Below is the text version of the Co-Optima webinar, “What unconventional engine-fuel combinations show the greatest promise for efficiency improvements beyond current light-, medium-, and heavy-duty technologies?” Watch the webinar recording.
[Begin audio]
Anthe George: So today I'd like to introduce Magnus Sjöberg. Magnus is a scientist at Sandia National Lab. And today he's going to talk to us about the work that Co-Optima has been conducting over the past few years on multimode and ACI technologies. So Magnus, please take it away.
Magnus Sjöberg: Thank you, Anthe, for that kind introduction. Yes, my name is Magnus Sjöberg. I work at Sandia National Laboratories, and I'm pleased to be here with you today to present this Capstone webinar. And today we'll be looking at what unconventional engine-fuel combinations can do to improve efficiency beyond what is currently found in terms of conventional technologies for light-, medium-, and heavy-duty vehicles.
So, Co-Optima promises to combine engine R&D and fuel R&D to produce better fuels and better engines. By the way, Anthe, can you hear me okay?
Anthe George: Absolutely. Yes, Magnus.
Magnus Sjöberg: Okay. Great. Yeah, the overview of this presentation is shown on the left here. I will start with an introduction to Co-Optima. I will give you key takeaways early on so you can decide if you want to stay for the rest of the talk. I hope you will. And then the main portion of the talk is divided into light-duty multimode and medium-/heavy-duty advanced compression ignition. And then we'll briefly end looking at the next steps.
So, the Co-Optima effort, which has been going on for roughly six years, is truly a nationwide effort. We have many national labs, shown in green here, across the country, as well as many university partners, shown in blue, as well as industry partners.
And we have many contributors, and I would like to acknowledge the contributions from everyone in the Co-Optima effort from across the teams. I would also like to acknowledge that this work was sponsored by the U.S. Department of Energy through the Bioenergy and Vehicle Technologies offices.
So, Co-Optima has been focused on liquid fuels and we have primarily considered known food-based biofuel feedstocks. As part of the program we have assessed well-to-wheel impacts for these biofuel options. We have provided data, tools, and knowledge. And for the fuels that we looked at for the marketplace, we have considered up to 30 percent blend levels in general; so, that's up to 30 percent bioblendstock and 70 percent petroleum. And in light of the current climate crisis, this may seem like a low blend level and there might be reason going forward to increase the blend levels. And we'll talk about that in the future steps. But I'd like to point out that the approach that we've taken here in Co-Optima is indeed applicable to fuels with high renewable content.
So, the research approach we have taken is to connect engine performance to fuel properties and to fuel chemistry. And we have taken a fuel-properties-based, composition-agnostic approach. And this is one of the main working hypotheses of that work, and that is equivalent fuel properties result in equivalent performance. In other words, it doesn't matter what bioblendstock you use in the fuel as long as you blend the fuels to meet certain fuel property requirements. However, we did see deviations from this hypothesis, and it turned out that in some instances the existing fuel property metrics were not sufficient. And one new metric that we have used I will be talking to you today about is called Φ-sensitivity, and we will get back to that when we talk about the medium-/heavy-duty ACI project.
So, in Co-Optima we have considered new engine designs needed to realize benefits and we have also developed new methodologies to quantify how benefits vary with fuel properties.
So, the scope of Co-Optima is focused on on-road transportation, spanning from light-duty to heavy-duty. We have focused on on-road transportation, spanning from light-duty to heavy-duty. And in both of these areas we have different time perspective on the research. We have done research that supports advanced fuels for conventional combustion technologies like turbocharged spark-ignition engines, and on the medium-/heavy-duty side on regular diesel combustion with alternative fuels.
What I'm going to focus on today is technologies that are less mature, and you might not find them in the marketplace, and in particular on the light-duty side, we're looking here at multimode engine operation, and for the medium-/heavy-duty side, advanced compression ignition. And for both of these focus areas today I will be concentrating on gasoline-range fuels.
