Battery death prediction could boost car range and shrink packs

A new model developed by a team at Stanford University can predict the ‘true condition’ of a rechargeable battery in real-time.

The algorithm, which provides greater certainty of a battery’s lifecycle, could let electric car manufacturers make the most of a pack’s capabilities.

The model combines sensor data with computer modelling of the physical processes that degrade lithium-ion cells to predict remaining storage capacity and charge level.

“We have exploited electrochemical parameters that have never been used before for estimation purposes,” said Simona Onori, assistant professor of energy resources engineering at the Californian university’s School of Earth, Energy & Environmental Sciences.

The new approach could help pave the way for smaller battery packs and greater driving range in electric vehicles. Manufacturers currently build in spare capacity in anticipation of an unknown amount of battery ‘fading’, which adds extra cost and materials, including some that are scarce or toxic. Better estimates of a battery's actual capacity will enable a smaller buffer.

“With our model, it's still important to be careful about how we are using the battery system,” said Onori. “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. Our system reveals ‘where the edges are’, so batteries can be operated with more precision.”

The accuracy of the predictions in the model – within 2% of experimentally-verified actual battery life, according to the paper – could also make it easier and cheaper to put old electric car batteries to work storing energy for the power grid.

“As it is now, batteries retired from electric cars will vary widely in their quality and performance,” said Onori. “There has been no reliable and efficient method to standardise, test or certify them in a way that makes them competitive with new batteries custom-built for stationary storage.”

Traditional battery management systems typically rely on models that assume the amount of lithium in each electrode never changes, said lead study author Anirudh Allam, a PhD student in energy resources engineering. “In reality, however, lithium is lost to side reactions as the battery degrades,” he said, “so these assumptions result in inaccurate models.”

Onori and Allam 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. They validated their algorithm in realistic scenarios using standard industry hardware.

The model relies on data from sensors found in the battery management systems running in electric cars on the road today. “Our algorithm can be integrated into current technologies to make them operate in a smarter fashion,” said Onori. In theory, many cars already on the road could have the algorithm installed on their electronic control units, she said, but the expense of that kind of upgrade makes it more likely that car makers would consider the algorithm for vehicles not yet in production.

The team focused their experiments on a type of lithium-ion battery commonly used in electric vehicles (lithium nickel manganese cobalt oxide) to estimate key internal variables such as lithium concentration and cell capacity. The framework is reportedly general enough that it should be applicable to other kinds of lithium-ion batteries and to account for other mechanisms of battery degradation.

The research was published in IEEE Transactions on Control Systems Technology.