Better batteries have revolutionised the automotive sector in recent years, enabling longer emissions-free journeys. Those improvements have seen them surge in popularity, with almost half of all new cars sold in the UK now electric vehicles (EVs) or hybrids.
EV batteries still have a major issue, however, according to researchers at Uppsala University in Sweden: they wear out too quickly, and are often the first component in a car to degrade. That drawback is wasting resources and slowing down electrification of the transport sector, the team claimed.
The researchers, led by Professor Daniel Brandell, set out to tackle the problem by developing a new AI model, which can reportedly increase the robustness of battery health predictions by up to 70%. The model could lead to longer life and enhanced safety for EV batteries.
“Being able to learn more about the life and ageing of batteries will benefit future control systems in electric vehicles,” said Brandell, who is director of the Ångström Advanced Battery Centre.
“It also shows how important it is to understand what happens inside the batteries. If we stop looking at them as black boxes that are simply expected to provide power, and instead acquire a detailed picture of the processes, we can manage them so that they stay in good condition longer.”
The study, carried out in collaboration with Aalborg University in Denmark, involved several years of battery testing. The researchers built a database of a large number of very short charging segments, which they then combined with a detailed model of the different chemical processes taking place inside the battery.
“Altogether, this gives us a very precise picture of the various chemical reactions that result in the battery generating power, but also of how it ages during use,” says Wendi Guo, a postdoctoral fellow at Uppsala University who conducted the study.
The model could also improve the safety of EVs, the team claimed. Safety problems that occur in batteries are often due to design flaws and side reactions, which can also be predicted by studying data from charging and discharging.
“The fact that we only use short charging segments is probably an added advantage. Battery data from electric vehicles is sensitive, both for the industry and from an anonymisation point of view for users. This research shows how far you can get without needing complete datasets,” Brandell said.
The research was published in Energy & Environmental Science.
Extracted from IMechE website, read more here