ZapAway – IoT-based EV Battery Protection System (2024)

One-line summary
ESP32-based IoT prototype designed to monitor EV battery voltage and automatically cut off power to prevent overcharging and improve battery safety.

Description
ZapAway focuses on improving EV battery safety by enabling automated monitoring and control during the charging process. The system continuously tracks voltage levels and triggers an automatic power cut-off under unsafe conditions using sensor-based relay control. The project was showcased at the AAC Project Expo 2024, where the team received recognition and certification.

Problem Statement
Overcharging and unsafe charging conditions reduce EV battery lifespan and can lead to overheating or damage. There is a need for an automated system that can monitor battery voltage in real time and ensure safe charging without manual intervention.

What We Built
We developed an IoT-based prototype using an ESP32 microcontroller, sensors, and relay modules to monitor battery voltage and control the power supply automatically. The system enables real-time monitoring and safety-driven automation during charging.

My Role
I worked on sensor integration and basic control logic, helping define conditions for triggering the relay-based power cut-off. I was also involved in testing the system and understanding hardware–software interaction.

Key Features

• Real-time battery voltage monitoring

• Automatic power cut-off for overcharging protection

• Sensor-based safety detection

• Remote status monitoring through an IoT platform

What I Learned
This project gave me hands-on exposure to IoT concepts and hardware–software integration. I learned how real-world safety systems are built, how sensors interact with microcontrollers, and how control logic translates into physical actions.

Challenges Faced
Ensuring reliable sensor readings and correctly triggering the relay under specific conditions was challenging. Debugging hardware connections and aligning sensor data with control logic required careful testing and iteration.

Tech & Skills (Project-level knowledge)
ESP32, IoT concepts, Sensors, Relay control, Embedded C/C++, Hardware–software integration, Problem solving

Description:
This project focuses on understanding how files and folders are organized within a system by programmatically traversing directories and displaying their hierarchy. It was developed as a practical exercise to strengthen Python fundamentals and explore basic file system concepts. The project was showcased at a project expo and certified for completion.

Problem Statement:

Visualizing and understanding complex folder structures can be difficult, especially when directories are deeply nested. A simple programmatic approach can make directory organization clearer and easier to analyze.

What I Built:

I built a Python program that traverses directories and outputs the folder hierarchy in a tree-like structure, making the organization of files and subfolders easy to understand.

My Role:

I independently designed and implemented the directory traversal logic, structured the output format, and tested the program with different folder structures to ensure correctness.

Key Features:

• Directory traversal for nested folders

• Tree-style representation of folder hierarchy

• Simple and readable output

What I Learned:

This project strengthened my Python scripting skills and helped me understand basic file system concepts such as directory traversal and hierarchical data representation. It also improved my logical thinking and problem-solving approach.

Challenges Faced:

Handling deeply nested directories while keeping the output readable required careful structuring of the logic and multiple test iterations.

Tech & Skills:

Python, Directory traversal (basic), File system concepts, Logical thinking, Problem solving

Description:
This project was initiated as a learning-focused exploration into applying machine learning techniques to social media datasets. The work primarily involved understanding the problem space, performing basic data preprocessing, and experimenting with introductory models. The project is currently paused due to academic commitments and a conscious decision to strengthen foundational software engineering skills before continuing.

Problem Statement:

Identifying micro-influencers from large volumes of social media data is a complex task that requires data-driven analysis. This project aimed to explore how machine learning could assist in such identification at a basic, conceptual level.

What I Worked On:

I focused on planning and coordinating data collection for the project. Initially, the approach was to use publicly available datasets due to restrictions on data access from major social media platforms. As the project evolved, we explored data scraping as an alternative, and I began understanding the fundamentals of the data scraping process before the project was paused.

My Role:

I led the early stages of the project by coordinating team efforts, managing data collection planning, and guiding the transition from dataset-based approaches to exploring data scraping. My role involved organizing tasks, understanding feasibility constraints, and supporting the team during the exploratory learning phase.

Key Areas Explored:

• Data preprocessing and cleaning

• Introductory machine learning experimentation

• Exploratory analysis of social media data

What I Learned:

This project helped me understand the importance of clean data, experimentation, and foundational knowledge in machine learning workflows. It also clarified the need to build stronger software engineering fundamentals before working on more advanced ML problems.

Challenges Faced:

The steep learning curve of machine learning concepts and the time required for experimentation, combined with academic workload, made it difficult to continue at the desired depth, leading to the project being paused intentionally.

Tech & Skills:

Python, Data Collection, Data Scraping (introductory exposure), Machine learning concepts (introductory), Exploratory analysis, Analytical thinking, Team coordination