The high complexity of Li-ion battery behavior under extreme conditions limits the accuracy of conventional EECMs. This work explores enhancing EECMs using ML methods to capture nonlinear and dynamic relationships. With data from four cylindrical Li-ion cells (NCA, NMC, LFP), ML-supported EECMs claim improvement using advanced hybrid techniques like time series cross-validation and end-to-end concatenation.