Heavy-Duty Battery Electric Vehicle Infrastructure Scenario Analysis Model (HEVISAM) (Text Version)

This is a text version of the video for Heavy-Duty Battery Electric Vehicle Infrastructure Scenario Analysis Model (HEVISAM) presented on April 3, 2024.

Heavy-Duty Battery Electric Vehicle Infrastructure Scenario Analysis Model (HEVISAM) Webinar
AISHWARYA KRISHNAM: –website in the next seven business days, and Clean Cities and Communities website in the next seven business days, and might also be used internally. If you speak during the call or use video, you are presumed to consent to recording and use of your voice or image. Now, I will pass things over to Raphael Isaac, Technology Manager and the US Department of Energy's Vehicle Technologies Office. Raphael and his colleagues in VTO's analysis sub-program are the DOE sponsors of Argonne's HEVISAM tool. Raphael, it is your turn.
RAPHAEL ISAAC: Great. Thanks Aishwarya. And great thanks for putting up that slide. Yeah. So I'm with Vehicle Technologies Office, and that slide kind of gives you an overview of all of the parts of ETO and the topics we're looking at. We do have our batteries and electrification team, we do have materials technology team, our AIMS team which looks at mobility systems.
We also are looking at off-road, air, marine, and rail applications. And then at the bottom you see the technology integration or TI team, which is the group supporting the webinar today. Just to give you a little bit more background about VTO, we do fund research development, demonstration deployment of new efficient and clean mobility options that are affordable to all Americans. And our research leverages among other resources unique capabilities and world class expertise of the nation's national laboratory system of which Argonne is one example.
So I will just very briefly introduce the HEVISAM model, and then I'll let Amgad and his team go into more detail, of course. But it was publicly released in 2023, and it's a technoeconomic analysis tool that the analysis part of VTO, that's my part of the Vehicle Technologies Office program. And this tool evaluates the levelized cost, fast charging scenarios of BEVs.
It takes into account station configurations, fleet parameters, and cost data, and outputs numerous variables including station capital and O&M costs, levelized cost and others, all of which are valuable to both government agencies in addition to our own, as well as industry stakeholders. And with that, I will send it back to you Aishwarya and the team. Thank you.
AISHWARYA KRISHNAM: Thanks Raphael. And now I will introduce our presenters for today's session. Our webinar presenters today are Amgad Elgowainy and Sajag Poudel. Amgad is a senior scientist distinguished fellow and the leader of the electrification and infrastructure group at Argonne National Laboratory. He has been supervising the development of many environmental life cycle analysis and technoeconomic analysis tools including today's topic, HEVISAM.
His models are recognized by thousands of users globally. Dr. Elgowainy has authored and co-authored over 200 technical publications. Sajag Poudel, is an energy systems analyst at Argonne National Laboratory. His research focuses on modeling of energy infrastructures, including innovative charging of electric vehicles, hydrogen delivery cost modeling, and thermodynamic and fluid modeling. He contributes to expanding Argonne's numerical tools including HEVISAM and HDSAM. Amgad, the floor is yours.
AMGAD ELGOWAINY: Thank you, Aishwarya. And good morning, good afternoon, everyone depending where you are. I will share the screen. But please let me know if you can see my screen.
AISHWARYA KRISHNAM: Yes, looks good.
AMGAD ELGOWAINY: OK, so I will go over a tool we developed to support DOE analysis team. Raphael, thank you for the introduction. Like Aishwarya mentioned, HEVISAM stands for heavy duty battery electric vehicle infrastructure scenario analysis model. This tool was developed in support of DOE, but usually, we make our tools public if they could be of value to the stakeholders.
Here we will show two case studies exercising that tool, one for port application, one for school bus applications. And I will go over the model at the end. If we have time, I will do a quick demo on the tool, and we will also take questions and try to answer. I would like to acknowledge my colleagues, Krishna Reddi, who is not able to join due to some emergency, and Sajag, who is ready online. He will help with answering some of the questions on the chat.
So HEVISAM is a technoeconomic analysis tool. Basically, it is Excel-based. It populates some cost data and some financial economic assumptions, and they try to look at the levelized cost of charging. The focus here is on some heavy duty. These could be trucks, this could be heavy, heavy forklifts, this could be any equipment with significant battery capacity that will require fast charging.
