Raspberry Pi 5 EV Battery State of Charge Predictor Kit – Build an AI‑Powered BMS

Every part needed, pre‑tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3‑5 days.

You’ll assemble a real‑time battery monitoring system that uses voltage, current, and temperature data from a LiFePO4 cell to predict state of charge. By combining a Kalman filter for noise reduction with an LSTM neural network on the Raspberry Pi 5, you’ll create a miniaturized version of the algorithms used in electric vehicle battery management systems.

What You’ll Build

A functional EV battery SoC predictor with a live dashboard, a trained LSTM model achieving under 5% estimation error, and a solid foundation in sensor integration and edge AI. The project puts you on the path toward advanced IoT and smart energy design.

What You’ll Learn

• Interfacing INA226 and DS18B20 sensors to Raspberry Pi 5 over I2C and 1‑Wire
• Implementing a Kalman filter in Python to smooth raw battery measurements
• Designing and training an LSTM neural network for time‑series SoC prediction
• Deploying the full pipeline on Pi 5 and visualising results with a real‑time dashboard

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Aspiring CBSE Class 11‑12 students building AI‑driven science projects, B.Tech ECE/EE undergraduates prototyping for Smart India Hackathon or capstone, and ATL Tinkering Lab mentors guiding learners through real‑world IoT and AI integration. Anyone eager to bridge hardware sensors with machine learning on the edge.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We’ve spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.