Advances in Control Strategies for Electric Motors in Electric Vehicles: Systematic Review 2021-2025 and Comparative Analysis
DOI:
https://doi.org/10.61799/2216-0388.2030Keywords:
electric drives, control strategies, electric mobility, induction motor, power converter, semiconductorsAbstract
Electric mobility has driven the development of more efficient and reliable electric motors for vehicle applications. Technologies such as the induction motor, the permanent magnet synchronous motor, and the brushless direct current motor have evolved alongside new control strategies and power converters. Nevertheless, challenges remain related to load variations, demanding dynamic conditions, and energy efficiency requirements.To systematically analyze recent advances in control strategies applied to electric motors in electric vehicles from 2021 to 2025, identifying research trends, comparative advantages, and existing gaps.A systematic literature review was conducted using the Scopus database following the Prisma methodology. An initial bibliometric analysis was performed, followed by a qualitative and quantitative comparative evaluation of one hundred fifty selected studies, considering performance, implementation complexity, efficiency, and adaptability to dynamic conditions.Findings indicate that vector control remains the most balanced technique in terms of precision, efficiency, and industrial feasibility. Predictive, intelligent, and hybrid strategies demonstrate superior dynamic performance and higher energy efficiency, although they involve greater cost and computational complexity. Overall system performance strongly depends on the effective integration of the motor, power converter, and control system, as well as on the adoption of advanced semiconductor materials.The joint evolution of motors, converters, and control strategies is essential to optimize electric mobility systems. Advanced control techniques represent the technological direction of the sector, yet their large-scale implementation requires reductions in cost and computational demand.
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