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<p>This work presents an integrated power management system for EVs combining solar Photovoltaic (PV) energy, battery storage, and optimized control strategies to enhance overall efficiency and performance. A novel High Gain Re-Lift Luo Converter with coupled inductors is used to boost the voltage with minimal losses. The system addresses key challenges such as inefficient energy extraction from the PV system, energy losses in power conversion stages, and suboptimal battery utilization. A Brain Storm Optimized Radial Basis Function Neural Network (BSO-RBFNN) Maximum Power Point Tracking (MPPT) technique is implemented to ensureaccurate tracking of the maximum power point under varying environmental conditions, maximizing the energy harvested from the PV system. The system integrates DC-DC Converter for efficient control of power flow, enabling both charging and discharging. Additionally, a three-phase Voltage Source Inverter (VSI) with an LC Filter provides continuous integration with the electrical grid, allowing for grid-connected operation during charging and regenerative braking. The proposed power management architecture improves energy efficiency and ensures reliable operation of the EV under different driving conditions. Simulation results indicate significant improvement in conversion efficiency to 96.8%, tracking efficiency to 99.27%, reduces power conversion losses, and increases overall vehicle performance.</p>

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High-Efficiency Electric Vehicle Power Management System Using Brain Storm Optimized MPPT and Advanced High Gain Re-Lift Luo Converter

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • A. R. Danila Shirly,
  • M. V. Suganyadevi

摘要

This work presents an integrated power management system for EVs combining solar Photovoltaic (PV) energy, battery storage, and optimized control strategies to enhance overall efficiency and performance. A novel High Gain Re-Lift Luo Converter with coupled inductors is used to boost the voltage with minimal losses. The system addresses key challenges such as inefficient energy extraction from the PV system, energy losses in power conversion stages, and suboptimal battery utilization. A Brain Storm Optimized Radial Basis Function Neural Network (BSO-RBFNN) Maximum Power Point Tracking (MPPT) technique is implemented to ensureaccurate tracking of the maximum power point under varying environmental conditions, maximizing the energy harvested from the PV system. The system integrates DC-DC Converter for efficient control of power flow, enabling both charging and discharging. Additionally, a three-phase Voltage Source Inverter (VSI) with an LC Filter provides continuous integration with the electrical grid, allowing for grid-connected operation during charging and regenerative braking. The proposed power management architecture improves energy efficiency and ensures reliable operation of the EV under different driving conditions. Simulation results indicate significant improvement in conversion efficiency to 96.8%, tracking efficiency to 99.27%, reduces power conversion losses, and increases overall vehicle performance.