Good afternoon to, everyone, and thank you for coming here today so. I, would like to thank the organizing. Committee for inviting me here and too, and give me this opportunity to, talk about the research I'm conducting, with my students, at Stanford and in. Particular today we're gonna look at how, we can, design that, we can use, but that is better through, physics based modeling, and control and in, particular we're going to look at automotive, applications. So, there, are so many things here that we are going to need. To know to. Understand, what what we do so we, are going to talk about energy storage systems, physics, based modeling, control, theory and automotive, batteries, so. I, did. Some homework and I realized notice. That there hasn't been much control. Covered, in this seminar, series so. There's, going to be some equations, some mathematical. Modeling so I'll try not to go too deep but. It's. Important for you to know what type of tools were using to understand, whether also the challenge is to. Develop those those, algorithms. So. Let me just start off with. Describing. What, is a model, base estimator, so. We have a system and this system can be, this. Case a battery, battery cell and out, of this system we want to recognize what, are the inputs, outputs and, States so, the inputs are D what we call actuators, so there are the signals that we use to, drive the dynamics, of a system to our reference points. Sensors. The output are the signals. That we measures, through. Some sensors, some hardware and then. Inside this box we have dynamics, that evolve over time which, we model through. States. Now. An inverse problem, is ready to try to reconstruct, and. Note the state by. Information. From the outputs, what, we want to do is something a little bit more sophisticated. Because, what we want to try to realize. In, the build is an observer, an, algorithm, that from measurements. From, the output measurements, from the input measurements, of the real system is, able to reconstruct the, states of the system itself and we also want to guarantee the disease. This estimate come. Urges to the realvalue okay. And we. Do this by first of all proving that the system is observable, so we want this a structural. Property, that we need to require, for. The system to to have in order to reconstruct. This. The state I'm also going to use feedback the, feedback allow, us to really guarantee. The convergence, of this image to the real States and, so. There are Maine. So Maine some, important, choices that we have to make. And. The first one is what is the model that we can use or we should be using and what. Is the algorithms we want to use for the observable design and so. Many of these are the two main, choices so we're going to look at in this talk modeling. And. We're. Going to also show some. New observer, algorithm. So. Energy. Storage technologies, today when we talk about electrochemical. Energy storage, technologies, we, think, of a lithium ion batteries, right but. There is more than that and, one.
Way To. To. Summarize, those, the available energy storage, technologies. Current. You, know from the past so the ones that were going to have in the future is by using what, is called Ragan. II plot, Ragunan, was a professor, at Carnegie Mellon the time he published, his paper in. 1968. And what he did was to, propose. A way to. Describe. Energy, storages in terms of two parameters. Specific. Energy and specific, power. Specific, energy on the y-axis and specific, power on the x-axis and. In. Those two metrics were chosen because in, his work he was looking at. You. Know selecting. Systems. For electric. Vehicles, at. The time it, was ahead of his time and so, specific, power is ready to acceleration, so how quickly can extract, the energy out of our devices basically. I mean to. Describe. Acceleration. Properties. Specific. Energy, gives, you an, idea or a proxy of what range you. You know the range you can drive and so. This. Is analog log scale and what, he also can notice here those are those diagonal, lines and those are basically, they. Tell you how. Fast, you can discharge your device so, they tell you the time scale, of your energy storage okay, so super capacitor, capacitor, here there are devices. That you can discharge, in charge very fast let. Us see batteries, little, marine bodies bodies are higher. Specific. Energies, so. They. Go through some electrochemical. Reactions, so they are little bit slower than capacitor. You. Can see here that a single, device is not. Represented. By a dot it's, not located in one particular, position but there is a curve well, that says is that the energy decreases, as in more power you want extract out of it and so these are important, consideration. For the design. You. Can also use this dragony plot to, and this is what the Department, of Energy's none -. To. Tell you what are the targets, of your energy storage. System. In terms of application, so. For if, the all-electric. Vehicles plug-in at three vehicles, and sure, sustaining, Hybrid those, are the requirements, for the energy storage at, the end of the day we want those, those. Devices to be as close as possible in, terms of performance to, the internal combustion engine so. Internal combustion, engine is not an energy storage devices is an energy conversion but he can extract, very, nice. Fishing, trip a very with, very it's very effective, in extracting, and converting, a chemical energy in the fuel into mechanical, energy so, the end is so it's not just about it's. A matter of cost, for. Electric vehicles to be, you. Know suitable, solution. But it's a matter of having. The, right, energy storage, component. That is, performance. Close as possible to the internal combustion engine it's used to be keep. This in mind because you're going to see this later on the PT ratio, is very important, it's nothing but those diagonal. Lines okay it's also called the in the battery world see rate. Okay. So energy storage, vehicle, technologies. So, today we have. Lots. Of options in terms of vehicle. Power trains and sometimes we don't really know what those. Are right and, so, one way to plug, them and to see a look at them is by. Describing. Term in them in terms of size of internal combustion engine with respect to size of electric models what that allows you to do is to, travel. Along this route this, line and, as. You go from here to here the degree of electrification, increases. So the electric model becomes bigger. So the energy. Storage becomes bigger and D and at, some point you get you're gonna get rid of the internal combustion engine so. The. Common denominator of those powertrain. Technologies, is the energy, storage, and I'm not saying that it's battery, I want to set its energy storage because one. Of the flaws the design power stream design today is that we go straight blandly. To. To. Think that I think that a little my mother is the. The. System to be used which is probably not the right approach. Okay. So. Melissa. My mother is what has allowed this. Wide range of, options, to.
