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<p>This study focuses on a key challenge in the design of switched reluctance motors (SRMs) for light electric vehicles (LEVs). The goal is to achieve high torque output while minimizing torque ripple and improving efficiency. These performance aspects are critical for ensuring smooth operation, better energy utilization, and suitability for real-world EV applications. SRMs are increasingly favored in electric mobility due to their simple construction, high fault tolerance, and cost-effectiveness. However, their nonlinear characteristics and coupled multi-physics behavior render the design optimization process complex. To tackle this, a novel multi-objective driving training-based optimization (MODTBO) algorithm is proposed. It enhances the original DTBO framework by integrating Pareto dominance, non-dominated sorting, and crowding distance mechanisms to support multi-objective search. The performance of MODTBO is validated using standard multi-objective benchmark functions as well as real-world engineering test problems. Results show improved convergence and solution diversity compared to MOEA/D, MOPSO, NSGA-II, and SPEA2, based on metrics such as HV, IGDp, and DeltaP. The proposed algorithm is then applied to optimize a four-phase, 8/6 SRM, targeting objectives of maximizing average torque, minimizing torque ripple, and enhancing overall efficiency. Critical design variables are identified through sensitivity analysis, and the final Pareto solutions are evaluated using Ansys Motor-CAD for electromagnetic performance. Three designs D1, D2, and D3 are selected for further analysis from the final Pareto solutions. Among these, Design D1 is selected for its balanced trade-offs demonstrating the real-world applicability of MODTBO in improving SRM designs for next-generation LEV applications.</p>

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Design of Switched Reluctance Motor for Light Electric Vehicles Using Multi-objective Driving Training-Based Optimization Algorithm

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Sikandar Ali Khan,
  • Suman Bhowmick,
  • Madhusudan Singh

摘要

This study focuses on a key challenge in the design of switched reluctance motors (SRMs) for light electric vehicles (LEVs). The goal is to achieve high torque output while minimizing torque ripple and improving efficiency. These performance aspects are critical for ensuring smooth operation, better energy utilization, and suitability for real-world EV applications. SRMs are increasingly favored in electric mobility due to their simple construction, high fault tolerance, and cost-effectiveness. However, their nonlinear characteristics and coupled multi-physics behavior render the design optimization process complex. To tackle this, a novel multi-objective driving training-based optimization (MODTBO) algorithm is proposed. It enhances the original DTBO framework by integrating Pareto dominance, non-dominated sorting, and crowding distance mechanisms to support multi-objective search. The performance of MODTBO is validated using standard multi-objective benchmark functions as well as real-world engineering test problems. Results show improved convergence and solution diversity compared to MOEA/D, MOPSO, NSGA-II, and SPEA2, based on metrics such as HV, IGDp, and DeltaP. The proposed algorithm is then applied to optimize a four-phase, 8/6 SRM, targeting objectives of maximizing average torque, minimizing torque ripple, and enhancing overall efficiency. Critical design variables are identified through sensitivity analysis, and the final Pareto solutions are evaluated using Ansys Motor-CAD for electromagnetic performance. Three designs D1, D2, and D3 are selected for further analysis from the final Pareto solutions. Among these, Design D1 is selected for its balanced trade-offs demonstrating the real-world applicability of MODTBO in improving SRM designs for next-generation LEV applications.