So, I'll give you the key takeaways, and I'll start with the medium-/heavy-duty side. So, on the medium-/heavy-duty side, we have found that advanced compression ignition, ACI, with gasoline-range fuels can provide higher efficiency than diesel engines and with much lower engine-out emissions. We have also found that fuels can be designed to provide properties that enable ACI, even at high bioblendstock levels. And we'll talk more about the medium-/heavy-duty results at the end of this presentation.
But now let's switch gears to light-duty and focus on that for the majority of this talk. And the key takeaway from the light-duty side is that advanced combustion does provide efficiency gains greater than 10 percent, in addition to any gains we have realized on the conventional boosted SI side. We have also found that fuel properties can play an important role to enable advanced combustion.
The light-duty goal is to determine fuel properties that enable advanced combustion modes with higher efficiency than conventional, stoichiometric, spark-ignition gasoline engines. And why do we care about efficiency? Increased engine efficiency can lower fuel consumption and carbon dioxide emissions. The need for light-duty transportation is immense in the United States. Light-duty vehicles travel nearly three trillion miles annually, and to do so they need a lot of energy in the tank, as shown here. If we can improve the fuel economy from today's around 22 miles per gallon to, let's say, 50 miles per gallon, this energy need for the vehicles can be greatly reduced, and with that also a strong reduction in the CO2 emissions from the light-duty transportation sector. Improved fuel properties can contribute to increased engine efficiency, and this applies both to conventional and advanced engine combustion.
So, what is multimode engine operation? Multimode engine operation uses advanced combustion at lower loads in combination with conventional stoichiometric-boosted, spark-ignited operations at high loads. Conventional spark-ignited operation is illustrated with this movie. It shows high-speed imaging where the turbulent flame is initiated by the spark in the center of the combustion chamber, and then we have a turbulent flame that propagates across a charge and consuming the charge.
Such a conventional combustion system can provide high-peak, high-powered density because it can operate at high speeds and loads. And for some portion of the operating map, this conventional operation can also be quite efficient. Unfortunately, we see that the regions where the engine is efficient in terms of speed and load tends to be at relatively high loads, and for normal engine operation the engine spends very little time in this efficient region. As indicated by these dots here from a typical drive cycle, the majority of the time is spent at lower engine speeds and lower loads. And if you see in this region, at lower loads this conventional engine tends to have relatively low efficiency.
So, the idea is to induce an advanced combustion concept, a lean combustion concept in the lower speed range indicated by this box here, and that way we get fuel economy gains by improving the efficiency of the engine where it spends lots of time.
So the question is, what fuel properties can enable this multimode operation? Well, since the multimode operation needs to be able to revert to a boosted SI operation, the multimode fuels also need to enable boosted SI. The multimode fuels also need to enable good low-to-mid load coverage.
So, let's first take a look at what fuel properties enable higher efficiency in conventional boosted SI engines. And this was discussed in more detail in the previous Capstone webinar back in March. You can find the recording online if you are interested in this. So, what we found when we studied boosted SI operation is that, starting from a baseline case with a baseline regular gasoline, we can realize up to 10 percent gains for this conventional combustion strategy. But it also requires a substantial increase in the research octane number of the fuel, and also a significant increase in the octane sensitivity, as well as an increase in the engine's compression ratio.
So, what is RON and sensitivity? Well, it turns out that when you operate the spark-ignited engine in a conventional mode you need to avoid autoignition occurring in the end gas because that creates engine knock. And one way to measure, or the traditional way of measuring the tendency for fuel to cause or to resist engine knock, is with two different octane tests. We have the RON test and MON test. And these are determined in a special test engine shown on the left here and we can compute the difference between these two octane numbers. RON minus MON gives us octane sensitivity, and this turns out to be a key metric combined with the RON value of the fuel. So, increased RON and increased sensitivity is good for the conventional SI combustion.