To. Exist okay, without this this. System. We won't be talking today about, electric. Vehicles okay. And so, good enough in late. Seventies, and eighties. Sure. That we, can realize a high energy density little, my mother is by introducing. A. Transition. Metal oxide, at the cathode so he could get very, high voltage out of these little. Minion batteries and this. Has revolutionized, the, portable electronics industry, at the time so. The first. Lithium-ion. Batteries, was commercialized, by, sun in the 1991. 1991, today. With. My own batteries are revolutionizing, the, automotive industry. And. Then what's happening, is that the electric utility sector has been impacted. In this, we have seen from, PG News last week by, this technology. So it's a big deal so, we need to understand, we to know how it works and eventually we also want to learn how to model, it, solely, to my own brother is the. Work based, on the redox, reaction. So there is a. Reduction. Oxidation reaction. Happening, and so, we have materials, at the anode that. Has. Been selected, in solid oh my mother let me tell you that it's it's a very nice marriage marriage, because, it's a donor acceptor, couples, so are not done. Donates. Electrons and, the cathode is made of a material that accept electrons, so it works very well and, as, the electrons basically. Travel. Along the external. Circuit, the ions, basically. Getting the intercalary from the anode and they pass through the. Separator. Into the electrolyte and go today to the cathode, those. Are also called rocket, charge battery that's just because the lead to basically go, back and forth between the two cut the electrodes, and the. Separator, is important, because allows, ions to, go through it so. But. It prevents electrons, okay, it prevents, the the two lectures to touch it prevents short circuit de madre and thermal, runaway and so forth. Okay. So, that's little more embroidery so if you ever ask yourself what, is a battery, so. Sometimes. It's not clear so, batteries. Is when we make a little, money on batteries for commercial, use what. We do we make a dough cylindrical. System. You, know they're very small I shoulda brought one with me, but. Those. Little guys they. Have 65, millimeter lankton eighteen millimeter diameter. They. Also can come a little money but that itself can come in the form of pouch cells from, this type deals they all have a nominal. Voltage of four bolts and. But. If. We. Don't make huge batteries what we do we create a battery, pack by connecting. Little minor cells in, series in parallel to. To, create a, pack that as the. Voltage. And the power that is needed you know in your vehicle, so does our attraction, battery pack. Tesla. Uses the cylindrical, cell. Nissan. Leaf uses the pouch cells and so forth so. There isn't why I'm talking about these, cells. Versus, pockets because we are going to look at later on some, of the. Characterization. Of those two different domain, which model it. Ammonium batteries work okay. So because of this. Structure. And. Because, we have many. Of those in simple, systems. Relatively. Simple system in a battery pack we. Need to make sure that everything. Is working properly and, that. Is done. By the battery, management system BMS and. That is basically the equivalent of, ECG or the engine control units, what it does gets. Information through, sensors, some measurement. In particularly, sense measure, current, voltage and temperature and, this information is used to prevent, to the battery to be overcharged, over this charge to go to some short, circuit, and to overheat, this is very important, and there. Are. There. Are many tasks. Many. Tasks that many, function, that we, ask the BMS to perform, and by, only using, those, measurements, and. And. So we want them to you know we want to get the right measurements, we want to control. Both.
The Temperature we want to balance the cell within the park we make sure that they are equally. They. Have all the same, amount of energy we don't want any unbalanced. Within. The pack for safety reason, monitoring. The state of charge which is a proxy of which is basically. Information. That gets translated into the driver language, in terms of how many mods I can still drive with my car with my battery we, want to make sure if something goes wrong we have a means to detect that to, to. Warn the driver and why, not you also want to do prognosis we want to be able to not only say. How. Old my. Battery so that's the state of health but also, wanna know how, long in the future I can still use my battery, so that is prognosis and the, all those functions are today implementing, BMS prognosis not yet body will very soon so. What we do in our lab is to design advanced BMS. And so what that means is that yes. We do have those we, can rely on those measurements, but we can do more we can create a beautiful measurement. So this word sensor, by means of estimators, so estimators, are used, by us as I. Mean, to look at what's inside so to track. And monitor the internal the concentration. Variations, the over potential some other importantly. Critical, parameters, Madhuri, there's not related to aging and so. Physical, inside are important, but also the control. Here is important, to develop estimator. That allows. You to, rigorously. Understand. When you're content to the right. Value. Of the parameters, and so. What. The industry uses is this safe, operating. Area. So. What you can see this is a. Temperature. Versus open. Circuit voltage. Diagram. And what. You can see is that the, battery is only using this green, area so. It's overly. Designed for. And the, design is very conservative, and that's, because we don't know my we don't want to over discharge in the over the other. Charge, and over discharge you, want to stay away from those. Edges. Because, the degradation is, going to be more pronounced, there and also because it's safer, to be within this but. The other reason is because we don't really know when we are here when we are here okay. So that's what we're leaving we, being. Even more conservative so. If you think about the, available. Power that, you can extract extract out of the body in terms of voltage that's the only that's. The only window that, you can use okay, if you want to stay within the normal function area within the body so this is the open circuit voltage response. And so, we only operate between usually, 20, and 80% state, of charge so the, rest is not being used at this point in time so. So. As I said initially one, of the critical question that we have to ask ourselves when, we go and develop an.
