Data Processing and Selection for Improving In-field Data Quality in Battery Health Management

Abstract:
In recent years, there has been an increasing focus on the climate and findings ways to mitigate global warming by reducing the emission of CO2. Using electric vehicles instead of combustion engine vehicles is a good way to reduce people’s environmental impact but battery production still emits a large amount of CO2 meaning that batteries need to be more efficient and live longer. This project will improve this by developing a data processing and selection strategy to make battery management systems more efficient in their data use while increasing the accuracy of models used to control supply of power to the battery. This will make battery management system better informed on the state of the battery and enables it to take better action for extending the lifetime of the battery, reducing the number of new batteries which need to be produced. The expected outcome of the project is to improve established models for estimation of battery states and parameters by only using the parts of the data which has the most information. This will be validated both by simulation but also experimentation of in-field data. This outcome will help make batteries live longer by making the management system smarter and develop a method which can be applied in other scientific domains where data efficiency is a key issue.

Funding: Fully funded by Smart Battery project, granted by Villum Foundation in 2021 (project number 222860).