So, now for multimode operation, we're going to use lean combustion. And why do we want to operate lean? Well that's shown in this graph here. We start from stoichiometric combustion, and we add more air going in this direction. The measure of the stoichiometry is called the Kluns ratio, and if we decrease the Kluns ratio we see that can gain efficiency gains. And this comes from improved thermodynamics, reduced pumping losses, and reduced heat transfer losses.
We also see that the gains that we can achieve depend on how we operate the engine. And at some point, we actually start losing efficiency if we add more air, and this is partially related to combustion instabilities. So, conventional combustion, like down here, has very stable combustion. We have the piston compression, and we have the combustion occurring here, and then expansion. And you can see that this cycle variability is low. If we operate out here, we are operating too lean, and now the combustion is not stable from cycle to cycle, and this is not acceptable.
It's possible to operate the engine while mixed and have high efficiency and sufficiently stable combustion but it is sensitive. So, next I want to talk to you about a technique that we can use to overcome the sensitivity of this lean combustion, and that's shown with the computer animation on the left. I'll be showing you computer simulations of lean combustion. We have the intake valves on the right; we have the exhaust valves on the left. You can see the spark plug in the center. And what you don't see is that we injected most of the fuel early, and we have now created a lean charge throughout the majority of the combustion chamber.
But to improve the stability of the deflagration of this lean charge, what we're going to do now, as we ignite, we're also going to inject a little bit of extra fuel at the time of spark ignition. And this little extra fuel needs a little bit of stratification, and we call this partial fuel stratification. Partial fuel stratification in combination with spark ignition gives us stable flame development from the spark plug area.
So, we have the deflagration occurring, indicated by the blue flames from the spark plug. But you can also see that we have a transition to what is shown here in the computer model as red flames. These are actually flameless combustion. This is autoignition-based combustion, which is shown in this schematic. So, we have the heat release rate as a function of the combustion progress from zero to 100 percent. And we have the flame-based combustion shown with the blue curve. And what's noteworthy is that the flame-based combustion slows down at the end toward the combustion process, and this is not good, but we can compensate for this slowdown by this red autoignition event, and we can maintain a high combustion rate throughout the combustion event. And this is one of the keys to getting high thermal efficiency of the engine.
So, we can use this combustion system I'm going to call spark-assisted compression ignition, SACI for short. So, this is one of the advanced combustion modes that we will be investigating in this multimode operation. Another combustion approach that we have used is stratified charge operation. In this case we inject all the fuel quite late before spark ignition and we have a stratified charge with strong fuel gradient and the fuel centered in the middle of the combustion chamber. This approach gives us high efficiency, but it can give us also some challenges with soot formation, as you can see in this movie. And as we go forward and look at fuel economy benefits from these combustion modes, we will be using a SACI approach for the mid loads and stratified charge for the lower loads.
Another advanced combustion strategy we have used in Co-Optima is called HCCI, or homogenous charge compression ignition, and this is a flameless combustion event shown in this movie here. And this can give us high efficiency. I will not be using this combustion mode for the fuel economy assessment for the light-duty side today, but we will come back to the use of HCCI for the medium- and heavy-duty project.
However, what we will do on the light-duty side, I'll share some results with you today, the use of SACI and the multimode combustion together in a hybrid powertrain, because we were wondering as we went along with this project if we now see electrification occurring in the marketplace and increased vehicle fuel economy from the advanced powertrain, if we now apply advanced combustion to the engine, do we still get the similar benefits as for a conventional powertrain? And I'll share some insights on that later.
So, the takeaway from this light-duty effort is that multimode implementation can indeed provide greater than 10 percent fuel economy gains over the boosted SI baseline engine. And this is in addition to the gains that we have realized from the increased ROM and sensitivity for the boosted SI baseline. We have also observed that multimode operation in a hybrid powertrain can provide greater than 15 percent fuel economy gains. And the fuel properties that turn out to be most important are RON and octane sensitivity. So, higher RON is better for the SACI load coverage and higher octane sensitivity is also better for the SACI load coverage. So in this sense, the fuel property requirement of SACI is fully aligned with that of the boosted SI engine, which also benefits from increased RON and octane sensitivity. And we have looked at blendstocks that can provide these desired fuel properties. We have identified that alcohols have high potential, for example, ethanol, iso-olefins, diisobutylene, and alkylfurans.
