleets. Regulation 443/2009 [9] has for instance defined a two-stage process to reduce GHG emissions of 20% by 2020. Each car manufacturer will get an annual specific emission target based on its average fleet weight [10]:
Specific emission of CO2= 130 + a × (M − M0) (1.1) where:
M is the average mass of the manufacturer’s fleet (in kg) M0 is the reference mass (1372.0 kg in 2012)
a is 0.0457
For instance, Audi AG’s average mass was 1579.0 kg in 2012, which led to a 2012 target of 139 g/km CO2. However, individually, the car manufacturer had already comfortably met this requirement with an average CO2
emission of 122 g/km [10].
The average of the specific CO2 emissions over all manufacturers is nothing but the EU 2015 target of 130 g CO2/km. Until 2015, only a certain percentage of each manufacturer’s fleet will be taken into account for the average (65% in 2012, 75% in 2013, 80% in 2014 and 100% in 2015-2016). During this period of time, the phase-in will only concern the best-performing registered cars. Super-credits are also given to vehicles emitting less than 50 g CO2/km, giving a bigger weight to electric or very efficient hybrid vehicles in the fleet average.
Even if this super-credit system has been criticized for misrepresenting the real carbon performance of car manufacturers [11], they constitute one more tool in favour of car hybridization.
Audi AG, with an individual target of 139 g/CO2 is one of the 9 manufacturers that had already met their 2015 specific CO2emission target in 2012. Audi AG is currently part of a joint pool with the VW group. The pool system allows manufacturers to form an alliance that will be considered as one entity with one specific CO2
emission target. Table 1.3 shows the different performances within the VW pool. By doing that, the advance of some car manufacturers regarding their target might be used to compensate other high-carbon fleets within the pool. This is often used between regular and sport brands, like in the BMW Group pool between Bayerische Motoren Werke AG and BMW M GmbH.
Table 1.3: Distance to target for the VW pool in 2012 [10].
Manufacturer Average emissions [g CO2/km]
Target [g CO2/km]
Distance to target [g CO2/km]
Audi AG 122 139 -17
Audi Hungaria Motor KFT 137 134 3.7
Bentley Motors Ltd 310 181 129
Bugatti Automobiles S.A.S 539 157 382
Automobili Lamborghini S.p.A 344 144 199
Dr. Ing. h.c. F. Porsche AG 188 153 35
Quattro GmbH 219 147 72
Seat SA 115 127 -12
Skoda Auto AS 120 127 -6.6
Volkswagen AG 119 131 -12
VW Group PC 120 132 -12
The EU target for 2020 is 95 g CO2/km (already mentioned in the Regulation 443/2009), with a weight slope (see formula 1.1) of a=0.0333 that should be confirmed in an amendment to be issued soon. Based on their previous CO2emission reductions, some car manufacturers are already on good track to reach this target, such as Toyota with an overall progress required in the fleet CO2 emissions of 3.4% by 2020 to be compared with the 4.6% that the Japanese firm has achieved over the past 5 years.
CHAPTER 1. INTRODUCTION 1.1. HYBRID CARS AS AN ANSWER TO A GLOBAL TREND
Some local and national initiatives exist as well to further reinforce the European emission policy. Several governments financially support the development of low emission vehicles, either by funding R&D programs such as in the UK, or by offering a range of purchase rebates depending on the car’s CO2 emission level, like in France and Spain. Finally, some countries also grant EV and HEV vehicles very attractive advantages such as road tax and toll exemption and free parking in the cities [12].
The combination of regulation standards and targets creates a monitoring context. Emissions restrictions aim at controlling both current car environmental performances and the automotive industry’s strategy in the medium term.
The requirements of such an ambitious regulation tactic would not have been met without tremendous improvements in the engine efficiency, as well as exhaust treatment systems.
1.1.2.2 Pollutant formation
Internal combustion engine functioning is based on the transformation of chemical energy into mechanical work.
The chemical energy is provided by the combustion of fuel with air, during which reagents are consumed and products appear. As the combustion takes place in the combustion chamber, these newly created components will be evacuated through the exhaust and ultimately sent out in the atmosphere.
Classically, the complete combustion of gasoline in the air could be written as follows:
C8H18+ 12.5 (O2+ 3.773N2) → 8CO2+ 9H2O + 47.16N2 (1.2) However, combustions in traditional engines are far to be complete and perfect. The type and proportion of exhaust gases depend on the combustion reaction parameters: temperature, type of fuel, reagent proportions.
For fuels such as gasoline and diesel, the main emissions are unburned hydrocarbons (HC), nitrogen oxides (NOx), carbon monoxide (CO) and soot [13]. In addition to these, nitrous oxide (N2O), methane (CH4), ammoniac (NH3) and urea will also be found. Biodiesel fuels usually leave less unburned hydrocarbons because the fuel already contains oxygen. However, the NOx proportions increase with that type of fuel.
The main exhaust gases and their effects on health and nature are described below:
• Nitrogen oxides: they are produced in high temperature conditions, provided that there is nitrogen and enough oxygen to oxidise it [14]. Because NOx react with moisture and sunlight, then can cause respiratory problems when inhaled. NOx emissions also lead to a mechanism which destroys ozone in the stratospheric layers.
• Carbon monoxide: CO is produced in big quantities during the combustion of rich fuel/air mixtures, because of a lack of oxygen to form CO2. The risks for health are significant, especially since this gas has no specific smell or color. When inhaled, CO reduces the ability of the body to convey oxygen, as hemoglobins are affected. On a larger scale, carbon monoxide is a short-life atmospheric pollutant which contributes to form smog in urban areas.