The reason we did not focus on level 2, for example, or slow charging because these will likely be for light duty vehicles, and many of these may able even to charge at home. So here we are looking at stations that will be developed to satisfy these heavy duty vehicle charging.
So the essence of the tool is to evaluate scenarios, what if? What if we have these many trucks, for example? How fast we want to charge them? Do we have opportunities for like multiple charging per port, for example, and things like that? And try to identify what are the key cost driver, and also give some estimate about what is the bottom line dollar per kilowatt hour charge into the vehicle?
So the levelized cost of charging incorporates the key cost elements, so capital costs including the equipment and also installation, operation, maintenance, energy like the electricity rates. This will be like time of use rates, or demanded charges, or any additional like fixed monthly charges. The model will calculate the cash flow discounted actually too for a net present value of zero, and to provide a levelized cost in dollar per kilowatt hour embedding there are some rate of return.
So the HEVISAM allows the user to define the different charging scenarios across all user input. We have some default their electricity rates, depending where you are and the utility you are sourcing from among other parameters of interest.
So the tool actually has input fields. I mean, the input would be at a very high level. What station configuration we should consider, how many charging ports, for example? Are we talking about plug-in, pantograph charging, the level of charging, and then the fleet or the battery electric vehicles that we would like to charge.
How big is the fleet? How big is the battery size, for example? When do you want to charge these vehicles? Do we charge them collectively do you have opportunity for charging within the day, for example? The battery charging profile itself, actually how fast you could charge it from a low state of charge compared to a high state of charge.
And then, of course, the equipment cost data, the electricity rate, and then on top of that we need some economic and financial assumptions, I will go over some of these. The model, again, is available in public domain. You see the link at the bottom of the slide. This is where you could click there with simple registration, download the model. And with the user manual it will provide some guide. This webinar is to help really give some introduction to the model, and how to use it, and what cases studies could be evaluated.
The outputs will be, what is the total capital aggregate? What is the other capital investment that may be really fixed costs overhead? What is the operation maintenance, energy cost? And how do they contribute to a levelized cost of charging? There will also be some cash flow tables shows actually when the project can break into the positive cash flow territory.
So this is a depiction of the tool. Actually, on the left, you will see an hourly charging profile. This will be important because this will tell how many equipment will charge in parallel, what is the implication of that on the load on the station, and this will tell us how many charging ports. I mean, how we can size the transformer or the switching gears among others.
It will also tell us the average utilization of the station equipment and things like that. And then the model will size really these components, like size the– depending on how fast you want to charge will really tell how many charging ports will be required, how many transformers, the capacity of these transformers, switchgears among others, and then other capital costs that will be required.
And then with financial economic assumptions, the model will able to calculate the bottom line dollar per kilowatt hour charge into the vehicle, and to provide some bisection really about the contribution of capital, versus operation, versus the energy, and to provide also some cash flows. Again, this model was developed to support DOE, and they are to inform their R&D program, and to understand what are the cost implications, and how some of these technologies will fare against other technologies, and systematically examine the impact of these parameters, basically understanding what are the key cost drivers.
And with some R&D, I mean, some of these could be mitigated to allow DOE to reach some of the cost targets. So where do we get our data from? Our data comes from different sources, so we did a lot of expert interviews to understand when they operate, really, how much is the source of their capital? What are the energy costs, other operating costs?
I mean, things related to warranties, or extended warranties, or insurance among others. And we looked at vendor websites to look at some of the equipment, the capital costs associated with these. We extensively looked at industry reports and also surveyed the literature.
These are mainly to populate default values, but all these values can be changed by the user. As we know really, these are not static. Actually, depending on the time, the location, and the size of the project, these cost numbers and operating parameters depending on the fleet composition among others will change for each deployment. So these can be varied by the user.
This will be like the interface. And again, if I have time, I will go over at the end of my presentation just to highlight where are the key inputs in the model. Here we show actually, the key parameters, how many vehicles? What is the size of the battery? I mean, when you go to the charging station, how much battery is left? What is the SOC?
I mean, when do you want to charge? What is your target end of SOC? Among others. So these actually will be user input and then some economic financial parameters. What is rate of return, for example? What is the analysis period for the project? What is the debt equity ratio, interest on debt among others? And all of these can be populated and then the user will hit Calculate basically, to evaluate the levelized cost of charging.