So, the research approach we took in the light-duty effort is to start with engine and fuel experiments, and then we expanded the combustion parameter space using computer modeling, and lastly, we assessed fuel economy impacts using vehicle-level drive cycle simulation.
So, we generate validation data with the advanced combustion modes like this SACI mode, with a combination of deflagration and autoignition, for different fuels and fuel types. We can use the validation data to tune our computer model. It's called the GT-Power model. This model can now be exercised across a wide operating range of the engine and generate the ensuing temperature and pressure history of the combustion. And this can be fed into a program called CHEMKIN, which is used to compute autoignition timing. This in turn can be used to determine the load range for this SACI operation. So, what this result here shows is that if we increase the research octane number of the fuel the upper load limit of SACI operation, that increases. Also, if we increase octane sensitivity of the fuel, that also increases the load limit, the upper load limit of SACI operation.
And from this information we can then determine the coverage of our lean combustion mode. In this case for RON 100-S12 fuel, we will have this coverage for the SACI. Below that, stratified charge. And outside this region stoichiometric SI operation. And with this information we can now run Autonomie and drive cycle simulations to determine the fuel economy.
So as I said before, we have observed more than 10 percent fuel economy gains from multimode engine operation, and that is quantified in this graph in the lower left. So for highway and urban drive cycles, we observe between 9 and 14 percent fuel economy gains. You see a tendency here as we increase the RON and sensitivity of the fuel going to the left, we see increased fuel economy gains for both of these drive cycles. And part of the reason why we see increased efficiency gains with a fuel that has high RON and sensitivity is the good load coverage, as indicated by this green box. So, for high RON and high sensitivity fuel, a majority of the engine operation, indicated here in the red, falls inside this more efficient area, with only limited operation indicated by the blue dots, in the conventional stoichiometric mode.
Another benefit of increasing RON and sensitivity of the fuel is that we can reduce the number of mode transitions in and out of this lean region. And this is certainly a great benefit to ease the practical implementation of this multimode operation.
So, to summarize and put this in context with the boosted SI results, let's take a look at this graph to the right here. So, if we start from a baseline operating point with a certain fuel economy (100 percent fuel economy), here we use a regular gasoline and a moderate compression ratio. If we now follow along here with the boosted SI and we increase RON, sensitivity, and compression ratio together, we can gain up to 10 percent fuel economy for the conventional combustion. But you also notice that we have to go to quite extreme RON values and octane sensitivity and also quite extreme compression ratio. So, what multimode operation allows is to realize even greater gains at much more moderate increases of RON and sensitivity and also at a much more reasonable compression ratio. So, from this it's clear that compared to boosted SI operation, multimode operation includes substantial fuel economy gains with a less extreme requirement for RON and octane sensitivity.
So, what fuels can provide the required fuel properties? Since the most important fuel properties for the SACI operation are RON and sensitivity, one can say that we are fully aligned with the boosted SI fuel requirements. So, what I'm showing you here are the top 10 bioblendstocks that were identified as a part of the boosted SI effort, and those include alcohols like ethanol and methanol, as well as furans. We'll come back to the furans in the medium-/heavy-duty project.
Before we switch gears to medium-/heavy-duty, let me talk briefly about the use of multimode operations for hybrid configurations. A power split hybrid can create an extremely efficient system, and here we see a schematic of this system as used in this autonomy drive cycle simulation. So, we are focusing on changes to the engine and the fuel right here. But first, let's take a look at the gains in fuel economy from implementing this hybrid system.