• Hydrocarbons: HC come from completely unburned fuel, for one third [15], and partly burned fuel. Some of them end up in the exhaust gases due leaks and quenching phenomena. Such chemical components are toxic and carcinogenic to humans, and may lead to lungs and heart diseases. They also help in the formation of low level ozone, which both generates smog and an enhanced green house effect.
• Particle matter: particle matter and soot are typical of Diesel combustions. Because they are bigger than the other pollutant existing in the exhaust gases, they can cause lung irritations and lead to cancers.
Their effect on nature is also devastating, as such particles indirectly block the photosynthesis process [13].
• Carbon dioxide: CO2 is one common and natural gas. It comes as a normal combustion product (together with water). Carbon dioxide is also one major green house gas and leads to ocean acidification.
However, CO2 does not represent a threat for nature and human in normal concentrations.
• Sulfur oxides: Fuels generally contain sulfur compounds, which are oxidized and released in the atmo- sphere after the combustion. Sulfur oxides are major contributors to rain and ocean acidification.
• Nitrous oxide: adding N2O in the combustion chamber increases the level of oxygen, allowing to burn more fuel for a more powerful combustion. Nitrous is mostly used in racing cars. But it plays also a potent role in the greenhouse effect and the air pollution. Nitrous oxide is also known as laughing gas.
• Methane: CH44 emissions come from incomplete fuel combustions [16]. Methane has a significant impact on the environment, as a significant greenhouse gas.
• Ammoniac: In a SCR (Selective Catalytic Reduction) system, the thermolysis and hydrolysis of urea produces NH3 [17]. A failing in the SCR could cause a release of ammoniac through the exhaust. This chemical compound reacts with organic matter, such as human skin, which makes it dangerous to manip- ulate without accurate protection.
• Urea: Injected shortly upstream the SCR catalyst, urea’s aim is to transform the NOx into ammoniac.
The NH3 will then be involved in a reaction (intermediate, fast or slow) that will eventually consume it to produce water and dinitrogen. Urea is not particularly dangerous for humans, as the body produces this molecule.
Tremendous technological improvements have stemmed from the need of a cleaner exhaust, confirming the stimulating role of environmental standards for the car industry.
Gasoline
GDI, Gasoline Direct Injection, is based upon a fuel injection directly inside the combustion chamber. The idea is to obtain a better control of the combustion by modifying parameters shortly before, during and after the combustion. Significant advantages of this technology are the regulation of the combustion temperature and the possibility to stratify the charge [15]. The mix could then be around lambda 1 near the spark, meaning that the air and fuel are close to the stoichiometric proportions, and lean everywhere else. Besides, as the fuel is directly injected inside the cylinder, it evaporates there too. Since an evaporation consumes energy (heat in this case), the combustion will be colder. This reduces the risk of knocking and the production of NOx as well.
A 3-way catalytic converter is a post-cylinder system that oxidizes HC and CO and reduces NOx. To work properly, the combustion needs to be close to lambda 1. Basically, the catalyst stores oxygen and uses it to reduce the hydrocarbons and the carbon monoxide.
Diesel
Some techniques used to combat the NOx emissions for Diesel engines are [18]:
• injection delay, leading to a later combustion during the expansion, hence lowering the temperature and the amount of NOx produced.
• use of a pre-chamber in which the combustion starts and where the the conditions are stoichiometric due to the low amount of air available. However, smokes produced during the start of combustion will need to be eliminated later on.
• intake air cooling, usually through an intercooler, which will lower the combustion maximal temperature and thus reduce the NOx emissions.
• Exhaust Gas Recirculation (EGR), which consist of mixing exhaust gases to the intake air. This dilution will reduce the oxygen concentration and lower the combustion temperatures.
• a selective catalytic reduction in which Urea is injected to reduce efficiently NOx into N2 [19].
Moreover, particulate matter and soot concentrations can be significantly reduced by using particle filters.
These filters will either heat and inject fuel to burn the soot externally, or use internal disposable coating to catch soot particles.
1.1.3 Hybrid cars
If the previously mentioned exhaust controls strategies have had a significant effect on the environmental impact of a car, treating the problem at its source is theoretically much more interesting. This thought led to the investigation of new alternative fuels as well as other sources of energy. Biofuels, alcohol fuels, hydrogen, compressed air have better environmental performances in terms of emissions. By replacing the tank with a
CHAPTER 1. INTRODUCTION 1.1. HYBRID CARS AS AN ANSWER TO A GLOBAL TREND
battery, a no-emission solution for transports would be offered through the use of an electrical machine, though not totally a clean product when considering the footprint of electricity generation. A compromise solution consists of combining an ICE and a electric drivetrain to reduce the fuel consumption as well as meeting the emission standards dictated on European and local scales.
1.1.3.1 Electricity generation
The most widespread and industrially used electricity generation solutions are nuclear powerplants, fossil fuel power stations, hydropower, wind and solar energy.
Fuel per kWh is cheaper than electricity, approximately half its price in Germany. However, with the bigger efficiency of electrical systems, a larger part of the incoming energy (electricity) is converted to outgoing energy (mechanical energy for engines). The energy savings are such that they compensate a higher price at the source, which in hybrid and electric cars enables a lower cost per km for electric powertrains than with traditional internal combustion engines.
1.1.3.2 Hybrid technology
The term hybrid refers to a configuration where two power sources coexist within the vehicle in new propulsion systems technology, : fuel stored in a tank and electricity stored in a battery.
