AI-Powered EV Battery Fire Prevention System
An internship project focused on real-time thermal anomaly detection and predictive fire prevention in lithium-ion battery packs.
Executive Summary
Lithium-ion battery fires remain one of the most critical safety challenges in the EV industry. This internship project tasks candidates with designing an embedded AI system that monitors cell-level thermal data, detects early-stage thermal runaway signatures, and triggers automated safety interventions before ignition occurs.
Why it matters
A single battery fire incident can cost an OEM billions in recalls and destroy public trust built over years. Proactive, AI-driven prevention is the next frontier — combining embedded ML inference, fail-safe circuit design, and real-time data pipelines into a unified safety architecture.
Technical Understanding
Basics
Thermal Runaway Fundamentals: Understanding the chain reaction of internal short circuits, electrolyte decomposition, and heat generation inside lithium-ion cells.
Sensor Architecture: Deploying a mesh of thermistors, voltage monitors, and IR cameras across the battery pack to create a real-time thermal map at cell resolution.
Mid-Level Engineering
ML Model Design: Training anomaly detection models (autoencoders, LSTM-based time-series classifiers) on thermal signatures to identify precursor patterns 60–120 seconds before critical thresholds are reached.
Edge Inference: Deploying quantized models on microcontrollers (STM32 / Cortex-M series) for sub-millisecond latency safety decisions without cloud dependency.
Advanced View
Automated Safety Interventions: Designing hardware-triggered isolation circuits that disconnect faulty cell groups, activate venting systems, and send CAN-bus alerts to the vehicle BMS and telematics stack.
Certification & Standards: Aligning the system design with ISO 26262 functional safety (ASIL-D), UN ECE R100, and AIS-038 Rev 2 (India) requirements for battery safety.
Key Takeaways
- • Thermal runaway prevention requires cell-level, millisecond-resolution sensing.
- • On-device ML inference is mandatory for fail-safe latency requirements.
- • Hardware isolation circuits must operate independently of software stacks.
- • Compliance with global safety standards is a non-negotiable deliverable.