With the growing importance of electromobility, the efficient planning in the production of lithium-ion batteries has become a critical factor in maintaining competitiveness. This article focuses on the optimisation of batch scheduling on parallel processors – an essential challenge in a complex manufacturing environment characterised by hybrid (sequential-parallel) processes, technological dependencies, and high variability. Based on a formal mathematical model of the P \(\mid \) batch, \(r_j, s_j \mid C_{\text {max}}\) type, a method is proposed that integrates batch planning, non-linear setup times, multi-objective optimisation, and robust scenario-based control. The model was implemented in the AMPL (A Mathematical Programming Language) environment and tested using real production data from battery manufacturing at the famous production company. The results demonstrate significant improvements over conventional methods (e.g., FCFS, LPT), including a reduction in production time of up to 10%, a 6% decrease in setup operations, and an increase in capacity utilisation (OEE) by more than 10%. Moreover, reductions in energy consumption and enhanced schedule predictability were observed. The model is designed to be integrable with MES/ERP systems and offers both scalability and adaptability across varying production scenarios. The findings confirm that the combination of a rigorously defined optimisation model and operationally validated data can significantly enhance planning efficiency in battery system manufacturing for electric vehicles.