The different degrees of hybridization and their features are presented in Table 1.4.
Table 1.4: Hybridization degrees [20].
Micro hybrid Mild hybrid Full Hybrid Plug-In Hybrid
Start & Stop X X X X
E-boost X X X
Regenerative
braking X X X
Engine load
point shifting X X
Electric only X X
Electric only
> 5 km X
Charging on
the grid X
Hybrid vehicles have several combined features that enable comfort and energy savings. On the following pictures, the energetic distribution of these different features are shown. The ICE is located at the front of the car (left-hand side). The battery is represented by a pink rectangle at the rear (right-hand side). The energy coming from the battery is pink, the energy coming from the ICE is cyan and the energy going to the battery (during regenerative braking for instance) is yellow. The arrows indicate the direction of energy transfer.
Start & Stop
This system equips a large part of recent vehicles. It turns off the engine when the car stops - at traffic lights and tolls for instance - and starts it again when the driver pushes the gas or clutch pedal. Of course, this general rule has plenty of technical exceptions preventing the engine from being turned off in critical situations, such as a high power requirement on the air conditioning system. To further improve this functionality, a driving situation recognition has been implemented at Audi. With this technology, the car automatically detects the type of driving environment - traffic jam for instance - and sets the Start & Stop parameters accordingly [21].
Finally, some hybrid cars also coast by shutting the engine off during some deceleration phases.
E-boost
In case of a need of extra power at the wheels - overtaking for instance -, both the electrical machine and the ICE can be activated at the same time to meet the performance requirements. Figure 1.1 shows the energetic sources during an E-boost.
Figure 1.1: Schematic energetic diagram of the E-boost [22].
Regenerative braking
An electric motor can also act as a power generator when the rotor spins at a speed greater than the syn- chronous speed (negative slip). This property is due to the reversible nature of the electro-magnetic phenomena involved. During a deceleration, an active braking control will decide the proportion of mechanical brakes to be used. The complementary part will be taken care of by the electric motor, hence generating energy and sending it back to the battery, as shown in Figure 1.2. However, this feature is mainly active for braking producing up to 40 kW approximately.
Figure 1.2: Schematic energetic diagram of the regenerative braking [22].
Engine load point shifting
Being able to use the ICE at its most efficient load points is not always possible for traditional cars. To optimize the load point, the engine speed would for instance be slightly increased, maybe leading to a higher fuel consumption, but with a very interesting gain in power output. In hybrid vehicles, this extra power could be used to charge the battery at low cost. By analysing the engine efficiency map, the ECU (Engine Control Unit) shifts the load point to a more efficient area.
Figure 1.3 shows the basic principle of a load point adjustment. On this torque vs engine speed map, the level curves represent iso-efficiency areas. The goal of a load point optimization is to move the load point to a local maximal efficiency zone. Most of the time, it implies an increase in the torque, such as shown by the grey arrow. The extra torque produced can then be stored as electric energy in the battery.
CHAPTER 1. INTRODUCTION 1.1. HYBRID CARS AS AN ANSWER TO A GLOBAL TREND
Figure 1.3: Engine map and effect of a load point increase/decrease [23].
The resulting consequences in terms of performance are then taken on by the electric drivetrain. If the load point has been lowered to a zone with a smaller torque than required, then the complementary energy will be taken out of the battery to compensate (see Figure 1.4(a)). If, on the other hand, the local optimum in terms of specific consumption is in a region with a higher torque, the extra output energy will be stored into the battery (see Figure 1.4(b)).
(a)
(b)
Figure 1.4: Schematic energetic diagram of a lower load point (a) and a higher load point [22].
Electric only
The «electric only» feature is exclusively available on Full-hybrid cars (including Plug-In Hybrids) and allows the car to run on the electric machine without any contribution of the ICE (see Figure 1.5). The normal driving mode involving the ICE only is still available (Figure 1.6).
Figure 1.5: Schematic energetic diagram of the full electric mode [22].
Figure 1.6: Schematic energetic diagram of the normal drive [22].
As this work deals with Plug-In Hybrid technology, most of the oncoming studies will focus on this technology rather than other degrees of hybridization.
In a Full Hybrid or Plug-In Hybrid vehicle, the hybrid technology can be implemented through several architectures. A basic classification gives three main types of drivetrains:
• series hybrid, such as the Audi A1 e-tron
• parallel hybrid, such as the Audi Q5 Hybrid
• power-split, such as the Toyota Prius
Note that other architectures exist, like the axle-split technology used on the BMW i8 which is powered by the electric machine on the front axle and by an ICE on the rear axle.
All architectures have most of the components in common:
• Batteries: to store electrical energy. There are usually two batteries: one is a high voltage battery aiming at supplying high voltage current for the hybrid functions, such as E-driving and regenerative braking.
The second battery is typically a 12-Volt Battery that supplies the onboard electrical network and its features (air conditioning, radio, displays, . . . ).
• Converter: usually a DC/AC converter, adapts the voltage of the current coming out of the high-voltage battery to the electrical machine, as well as the current from the machine to the high-voltage battery.
• Electric motor: 3-phase AC induction motor, 145 V for the Audi Q5 Hybrid [24]. This type of motor is made of two main parts: a rotor, usually a cylindrical metal cage with conducting materials in its surface, spins under the effect of a rotating electro-magnetic field generated by the stator.
