Lithium-ion batteries have a wide range of applications, and their safe and reliable operation is essential. However, due to the complex electrochemical reaction of the battery, the battery performance parameters show strong nonlinearity with aging. Therefore, as the main technologies in BMS, battery state estimation and lifetime prediction remain challenges. Artificial Intelligence (AI) technologies possess immense potential in inferring battery state, and can extract aging information (i.e., health indicators) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process. Therefore, this course aims to introduce the application of AI in Smart Battery state estimation. 

The two-day course introduced AI methods for estimating/predicting batteries’ state of charge (SOC), state of health (SOH), state of temperature (SOT), and remaining useful life (RUL). Key aspects included laboratory data preparation, data preprocessing, AI model training and selection.

Thanks to our guest lecturer, Dr. Yicun Huang from Chalmers University of Technology, as well as Prof. Remus Teodorescu, Nicolai Weinreich, and Dr. Roberta Di Fonso from CROSBAT at Aalborg University.