Hyundai Motor Group has shown how digital twin technology can predict an electric vehicle’s lithium-ion battery lifespan and optimise its battery management and performance following a project with Microsoft Korea.
Using Microsoft’s cloud service Azure, Hyundai created digital twins of EVs based on real-world driving data collected from vehicles and used the information to predict the battery lifespan of each vehicle.
Hyundai said that compared to traditional battery management systems that calculate current performance, digital twin technology is more accurate because it continuously recalculates and analyses factors based on actual vehicle driving history
The aim is to improve the accuracy of battery lifespan prediction and customise battery management systems for each EV model.
Based on the project’s success, the South Korean vehicle OEM will implement digital twin technology as a way to improve battery performance going forward.
Jenna Lee, head of Asia technical sales, IoT & MR technology at Microsoft, said: “The importance of batteries is becoming bigger with the commercialisation of EVs.
“The collaboration is more meaningful as it is the first case of using Microsoft Azure Digital Twins platform to improve battery performance of EVs.”
Creating battery twins
The data-integrated analysis model uses artificial intelligence (AI), machine learning and physical models to analyse information such as charging and discharging cycles as well as parking and driving environments.
By using digital twins, Hyundai expects to devise customised battery management measures for each vehicle to help maintain optimal performance.
The project also implemented technologies that provide advice to motorists on how to better manage vehicle performance, such as ‘Use slow charging when you have time,’ ‘Speed affects battery life’ and ‘Ventilation seats are more energy efficient than continually using air conditioners’.
In addition to this project, the OEM is researching other ways to improve batteries and EV performance and plans to review application of related technologies in mass production.