So, the conventional powertrain is shown for each of these drive cycles (highway, urban, and aggressive drive cycles) in the beige/brown bar to the left here. And if we now change from the conventional operation or conventional powertrain to a hybrid powertrain, for the highway cycle we actually see a detriment, but we see great benefits on the urban drive cycle (up 43 percent) and also on the aggressive drive cycle (up 16 percent).
So now we ask ourselves with this and a more efficient powertrain, if we go to the trouble to add a multimode operation in terms of a hybrid SACI implementation, do we get gains? Yes, we do. So now we go from this blue bar to the green bar. And for the highway cycle, we see 19 percent gains from the multimode engine operation, for the urban cycle we get 12 percent gains, and for the aggressive cycle we see 7 percent gains. And this is when we implement SACI only. And it turns out that that's the only combustion mode we need to implement on the lean side because if we go to the additional effort to implement a stratified charge operation in combination with SACI, then we see essentially no additional gains for this hybrid configuration.
So why is that? And that is because the controller of this power split hybrid is focusing the engine operation to regions where the engine is most efficient. So, you can see here we have different load coverages for the low-RON, low-sensitivity fuel versus a high-RON, high-sensitivity fuel. But in either case, the heat map here shows that the engine has really focused its operating time in the upper load range of this SACI operation, so it will never or very seldom want to use any low loads where we could benefit from the stratified charge operation.
We also see that the fuel economy, as a function of the fuel properties, RON and sensitivity show very little dependence on the RON and octane sensitivity, and that is again because the engine controller adapts to the available lean operating space and utilizes that really well.
So, with that I'm ready to switch gears to the medium-/heavy-duty project. And the goal for this project was to determine fuel properties that enable implementation of ACI techniques.
Why do we care about full-time ACI engines? Well, ACI engines can lead to high efficiencies and low harmful emissions. And one variant of this is called low-temperature gasoline combustion, LTGC. It has demonstrated good performance over the entire operating map of the engine, and efficiencies are somewhere between 14 and 30 percent above a generic diesel engine.
We know that bioblendstocks can significantly reduce the carbon footprint of combustion engines. But the question we asked ourselves is: Can renewable fuels also assist in the implementation of this advanced combustion mode?
So to answer this, we performed engine and fuel experiments. We defined new fuel property metrics, namely Φ-sensitivity. And we developed new fuel-blending strategies and we expanded the parameter space using computer modeling.
So the fuel requirement for this LGTC engine, it needs to support both ACI operation, it needs to enable high loads in ACI mode, it needs to ensure that we have low intake heating requirements for this engine, and also high Φ-sensitivity, which we'll talk about in a second. But to be attractive in the marketplace, this gasoline range fuel should also provide benefits for boosted SI engines, so it should provide high RON and high octane sensitivity.
And speaking of Φ-sensitivity, it turns out that Φ-sensitivity is important for LTGC operation when we use LTGC in combination with partial fuel stratification. Partial fuel stratification is when we introduce a gradient of fuel concentrations in the combustion chamber. And if we do, then this is how the combustion looks. We get a progressive autoignition event starting from the rich regions on the right moving toward the leaner regions on the left-hand side in the combustion chamber. So, this progressive autoignition event improves both combustion control and it reduces engine noise, which are two important things for practical implementation of advanced compression ignition.
To determine how we can formulate the fuel to meet these requirements, we developed a new modeling methodology that's based on CHEMKIN simulations. So, this includes CHEMKIN simulations that compute the RON and MON of the fuel as well as these HCCI reactivity metrics. And with this methodology, we tried two different fuel-blending strategies. And the first one that we tried turned out to be not very effective. So in this case, we looked at different bioblendstocks, shown in the top here. We looked at those bioblendstocks to produce the Φ-sensitivity that we wanted, and then mixed these bioblendstocks with lower-reactivity species, shown at the bottom here, to provide the increased RON and octane sensitivity. But as you can see, this approach wasn't very effective. It was very hard to exceed the Φ-sensitivity compared to that of regular gasoline.