Estimator, Is what is the model we can use who we should be using and so, if you look at battery cells battery, weather. Is a, multi-skilled, system new. Timing light so. Things. That happenings. Dynamics, that evolve at the particle, scale atomic scale don't really matter at the system scale for, on board, we. Have time execution, they do matter for, design, but. Not for real-time. Application. And and. So, it's. It's a it's a it's a tough choice okay we don't really know where. To start what to do and so, but, that's a very important, it's a very important question and so if. Material. Scientist, and the, little chemist in chemistry, chemical. Engineers they work, very, hard to design by the batteries it's our job to as. A control, engineers, to really be able to extract the maximum performance we, don't want to waste anything, and so, if we, are given a battery with a nominal capacity of, a certain values with nominal potential, we want to be able to really get the most out of it now it can be done through some. Hard, Ock model-based. Estimation. Theory. So. That's. What we do in our lab, and. There our purpose, is to really link science, in control engineering, it so to really. Understand. And, bring what happens at the. Small. Scale up to the system, level scale and I. What. You see here is a schematic of a three-way catalyst and the gasoline particulate, filters it's, not a mistake that's, those, are the two type of systems, I. Do. Research on with other, students, when. I don't do battery work so but those systems they have lots of in common believe. It or not and so, and. So the, main the, main important, job. Is to fit to derive, and construct, physically, meaningful control, oriented model because. Then we can go ahead and do our advanced. Model, controller, development. And, also, vehicle, based, optimization. And so forth so. Modeling. Battery, modeling. There, are many two groups of modeling one is physics based the other one empirical, or control oriented and, the, people have used those different, people have used the. Two, groups of model for different purposes we do use physics based modeling, for design. So. If you want to design bodies. You want to change some, materials. You know, parameters. Like porosity or mm, T and you want to see the performance of the system level but, for a runtime. Stage so for onboard application, and we use control oriented models, and these. Two type of models have been very very very, separated. Until. Recently. And I. Will note also the model fly right they are wrong. But and what we are after here is to try to understand, how trustworthy, among.
Battery Model is because. The. Way I see it is that all, models are good if you understand, the limitation, and what. Is that you want to do with it so does. Empirical, models, also called equivalent circuit, models, are very good, and are today being very used for, EMS. Implementation. If you want to extract information, like, power and. Predictive. Altered response the problem with those models that is, it you to run you know in a real hardware, but. They rely on a very heavy. Calibration. Campaign, so, those parameters, here there are chemistry. Dependent, and they need to be calibrated across, different, temperature. Ranges and, see. Rate instead of charge and so forth but yet, very very, very useful. On. The other hand there is a new or different class of models called physics based models I told you that, what they do they try to look at the concentration. Of lead you within the particles, within the electrodes and also. Predict the electrolyte, dynamics, and the world. Potential, so forth and so, going, this way we. Find in the single particle model and the single. Particle model sendee also, the porous, electric electric, models are called the DF NOP, 2d model now. These are you. Know these, models, increasing complexity, as we go this way and body. Also increasing predictability, so we, get better and better response, out of this model as we add. Dynamics. And so, in this particular case the single particle model looks, at the electrodes. As if, there were two spherical. Particles, and we basically. Tracked. The the. Concentration, of lithium through, diffusion mechanism, inside those particles, and they, hence particle, models look at the dynamics. Of the electrolyte, single, band Peter deals in the still we make the assumption that you have two spherical electrodes. Two, spherical particles. In. The electrodes and then here the basically. This assumption, one electorates basically. Removed so, these are PDE partial, differential, equation model base. Model, and their base amass a charge, conservation in. The solid and electrolyte, phase and so. The question is can we use those model for control oriented purposes. And also, is this the best model to use so, the dfn model is the, model has been commonly, used for. Design purposes, but, also for, control. And one. Of the, criticism. And that I need to move to my control to control communities. That people. Have taken this model for granted, and they, have applied for traction. Batteries, in automotive, applications, and and. This. In. This business has created some summation. Terms of accuracy so, if you. I. Will, ago we started, looking at this modeling. Approach and with a, colleague. From my department Alina, Machado and, my. PhD student teri cashews now with Farah the future we, developed, microscale. Modeling. Framework. And. A. New micro scale modeling framework for little money on batteries and so i want to talk about the limitation, of this model again this is the one that is used today by everyone, control. People, and decider. So, neumann, developed. This model at the macro scale continuous. Model in 1993. And. If. A couple of years after the first lady my imbalance was introduced, and. It's. Based on very strong assumption, and assumptions, that the. Particles. Within, the electro in the electorate are spherical it's, very strong and also.