So this is actually some, I mean, economic assumption, and these are some of the financial assumptions how you depreciate your equipment among others. So utility rates as you will see later is a key cost driver. So in addition to the capital, the energy cost itself could be significant. Here we provide a scenario from a utility in the New England region. Actually, you see here the blue line pointing to the primary vertical axis shows the utility rate.
Actually, in this case it is flat throughout the day around $0.09 per kilowatt hour. And then you see the orange curve, which is pointing to the secondary vertical axis showing what is the demanded charges per kilowatt? And this means that if there are many vehicles charging concurrently, and depending on how fast they charge this could be a significant cost component.
And then there will be some other like if you have a fixed monthly charge, actually, then this will go on top of this. So these are all inputs that the user can specify by the hour of the day, time of use rate, demanded charges, and any fixed monthly charges. The equipment cost is another user input that can override the default. In this case, we'll look at a scenario where we use 150 kilowatt charger.
I mean, the battery in this case, as I mentioned earlier, will be for these vehicles 180 kilowatts, so 150 kilowatt actually will charge this vehicle relatively quickly. These are the costs assumed for the charger, for the transformer, and for the switchgear, and plus the installation cost. The annual maintenance cost is specified as a percentage of the installed capital.
And in this case, the charger is assumed to be the annual cost will be 5% of the installed capital. And then for the transformer and switchgear, it is only 1%. How fast you charge battery. Battery do not charge at the same rate. It is like as if as you charge the battery more, you build some back more resistance, basically, to allowing the charge into the battery.
So this is the charging profile, and this is, again, can vary by the battery pack. And we are using the argon backpack model really to populate some default there. As you see on the left, really, depending on the SOCs, the type of the battery, and how the capacity of the battery to receive the charge. Your charging rate as a percentage of the maximum possible will depend on the SOC, the state of charge as you start the charging of the battery.
So if you are below 75% the state of charge, and you can really charge at the maximum rate. And here you will see as you really go beyond that, then the charging rate will drop over time to top it off, actually, from 95% state of charge to a full 100%, then this will be charged at a rate, a quarter of the maximum possible rate. Again, this will be a user input. Depending on the type of battery, this could be changed by the user.
This will be important to examine how long the vehicle will need to be at the charger to reach a certain state of charge at the end of the charging session. So let's start with two demos. One on port equipment, and which is just a scenario just to demonstrate, and then later we'll look at a school bus scenario.
So here we are looking at 140 trucks, each carrying a battery of 180 kilowatt hour. The average use of these over the duty cycle is about 12 kilowatts, this is power not energy. And then in this case, we assume for 100 kilowatt hour if we have 150 kilowatts at the end, again, depending on the previous slide, this may take like somewhere between an hour to two hours really to go from a low state of charge to a high state of charge.
So we'll look at several cases. One case here is to have these 100 vehicles divided into two groups, we call them group A, group B. And the idea here is to try to maximize or to improve the utilization of the charging equipment because the capital cost is a key cost driver. So rather than having 100 charging ports, one for each vehicle, we will say, well, we'll have one port to charge two vehicles. And we'll segregate them so that group A will come charge, and then we'll allow five minutes in between, and then group 2 can come and charge. So this is basically the scenario here.
So and the table really will provide some details about when they come, you will see it also on the graph. The green shaded area, this is when the group A, and then followed by group B, which is the dashed line really come to charge. And as you could see from early morning, you operate the vehicle, this is the orange portion of the chart. Your SOC will drop, and then you'll have chance to charge around 10 AM for group A, 10:30 AM for group B.
For half an hour this will raise your state of charge to something around maybe 80%, 85% and then you operate again. Then in the early afternoon you may have another opportunity there, and then in the late afternoon. And then overnight, you could really have this charge fully to 100% for the start of the next day. So this is, again, a scenario. It could be anything, but in this case the key factor here is that, two vehicles per port.
This is a scenario we will say, we want one port per vehicle, and this vehicle can come any time because each vehicle has a dedicated port. And we will assume similar scenario, but in this case really will see only like one group of vehicle. I mean, these are the 100. Actually, each one has its own plug or port, and then they will come and charge again.
Now, this vehicle will rather than having them segregated, I mean, in this case, I mean, showing that if these vehicle come during their break to charge collectively, this will raise really the load actually in terms of how many kilowatts or megawatts actually demand on the session, which will exacerbate the not only the number of charger, but the demand demanded charge, the transformer needs, the switchgear need among others. So this is just to provide a contrast.
