News ID: 274259
Published: 0809 GMT September 16, 2020

New algorithm to help predict battery life

New algorithm to help predict battery life
MILES WILLIS/GETTY IMAGES

Estimating battery life is one of the great modern dilemmas, and the older the item, the harder it gets.

But researchers at Stanford University, US, may be about to save us all. They’ve developed a model they say can predict the true condition of a rechargeable battery in real-time, Cosmos magazine reported.

The algorithm combines sensor data with computer modeling of the physical processes that degrade lithium-ion battery cells to predict the battery’s remaining storage capacity and charge level.

“We have exploited electrochemical parameters that have never been used before for estimation purposes,” said Simona Onori, coauthor of a paper in the journal IEEE Transactions on Control Systems Technology.

In trials, the new approach made predictions within two percent of actual battery life.

It could have particular application in vehicles, by paving the way for smaller battery packs. Carmakers build in spare battery capacity as a buffer which adds extra cost and materials. Better estimates of an actual capacity would allow a smaller buffer.

“With our model, it’s still important to be careful about how we are using the battery system,” Onori said, “but if you have more certainty around how much energy your battery can hold throughout its entire lifecycle, then you can use more of that capacity.”

All batteries have two electrodes (cathode and anode) sandwiching an electrolyte, which is usually a liquid. In a rechargeable lithium-ion battery, lithium ions shuttle back and forth between the electrodes during charging and discharging. An electric car may run on hundreds or thousands of these small battery cells.

Traditional battery management systems tend to rely on models that assume the amount of lithium in each electrode never changes, says lead author Anirudh Allam, when in fact lithium is lost to side reactions as the battery degrades. So the assumptions are wrong.

Allam and Onori designed their system with continuously updated estimates of lithium concentrations and a dedicated algorithm for each electrode, which adjusts based on sensor measurements as the system operates.

The model relies on data from sensors found in the battery management systems running in electric cars on the road today and thus could, in theory, be integrated into current technologies, though cost would be a factor.

The researchers worked with a battery commonly used in electric vehicles (lithium nickel manganese cobalt oxide), but Onori suggests the framework should be applicable to other kinds of lithium-ion batteries and to be able to account for other mechanisms of battery degradation.

 

 

   
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