Scholarlink

<p>Efficient battery usage is crucial for extending the driving range and improving the overall efficiency of battery electric vehicles (BEVs). This paper presents an energy-efficient adaptive linear model predictive control (AL-MPC) strategy for a permanent-magnet synchronous motor (PMSM) drive. Unlike the conventional linear predictive model, which cannot represent the highly nonlinear electrical behavior of the PMSM plant inherent in BEVs, the proposed controller employs a real-time adaptive model continuously updated with the estimated traction torque and stator reactance. These updates allow the predictive model to reflect the actual motor dynamics more accurately. A moving-average-filtered third-order generalized integrator (MAF-TOGI) observer provides noise-resistant estimates of the electromagnetic torque and reactance, enabling accurate current reference tracking and precise realization of the maximum torque-per-ampere (MTPA) control. Consequently, battery energy consumption is reduced, and system efficiency is enhanced. Simulation results demonstrate smoother torque response, improved speed tracking, and a 7.4% reduction in battery energy use compared with a conventional linear MPC baseline under identical driving conditions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Observer-Aided Adaptive MPC for Battery Energy Reduction in PMSM Electric-Vehicle Drives

  • 期刊论文

  • 发表日期：

    2026年5月5日

查看全文
  • Moustafa Magdi Ismail,
  • Mujahed Al-Dhaifallah,
  • Mohamed M. Refaat

摘要

Efficient battery usage is crucial for extending the driving range and improving the overall efficiency of battery electric vehicles (BEVs). This paper presents an energy-efficient adaptive linear model predictive control (AL-MPC) strategy for a permanent-magnet synchronous motor (PMSM) drive. Unlike the conventional linear predictive model, which cannot represent the highly nonlinear electrical behavior of the PMSM plant inherent in BEVs, the proposed controller employs a real-time adaptive model continuously updated with the estimated traction torque and stator reactance. These updates allow the predictive model to reflect the actual motor dynamics more accurately. A moving-average-filtered third-order generalized integrator (MAF-TOGI) observer provides noise-resistant estimates of the electromagnetic torque and reactance, enabling accurate current reference tracking and precise realization of the maximum torque-per-ampere (MTPA) control. Consequently, battery energy consumption is reduced, and system efficiency is enhanced. Simulation results demonstrate smoother torque response, improved speed tracking, and a 7.4% reduction in battery energy use compared with a conventional linear MPC baseline under identical driving conditions.