So instead we had to change to a different strategy. In this case, we achieved the high Φ-sensitivity by using higher-reactivity species that you can find in petroleum. This provides the high Φ-sensitivity, and then we blended these species with lower-reactivity bioblendstocks shown at the bottom here that can provide high RON and octane sensitivity. And you can see that this strategy was effective. We have several examples, which substantially increased Φ-sensitivity relative to the regular gasoline.
So with this approach, we formulated a better fuel, which we call CB#2. And it has 40 percent bioblendstock content in the form of furans. And we confirmed experimentally that indeed we see a great increase in the Φ-sensitivity, which helps to enable LGTC implementation. We also observed increased RON and increased octane sensitivity.
So, it also has higher bioblendstock content, and that on its own will reduce the greenhouse gas emissions compared to regular gasoline. In addition, this fuel can provide even higher efficiency for the engine. Here we have the efficiency map for this LGTC engine when it is operated with regular gasoline. We haven't yet tested the CB#2 fuel across the load and speed range fully, but the data we have so far indicated increased efficiency with this CB#2 fuel.
And as a part of Co-Optima, we are also looking at the well-to-wheels greenhouse gas emissions and the reductions that can be realized with advanced bioblendstocks and advanced combustion technologies. And we see that certain combinations of bioblendstocks and fuel production methodologies combined with a highly efficient ACI operation can really reduce the greenhouse gas emissions relative to petroleum gasoline. So in that sense, the use of fuels with high bioblendstock levels and highly efficient ACI engine operation is an attractive combination.
So, next steps. We need to ensure that we have vehicles with clean exhaust. And we also need to work on increasing the blend levels to enable net-zero carbon solutions. So, clean exhaust is imperative for market introduction. And we have observed that lean engine operation comes with unique aftertreatment challenges. So, for example, we may see different compositions of the gases. We may see sometimes increased hydrocarbon emissions from the engine. So we need to ensure that the aftertreatment catalysts are efficient at cleaning up what comes out of the engine. And in this respect, we have observed fuel effects that, on these catalytic reactions… but I won't go into details on this today. But anyway, this is an important aspect of fuel engine co-optimization.
To realize the potential of what we have done, we also need to scale up the production of these bioblendstocks for commercial production. We need to overcome adoption barriers. And the benefit of this approach has been analyzed and you can read more about this in this relatively recent paper that came from our research team in Co-Optima.
So, next steps, there are clearly large uncertainties in the future direction of transportation, but the net-zero carbon emissions pathway may include powertrain technologies that use low carbon and renewable fuel internal combustion engines, ICE-hybrids, fuel cell hybrids, and battery electric powertrains. And I would say that coordinated national lab efforts like Co-Optima should be well positioned to contribute toward net-zero carbon solutions.
So, with that I would like to end this by acknowledging the support from the Department of Energy across all levels of management at DOE. I would also like to acknowledge the dedicated effort by the Co-Optima leadership team here at Co-Optima, as well as all the contributors across the teams in Co-Optima.
So with that, I will thank you very much for your attention and I will be very happy to take any questions you might have. Thank you.
Oh, sorry, I should mention, please stay tuned for next month. Next month we will be hearing from Robert Wagner and Dan Gaspar. They will be talking about what we learned at Co-Optima and where we should go next. And with that, I will be happy to take any questions.
Moderator: Great. Thank you, Magnus. So, if anyone has any questions, feel free to raise your hand or type a question in the chat and we will call on you.
Magnus Sjöberg: Maybe it was all crystal clear. Or totally confusing.
Audience: I have a question, Magnus. This is Matt.
Magnus Sjöberg: Hi, Matt.
Audience: Hi. Thank you for a very nice presentation. I really enjoyed the overview. My question is with regard to the multimode operation. And what has Co-Optima done in regard to addressing managing especially NOx emissions in the lean operation, emission control systems integration to multimode operation?
Magnus Sjöberg: Yeah, we have certainly looked at aftertreatment in the multimode project, and like I mentioned we have identified fuel effects, in particular on the oxidation aspects of the aftertreatment, oxidation of hydrocarbons.