CHAPTER 1. INTRODUCTION 1.1. HYBRID CARS AS AN ANSWER TO A GLOBAL TREND
Series hybrid
The series hybrid architecture specificity is that the electric machine is the only engine that is directly coupled to the wheels. The role of the ICE is to supply the electric drivetrain with power, to the battery or directly to the electric motor, as shown on Figure 1.7. An energy storing system, which is optional for this architecture enables regenerative braking.
This architecture is also used for electric vehicles equipped with a range extender (EREV2) . A range extender is an ICE that aims at increasing the range of an electric car. In 2010 Audi presented its A1 e-tron at the Geneva International Motorshow. The A1 e-tron is a range-extender equipped version of the A1. To support an electric machine of 45 kW, a Wankel engine producing 15 kW has been installed. Although its performance is limited, the range extender can bring 150 extra kilometres of range [25].
The main advantage of a series hybrid architecture is that the ICE can be run at its best load point most of the time [20]. As it does not directly drive the wheels, the engine speed and torque can be chosen freely depending on the electrical needs. Any extra energy generated can be stored in the battery, whereas a lack of power would be compensated by the very same battery. Moreover, as the electric machine benefits from a direct access to the wheels, the gearbox is drastically simplified while regenerative braking presents an enhanced efficiency. However, these vehicles have a lower overall fuel efficiency due to the energy conversion losses. The complex architecture implies a heavy drivetrain with a sustained demand on both engines to allow a reasonable speed range.
Figure 1.7: Schematic diagram of a series hybrid drivetrain [23].
2Extended Range Electric Vehicle.
Parallel hybrid
The parallel hybrid is one of the most common architectures among the hybrid vehicles. It differs from the series architecture by the fact that both the ICE and electric machine are able to directly power the wheels (see Figure 1.8). Differential gears are here to ensure the torque transmission to the wheels as well as the coordination between electric machine and ICE. The Audi Q5 Hybrid is an example of a parallel hybrid. That SUV is equipped with a 180 kW four-cylinder engine, assisted by a 40 kW 3-phase electric machine supplied by the 1.3 kWh battery [24].
Another parallel architecture type is the «through the road» layout. In this case, the ICE powers one axle, usually the front one, whereas the electric machine powers the other one. This system is used on the Volvo V60 Plug-In Hybrid where the rear axle is electrified and the front one powered with a turbo diesel injection engine.
Although no mechanical connection exists between the electric machine and the ICE, the architecture is still a parallel hybrid with all related features.
Parallel hybrids have the advantages offering a better efficiency on long distance driving as well as a consid- erable flexibility regarding the equipment and operation. The electric machine does not need to be as big and powerful as for a series hybrid, as it only assists the ICE. On the other hand, because the ICE cannot be run at optimal load point at all time, parallel hybrids are less efficient in city driving.
Figure 1.8: Schematic diagram of a parallel hybrid drivetrain [23].
CHAPTER 1. INTRODUCTION 1.1. HYBRID CARS AS AN ANSWER TO A GLOBAL TREND
Power-split
This solution shown on Figure 1.9 is an intermediary architecture between series and parallel. The mechanical coupling between the ICE and the electric motor through differential and planetary gears allows different types of power paths - either mechanical or electric. As a result, a series hybrid configuration can be used at low speed, by simply blocking the direct mechanical link existing between the internal combustion engine and the wheels.
During cruising, a parallel layout will be used for maximum efficiency. The high flexibility and adaptability of this solution was one of the elements of the success of the Toyota Prius. However, the level of complexity of this technology is high, making it rather expensive to produce and buy [20].
Figure 1.9: Schematic diagram of a power-split hybrid drivetrain [23].
Battery and charging options
All of these technologies rely on batteries, more specifically on high-voltage batteries. Their aim is to store energy to make it available for the electric machine when needed.
Figure 1.10 shows the difference in energy density between the different fuels and main battery types. It is interesting to notice that batteries cannot compete with fuels in terms of energy per weight.
Figure 1.10: Energy density of some fuels and batteries [12].
A choice based on the application has to be made when selecting the battery type. An HEV vehicle will use the electric drivetrain mainly as a power support unit for E-boost for instance. The battery does not aim at providing a lot of energy as the all-electric mode of HEVs is quite limited. Consequently, a high power density battery will be preferred, with a capacity of approximately 1-2 kWh. NiMH batteries are widely used for this application.
On the other hand, Plug-In Hybrids and EV cars are designed to provide a much larger electric experience than conventional hybrids. This means that the electric resources and features are used for a longer time and thus that the battery should be able to store a considerable amount of energy: For these applications, high energy density lithium batteries will be chosen, providing a capacity up to 85 kWh3.
If 100% of a battery capacity can theoretically be used, at least for new batteries, the exploited range of State Of Charge (SOC) is narrower. The Depth of Discharge (DOD) plays a major role in the battery life. As no memory effect affects the Lithium-Ion batteries, it is absolutely acceptable to partially discharge it before charging it again. Usually, a lower stress level on the battery components - i.e a lower DOD - will increase the number of possible discharge cycles. Due to ageing of the cells, an old battery cannot be charged at 100%
of its initial capacity. Moreover, as the battery capacity is not always absolutely exactly known, it is safer to define SOC boundaries that prevent to reach extreme states of charge. Finally, a Lithium-Ion battery is permanently controlled by a Battery Monitoring System (BMS) which would put the battery into sleep mode if the SOC become too low. For all of these reasons, energy management programs and systems always work with a restricted SOC range, between 20% and 80% for instance.
For PHEVs and EVs the battery charge is a challenging topic. To compensate the limited range of these vehicles, the battery recharge should be as quick as possible. However, in some cases it can last up to 20 hours.
