Driving Intelligent Battery Health Monitoring in BatCAT project

Paper entitled “Combining Autoencoder and Cellular Neural Networks for Enhanced Direct Multi-Step Forecasting of Short and Long-Term Multivariate Time Series in Battery Health Monitoring: A Preliminary Feasibility Analysis” was presented by Mohamed El Bahnasawi at the “Applied Mathematical Methods and Computational Methods in Engineering” session of the International Conference on Applied Mathematics & Computer Science (ICAMCS 2024) in Venice, Italy this September.

Mohamed takes an active role in BatCAT project, as a researcher employed by Alpen-Adriatic University Klagenfurt (AAU) and focused on time-series forecasting to estimate battery health.

In his talk, Mahamed addressed prospects of applying Cellular Neural Networks (CeNN), its enhanced forecasting capabilities and future directions.

More information can be found at the conference WEB-page.