And of course, here one port per vehicle is meaning low utilization of that capital compared to case 1. What does it mean if we compare the two? Actually, this is case 1 on the left. You see the pie chart, you see the contribution of the capital compared to case 2. Case 2 actually is nearly double the capital because you double really the number of ports. You see also the orange section of the pie for case 2 is higher because you have higher demanded charges.
And then you will see the– I am sorry, because you have more equipment, you will have more operation and maintenance. And then the green section, you will see it is also higher because of the demanded charges. So this is a table at the bottom shows that roughly the capital investment from case 1 where you have two vehicles per port, and to case 2, which you have only one vehicle per port. Your capital costs will double, your levelized cost will go from nearly $0.40 to $0.60 per kilowatt hour.
So this is really what the model can really show, where are the trade offs actually from how you schedule the operation, whether you have this opportunity or not, and what will be the implication for this. This was really the key factor here I mentioned really the demand charges. So here you will see on the left for case 1 what will be the power required. This will be the blue curve, the energy use actually for that station.
And for case 2, you will see nearly the power doubles. And this means that your demanded charges will be significant in addition needing more transformer, more switch gears, in addition to the larger number of port. And therefore, you will see really the implication of that, of low utilization will be really key into your levelized cost of charging. Now, what if we have an opportunity to avoid the demanded charges, basically, to put an energy storage system, a battery energy storage?
Basically to charges when the time of use rate is low, the demanded charges are low or non-existent. And then when we have high time of use rate or high demand charges, then we let the battery discharge. So this will be like a trade off by putting some capital upfront, and then trying to reduce our energy cost. So this is a scenario we show in this case, we put like an eight megawatt hour battery or 8,000 kilowatt hour battery with a capital cost of slightly over $2 million.
This is roughly less than $300 per kilowatt hour including what you see in the formula there are some manufacturing tax credit and investment tax credit under the IRA. So in that case, what does it mean? We are increasing the capital. What we would like to understand, what is the net reduction on the energy cost? And whether the net actually will be positive or negative.
One thing here to note is also, when we put the battery, we add some capital cost. But because we avoid the significant power at certain periods, then we can reduce the number of transformer and the switchgear, and this will partially offset the capital cost of the battery. And with the hope that the reduction in energy costs can do the complete offset or turn actually into some benefits.
So here it shows really what that scenario actually is. This will be like case 1 we had before, but now we are adding a battery energy storage. And here you will see the profile. There is you charge the battery at the low demand charges and then discharge really in the other periods to avoid these charges. So case 1, case 3. You will see, again, the case 1 is, we had two vehicles per port, case 3 is the same, but case 3 has additional capital in this case the battery.
Partially offset by the number of transformer and switchgear, but still you see the contribution there is higher. This is the blue section and but you see the energy cost is significantly lower here. $0.11 contribution per kilowatt hour for the battery case compared to $0.19 in the baseline case.
So despite having an additional capital, potentially, these battery if the cost is low and if it is used in a way to avoid the demanded charges and reduce some of the capital, the net-net actually could be positive, so rather than near $0.40 it will be closer to $0.33 in that case. This will be strongly dependent on the cost of that battery energy storage and, of course, the life of that battery, and how strategically this could be deployed to avoid some of the energy rates with their time of use rate or demanded charges.
Finally, we'd like to look at the opportunity charging. What if we are able to schedule the fleet charging to avoid periods where we have high demand charges? I mean, at least, partially. And here you will see case 1, and you see the blue profile there is where you have really these demanded charges. And in case 4, we try to put the green sections outside really the demanded charging period. What does that mean? And this is, again, depending on the flexibility of the charging mode, including even any batteries. Actually, what does that mean? And how much cost saving that could be, and this will be for this very specific scenario.
So here you will see for that scenario that the cost will be lower than the cost in case 1, I believe case one was, again, $0.39 a kilowatt hour. And this was really pushing that $0.19 per kilowatt hour into only 11, actually, will have some significant cost reduction. So in this case, actually, you will have a similar number of charger, similar number of transformer, but basically you try to have the charging station with some flexibility to maximize your benefit from the utility rates.
