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<p>As the demand for sustainable transportation solutions grows, incorporating renewable energy sources becomes crucial for enhancing both energy efficiency and vehicle performance. This paper proposes an intelligent hybrid energy management approach for Brushless DC (BLDC) motor-driven electric vehicles, powered by a combination of solar, battery, and supercapacitor systems. Optimizing power flow management under various load circumstances is a major problem in such systems. A consistent power supply is maintained during low-intensity periods, and the surplus energy produced during high-intensity periods is stored in an energy-storage system to ensure efficient operation. In this hybrid system, the supercapacitor provides instantaneous load power, whereas the batteries provide a continuous energy supply. To optimize and coordinate six major controllers in solar, battery management, supercapacitor, BLDC drive, and energy management, four nature-inspired algorithms, including Artificial Bee Colony, Grey Wolf Optimization, Harris Hawks Optimization, and Moth-Flame Optimization, are implemented and compared in the proposed system. Using MATLAB/Simulink software, the effectiveness of the suggested system is modelled and validated. The results confirm significant improvements in energy utilization, system reliability, and overall vehicle performance. These findings highlight the potential of renewable energy integration in advancing electric mobility. This study advances sustainable transportation research and demonstrates the efficacy of novel nature-inspired optimization for enhanced hybrid energy systems.</p>

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Improved Performance of Solar, Battery, and Supercapacitor Powered BLDC Motor-based Electric Vehicle Using Nature-Inspired Optimizations

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Vineet Kumar Tiwari,
  • Awadhesh Kumar,
  • Shekhar Yadav,
  • Nitesh Tiwari

摘要

As the demand for sustainable transportation solutions grows, incorporating renewable energy sources becomes crucial for enhancing both energy efficiency and vehicle performance. This paper proposes an intelligent hybrid energy management approach for Brushless DC (BLDC) motor-driven electric vehicles, powered by a combination of solar, battery, and supercapacitor systems. Optimizing power flow management under various load circumstances is a major problem in such systems. A consistent power supply is maintained during low-intensity periods, and the surplus energy produced during high-intensity periods is stored in an energy-storage system to ensure efficient operation. In this hybrid system, the supercapacitor provides instantaneous load power, whereas the batteries provide a continuous energy supply. To optimize and coordinate six major controllers in solar, battery management, supercapacitor, BLDC drive, and energy management, four nature-inspired algorithms, including Artificial Bee Colony, Grey Wolf Optimization, Harris Hawks Optimization, and Moth-Flame Optimization, are implemented and compared in the proposed system. Using MATLAB/Simulink software, the effectiveness of the suggested system is modelled and validated. The results confirm significant improvements in energy utilization, system reliability, and overall vehicle performance. These findings highlight the potential of renewable energy integration in advancing electric mobility. This study advances sustainable transportation research and demonstrates the efficacy of novel nature-inspired optimization for enhanced hybrid energy systems.