As the market of battery systems is flourishing, the technological development is evolving at a fast pace.
Several recharging technologies exist today and can basically be classified into two types:
• Plug-in charging
• Wireless Power Charging (WPT)
Plug-In charging involves a power cable that connects the vehicle inlet to the charging facility. This option is today the most widely used for both public and domestic purposes. The EV or PHEV can then be charged while shopping at the mall or during the night at home. The IHS Inc.4 report about batteries [12] mentions three levels of plug-in charging technologies planned in the US:
3The Tesla Model S high capacity battery
4Formerly Information Handling Services Incorporated, global market and economic information provider.
CHAPTER 1. INTRODUCTION 1.2. HYBRID TECHNOLOGY AT AUDI
• 120V-15A AC source. Common for household purposes, the drawback of this technology is the duration of the recharging process. In the example of an EV such as the Nissan Leaf, it can take up to 19 hours, which brings scheduling issues.
• 240V-30A source. This option halves the charging time and can be integrated directly at home, as some domestic devices already work on 240V. The technology is likely to be the most widespread one for EV and PHEV cars recharging in a very near future.
• 480V three-phase DC. Industrial technology offering a high speed recharging. However, beyond the costs, it also needs specific plugs and charging facilities. The Nissan Leaf can be charged up to 80% in 26 minutes.
In order to avoid as much as possible compatibility issues related to plugs as well as limiting the onboard electronics dedicated to charging, a wireless charging method has been developed recently.
WPT is based on inductive coupling: a coil located under the car carries an unsteady current and thus generates an electromagnetic field. The second coil installed in the car is close enough to the first one to be influenced by the generated electromagnetic field. Following the Neumann induction principle, this field induces a current in the second coil and consequently charges the battery. This technology reduces the issues of compatibility and electronics mentioned above, but raises new matters, especially regarding alignment with the underground coil and efficiency. Volvo is leading a project called Continuous Electric Drive which is based on the plugless charging of a C30 (see Figure 1.11).
Figure 1.11: Volvo’s Continuous Electric Drive system, Volvocars news webpage.
The battery capacity and the charging technologies will decide of the viability of PHEVs and EVs in the future. They are driving parameters of the electromobility development which keep being improved and chal- lenged.
In parallel to that, a small revolution regarding the onboard power-net is also taking place. Most of today’s cars are supplied with 12V. However, more and more modern integrated devices demand a significant power supply, such as the CPT SpeedStart Starter generator [26] whose energy recovering and generation features are considerably boosted when powered at 48V. The technical and economical issue has triggered some big OEMs such as the VW Group, BMW and Daimler to start integrating a 48V subnet to their onboard systems, connected to the conventional 12V net via a two-way DC/DC converter.
1.2 Hybrid technology at Audi
1.2.1 Audi AG
History
August Horch was born in western Prussia in 1868. Following a degree in engineering and some experience in shipbuilding, Horch decided to found his own business A. Horch & Co in Cologne at the age of 31. Two years later the first Horch automobile, a two-cylinder engine car, was produced. After moving his firm out of Cologne,
Horch is forced to rename it following a court decision: Audi Automobilwerke GmbH is officially registered on the 25 April 1910, Audi being a latin transformation of Horch.
In 1932, Audi, DKW, Horch and Wanderer gather and form the four ring company AUTO UNION AG.
AUTO UNION GmbH is founded is Ingolstadt in 1949, becomes a Volkswagen owned subsidiary in 1966. NSU Motorenwerke AG joins the alliance three years later.
The company is finally renamed AUDI AG, its current name, in 1985, and is today one of the leading international suppliers of premium automobiles.
Management and strategy
Audi is part of the VW Group, together with 11 other vehicle manufacturers, as shown on Figure 1.12.
The firm is coordinated by a board of management of 6 directors and chaired by Rupert Stadler.
Figure 1.12: Chart of the VW Group and its subsidiaries.
Audi’s vision 2020 is to be the premium brand. The mission plan is oriented around 4 different axis:
• Living responsibly, by making the difference through social involvement, green and resource-efficient value chain and economic responsibility.
• Defining innovation, via technological programs such as the Audi e-tron, Audi ultra5 and Audi design.
• Creating experiences with innovative sales formats, by presenting the brand in an authentic and likeable way and providing personal customer relationships
• Shaping Audi towards flexibility in high performance processes and structures, global presence and com- petence and agility for growth and new business areas
The Goals of Audi pertain to acquiring a superior financial strength, maintaining a continuous growth, becoming a global image leader as well as an attractive employer worldwide.
Key figures and facts
In 2013, Audi employed 52 563 persons [27], 37 400 of which working in Ingolstadt.
The operating income of Audi amounted 5 billion euro in 2013 [28], with an increasing turnover of 49.9 billion euro that will contribute to the large 22 billion euro investment planned by 2018.
In 2013, the Audi fleet was constituted 12 models giving more than 50 variants (Figure 7.1 in the appendix, section 7.1). Audi produced 1 605 926 units and sold 1 575 480 cars in 2013. 31% of these cars were sold in China, 35% in Europe and 10% in the USA. Figure 1.13 shows the model distribution in the 2013 sales.
5Low Weight Car Body Project
CHAPTER 1. INTRODUCTION 1.2. HYBRID TECHNOLOGY AT AUDI
Figure 1.13: Shares of Audi models in the 2013 sales [27].