So this is really just comparing these four cases. Again, case 1, two vehicles per port, case 2, one vehicle per port, case 3 is similar to case 1, but with a battery energy storage, and the case 4 is similar to case 1, but really with some flexibility of charging outside the high demand charging hours. And you will see there, the levelized cost of charging in the top, and you see the capital investment. Case 2 is much higher, almost double because we have really less utilization of the capital, many more number of ports, higher number of switchgear transformer.
Case 3 is with the battery. Actually, is higher than case 1 basically because of the battery with some offset from the transformer and the switchgear, and the case 4 is similar to case 1, actually, but with flexibility really to reduce your energy cost. Now, we'll go to another scenario. Actually, what if we're looking at a bus fleet? Actually, and in this case we'll look at something somewhat different, and these are scenarios that could be done.
And here we look at 80 battery electric buses, much higher battery capacity here for 40 kilowatt hour. And then we assume that these will all charge overnight with a few of them have the opportunity to charge during the day. And here we put them like the 80 into like three groups. We say, 40 of them actually will charge at 50 kilowatt. And 20 of them will charge at a faster rate, and these actually these faster charger will allow for even some opportunity charging.
And then we have group 3, actually, 20 will charge at 50 kilowatts, but with some opportunity charging during the day as you see in the curve on the right there. Actually, you see really some charging activity around noon. So this is a scenario– again, this is a scenario that could be changed actually, and this could charging how fast you charge, the starting batteries we see among others when you charge, all of that will be input to the model. But we'll give you a feel of what does that look like.
So here you will see that there is an opportunity for– there is actually 60 active charging units with 50 kilowatt each, and then we have 20, 150 kilowatt much faster charger that could be used also for overnight charger. We add few back ups, but this is unnecessary actually, and this could be more or less depending on the user input. And then, the number of transformer that will be required and switch gears will depend on the peak power as you see on the figure on the right.
The levelized cost of charging actually, you will see for that scenario. I mean, the capital will be dominant, operation, and the maintenance, and the energy costs actually, will add up to the total contribution of that case. Now, does it matter if we build these stations for a small fleet versus a larger fleet there is some economies of scale? So here we look at two scenarios actually, one that is 80 buses, one is a depot that has only 20 buses, for example.
And we maintain actually the proportion of the 50 kilowatt versus 150 kilowatt charging. And on the figure on the right, you will see really that lower number of vehicles per station actually have some impact, but not very dramatic. So for the scenario, the best scenario here we were talking about $0.33 per kilowatt hour, and that will increase to $0.38 or $0.39 a kilowatt hour, so some economies of scale, but not very strong.
You see the energy cost did not change in this case, but the capital, and the operation, and the maintenance did differ slightly. So with that, I will stop. Of course, I would like to acknowledge our sponsors at DOE Vehicle Technologies Office analysis program, so thank you Raphael for your support. And here is our information. If you have any questions, you could reach out to me, or Krishna, or Sajag who is online. Again, the model is free for download at the link you see here, and I will be happy to take questions.
SAJAG POUDEL: Amgad, there are a couple of questions in the chat, so maybe we can start with those.
AMGAD ELGOWAINY: Thank you, Sajag. So let us see. Do you consider vehicle to grid? So yeah, in this model we do not consider that. Again, this is the first release of the model, so we would appreciate some feedbacks and also what features you would like to see. We put the battery energy storage, we thought this will be useful. We do not include vehicle to grid for that case. So this is something perhaps we could explore with our sponsor if we can expand the model to cover that feature.
Is HEVISAM capable to simulate the charging station with PV panels? Yes, with some modification. We did model, use the model to look at some off-grid scenarios basically, to see if we can be completely independent with some PV canopy. And basically, the way we did it in HEVISAM, of course, we can formalize that is basically, to tag that into the capital cost.
And also make sure that we incorporate it in a place where the operation and maintenance will be consistent, so that it actually flows also well through the existing cash flow tables. Ideally, we want to have that as a feature, and I think this will be an important feature to look at off-grid scenario.

AMGAD ELGOWAINY: Can I use the HEVISAM to simulate for light duty vehicles or electric cars? The answer is yes. Although, it was not developed for that, but there is no reason why this could not be there. The only caveat is that we did not built in defaults for level 2, for example, charging. And if you have these, you could just hijack, really, let us say the 50 kilowatt and to make it really populate the course with level 2, so hypothetically, yes.
Again, this could be a feature that we could expand and add the level 2 charging, actually, into the default scenario, so that this will also be not necessarily just for heavy duty, but also covers the light duty. And I hope Sajag you just make note of these, these are all potential opportunities to expand the features of the model.