There's. No way to accommodate. This model, to, for, different shape. Of those particles. Another. Limitations, that we Newman developed this model with this student, leader, my own batteries were being used for portable electronics. And. For. Those applications, the, power rate, is very low, plus. Another. Issues that you. Wouldn't mind to replace your battery every two years in your laptop, right you, do mind or replacing, your battery, every, two years from electric, car so, that is not even an option and so, this model does not capture, any aging dynamics, and it. Does not work well, properly. At the high C rate of operation, and delete. And. Energy. Doesn't capture what, happens at the low state of charge and, so those are very critical. Important. Elements, when you want to design a traction, battery, for electric vehicle and the. Literature is show some of the. Inaccuracy. And divergence, of, simulation. From. Newman. Model at IC rate and. You. Know and. Towards. The end of this chart so. Lower, state of charge capturing. Dynamic celerity the charge is very important, because it gives you the ability to really understand, what's your effective, range. IC. Rate is important for fast charging and so, and, even, more so, so. In, the if. You look at the, scanning. Electron. Microscope. Images you see also for that four different chemistry, a nickel. Metal nickel. Manganese cobalt and. Lithium. Cobalt oxide and. Graphite and so forth those. Particles, are not spherical, and if you're trying to use the Newman, model for those different, chemistry, you, know necessarily you. Wanna get. The, an. Accurate, answer, and. So we, we, develop our model, and I, want I don't think have time to go into the detail but the. The main differences that, Newman. Models based on a volume averaging, approach while. My. Colleague Elaine is an expert in homogenization and, with her we, developed this. This. Mewn model. And. Then what we did was to take a unit cell within the porous electrode, and define the scale. Separation Thompson. Separation, parameters, and then we basically, we. Did asymptotic, expansion, technique. And rigorously. And tragically, we have obtained. A continuous, time scale, model which is a 3d. Model the. Newman, model is on the other hand is a p2, D and P stands for pseudo, 2d, model, and students because we have spherical coordinates. And within, the diffusion in, the electrodes and then the. DX. Coordinate. For. The. Mass. Gradient. Concentration gradients. Within the electrolyte and so, one of the main difference, also, between, those, two models is that in. The electrolyte mass transport, equation, we do have electro, migration. Mechanism. So the, potential. Gradient. That, effects basically the electron, mass transport, which is not which. We don't have in the DF and model so and there are more other. Differences. Within. These, two, approaches. An, important, aspect that we're investigating right now is the calculation, of those, effective. Parameters. Average. Parameters, so diffusion and the conductivity, so. The. Different model what it does it calculates, those effective parameters, by using an empirical, law. Which is the bruceman law that everybody is using that is based on a very, rough approximation, of, of. This, parameter, centers based on the knowledge of the porosity. And. Uses. This coefficient, equal to 1.5. To. Calculate, those effective, parameters. Now. If. You look at different chemistry, and. Brush. Manta coefficient has been mapped over different, three, different. Dimension. And. Z. Is, our basically, perpendicular. Direction the one over which across, which we do the modeling and, you can see that for graphite, this, coefficient, this exponent, is almost three and so, if you if you have two different chemistry. And which. Have the same velocity value. Etta and use this value, you come up with the wrong answer because the factory d the, real one is is a it's. A little bit, higher. Than one point five and, so those are some of the flaws, within, the FN model that, haven't been really, addressed, by the, community, well we do on the other hand. Is. To solve a closure problem and, that. Allows you to bring. That the poor scale, information. Up to the, micro. Scalar continuous, scale okay. Now in this particular case we solve the closure problem using, a. Spherical. Particle, assumption.
