{"id":7873,"date":"2026-04-03T14:16:55","date_gmt":"2026-04-03T11:16:55","guid":{"rendered":"https:\/\/nti.ukrintei.ua\/?page_id=7873"},"modified":"2026-04-03T14:19:33","modified_gmt":"2026-04-03T11:19:33","slug":"science-technologies-innovations-%e2%84%96137-2026-80-89-%d1%80","status":"publish","type":"page","link":"https:\/\/nti.ukrintei.ua\/?page_id=7873&lang=en","title":{"rendered":"Science, Technologies, Innovations \u21161(37) 2026, 80-89 \u0440"},"content":{"rendered":"

<\/a><\/p>\n

https:\/\/doi.org\/10.35668\/2520-6524-2026-1-08<\/strong><\/em><\/p>\n

Pasieka B. V.<\/strong> \u2014 Postgraduate Student, Department of System Analysis and Information Technologies, Vinnytsia National Technical University, 95, Khmelnytskyi Highway, Vinnytsia, Ukraine, 21000; +38 (095) 442-92-20; bogdanpaseka2000@gmail.com; ORCID: 0009-0002-9975-2896<\/p>\n

CONTEXT-DEPENDENT ADAPTIVE REDUCTION AND EQUIVALENT MODELING <\/strong>OF AN ELECTRIC VEHICLE WITH DRIVING MODE SWITCHING FOR A DECISION <\/strong>SUPPORT SYSTEM<\/strong><\/p>\n

Abstract.<\/strong> 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.<\/p>\n

Keywords:<\/strong> electric vehicle, equivalent model, decision support system, adaptive reduction, mode switching, optimal movement, traction electric drive.<\/p>\n

REFERENCES<\/strong><\/p>\n

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    https:\/\/doi.org\/10.35668\/2520-6524-2026-1-08 Pasieka B. V. \u2014 Postgraduate Student, Department of System Analysis and Information Technologies, Vinnytsia National Technical University, 95, Khmelnytskyi Highway, Vinnytsia, Ukraine, 21000; +38 (095) 442-92-20; bogdanpaseka2000@gmail.com; ORCID: 0009-0002-9975-2896 CONTEXT-DEPENDENT ADAPTIVE REDUCTION AND EQUIVALENT MODELING OF AN ELECTRIC VEHICLE WITH DRIVING MODE SWITCHING FOR A DECISION SUPPORT SYSTEM Abstract. The problem of the practical … <\/p>\n

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