SAJAG POUDEL: And also just visit a link for one of the report where we saw the– demonstrate the kind of off-grid and light duty vehicles by user could be [INAUDIBLE] to someone to the test.
AMGAD ELGOWAINY: Thank you. Is the battery state of charge considered within modeling? Yes. So this is important. You remember earlier I showed that table where it shows, I mean, the percentage of the maximum charging rate, we said up to 75%. This is by default it can be changed by the user. You can charge at the maximum rate, and then it has really to slow down until when you get near topping of between 95% to 100% state of charge it will be roughly at a quarter of the maximum charging rate.
So we do that, the model also specifies at the end of the day, do you have 20% or 30% state of charge? And where the maximum you want to be actually the 90%, is it 100%, is it 85%? So the model will allow you to do that, and then it will calculate based on that charging rate profile how long it will take to charge a given battery.
Do you consider scheduling the charging session to align with low tariff times? So this will be a user input. You remember scenario 4 or case 4, where we try to schedule the charging to avoid the high demand charge rate session. So the model will allow you to look at these scenarios of both whether these are practical given the operation. For example, if you look at transit buses, they may not have, they have certain schedules, they have really to serve their customers at certain times, and they may have only limited window of opportunity to charge including, of course, overnight.
But in other applications, you may have that flexibility, and the model will allow you to schedule the charging session outside, really, the high charging rates with our time of use or demand charges, so the answer is, yes. And I will likely use the last 5 minutes or 10 minutes to go and show you where exactly you can do that. OK. This is the report, thank you, Sajag.
What validation have you done to ensure the model is providing the correct data? So I believe this is a good question. So we built this model on a platform that was previously used to look at hydrogen fueling, which is much more complex. And we just use it as a backbone or like the framework for the cash flow and all of that, and just to structure it really to be consistent with a charging for a battery electric vehicles.
We run these models by independent consultant, who is very, very capable actually in looking at these. And they replicate the results with their own independent platform to see if we can match actually, and this is really the way we validated that model. Thank you for that question. The state of charge the question was on battery assisted EVSE not the vehicles themselves, that they change– does that change the answer? OK, so let me go back to the state of charge.
OK, I see. So you are talking about the storage system, right? So the Z battery, I believe, we limit the SOC not to below 10%, and not to exceed I believe 95%. So of course, that battery energy storage system, again, the cost of it with our cycling, a deep cycling or shallow cycling, impacts the life of the battery is also a key question, but this is something that the user can define in the model.
We allow to look at the, not only the cost of the battery, but whether this is battery that was built for primary use. Is this a secondary use battery? The cost of that the life of that, all of these are input into the model that the user can verify. So yeah, not every battery will have 100% usable capacity. I think, this is the essence of that question.
When was the cost data for inputs collected? Will you continue to update it? The data was collected in the year 2021, 2022. And this is a model like Raphael mentioned, was released last year. So the model is fairly new, and the cost of data, of course, could have changed over the last two years or so. But again, these are representative costs, and they likely will change over time, but they are also user input.
So let us say if I am a fleet operator, I will reach out and get quotes on like charging equipment, then I can go to the model and change that depending really on the cost I have received, including the installation cost, including whether we have warranty or not, or extended warranty or not, and things like that. So all of these could be changed in the model.
I think, I got to the last question. I will be happy to answer more as we go, but let me load the model just to go quickly over it. And again, any feedback on that will be helpful. So I will stop sharing, and then share again. Let me just load the model quickly. OK, so I am sharing the screen now. And let me know if you can see this at all.
AMGAD ELGOWAINY: So OK. So this is at all, this is some introduction to the tool. All blue cells are calculation cells, so these are not user input. Of course, you can still override the formula there, but sometimes if there is a cell dependent on that, it might really result in the model not functioning as designed or as required.
The user inputs are always in this color, the peach color, or the beige color. Actually, that will be the user input cells. The green cells are for information, so if you want to add a comment or a note, actually, this will be where we put that. This is for information purposes only. And then if we have a drop down menu just to highlight these sometimes, these are not obvious, we just really work with a darker color of the peach basically, that.
