The article presents the methodology of isolating aperiodic phenomena constituting thebasis of the energy balance of vehicles for the analysis of electromobility system indicators. The symp-tom observation matrix (SOM) and experimental input data are used to analyze periodic phenomenasymptoms. The multidimensional nature of the engine efficiency shortage has been well defined andanalyzed in terms of errors in the general model using neural networks, singular value decomposi-tion, and principal component analysis. A more difficult task is the analysis of a multidimensionaldecision-making process. The research used a data fusion method and the concept of symptomreliability, which is applied to the generalized failure symptom obtained by applying the singularvalue decomposition (SVD). The model research has been based on the gray system theory (GST) andGM forecasting models (1,1). Input data were obtained from the assessment of driving cycles andanalysis of the failure frequency for 1200 vehicles and mileage of 150,000 km. Based on this analysis,it can be concluded that with the current infrastructure and operating costs and the frequency offailure of PHEV and BEV drives, ICEV vehicles are unrivaled in terms of their operating costs.