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February, 2007
Batteries of the Future I
Modeling Lithium-ion Battery Behavior

The BATT Program (Batteries for Advanced Transportation Technologies) is a $6 million DOE program that aims to develop the next-generation batteries for use in electric, hybrid-electric, and plug-in hybrid-electric vehicles. Berkeley Lab's Environmental Energy Technologies Division (EETD) assists the U.S. Department of Energy in managing research conducted under this program, which takes place not only at Berkeley Lab, but other national labs, universities, and private companies.

The next generation of batteries in your car is coming from laboratories — and from computer models. Advanced battery development is no longer just a question of trial and error engineering; scientists increasingly use computer models to design the best possible battery.

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The future success of electric vehicles like the Chevy Volt pictured here will depend upon greatly improved battery power and energy.

Batteries based on lithium are considered by many experts to be the most promising, in part because of their high cell voltage — as much as 3.7 volts, as compared to 2.0 volts for a lead-acid battery or 1.2 volts for a nickel metal hydride cell. This high voltage translates directly into higher energy, which has been key to commercializing lithium ion (Li-ion) batteries for cellphone and laptop applications.

And lithium batteries, says Venkat Srinivasan, a staff scientist in Berkeley Lab's Environmental Energy Technologies Division (EETD), "will also allow for significant improvements in the presently available hybrid-electric vehicles, HEVs. In addition, it is hoped that lithium batteries will pave the way for the development of plug-in HEVs and the electric vehicles of the future."

For lithium batteries to become widespread in vehicular applications, however, their performance and life need to improve, their safety must be enhanced, and their costs need to decline. "While the HEV market will be the low-hanging fruit, with plug-in HEVs expected within the next decade, pure electric vehicles will be a major challenge," Srinivasan says. Even fuel-cell-powered vehicles will need high-performance batteries, because only batteries can provide the necessary acceleration. Fuel cells can't ramp power up and down fast enough for rapid acceleration.

"The mechanism of charge/discharge in lithium cells involves shuttling the lithium between an anode and a cathode," explains Srinivasan. "The choice of materials for the anode, cathode, and electrolyte has a major impact on the various problems facing lithium batteries today. Even after a decade of research, no magic combination of material has been found that has all the good attributes. So, research continues on three classes of cathode materials, four classes of anodes, and three classes of electrolytes, all in the hope of finding the right combination that will allow for commercialization."

Srinivasan and other researchers in EETD are studying batteries in many different ways, including synthesizing new anodes, cathodes, and electrolytes; fabricating test batteries with advanced materials and measuring their performance in the lab; understanding their behavior using advanced diagnostics, including microprobe techniques; and by creating computer models of battery behavior.

This last is the approach taken by Srinivasan, who works in EETD's Electrochemical Technologies Group. Typically the attempt to produce improved batteries involves trial and error, but Srinivasan is using a more systematic approach to help both the materials scientists who develop new materials and the engineers who are trying to optimize whole battery systems.

Srinivasan uses mathematical models of battery chemistry to evaluate the performance limitations of particular Li-ion chemistries. He simulates the performance of a particular chemistry and compares it to experiments performed in the lab to see how well his model results hold up. From the results he extracts information about what factors in a particular material are limiting the performance of the battery. Material developers and battery engineers can use the information to design a better battery that comes closer to meeting the needs of real applications.

"We get the physics from simple lab-scale experiments," Srinivasan says, "and then we use equations to describe this physics. If the model shows that the material looks promising for, say, a plug-in HEV, then we can spend the time and effort to make large amounts of this material, to make prototype batteries with it, and to see how they will perform when used in the real world." What particularly interests Srinivasan about the work "is that I can connect the materials development scientists with those who are optimizing the batteries, and I can make this connection quickly."

Acceleration and range

In Srinivasan's presentations he uses a key image, which has become widely popular because of how clearly it summarizes where the field lies right now. It's a map depicting the current performance of batteries and other technologies, and where they have to go to be useful for electric vehicles.

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A map developed by Venkat Srinivasan from product fact sheets compares the specific energy (in watt-hours per kilogram) of vehicle power sources, an indicator of their range, with their specific power (in watts per kilogram), an indicator of acceleration. Dotted lines indicate acceleration and cruise times, while blue stars show DOE's energy and power goals for electric vehicles and hybrids. Internal combustion engines still out-perform all other power sources, but battery researchers are confident that they can improve the profile of lithium-ion batteries substantially.

