{"product_id":"kit-pi-5-ev-battery-state-of-charge-predictor","title":"Pi 5 EV Battery State of Charge Predictor","description":"\u003ch1\u003eRaspberry Pi 5 EV Battery State of Charge Predictor Kit – Build an AI‑Powered BMS\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre‑tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3‑5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 5‑6 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 15‑21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Kalman filter \u0026amp; LSTM implementation for battery SoC\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eYou’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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You’ll Build\u003c\/h2\u003e\n\u003cp\u003eA 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You’ll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eInterfacing INA226 and DS18B20 sensors to Raspberry Pi 5 over I2C and 1‑Wire\u003c\/li\u003e\n  \u003cli\u003eImplementing a Kalman filter in Python to smooth raw battery measurements\u003c\/li\u003e\n  \u003cli\u003eDesigning and training an LSTM neural network for time‑series SoC prediction\u003c\/li\u003e\n  \u003cli\u003eDeploying the full pipeline on Pi 5 and visualising results with a real‑time dashboard\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eINA226 Power Monitor\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDS18B20\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eLiFePO4 Cell 3.2V\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 128GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e20\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre‑tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step‑by‑step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3‑5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eAspiring 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.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eScan the QR code on the box to open your project‑specific AI companion. It walks through every step, and if you need a human, our WhatsApp support team responds within a few hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior AI knowledge to complete this project?\u003c\/summary\u003e\u003cp\u003eNo. The kit includes a pre‑trained LSTM model and Jupyter notebooks that explain the code line by line. Basic Python familiarity is helpful, but the AI companion fills any gaps.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I test other battery cells with this setup?\u003c\/summary\u003e\u003cp\u003eThe design is based on the included LiFePO4 cells. You can adapt it for other chemistries with proper safety precautions; the AI companion outlines the necessary sensor range checks.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes the kit include a display for the dashboard?\u003c\/summary\u003e\u003cp\u003eThere is no dedicated LCD, but the live dashboard runs on the Pi and can be viewed via any HDMI monitor or VNC. The AI companion helps you set up a headless connection in minutes.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eVoltage, current and temperature data from a LiFePO4 test cell feeds a Kalman filter + LSTM hybrid on Pi 5 for SoC estimation.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eINA226 Power Monitor\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/ds18b20-digital-temp-sensor-module-1-wire-55-to-125c\"\u003eDS18B20\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003eLiFePO4 Cell 3.2V x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 128GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x20\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 EV Battery State of Charge Predictor?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 EV Battery State of Charge Predictor includes all components needed: Raspberry Pi 5 4GB, INA226 Power Monitor, DS18B20, LiFePO4 Cell 3.2V, NVMe SSD 128GB and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 EV Battery State of Charge Predictor?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 15-21. Voltage, current and temperature data from a LiFePO4 test cell feeds a Kalman filter + LSTM hybrid on Pi 5 for SoC estimation. Estimated build time is 5-6 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 EV Battery State of Charge Predictor online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 EV Battery State of Charge Predictor is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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