1.2.2 Hybrid technology
1.2.2.1 Electric Drivetrain Development in the R&D
The Research & Development Unit6 is located in Ingolstadt, Germany, on the main Audi facility where more than 37 000 people work.
Among all the different departments gathered on this site, one, called EA7, deals with Powertrain Devel- opment and is chaired by Dr. Hirsch. Figure 1.14 gives an overview of the organization of the Entwicklung Aggregate Department. One of its sub-departments, the EA-2, is responsible for the development of electric drivetrains. EA-2 is lead by Mr Enzinger and also chaperons 4 groups, including the EA-24 in charge of the functions and applications for electric drivetrains. More specifically, a team of a twelve engineers works on the drive strategies under the name of EA-242. This thesis has been conducted within EA-242, which carries out projects pertaining to the Start & Stop functions, as well as predictive drive strategies.
Figure 1.14: Organization chart of the Powertrain Development Department at Audi, focus on the group EA-242 where the thesis has been conducted.
6Commonly referred to as TE, which stands for Technische Entwicklung
7Entwicklung Aggregate
1.2.2.2 Audi hybrid models and technologies
Start & stop systems equip most of new Audi cars today, and the firm will extend its range of hybrid vehicles over the coming years.
The programme e-tron was born in 2009 at the International Auto Show in Frankfurt with the unveiling of the Audi e-tron, an R8 look-alike EV sportscar. Ever since, other e-tron models of existing cars have been designed, such as the A1 e-tron, A3 e-tron and R8 e-tron which will soon start a small-batch production. A short individual presentation of the current Audi Plug-In Hybrid models and concept studies follow.
A1 e-tron with Range-Extender
Table 1.5 shows some of the main characteristics of this electric vehicle equipped with a range extender.
Figure 1.15: Audi A1 e-tron.
Production status Under production Hybrid type EV with Range-Extender Hybrid architecture Series-hybrid Electrical machine 75 kW
ICE Wankel engine:15 kW
Battery Lithium-Ion:12 kWh
EV Range 50 km
Table 1.5: A1 e-tron technical information.
A3 e-tron
The technical details of this newly introduced Plug-In Hybrid by Audi are gathered in Table 1.6.
Figure 1.16: Audi A3 e-tron.
Production status Under production
Hybrid type PHEV
Hybrid architecture Parallel-hybrid Electrical machine 75 kW/330 Nm
ICE 1.4 TFSI:110 kW/250 Nm
Battery Lithium-Ion:8.8 kWh
EV Range 50 km
Table 1.6: A3 e-tron technical information.
CHAPTER 1. INTRODUCTION 1.2. HYBRID TECHNOLOGY AT AUDI
A6 L e-tron
Designed to promote electromobility for the Chinese market, the A6 L e-tron concept was revealed during the Beijing motorshow 2012. Its characteristics are given by Table 1.7. Note that Audi did not release any specification regarding the battery capacity.
Figure 1.17: Audi A6 L.
Production status Concept
Hybrid type PHEV
Hybrid architecture Parallel-hybrid Electrical machine 70 kW
ICE 2.0 TFSI:155 kW
Battery Lithium-Ion
EV Range 80 km
Table 1.7: A6 L e-tron concept technical infor- mation.
Sport Quattro Concept
Presented at the Frankfurt Autoshow IAA 2013, the Audi Sport Quattro concept shows tremendous perfor- mance (see Table 1.8) due to the intelligent coupling between a powerful ICE and the electric machine.
Figure 1.18: Audi Sport Quattro Concept.
Production status Concept
Hybrid type PHEV
Hybrid architecture Parallel-hybrid Electrical machine 100 kW/400 Nm
ICE 4.0 V8 TFSI:412 kW/700 Nm
Battery Lithium-Ion:14.1 kWh
EV Range 50 km
Table 1.8: Sport Quattro Concept technical information.
Start & Stop and Drive situation identification
The Start & Stop technology stops the engine when no contribution from the ICE is necessary, and starts it again as soon as power is required, as explained earlier in this report (see subsubsection 1.1.3.2). The Start &
Stop complexity level has increased over the past years, with add-on technologies such as the Drive Situation Identification, developed at Audi by the EA-242 team. Originally named «Fahrsituationserkenung f¨ur Start Stopp 2.0» [21], the feature aims at reducing the unpleasant short stops during which the engine goes off and on again less than 10 seconds later. The algorithm uses a deterministic approach which consists of identifying the current drive situation based on the past speed history, more particularly the stop times.
EV mode
The Audi Hybrid cars are equipped with an EV mode, that can be activated by a simple push on the «EV»
button located on the center console, if the battery state of charge allows it. In this mode, the ICE on/off power limits are higher so that the engine will start less frequently than in a regular hybrid mode. This increases the electric driving but depletes the battery faster.
Short Range Predictions
Using very precise GPS data, short-range information about the oncoming topography and changes on the
route can be computed. These details pertain to curves, gradients (up-/downhill) or speed limitations for instance and will be used by the control units to prepare the car accordingly. This technology enables an enhanced driving experience, as the car would react on time and with an appropriate behaviour to the route.
The gear could be for instance properly set just before entering a curve, while improving the steerability and driving stability in the bend.
Basic drive strategy
The regular drive strategy pertains to the management of the energy between the wheels, the electrical machine and the ICE. The ICE on/off power thresholds are determined by the drive strategy, as well as the amount of regenerative braking and the torque dedicated to the battery charge. The drive strategy can be improved by taking into account predictive data or analysing the driving situation.
