Batteries fade as they age, slowly losing power and storage capacity.
[...] Now, a model developed by scientists at Stanford University offers a way to predict the true condition of a rechargeable battery in real-time. The new 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 [stanford.edu], assistant professor of energy resources engineering in Stanford’s School of Earth, Energy & Environmental Sciences [stanford.edu] (Stanford Earth). The research [ieee.org] appears Sept. 11 in the journal IEEE Transactions on Control Systems Technology.
The new approach could help pave the way for smaller battery packs and greater driving range in electric vehicles. Automakers today build in spare capacity in anticipation of some unknown amount of 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,” Onori explained. “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 this model – within 2 percent of actual battery life as gathered from experiments, 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,” Onori said. “There has been no reliable and efficient method to standardize, test or certify them in a way that makes them competitive with new batteries custom-built for stationary storage.”
Not just car batteries -- I'd like to know if it could be applied to cell phones, tablets, and laptops.
Anirudh Allam, Simona Onori. Online Capacity Estimation for Lithium-Ion Battery Cells via an Electrochemical Model-Based Adaptive Interconnected Observer - IEEE Journals & Magazine, (DOI: 10.1109/TCST.2020.3017566 [doi.org])