The map's horizontal axis is power, and represents acceleration; for acceleration comparable to internal combustion engines, electric cars need to be able to ramp up power quickly. The map's vertical axis is energy, representing the amount of energy a battery can store. It's a measure of range — the more energy the battery stores, the farther the car can travel.

Different types of batteries are represented on the map by curved lines, which show the decrease in stored energy as power increases. All batteries show a big decline in energy — that is, range — as they achieve more and more power, or acceleration.

A star on the lower right of the map represents the U.S. Department of Energy's goal for hybrid electric vehicles. Some lithium-ion batteries on the market today already meet the goal established for hybrid vehicles; these batteries provide sufficient acceleration but not much range. Nickel metal hydride batteries fall just short, and lead acid batteries, the oldest of all technologies, trail the pack.

The upper star on the map represents DOE's range and acceleration goal for future electric vehicles. Internal combustion engines sit high on the performance curve, but no battery technology currently meets the goal, although lithium-ion batteries come closest. According to some claims, fuel cells could theoretically come close to the range and acceleration needs of electric vehicles, but this technology is still unproven.

From real batteries to models and back again

Srinivasan models lithium-ion materials sent to Berkeley Lab from many groups throughout the world who are developing these materials. A model's output for a specific material might be a plot of how its voltage and capacity changes with increasing power, for example.

Srinivasan and other Berkeley Lab researchers perform lab tests on the materials, and similar battery chemistries from different sources are compared. Srinivasan's model can tell whether differences in performance are caused by a battery's design or by something intrinsic to the material itself. Anything from electrode thickness, to porosity, to particle size, to the parameters of the battery's chemical reactions can affect the results.

The basic model that Srinivasan starts with was developed by John Newman, head of the Electrochemical Technologies Group at Berkeley Lab and a professor of chemical engineering at UC Berkeley. Newman's group has been modeling batteries since the 1970s, and their approach is widely used throughout the field. Fitting the model to the specific chemistry he's working with allows Srinivasan to get close to a battery's actual performance.

"This is what I love about batteries," he says. "Each one has its own idiosyncrasies; there's something a little different about each battery chemistry. To get the right physics, you have to keep adding more details."

Srinivasan has graphically summarized some of the materials he has modeled recently, again plotting their energy against their power. Materials come from all over the world — from Berkeley Lab's own groups, from MIT, from a researcher in Slovenia, and from the Canadian power company Hydro-Quebec, which sent a commercial prototype. So far no material has come close to the theoretical maximum performance, which Srinivasan represents by a curve labeled "ideal." The ideal battery material would have the particle size of the MIT sample and the conductivity of the Hydro-Quebec sample, so there is still a lot of room for improvement in this particular set of chemical combinations. Particularly promising are compounds of lithium iron phosphate with graphite, an electrically conductive form of carbon.

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The performance of batteries developed by groups at Berkeley Lab, MIT, Hydro Quebec, and in Slovenia is compared. All batteries use similar lithium compounds but are engineered differently. The red curve, based on a model by Venkat Srinivasan, shows the theoretical maximum performance of electrodes using compounds such as lithium iron phosphate with graphite.

One important conclusion Srinivasan drew from this study was that research groups who provide the materials could identify the maximum energy density of a battery cell by varying the porosity and thickness of the electrodes.

"My hope is that five years from now, we will have a plug-and-play model for these battery materials," says Srinivasan. "Lithium ion batteries are much more complex than lead acid cells, partly because of the wide variety of materials under consideration."

Although he concedes that "We are not at that stage right now," he notes that computer models have gotten better over the years. "This is because our understanding of the physics is getting better. As better diagnostics tools are developed, researchers are beginning to understand the numerous complexities that characterize batteries."

This has happened because interest in batteries has led to increased funding and more people studying the problems. "You need a critical mass of researchers thinking about batteries every day to make progress," he says.

"And there are still other battery-related problems to solve," he adds. "For example, we don't really understand why batteries fail."

But that's another story, introduced in this issue of Science@Berkeley Lab in Batteries of the Future II, Building Better Batteries Through Advanced Diagnostics.

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