This is where you could download the model, and this is where you can contact us if you have any questions. But getting to the scenario actually, this is an important one. So you will say I want to consider a system without an energy storage or with energy storage, for example. This will be some of the economic assumptions or what, for example, discount rate you want to consider, how long is that the project that the analysis period you want to cover, what did you may have actually versus equity? What is the interest of that if you have any and how long actually is the period of that date?
So all of these will be important parameters. How long the project will take to construct, and so on, and so forth. Here it is just a pre-populated with the example I mentioned, so this is truck group A, B, truck group A, B. So let us say truck group A will come overnight. This is actually coming at 1 AM, and we would like comes with half empty, and would like at the end of the session to be 100%, for example.
And then this will be the same for the other actually group. And then you will see at 10 AM, group A will come. And then after half an hour, group B will come. Remember, two vehicles per charging port. And similarly, in the midday and then at the end of the day actually, the late afternoon. So this is a similar charge, similar scenario we have developed before.
This was the 150 kilowatt hour charging rate. The vehicle or the battery will allow 132. This will be defined based on the exact vehicle battery characteristics. And then here we look at the 150 kilowatt battery charger, and then you will see here the 50 and the 350 if you want to look at other scenarios. We will keep updating this with other charging equipment. I mentioned, we do not have level 2, for example, so this is something we will need to look for. Buses actually, whether you want to do pantograph or plug-in, if it is really plug-in you say no, if it is pantograph you will say yes, basically.
Now, if you want to look at the electricity rate, I remember this was a question. This you will click there. It will bring you to the energy sheet or the utility rate. This is the time of use rate depending on the utility. This is the $0.09 you remember we had it flat over throughout the day. These are the demanded charges actually per kilowatt. You can define them in that block here, and this will basically set your utility rates for that. If you have a fixed monthly charge, it will appear there.
And then here it you will see based on your charging profile there, and the rates here it will tell you what will be your energy consumption. So basically, this is a chart that we copied and paste into our point, PowerPoint, actually. So it will tell you actually what will be your maximum peak, when, and things like that in addition to the energy. If I go back to the scenario and look at specified station details, for example.
And remember, we said how many ports per vehicle actually, and then here whether you have extended warranty or not, and the things like that. What is the power of your transformer, for example, in kilowatts? I mean, again, here station energy storage. You remember I told you, is the battery new? Does it come from a second life? And things like that. So all of these will be changes that the user can put into the model.
This is a lifetime. This means over 30 years we will need to replace this. Actually, this could change. This is a user input, it is a cell. This is how you depreciate your equipment. If you want to depreciate the charger over 10 years, actually, this is, again, a dropdown menu. You can pick any of these. Typically, this will be depending on the IRS schedule. Actually, this is the MACRS depreciation schedule. And then some state tax, federal tax actually, and the inflation rate, this can really propagate throughout the cash flow.
Let us see. Cost data. I believe if I scroll down here cost data here, this is the depreciation. So typically, most will not really look at these cost indices. Actually, this is the inflation. Actually, especially for equipment, but this is an important one. Actually, the charging equipment rating depending on the power, whether you have a single dispenser per charging equipment, or dual dispensers.
Actually, this is something we did not really show as a case study for that, whether you want to have extended warranty and things like that, this will be that the pantograph if you use it for buses or other actually, what will be the cost of that? So all the features out there are user input. This is a different power rating for the transformer, this is the switchgear. And then some of the battery actually, new battery, second life battery.
So all of these will be user input, again, these are the peach results. So if I go back to here. And once you define your scenarios, you can click Calculate, and then here this table will populate the results. So here this is our case 1, you remember it was $0.39. The capital investment was about $10 million, and the annual operation and maintenance was about 20% of that.
And then this is the contribution of the capital, the energy, and the operation, and the maintenance. And the energy, it shows you also actually, what I mean a pictorial graph of how the charging will occur during the day. This is a very high level overview of the model. And it is again, a simple model, first version. It is complex in some details.
And here actually, you can look really at the result summary. This is really what we copied actually into the PowerPoint for the case 1 study. I think this is it, so I will turn it back to Aishwarya. And thank you all for listening.
AISHWARYA KRISHNAM: Great. Well, thank you all for joining us today. I'm going to just really quickly, again, drop a link to the tool in the chat, as well as contact information for our presenters today, and thank you for attending. Please let us know if you have any questions, we can pass those along to the presenters later. And like I said earlier, we'll have a recording posted on the Clean Cities and Communities website in the next seven business days. Have a great rest of your day.