Just, So that we could compare. Our results but. The model allows you to really look and change, the the particle, size. And the line is looking at this particular, work. Right. Now so this simulation were performed, by a research scientist. Slava. And what he got, is. That there. Is a 20% difference between, the. Effective. Literal, light diffusion this particular, case is obtained, from the close-up problem in the bluesman and. And. So we. Are keep working on that route. At the same time we have implemented. Our full, emoji noise model, and we, have identified parameter. And we have set, up a co-stimulation, environment, using, Matt well Matt Robin comes from with the physics software, and. In. The results. That. We obtained so, we do experiments, a, different. Temperature same, C rate of discharge 1c but. For 4 different templates 23 40, 45, and 52 degrees. C and what, we see is that something, that we already kind, of saw in the Litoral is that new more modest really start being. Less accurate, earth the end of this charge here and even more here and and. Also we plot the voltage error, as, a function of stock and temperature. So. These. Are we are going to present with our cache these these results, in two weeks at the CDC. So. We, are we're, doing. Additional. Experiments to identify. The models of a different, CEO, rate of operation, and and. The. The way we see is that this, model can really be. Used to better design batteries. Especially, for this application and, even more for greater storage application, well, you really want to go down to the lower state of charge values you want to be able to really, have a meaningful and, accurate information, we. Can use this model also to generate proxy. Data or synthetic. Data for machine learning algorithms, and and. Even. More importantly, we, want to use it we are looking at this right now how. To reduce, this model, and make it a control oriented model and, and. For. A real-time. Estimation, so as we do this work we are also working on the. Meantime we work with my PhD student, and you don't model identification and. Estimation. And. And. So. The. Problem is that we can't measure static, charges pseudo health but we really want to know those two parameters very well they're very critical, and and. So we go ahead and after. This the design electrochemical. Model based estimator now. This has been done by. Few. Researcher, in the world what what we found out is, that there, is a problem a weak observability. There and so there isn't me because this is. Due to this open, circuit potential at, the, Arnold does basically is pretty, flat, now. When you look at the voltage across the battery terminal, what you do is do the difference with those, two so this week of the Ribeira t problem. Has. Been addressed in a different. Ways in the literature but, all these ways have some disadvantages. So. Some of the approaches, would not allowed this the observer to be extended, and. To. Predict the aging because they're based on an assumption that lead to molt conservable, which is not true when the aging happens. In. This particular case that we're going to look at after we. We. Assume to know very. Well the. The concentration. Of lithium at the anode through an open-loop absorber that we assume to initialize, you, know well and. Indiana. The third approach requires the. Additional, extra temperature sensor to impose, some relationship, that too will enhance observability.
So, We wanted, to remove all those limitations. Because what we want to do we wanted to build in the final observer that, we could use to estimate concentration. Of lithium and the both, electrodes, that, we could use to estimate aging. Without, the, adding. An additional. Sensor. So. We we, look at the we starting from the inane single particle models because we wanted to be able to capture also the electrolyte dynamics for fast charging eventually, eventually, for first step fast charging. Application. And so, the key here is to. Use. Fun a different approximation, and, rewrite. The, partial. Differential equation to rewrite the system in terms of. States. Acts so concentration, alidium at the two electrodes and the. Electrolyte and. And. In. Know sometimes of you know output equation which is a nonlinear output equation, as. A function of the concentration temperature. In. The, electrolyte. Resistance, so, that's the that's the system we are working with to develop our estimator, so, before, we go ahead we can go to help to, develop the observer. We have to identify. Parameters. There are some parameters which, are unknown and, that is done through some most, objective. To musician, by. Setting up a more subjective or actually single. Object in this case but we also look at most objective, optimization. Problems. And. And. Then. We. We. Went ahead and propose our our scheme. So this screamer was the one that we developed, with Alex in 2016. Alex is now Bartlett is now a Ford, so. His idea was direct okay we have one, electrode, we assume that one electrode, is perfectly, known which. Is the anode and therefore the cathode we can develop a closed loop observer. Now. We want to get rid of this assumption, and so what did was to propose a concurrent. Interconnected. Adaptive, observer, and. What it does is that basically. We. Estimate. In close loop the concentration. Which electrode, separately. And we assume that yet the other electrode, is given me an open loop but the reality, meaning in practice, is given from. The other observer, in closed loop and so this mechanism, works, and allows you to prove. The convergence of the estimates, to the real concentration. Values, and, so. We we could prove asymptotic, stability so, this is just an idea very. Graphic. Idea, of what a synthetic stability, is so, if we define the error has been the difference, between, the. Real concentration, values, and the estimate and. So this is the initial, error initial, values of the arrow so, after, sometimes, we, can show that this error goes to do ok and and.
That Is possible through. Some, design, so if you set up the cap of the server and the another server is a as I mentioned. Earlier interconnected. Manner we, can show through somebody up on of stability theory, that this, conversion happens, and, and. This. Is some. So. We simulate, a uddi. Cycle. Here, and then we know we have the. The. Blue ones here, the blue dots represents the real concentration, of the two electrodes in the red, starts. Here represent, wranger, initialized, concentration. But then we show that we basically, converge. Today to, the real one so, that's. Fine we did that, but. What that observer, didn't include was any. Was. Not. We. Couldn't use that observer to predict the age and we. Do really want that because that's the most important, critical parameters, we want to track in, real time and so, when it comes to the age in. The. Degradation is, a big. Addition happens in many many ways there are many interconnected processes. Happening. Within the battery but, it manifests, itself, through. Symptoms. Which are basically related to capacity. Fade and power fade, very, simple. So. You need to charge your car, more often, and you won't be able to extract that energy as quickly as you could, initially. And so. Among. The it's. A very difficult problem, to model and so, what, the literature proposes, and shows is that the, SEL. Layer formation at the anode is the main aging mechanism, that basically, is, behind, the capacity, fade and power shred of the battery so the SEL layer means that the solid electrolyte interface, at the anode is basically, is this. Hemi. Represented, as this. Protective. Layer if. You will that, basically, creates, around the anode the during the first cycles of the battery so the reaction, between the electrode and electrolytes, that's basically. That's what. Gives. Rise to the. Problem. With this so, there is one good point of this jociel affirmation, SEL formations, that it prevents the anode the prevents. Corrosion of, the anode but, there are so, many negative, characteristics. And the, main ones is that first, of all it encapsulates all, idiom cyclically, do get tied up within this layer and so, it's not available for, reaction and, so. You have renal awesome capacity, plus, this layer, increases. In thickness and that, prevents, the ion, basically, to to travel, through it so that is. Originated. So increase of resistance, or power fade so. We, convince our staff that this really is the more and more important. Mechanism, to model and we, connected, we linked this phenom this. Variable. To some. Of the parameters within, the model in particularly. The. Diffusion, coefficient, and D also porosity, in the honored and there as well as the stoichiometric window. And and. And. Also the. The. Transport parameter. In. The hele and so these parameters are. Modely, now are basically in our model, and so by tracking adaptively. Track those parameters, we can predict, capacity, fade so, that's what we did the, other things that we did is. To. Was, to also include the uncertainties, in our model, so as i show you earlier there are many different type of models, so whatever you pick you know that you're going to make a mistake with respect to what the real experiments.