For the NOx control, this has worked, is maybe underway right now. But I think my co-team lead in the Advanced Engine Design Team, Josh Peel, I think he would be in a much better position to give you an answer to that question. But it's certainly a really important aspect. Lean NOx aftertreatment is a key challenge to realizing multimode engine operation.
Audience: Yes, thank you.
Moderator: All right. I see a question in the chat: "Most if not all gains were given in percent, but what is baseline efficiency?"
Magnus Sjöberg: The baseline efficiency varies. So if we go back here, let me jump out of this. So, we can take a look at any of these engine maps, let's say this one here. So, when we compute for the multimode operation, when we compute the gains it's relative to the same drive cycle.
Let me see here. So as we change the fuel, we also change the efficiency and the operation of the stoichiometric portion. So, you will see that these contours with efficiency as a function of engine speed and torque, they do change with the fuel for the stoichiometric mode as well. So, there's not a single number for the baseline but it does change with the fuel, and it's certainly not a constant number. So, I'm not sure if that answered the question.
Audience: So, hi Magnus. I was the one asking the question, so maybe I can actually ask it straight, if that's okay. Nice talk, by the way. I did like the use of HCCI, SACI, and all kinds of interesting acronyms. That's all good. But looking at this diagram, it says 37 percent efficiency, maybe approaching 40 in the best point in your advanced operating case. But isn't it so that if you buy a Toyota Prius or a Kia hybrid, they are already 41, maybe 42 percent efficiency?
Magnus Sjöberg: Right. And that is one good reason why we looked at the relative gain from this, because the efficiency that we show in this case is really based directly on the hardware that we have in the lab. And as you know, people have made huge strides to improve efficiency by, for example, reducing heat transfer losses and supports. So the combustion system that we used for our experiment may not represent that latest progress. So I would say if we were to take the latest engine platform, if we do our stoichiometric experiments, that would certainly raise the baseline efficiencies substantially. But I still expect that we can realize these relative gains by adding the lean portion to that more efficient baseline stoichiometric operation. That's my expectation, but if you know otherwise, please let me know.
Audience: I think those more advanced hybrid engines are using quite a lot of EGR with their stoichiometric operations, so it might not be as big of a gain but maybe still some.
Magnus Sjöberg: That's right. Yeah, they operate with significant EGR and that will give them on the order of maybe five percent relative fuel economy gain.
Audience: Okay. Thank you.
Magnus Sjöberg: Yep. Thank you.
Moderator: And I see another question from Isabel. She asked if you could say something about market potential or reactions from the market on your results or if it's too early.
Magnus Sjöberg: I'm not sure if I'm the right person to answer this question, to be honest. But what you see, what you have seen in the United States is a conversation has been going on between stakeholders. There has been interest from the vehicle manufacturer's side to increase RON and increase the sensitivity of the fuel. But in practice, you haven't seen much change in what's offered in the market and what the consumer is requesting. So I guess there has been so far not much change in the market in terms of the properties in the fuel in terms of RON and sensitivity.
Moderator: Thank you. All right. So, I don't see any other questions.
Magnus Sjöberg: And I can add to that one complexity is that as a vehicle manufacturer, they sell a vehicle that can be operated anywhere in the United States. So if you design the engine to require a certain higher RON and higher octane sensitivity, you need to be sure that the consumer has access to that new fuel wherever they want to drive, and also that the consumer really uses the right fuel and doesn't put in regular gasoline when the engine really wants or needs a premium gasoline. So that's another implementation challenge, I would say.
Moderator: Thank you, Magnus. All right. So if that is all the questions, I just wanted to remind everyone again that on September 30th, Robert Wagner and Don Gaspar are going to be presenting on "Co-Optima: The Past, Present, and Future: What We Learned and Where Do We Go Next?" So, we'll talk to you all next month. And thank you again, Magnus.
Magnus Sjöberg: My pleasure. Thanks, all, for attending.
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