Predictive drive strategy
Long-range predictions enable forecasts in terms of power over a predefined route. These predictions can then be used to compute the fuel and electrical consumptions of a PHEV, to eventually determine an optimal succession of drive modes in order to reduce the fuel consumption and fully take advantage of the battery. The predictive drive strategy is the topic of this master thesis and will be explained in depth in the next chapter.
Towards the optimal PDS: introduction
2.1 Principle
A drive strategy is a chosen set of energy management modes to be applied on the hybrid drivetrain, depending on some internal and external parameters, in order to reach the following specific goals as identified by Audi:
• achieve a reduction of fuel consumption
• enhance the EV-experience
• improve the comfort
• remove the need for a manual switch of mode while driving
The term predictive drive strategy (sometimes shortened to PDS) is used when the creation of this drive strategy involves data coming from route and energy needs predictions. This technology aims at reducing the fuel consumption by using an intelligent combination of drive modes on a given route [23].
The generic drive modes available for a Plug-In Hybrid are:
• EV: all electric through the whole power range of the electrical machine (40 kW power demand for electrical machine equipping the Audi A6 Hybrid).
• HEV: all electric up to a pre-defined limit (usually around 20 kW power demand).
• Charge: using load point shifting/increasing as much as possible so that the engine will be operated at its best operating points and possibly preventing the engine from stopping when the Start & Stop system would normally act, the charge mode aims at producing more power than needed in order to charge the battery.
To be able to determine how accurate each drive mode is, the route has to be known beforehand. The user simply enters the destination in the navigation system, which computes the route. The onboard computer will then analyse the route profile [29] and draw forecasts about some energetic characteristics. By doing this, anticipative information about the engine speed and torque demand [23] can be generated, which are called long range predictions.
GPS data are provided by a cloud of 32 satellites enabling a computing of the car position at anytime, almost anywhere, with a precision of some meters. By combining these data with a well-documented real-time updated cartography, it is possible to get further details about the road, such as the curve radii, traffic information and speed limitations. Such information are already used in Adaptive Cruise Control (ACC) technologies which aims at anticipating the car’s short-term dynamic needs, like gear shift or suspension and steering adjustment. Some technologies are already dealing with the reduction of fuel consumptions on traditional ICE powered vehicles by accurately choosing the gear with help of GPS-based predictions (see oncoming subsubsection 2.2.2.1). As
CHAPTER 2. TOWARDS THE OPTIMAL PDS: INTRODUCTION 2.2. STATE OF THE ART
there are more degrees of freedom for a PHEV, considering both the electric and conventional drivetrains, the gains are theoretically much higher too.
Based on these data, the route can be split into a certain amount of parts. Using recognition algorithms that will be explained later in this report, the onboard computer will determine an appropriate segmentation. For each segment, the expected electric and fuel consumptions will be calculated for all available modes (EV, HEV, Charge). The last step is to give this information to an optimizer that will create the optimal combination of modes to be applied on the route.
Once the drive strategy has been computed, it will be implemented by the energy management system connected to both the electric propulsion system and the ECU.
If some modifications concerning the computed itinerary occur while driving - such as an update of the traffic conditions or a diversion followed by the driver, the same loop can be repeated again and a new drive strategy will be calculated.
The master thesis
The topic of the master thesis is the «Optimization of a predictive drive strategy for a Plug-In Hybrid vehicle»1. The targets are the following:
• Identify the different steps towards the implementation of a predictive strategy in a Plug-In Hybrid Audi.
• Develop a tool capable of simulating the general operation of the predictive drive strategy algorithm.
• Analyse the challenges and limits of pilot programs and algorithms.
Due to the high level of confidentiality, information related to the planning of the Predictive Drive Strategy project is very limited. This technology is expected to equip the next generation Plug-In Hybrid vehicles of Audi within three years.
A total of 7 engineers have been attached to the project:
• 1 person from the team I/EA-211 (Concept development Electric Drivetrain), in charge of the fuel and energy consumption simulations
• 1 person from I/EA-222 (Powertrain functions and applications) dealing with long-range predictions
• 2 persons from I/EA-252 (Software Development/Drivetrain functions) working on the analysis of the long-range predictions
• 3 persons from the I/EA-242 supervising the project as well as focusing on measurements and strategy optimisation
2.2 State of the art
2.2.1 Research studies
In the field of predictive drive strategies, the PhD dissertation of Michael Back [23] from the University of Karlsruhe is a reference. This analytic report was issued in 2005 and includes several references to previous works in the area. It covers the hybrid variants, different technologies to generate predictive data, method of determining the drive strategy based on the non-linear discrete car model and their implementation in a parallel hybrid model. Note that the non-linearity comes from the gear shift.
Predictions are the base of an anticipated drive strategy. The route predictions come from:
• navigation system
• up-/downhills and slopes
• speed limitations
• curves
1Original title in German: «Optimierung der vorausschauenden Antriebssteuerung bei einem Plug-In Hybrid».
• driver’s behavior (reaction to speed limits and in curves for instance), and realtime informations (traffic info via RDS-TMC).
Three types of sensors can help in determining these predictive data:
• distance sensors: assess the traffic density by measuring an average car-to-car distance, gather information as to how the driver accepts to following a slower vehicle
• cameras: read road signs, assess traffic density
• weather sensors: evaluate driver behaviour with rain, take into account weather depending speed limita- tions
For further optimizations, a detection of traffic lights, stop signs, crosswalks and intersections can be con- sidered.
From all this route information, the acceleration and speed profiles are determined. The required torque and engine speed are then computed by sending the speed and acceleration profiles through the physical car model, while the driver’s torque and power demands are known.