Predicted, What you know how other high, fidelity models, can give you so the Rizal was going to be some uncertainties, so, included uncertainty, in our in. Our observer, design and, what, we went after this time was not asymptotic. Stability we, could because, now we have a. Uncertainties. But, we could show we are showing actual practical stability, so what we're saying is that the error between. The two the estimates of the states and the real concentration, and also the parameters, will, not converge to zero but, it will stay within a bowler whose, radius, is the function, of the uncertainties, so, the better you are in this none of your model, the, most predictive, you are in this on your model the better you can also the. Battery the performance, of us tomater is, and so, we elaborate. The previous scimitar. Scheme. A little bit more, in the way now we have accommodated. The adaptation, of the two parameters that I wanted to so the diffusion coefficient, the transport. Coefficient, electrolyte, and. We. We. We, did some math we derive our, proved. Our critical, stability, practical. Stability, and. If. I can run the simulation. No. I can't. Okay. I. Can. Now. So. The. This. Business, of, adaptively. Of estimating. Those parameters, allowing, us to also estimate. Capacity, because as the battery ages, those. Parameters, also basically. Changing. That will affect in creation, as well as capacity, so, now we have a. An. Observer, that is not, all estimates accurately. It with. Practical. Stability, property, stead of charge but also capacity, and to the best of our knowledge, something. Of this type hasn't been developed yet so our next step is to implement. This in real large order to make sure to. Show the validity of real, time implementation. Of. This, algorithm so, battery. Pack modelling initially, I show, you the difference between battery, stats and battery pack, and. So. Why is that important, this was a it's. A it's a research topic that I started, working. On with two visiting scholar stefano and carlo and now Anirudh is also, full. Time working on this research. So. It's. Very simple the problem is very simple battery cells are not created equal and so when you have ten. You know hundreds, or thousands, of those cells that you put in a park, something. Might go wrong right, and so the literature shows that there. Are manufacturing. Variances, within the cell in this particular case this. Is the impedance within, the imaginary. And the real axis. Plane. Impedance. Love of self they're different from cathode thickness, and the cutter particle, size so. We. Also see, there. Are evidence that shows the temperature, of a cells changes. And the thickness of the electrode changes, and so, those are again, a manufacturing. Presence. Effect. In. This study, what. I've done now they have to. Visit, have taken a bunch of cells 4080 from neuron and. They have assessed the initial capacity of those cells and then they let those cells, evolve, and the great basically, age with. The same cycle, and, what happens, after you know two, thousand circles that the trajectory. Of a capacity, has changed, at the verge quite a bit and then you know if you look at the histogram.
