Batteries in electric vehicles wear out too quickly and this is slowing down the electrification of the transport sector. Researchers at Uppsala University have now developed an AI model that can provide a much more accurate picture of battery ageing. The model could lead to longer life and enhanced safety for electric vehicle batteries.
It is not uncommon for batteries in electric cars to be the first component of the vehicle to age. This is a major waste of resources today and is holding back the transformation of the transport sector. To address this issue, the automotive industry is developing software, often based on AI, to optimise battery management and control. Researchers at Uppsala University have now produced a new model that can increase the robustness of battery health predictions by up to 70 per cent.
"Being able to learn more about the life and ageing of batteries will benefit future control systems in electric vehicles. 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," says Professor Daniel Brandell, who led the study and is in charge of the Ångström Advanced Battery Centre at Uppsala University.
Can map the battery life cycle
Several years of battery testing are behind the study, carried out in collaboration with Aalborg University in Denmark. A database was built up by collecting data from numerous very short charging segments. This was then combined with a detailed model of all 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, who conducted the study.
Reduces need for sensitive vehicle data
The discovery could also affect the safety of electric vehicles. The safety problems that can occur in the battery are often due to design flaws and side reactions, which can also be predicted by studying data from the battery's 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," says Brandell.
