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May 10, 2022


Energy Storage
Nadim Maluf

George Gianarikas, senior research analyst at Baird covering Quantumscape (NYSE: QS), asked the company’s CEO, Jagdeep Singh, on its Q1 2022 earnings call about “the kind of role software analytics play in the offering” that the company will give to OEMs.

Mr. Singh replied: “You cannot try to identify dendrite formation using software and then try to somehow change your charge protocol […] to sort of slow down or stop the growth of the dendrite.”

I beg to differ with Mr. Singh. In this two-part series, I will shed more light on the role that battery intelligence and software are playing in predicting the early presence of dendrites and avoiding unnecessary battery fires.

Let’s do a brief review of two fundamental physical principles of the lithium-ion battery, including solid-state batteries.

First, the principle of energy storage relies on the shuttling of lithium ions between two electrodes: the cathode and the anode. During charging and discharging, lithium ions go back and forth between the two electrodes. In traditional batteries that use graphite anodes, energy is stored when these ions insert themselves inside the graphite matrix. If you can imagine the lithium ions as drops of water, then the graphite is like a sponge. But in some of the newer solid-state battery designs, lithium ions instead recombine into a metallic lithium anode. Again, imagining the lithium ions as drops of water, then the anode is like a bucket of water; the drops just dissolve into the bucket.

In a simplified view of the electrochemistry, metallic lithium dendrites occur when lithium ions combine together just outside of the anode – on its surface to be more exact. So instead of getting stored into the anode as electrochemical energy, these lithium ions combine into a metallic form of lithium and deposit as dendrites. Over time (days and months), these dendrites grow into a filament that becomes an internal short-circuit between the two electrodes. The result is often catastrophic.

The second principle relates to energy density, i.e., the amount of energy the battery can store per unit of volume. This energy density relates directly to the number of ions that are shuttling back and forth; more energy equals more ion traffic between the electrodes. More ion traffic means more likelihood for dendrites to form. The vast majority of research and development in batteries including solid-state batteries aim at increasing energy density.

Manufacturing defects and certain operating conditions such as charging and temperature accelerate the formation of dendrites. Many defects may be detected at the factory, but not all. These rare and latent defects can lead to catastrophic fires and expensive recalls. This is what battery software and analytics can detect and prevent.

There are always precursors that lead to dendrite formation. As dendrites begin to form in their nascent stages, they have unique signatures that can be detected, often electrically but also with other methods such as ultrasound. These are small signals but intelligent software along with appropriate battery models (and data) are effective at identifying them. Once diagnosed, intelligent battery software can mitigate these potential defects with appropriate charge control schemes that greatly reduce, if not stop, the growth of these dendrites. In those rare cases where mitigation is difficult or impossible, then the software lights up the “check-engine” panel and the vehicle is safely brought back to service.

Battery analytics and software are essential components of the battery ecosystem as we scale the adoption of lithium-ion batteries into everyday life. It will be absurd to imagine the costs to society for expecting manufacturers to deliver defect-free batteries. With intelligent battery software and analytics, these few bad batteries can be identified, isolated and mitigated in the field instead at a far lower cost.

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