There Is a quite, big. Of a spread there and so. On. Top of that this. Study has shown that in. A module of 10 cell, in serious, exposed. To different, temperature. So. As opposed to a different basically, temperature. Gradient, the. The. Aging of the cells basically, evolved. Basically. There is an induced aging, of the, overall. Module. And this, last studies with very recent. Shows. That. These. People from either national lab have, used battery. Pack from Nissan Leaf and they, have tested. Those parks. As. Well as they have tested the cells within the pack. Independently. They what have shown is that two pack, packaging, was more, pronounced. Than cell, aging so, something happens within the park that we don't capture on the cell level and so, if, you think about the majority maybe you don't know but the majority of the modeling, and estimation work, is at the cell level I would say the 90% of the papers today, look. At the cell level. Estimation. And. And. So what, we try to do is to. To. Understand, what, happens at the pack level and the way we we. See a look at that is that by saying. That modularity. Doesn't. Hold true within, a pack so you cannot interfere or, basically, derive. The. Pack level properties. From single cell okay. And. So. And, so. Modularity. We, want to show we. Thought that modular it is no preserve upon interconnection. A battery pack and, there. Is a very nice theory, that's been developed, within the synthetic. Biology field. That. Looks, at what happens when you interconnect, system so, cell in a minor in a big module, and in. Some application, what people have shown is that when. You do connect system in this fashion not. Only does, the system. Downstream. Not. Only the system downstream, is affected, by the system upstream of it, but also the system, upstream of this interconnection. Is a, by, the. System downstream, through this reactive, signal so something, goes back here, that, you don't consider when, you basically, apply the modularity principle. And what this is called is, retroactivity, to the output so the. Dynamics, of the system in, this case downstream, upstream. Not. Only are affected, by whatever inputs, we have but also whatever, is basically, used in, the interconnection. Interconnection. Settings. And so we use this idea, and. We. Did some experiments at the time and then, we drill. The battery we look at the core temperature of the battery and we, we. Measure it so, that's the blue one is the core temperature of a single body linden we did the same experiments. By connecting, those cells in series and, what. We observe is that the battery temperature, was. Higher in this setting and we use the same cycle and this, was just due to the interconnection, and so, we we. Wanted to prove that we had retroactivity, happening. There and so, by running the thermal equation of our. Cell, which. Is connected. In series to two cells one upstream and one downstream, we, can recognize and see that there are two terms here that what. They do they are acting. As the retroactivity, term, so, the temperature, shall be is not only a function of you, know the temperature, of the skin temperature, the. Core temperature itself but, is also a function of the temperature. Upstream ta. And the temperature, downstream. Okay, and so, the environment affects. The performance of the cell in terms of and, while how that happens, is through these retroactivity, terms, and, and. So now if.
We Did, you. Know our study no single cell I realized that these stress factors, the aging factors are, mainly set, of charge C rate and temperature. With. Interconnection. Happening, we need to account for these, thermal, interconnections. Those thermal, retract, IVA T terms that induce, some extra. Dynamics. In the system and don't affect the aging of the system and so, our. Apologies, they wonder we are investigating, is there very attractive it is responsible. For AG to propagate, among yourself, so, aging. Propagates, because, of Terminator, activities, that's the concept, that we are developing, and, I. Want, to do that. So. One proof that that is basically. The right things to do is that if you look. At the. Rate. Of change of, the se la se, la. ER so that is the parameter that represents. The AG that we have discussed earlier you see that the SI the sei layer growth, of, Selby. Is not, only a function of you, know the parameters of the cell itself but also the parameters, of the additions, you know neighboring. Cells okay. Through, these thermal dynamics. Now. The, modeling, framework. That we are developing is, based on the singular. Perturbation problem. Singular, perturbation approach, we, are recognizing, that the, model of badr is evolving, across, three different scale, temporal, scale temperature. Concentration and, and. Aging, okay. And so what we see is that the. Agena needs to be we. Can only it's it's enough to, look at the aging dynamics, on these low manifolds. Because. The temperature dynamics converge to this low manifold, and so that's that's. Our that's, our. Idea. So. These are this work has lots, of implication, because allows you to accurately, estimate. Battery, state of health, and. That allows you to mitigate, aging during fast charging within the package we've seen they work from either National Lab has shown that the battery part is. Aging. Much faster, than single, cells so, if you can somehow mitigate, these, retract Eva T terms and design. A controller, that somehow. Impose. Or brings, back, that modularity, that you wanted. To apply in the first place that's that's, the way to go okay and, that can be done through some thermal control from a management control. Last. Application of, going to be quick here his. Hybrid vehicle optimization. And, this is a project that I'm doing now with. The army so Denise Reed so and. Define. And Abdullah were, the two postdocs that were on this project and they're, now at, washin the FCA, so. Going. Back to this vehicle, technology. Powertrain. Technologies. Options. You remember that now. If, we look at what. Is the requirement of. The. Energy storage within these, vehicles. Well we can see that in. Terms of a/c rate okay. Or p2e ratio, this is the PE ratio that we defined earlier the. PE ratio going. Of, the energy storage is different, okay it increases. As the, degree of electrification, increases. So you need more battery depite we should, be decreases so you need more energy than power okay, but for micro hybrids, for example, you need more you, know you need to be able to really use that energy very quickly and so, the question, is is, the ballistamon battery, dry technology, to use across all those vehicle. Power trains and. Aren't. We probably asking too much to, this technology, we're asking the lithium-ion batteries, to behave as close as possible to internal, combustion, engine on the other hand there are other technologies, here that we even people. Hasn't, even explored. Right, so what if we. We, share the burden and what. Is instead, we, maybe, try to use for. Example super capacitor, and trying. To help a little bio batteries, to achieve this goal and so. Can a hybrid, energy storage solution, can be valuable, in some applications. And. Super. Capacitors, are, very. Neat. Technology. They have very high power density they're they're. Very fast to discharge, they're very long life you can you. Can get up to a million of cycles, of out, of, super. Capacitor, and so in a way super, capacitors, have complementary, properties, of little million batteries so what, is the best way to electrify, those, should we. Diversify. The energy, storage, and see what is the optimal, size so those are fundamental, questions that we try to address. With. Our work so we were asked to develop some tools. For, the optimal selection, and size of energy storage and so, we look at this. This. Vehicle, the. Mine. Resistant, Ambush Protected altering. Vehicles it's, a pretty heavy vehicle, and what we did was -. This. Is a schematic of the powertrain, and what we did was to hybridize. So, we still have an, internal combustion, engines a diesel engine and, we want to advertise without lithium batteries, in this particular, case we did choose the nickel.
