This project presents a solar photovoltaic integrated sensor less PMSM-driven three-wheeler electric vehicle aimed at achieving efficient and intelligent solar-powered transportation with real-time IoT monitoring. The system measures solar voltage using sensors interfaced to a PIC microcontroller, which converts the sensed data into serial format and transmits it to the Raspberry Pi Zero 2W. Based on this solar voltage data, the Raspberry Pi generates a PWM signal to control the MPPT circuit using a Perturb & Observe (P&O) algorithm with adaptive step size inspired by AI optimization, ensuring maximum power extraction under varying environmental conditions. The optimized solar power is regulated through a charge controller, stored in a battery, and supplied to an inverter that drives a Permanent Magnet Synchronous Motor (PMSM) through Triac which is controlled by the PIC microcontroller based on received speed from POT. Senseless motor operation is achieved using a Zero Crossing Detector (ZCD) and Potential Transformer (PT), eliminating mechanical sensors while maintaining efficient drive performance. Vehicle speed is controlled through a user-adjustable potentiometer. In addition to MPPT control, the Raspberry Pi acts as the system’s intelligent processing and IoT interface. Key parameters such as solar voltage and motor RPM are monitored and uploaded to the ThingSpeak platform for real-time visualization and performance analysis, while data logging is supported via an SD card. The PIC microcontroller manages signal acquisition, serial communication, and system coordination, enabling seamless interaction between sensing, control, and cloud monitoring. This integrated architecture combining adaptive AI-inspired MPPT, Raspberry Pi–based PWM generation, sensorless PMSM drive, and IoT connectivity enhances energy efficiency, reduces system complexity, and provides a scalable smart solution for solar-powered electric vehicle applications.    

Objectives:

1. To design a solar-powered three-wheeler EV

2. To implement AI-inspired adaptive MPPT for maximum energy extraction

3. To develop a sensorless PMSM drive system

4. To eliminate mechanical speed sensors using ZCD technique

5. To enable real-time monitoring via IoT

6. To improve system efficiency and reduce hardware complexity

7. To implement machine learning–based adaptive control.  

Components Used:

• Raspberry pi zero 2W.
• Solar Photovoltaic (PV) Panel
• MPPT Controller
• Charge Controller
• Rechargeable Battery.
• Inverter
• PMSM motor
• PIC Microcontroller.
• POT (potentiometer)
• LCD display.
• SD card
• Power Supply.
• Zero crossing detector.
• PT(potential transformer)
• Triac.

  Software’s used in raspberry pi:

• Perturb & Observe (P&O) Algorithm (implemented in controller) Machine Learning.
• Raspbian OS.

Software’s used in PIC microcontroller:

• PIC-C compiler for Embedded C programming.
• PIC kit 2 programmer for dumping code into Micro controller.
• Express SCH for Circuit design.

   Regulated Powersupply:

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