Welcome to the Honours week of this course. This week we will study the idea of co-simulating the battery pack along with its load at the same time. What this means is that we simulate the load and the battery pack together in an integrated way. It's important to be able to do so before we go too far down a design path of designing and building our batteries. We want to know if there are any interactions between the battery and the load that we might not have anticipated at first that would influence how we design the battery. By performing this co-simulation, we can ensure that the battery pack will meet all of its performance requirements before we make a large investment in tooling a battery pack, battery manufacturing facility. Generally, we would consider co-simulation only for cases where a large investment is being made for the battery and where design improvements might be made based on new knowledge gained from the co-simulation. So, we might consider co-simulating electric vehicle systems, or grid connected systems, or even mobile phones which have a very large power demand and can benefit from power management strategies. We would probably not consider co-simulating a power tool or a flashlight for example, because there are very few opportunities for improving battery management or battery pack design that would cause any noticeable change in those cases. Now clearly, we cannot study all possible battery loads in one week. So, here we restrict ourselves to learning how to co-simulate a battery pack along with the type of vehicle application. In order to predict the demand on the battery by the vehicle, we must simulate the vehicle over a number of real world operating scenarios and these will provide a profile or profiles of required battery power versus time. We might consider co-simulating the battery with a hybrid electric vehicle. However, hybrid electric vehicle simulations are very complicated. In order to simulate the vehicle accurately, you must be able to simulate all of the complexity of the internal combustion engine combined with the multi-speed transmission, combined with the hybrid control algorithm. So, this is something that actually is routinely done in industry, but it's beyond the scope of this particular course. On the other hand, electric vehicle simulations are simulating a plug-in hybrid electric vehicle that's operating presently in electric-only mode is quite straightforward. There is no internal combustion engine that contributes to the need of simulating the same. There's only a single fixed speed gear ratio to consider. There is no power source blending algorithm that needs to be simulated to determine how much power comes from the internal combustion engine, and how much from the battery electric system. So, we are going to focus on co-simulating a battery pack with an electric vehicle this week. In order to be able to simulate an electric vehicle, we need two things. First, we need an accurate model or description of the vehicle itself. Second, we need to understand the task that the vehicle is required to accomplish. The vehicle description must include equations and parameter values that characterize the battery inside the vehicle, and this includes all the characteristics of the cells that are used, and how these cells are configured into modules, and how the modules are then configured into an overall battery pack. We also require a description of the motor of the vehicle and the power electronics that drive that motor which is usually called the inverter of the vehicle. We must understand the drivetrain of the vehicle and certain performance characteristics of the vehicle. The task of the vehicle is required to accomplish is generally specified using a profile of desired speed versus time, and this profile can also include the road grade profile as well. In this week, we will look at four common examples of profiles of desired speed versus time. The first is known as the Urban Dynamometer Driving Schedule, or UDDS profile. The second is known as the Highway Fuel Efficiency Test. Now, the first profile is used by the United States Environmental Protection Agency to characterize the city fuel economy of a standard gasoline powered vehicle, and it is considered a reasonable driving profile for city driving. The second profile is also used by the United States Environmental Protection Agency to characterize the fuel economy of a vehicle driving on a highway for again a gasoline powered vehicle, so it's considered a reasonable driving profile for highway driving. The third profile is the New York City Cycle. This is more representative of stop and go traffic that is experienced by taxicabs and by city buses in a large metropolitan area. And the fourth profile that we look at is known as the US06 or US06 drive cycle profile. This was actually recorded on the road here in Colorado by the United States National Renewable Energy Laboratory. They took a vehicle that was outfitted with sensors and they drove it along Highway six in Colorado near their headquarters. And this drive cycle is representative of quite a strenuous mountain driving scenario at relatively high speeds. The four profiles that I mentioned on the previous slide are drawn on the figures of this slide. The top left figure shows a profile of desired speed versus time for the UDDS profile. The top right shows the highway driving profile. The bottom left shows the New York City Cycle profile. And the bottom right shows the US06 drive profile. The two profiles on the left are for urban or city driving and you can see that there are many stop and start events as the speed goes between zero and relatively low maximum values. The two figures on the right are dominated by highway driving, although the US06 also has some city driving at the end. These two profiles have more consistent speed and less acceleration demand although they also have higher low due to wind drag and high speeds. I will provide these four drive cycles to you as resources on the website so that you will be able to simulate vehicles using them as well. Each one of these profiles has a sample rate of one update per second in desired speed. You will learn this week how to co-simulate a battery pack and an electric vehicle. And to do so, you will learn the basic equations that must be used to simulate all of these drive profiles over the next lessons. The general approach that you will learn to simulating a battery pack along with the electric vehicle is illustrated in this figure. We start with the top left block. We retrieve the desired speed for this point in time for any of the drive cycle profiles that you just saw. We compare the desired speed with the present actual speed, and from that, we compute what the desired acceleration must be in order to achieve this desired new speed. Once we know the acceleration, we can compute the desired force between the wheel and the road surface to achieve that acceleration, and that also will allow us to compute a desired torque produced by the electric motor. However, the desired torque that may be achievable by a particular motor design might also be less than the desired torque. So, the next step is to impose practical limits on the values that were previously computed in order to bring those values into the range that happens to be achievable by the motor in our design. The limited value of torque allows us then to recompute the actual road force that we are able to develop and this might be different from the desired road force that is required to achieve a desired acceleration. So then, we must also recompute the actual acceleration, and from that, the actual achieved speed of the vehicle. We also have to consider motor power limits and these are separate from and distinct from motor torque limits. And we must limit the motor torque and the motor power to achieve something that is feasible by the motor used in our design. Using the value of motor power, we can compute the demanded battery power. And then, we can update the battery state of charge in the model as well. And by the end of simulating one drive cycle, we know how much energy has been depleted from the battery pack to accomplish that drive cycle. And from that information, we can extrapolate the overall vehicle range for a full battery charge. So, to summarize this introduction to week about co-simulation, you have learned that it is valuable to be able to co-simulate a battery pack and its load. This week we will look at examples of an electric vehicle simulator which shows some relevant details about co-simulation that are simple enough to develop fairly quickly. You've learned that the approach will be to determine a desired acceleration and from that desired road force that is needed to meet the desired speed profile. And then, you have learned that we will impose the torque and power limits on these variables from the actual motor design that we intend to use for our vehicle. And then, that will allow us to compute the actual forces and the actual acceleration and the actual speed. One output of interest will be the demanded battery power versus time. Another output of interest will be the vehicle range which is extrapolated from the energy depleted over a single drive cycle.
