People who buy electric cars don't understand a lot about energy generation. They may believe that solar panels and wind are providing the energy, but even after $3 trillion in subsidies, those have not changed the percentage of energy generated by mainstream sources, like natural gas.

Their second introduction to reality is charging. If you are sleeping, and can sleep well knowing your electric car charging is equivalent load to an entire extra house on the grid, long charging times are fine, but it makes long trips a source of anxiety for most.

When the 1980s-era lithium-ion batteries that power electric vehicles are being charged, lithium ions migrate from one side of the device, the cathode, to the other, the anode. By making the lithium ions migrate faster, the battery is charged more quickly, but sometimes the lithium ions don’t fully move into the anode. In this situation, lithium metal can build up, and this can trigger early battery failure. It can also cause the cathode to wear and crack. All of these issues will reduce the lifetime of the battery and the effective range of the vehicle — expensive and frustrating consequences for drivers.

One solution is to tailor the charging protocol in a way that optimizes speed while avoiding damage for the many different types of battery designs currently used in vehicles but each solution is unique and developing optimal protocols requires a huge amount of data on how various methods affect these devices’ lifetimes, efficiencies and safety. The design and condition of batteries, as well as the feasibility of applying a given charging protocol with the current electric grid infrastructure, are also key variables.

Yet if people are going to use electric cars as more than a gimmick, we need better batteries or faster charging. machine learning techniques that incorporate charging data to create unique charging protocols. By inputting information about the condition of many lithium-ion batteries during their charging and discharging cycles, scientists reporting at the ACS meeting today say trained the machine learning analysis to predict lifetimes and the ways that different designs would eventually fail. The team then fed that data back into the analysis to identify and optimize new protocols that they then tested on real batteries.

“We’ve significantly increased the amount of energy that can go into a battery cell in a short amount of time,” says Eric Dufek, Ph.D. of Idaho National Laboratory. “Currently, we’re seeing batteries charge to over 90% in 10 minutes without lithium plating or cathode cracking.”

Going from a nearly dead battery to one at 90% power in only 10 minutes is a far cry from current methods, which, at best, can get an electric vehicle to full charge in about half an hour. While many researchers are looking for methods to achieve this sort of super-fast charging, Dufek says that one advantage of their machine learning model is that it ties the protocols to the physics of what is actually happening in a battery.

The researchers plan to use their model to develop even better methods and to help design new lithium-ion batteries that are optimized to undergo fast charging. Dufek says that the ultimate goal is for electric vehicles to be able to “tell” charging stations how to power up their specific batteries quickly and safely.