This information is then given to the optimizer, which focuses on fuel-consumption reduction. The optimizer works with discrete data and is based on dynamic programming instead of the search of a global optimum.
Dynamic programming reduces the original problem to a smaller, more limited sub-problem. In other words, a gliding window will define the predictive data that the algorithm has to consider in order to solve the problem.
In Back’s report, this window can be up to 65 seconds wide. At a time t0, only the predictions between t0
and t0+ 65s will be considered to optimize the fuel consumption in this window and use the electrical energy accurately.
Based on the oncoming route profile contained in the sliding window, predictions about the torque and power needs will be considered. For each optimization, a defined share of the battery capacity will be consumed. The electrical machine is used to support the ICE (in uphills for instance) while respecting this allowed electrical energy consumption. For some examples including an uphill followed by a downhill, this energy share can be equal to zero as the regenerative braking will compensate the energy used on the uphill. However, in most of the common cases, the amount is likely to be strictly positive to allow a visible improvement in the fuel consumption.
The result of this optimization is a set of regulation commands for the ICE, the electrical machine, the gearbox and the brakes.
The whole algorithm is then integrated in a car model and assessed in an experimental environment, with real speed profiles. For all tested configurations, the resulting fuel consumption is lower with a predictive drive strategy than with a conventional hybrid strategy. The author concludes that due to the high variability among the tests, more simulations are needed to provide statistic average fuel savings. Simulation conflicts in the full-car model arise from the gear box, as gear shifts are often suggested by the predictive drive functions and not implemented by the gear control unit. With a simple predictive drive strategy, the simulations have shown that a reduction of at least 2% is possible.
Many technical resources at Audi refer to Back’s «Pr¨adiktive Antriebsregelung zum energieoptimalen Betrieb von Hybridfahrzeugen». However, the approach implemented by the firm differs from it on several aspects, as described in the chapter 3.
2.2.2 In the industry
2.2.2.1 BMW: Predictive Power Management
In 2012, BMW had announced its new Predictive Power Management feature [30]. A range of GPS-based functions supports this new functionality by allowing an anticipative control of the gearshift. The road forecasts include a consideration of the up-/downhills, the curves as well as other road obstacles such as roundabout.
The Predictive Power Management is coupled to a panel of features to enhance the driver’s experience:
• Pure Dynamics: In Sport Mode, the car will adopt a dynamics and performance oriented behaviour. Based on the road forecasts, an optimized gearshift is implemented to make the right torque available at the right moment, such as at the end of a curve.
CHAPTER 2. TOWARDS THE OPTIMAL PDS: INTRODUCTION 2.2. STATE OF THE ART
• ECO PRO mode features
– ECO PRO Foresight Assistant: this functionality gives the driver tips to reduce the fuel consumption based on oncoming speed reductions, obstacles or possible use of coasting.
– ECO PRO Coasting: the engine can be decoupled when the driver releases the gas pedal (following an indication from the Foresight Assistant for instance), in a range of speed of 50 km/h up to 160 km/h. BMW announces fuel savings that can reach 5% with this feature.
– ECO PRO Route: the navigation systems allows the driver to choose between direct route and more economical ones to enhance fuel savings, while considering the driver’s driving behaviour.
– ECO PRO Analyser: simply analyses the driving behaviour and rates its level of eco-friendliness.
– ECO PRO Efficiencytainment: overall interface that displays which of the features are currently being used.
The engine temperature regulation is also based on the Predictive Power Management. These features are gathered under the ActiveHybrid technology package.
2.2.2.2 BMW: Anticipatory Energy Management
Already mentioned in the 2012 media release [30] as well, the use of Predictive Power Management to optimize the hybrid driving strategy has been confirmed at the 22ndAachen Colloquium Automobile and Engine Technology 20132.
Public information about this technology is rather rare but BMW has already acknowledged that the Antic- ipatory Energy Management system will equip the BMW i8 [31]. Just like the Predictive Power Management, the Anticipatory Energy Management considers the road profile, as well as up-/downhills, curve radii and speed limits to compute energy consumption forecasts for both the ICE and the electric drivetrain.
In a non-anticipated strategy mode, the vehicle would run electrically in cross-country and city areas. How- ever, the Anticipatory Energy Management feature will allow an intelligent management of the drive modes meaning that pure electric driving will occur where the efficiency is optimal.
The document [31] does not stipulate whether the Anticipatory Energy Management calculates an optimal driving strategy. It seems that a priority based optimization is used (see section 3.6 about the priorities) to ensure an EV driving for the last section of the itinerary3.
2.2.2.3 Daimler: predictive drive strategy for E-trucks
In 2011 two members the Institut f¨ur Systemdynamik of the University of Stuttgart had published a short article describing a project of drive strategy for hybrid trucks carried out in cooperation with Daimler [33].
Using predictions regarding the route topography and the speed limits, commands will be sent to the hybrid powertrain in order to reduce the fuel consumption, with a considerable energy input from the regenerative braking. The optimization is based on two different algorithms:
• a short-range non-linear optimizer which defines the gearshift and engine speed.
• a long-range linear optimizer which analyses the route, classifies its parts into different sections depending on their gradient profile and then determines progression of SOC of the battery.
As no further publication than [33] has been issued on this project by the authors so far, one can assume that the study is either still ongoing or protected by Daimler.
27 - 10 October 2013 in Aachen, Germany.
3Similar to an EV - coming home function developed by Audi [32].
