Traditionally, battery scientists have tested EV batteries in labs using a constant charge-discharge cycle. While effective for quick evaluations of new designs, this method does not accurately reflect the varied usage patterns of everyday drivers, the study published in *Nature Energy* on Dec. 9 reveals.
Although battery costs have fallen by approximately 90% over the past 15 years, they still represent about one-third of an EV's price. This research could provide reassurance to current and prospective EV owners about the longevity of their vehicle's batteries.
"We've not been testing EV batteries the right way," said Simona Onori, the study's senior author and an associate professor at Stanford's Doerr School of Sustainability. "To our surprise, real driving with frequent acceleration, braking, stopping for errands, and extended rest periods helps batteries last longer than previously thought based on industry-standard tests."
Machine learning algorithms were crucial in analyzing the extensive data, revealing that certain driving behaviors, like sharp accelerations, slowed battery degradation. This contradicted prior assumptions that acceleration peaks harm EV batteries. "Pressing the pedal hard does not speed up aging. If anything, it slows it down," explained Alexis Geslin, one of the study's lead authors and a PhD candidate in materials science and computer science at Stanford.
"We battery engineers have assumed that cycle aging is much more important than time-induced aging," said Geslin. "For consumers using their EVs for daily errands but leaving them unused most of the time, time becomes the predominant aging factor."
The researchers identified an optimal discharge rate balancing both time and cycle aging for the batteries tested, which aligns with typical consumer driving habits. Manufacturers could update battery management software to incorporate these findings, potentially extending battery lifespan under normal conditions.
The study's principles could apply beyond EV batteries to other energy storage systems, plastics, solar cells, and biomaterials where aging is a key concern. "This work highlights the power of integrating multiple areas of expertise-from materials science and modeling to machine learning-to drive innovation," Onori concluded.