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<p>This research presents a concept to develop the overall efficiency and functioning of Photovoltaic (PV) and Battery powered Electric Vehicle based Brushless DC (BLDC) motor system in the context of Internet of Things (IoT).The power produced by the panel is effectively converted into an appropriate voltage level for BLDC motor with the development of an innovative Hybrid Z-Source Coupled Inductor Boost (HZSCIB) Converter. Furthermore, the HZSCIB converter reduces voltage stress on switch and ensures high efficiency. A Maximum Power Point Tracking controller (MPPT) using Cascaded Type-2 Artificial Neural Network (ANN) is adopted to continuously monitor MPP of PV panels to improve system performance and efficiency. The State of Charge (SOC) is continuously monitored by an ANN controller to enable efficient battery management and system performance optimization. A bidirectional 4converter is implemented to maintain an energy flow during charging and discharging process. The IoT integration enables remote control and monitoring of the system. The monitored parameters including (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({V}_{PV},{I}_{PV},{I}_{M},SOC and speed\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi>V</mi> <mrow> <mi mathvariant="italic">PV</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>I</mi> <mrow> <mi mathvariant="italic">PV</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>I</mi> <mi>M</mi> </msub> <mo>,</mo> <mi>S</mi> <mi>O</mi> <mi>C</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>e</mi> <mi>d</mi> </mrow> </math></EquationSource> </InlineEquation>) are transmitted to a cloud-based platform through IoT connectivity, allowing real-time data analysis and system performance evaluation. Additionally, the IoT platform enables the application of remote control strategies, facilitating system optimization and predictive maintenance. Experimental outcomes validate the efficiency of the proposed Cascaded Type-2 ANN MPPT controller with HZSCIB Converter in augmenting the performance of PV-Battery-BLDC motor system. The system achieves accurate MPP tracking and optimal battery SOC management, leading to increased energy harvesting from PV array, extended battery life, and improved overall system performance.</p>

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IoT-Based Hybrid Power System with an AI-Driven MPPT Controller for Z-Source Boost Converters

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • N. Ananthasaravanan,
  • V. Megala

摘要

This research presents a concept to develop the overall efficiency and functioning of Photovoltaic (PV) and Battery powered Electric Vehicle based Brushless DC (BLDC) motor system in the context of Internet of Things (IoT).The power produced by the panel is effectively converted into an appropriate voltage level for BLDC motor with the development of an innovative Hybrid Z-Source Coupled Inductor Boost (HZSCIB) Converter. Furthermore, the HZSCIB converter reduces voltage stress on switch and ensures high efficiency. A Maximum Power Point Tracking controller (MPPT) using Cascaded Type-2 Artificial Neural Network (ANN) is adopted to continuously monitor MPP of PV panels to improve system performance and efficiency. The State of Charge (SOC) is continuously monitored by an ANN controller to enable efficient battery management and system performance optimization. A bidirectional 4converter is implemented to maintain an energy flow during charging and discharging process. The IoT integration enables remote control and monitoring of the system. The monitored parameters including ( \({V}_{PV},{I}_{PV},{I}_{M},SOC and speed\) V PV , I PV , I M , S O C a n d s p e e d ) are transmitted to a cloud-based platform through IoT connectivity, allowing real-time data analysis and system performance evaluation. Additionally, the IoT platform enables the application of remote control strategies, facilitating system optimization and predictive maintenance. Experimental outcomes validate the efficiency of the proposed Cascaded Type-2 ANN MPPT controller with HZSCIB Converter in augmenting the performance of PV-Battery-BLDC motor system. The system achieves accurate MPP tracking and optimal battery SOC management, leading to increased energy harvesting from PV array, extended battery life, and improved overall system performance.