Manganese. Cobalt, chemistry. And, we also did a brothers with the hybrid. Energy storage system Binet ssin of batteries, and super capacitor, connected through DC, DC convertors, on Zima active configuration. And and. We. Use in, this case for vehicle optimization. We use empirical, models that we. Validated. Experimentally so. Those models, again, simple. And very very effective with, this type of work and, and. So, and, we. Did that we, formulate. An optimization problem. The optimization, problem was been formulated, in a way that we. Wanted. To have on the same cost. Function, both, the. Energy. Management the, power. Split, variables. As well as the design variables the design variables are basically the number of cells of the. Super cap in, parallel in the series, as well as really to my own baggage so this values, these parameters. Here tells you a big deep pack the. Two packs are and this variables, here tells you how, you are performing, the split, in real time okay, from the request of power the wheel and so. So. These are the two configurations. And. What. We did was to formulate. Animal Tony function, that was accounting, for the again, the design and the energy management very, much at the same time so, we could solve this problem all, together and we. Defined steady states and we developed our algorithms. And the. Simulations. Show some very interesting results, so. These are typical, it's, a concatenation, of different driving cycles, that are used by the army they are very different from the driving cycle we use for passenger, vehicle application, they are very demanding very high charge, and discharge cycles. And so. Our work our results, have shown that if, you use Newton, so, we compare our starts, with, what. The ammunition have done so the DW, does the power train by using a item. Phosphate battery, not optimally, design they just put a battery there without. Any, optimal. Consideration. By just using, a different chemistry, and optimally. Design and. The. Size of it we, could get seven and fewer of. You, know 7%, but, if you did if, you do use, Super cups on the other hand with nickel metal. Nickel. Manganese cobalt batteries, you can get up to a, saving over 13%, or, so and those, guys here this vehicle, here in its conventional, configuration can, give you less, than five miles per gallon okay, so this saving is very, significant. What. We also, saw. The. Disturbs. You basically, the size of the Super Cup with respect to lithium-ion batteries, for this particular, cycle. Let. Me let, me just go to this take away. One. Of the things that we basically. We. Have discovered. That Super cups have been overlooked. There. Is some good reason maybe the, volumetric. Density. Is not. It's. Not high so. We haven't accounted that in our, optimization. Work and so. That. Will be our next step because from a volume, standpoint, they really are very demanding. The. Way we want to extend this work is by trying. To give. Guidelines, as to what energy, storage device to use Optima, by interrogating. A goonie plot properly. And so as you said you've seen before there is a wide range of vehicles that needs to be electrified, and so. There's not a one, solution. Of it's all okay so there, is different, requirements, to the application for the applications, of different solutions. The. The main challenge, also that we're trying to solve is scalability how can you scale up and down the solution, across those different vehicles you, know where the makes sense and so. Mmm. I'm we're, also doing some some, worker with my patient, student. Francis is also. Now a Tesla, part-time. But we are looking. At how to identify how, to address, scientific, and ability. Issues of electrochemical, models, by using GIS, test, data so, he is test is electrochemical, impedance, spectroscopy is a test, that you do in frequency, domain and, we, try to understand, how much information, we can get to solve and address the, lack of identifiability. Some.
Parameters, In those models, and I, also have the pleasure to work with NGO, is a freshman. To nueva. High school and. Andrew. Join our group last summer and what is doing, is, a very ambitious project. Here, he's trying to come up with a prognostic, algorithm, trying to predict, a remaining useful life from. From. Something. Field you know, experimental. Data, collected. On plug-in. Hybrid vehicles, and is using some correlation, and machine learning the. Other and. So. What, are the next research, design challenges. Indeed. Grid. Grid. Storage is the next one so, we have our. Our. Research that we are conducting in, the automotive batteries, but the, very next. Things. That we're doing is to look at. How. Do those, batteries, behave and. You. Know in a Tesla the battery pack has 85, kilowatt, all right so Haiti five kilowatt. Hour battery, now we are talking about one point one year. At our model, is much bigger system and we. Don't know what is the duty cycle, that those batteries, go through whatever modern, tools we need and how the age and you know there are so many questions that they are there that you. Know lots of opportunities, for research and we, are looking at those some of that with with, Nora who, just join your, this. Quarter. And. The other point is that I want to give. Is that we. Don't want to we don't need to reinvent the wheel so this, batteries are very similar to, the batteries, that are using, automotive. They, have differences, so it's important, to understand, what are the differences, in terms of usage when, we can leverage the knowledge and the experience that, we are acquiring in, the automotive, field the quite a bit and that will accelerate the, advancement. In. The in the field of grid storage and. With. That happy. To take any any, questions you might have and also would like to thank, our sponsors. Without. Which I would not be able to do this research and thank you so much.
2019-02-03