Context-Dependent Adaptive Reduction and Equivalencing of an Electric Vehicle Model with Driving Mode Switching for a Decision Support System

B. V. PASIEKA, Postgraduate Student

DOI: https://doi.org/10.35668/2520-6524-2026-1-08

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Keywords: electric vehicle, equivalent model, decision support system, adaptive reduction, mode switching, optimal movement, traction electric drive.

ABSTRACT

The problem of the practical implementation of mathematical models of optimal motion of an electric vehicle with an AC traction electric drive in driver decision support systems is considered. The necessity of the equivalencing of complex dynamic models to ensure real-time calculations is substantiated. A method of context-dependent adaptive model reduction is proposed, which involves automatic switching between simplified equivalent models depending on the current driving mode of the vehicle: a horizontal section, ascent, descent or turn. Criteria for the equivalence of reduced models are formulated based on minimising the integral error of the velocity trajectory and energy consumption. A hybrid model structure with switching logic is developed, which ensures the continuity of the motion trajectory when changing modes. Computer modelling has been performed in the MATLAB/Simulink environment to compare the computational complexity of the full and equivalent models. The modelling results confirm a four- to sixfold reduction in computation time while maintaining the accuracy of motion parameter prediction at a level of 2% to 5% relative error. The conditions for the applicability of each of the equivalent models were determined depending on the road profile and dynamic movement characteristics. An algorithm for integrating equivalent models into a decision support system software application was proposed, with the possibility of further expanding functionality through intelligent prediction algorithms. The prospects for applying the developed models to the creation of adaptive electric vehicle control systems based on artificial intelligence methods are outlined. The results obtained can be used in the design of intelligent decision support systems for electric vehicle drivers, aimed at improving energy efficiency, safety and adaptability of movement in real operating conditions.

Received by the Editorial Office on 08.02.2026
Accepted for publication on 26.02.2026

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