Battery-specific chargers are on the horizon.

By Can Emir

Researchers from Idaho National Laboratory are using machine learning and other advanced analysis to reduce electric vehicle charging times without damaging the battery, a press release revealed.

Despite the growing popularity of electric vehicles, many consumers hesitate to make the switch. One of the primary reasons is that it takes so much longer to power up an electric car than to gas up a vehicle powered up by an internal combustion engine. This hesitation is a reflection of range anxiety, and the solution for this anxiety is to get yourself a long-range electric vehicle, which can be a bit pricey.

In search of quicker powering methods

Charging the lithium-ion batteries that fuel electric vehicles is a delicate balancing act. Drivers want to power up as quickly as possible to get back on the road, but with current technology, speeding up the process damages the batteries.

When a lithium-ion battery is being charged, lithium ions migrate from the cathode and anode sides of the device.

The batteries can be charged more quickly by making the lithium ions migrate faster, but sometimes the lithium ions don’t fully move from the cathode to the anode. By doing so, lithium metal can build up, triggering early battery failure. It can also cause the cathode to wear and crack. All of these issues will reduce the battery’s lifetime and the vehicle’s effective range.

One solution to this enigma is to tailor the charging protocol to optimize speed while avoiding damage to various types of battery designs. But developing optimal protocols requires massive data on how different methods affect these devices’ lifetimes, efficiencies, and safety.

The design and condition of batteries and the feasibility of applying a given charging protocol with the current electric grid infrastructure are vital variables in the studies.

“Fast charging is the key to increasing consumer confidence and overall adoption of electric vehicles,” said Eric Dufek, Ph.D. from Idaho National Laboratory’s Energy Storage & Electric Transportation Department, at the fall meeting of the American Chemical Society (ACS). “It would allow vehicle charging to be very similar to filling up at a gas station,” he added. 


Range anxiety is seen as a key limitation by many consumers looking to purchase an electric vehicle. The two routes to alleviate this anxiety are through the development of higher energy batteries and batteries capable of charging in 10 minutes or less. Achieving either target is difficult and presents a suite of challenges spanning from material degradation through cell and electrode design. When performing extreme fast charging, many types of degradation emerge including Li deposition and cathode cracking. Early detection and understanding using electrochemical methods are complicated, but possible if using a multitude of different signatures. Here we describe recent efforts to jointly align electrochemical methods with targeted characterization and advanced analysis to detect failure modes. Specifically, machine learning and other advanced analysis approaches show promise to reduce the time and effort needed to predict life, delineate failure modes, and provide input to electrochemical models. Here we discuss the use of machine learning to perform early failure mode classification on cells used for fast charge applications. Using this information, it is then possible to feedback information for the refinement of advanced charging protocols designed to minimize specific aging pathways.