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Honda BEV thermal runaway even
Project Timeline
Aug 2024 - May-2025
OVERVIEW
Honda’s Marysville Auto Plant is transitioning to BEV production in 2025, with Intelligent Power Units assembled onsite. This project evaluates early detection methods for lithium-ion battery thermal runaway events in IPUs, focusing on proactive sensing strategies suitable for mass-production environments. The work includes analyzing thermal runaway mechanisms, assessing candidate sensing technologies, and recommending MAP-specific detection solutions to prevent hazardous thermal events and improve BEV manufacturing safety. collaborated with Honda’s BEV Infrastructure Development Team on an engineering capstone project focused on early detection of lithium-ion battery thermal runaway in Battery Electric Vehicles (BEVs). Evaluated multiple sensor modalities through battery testing, data analysis, and Python-based modeling in Jupyter Notebooks. Worked closely with lead Honda engineers to assess detection performance, integration feasibility, and safety implications for automotive manufacturing environments.
HighlightS
BEV Safety & Thermal Runaway Detection
- Contributed to a Honda-sponsored capstone project supporting the Marysville Auto Plant (MAP) BEV production transition (2025) by evaluating early detection strategies for lithium-ion battery thermal runaway in Intelligent Power Units (IPUs).
- Developed a proactive safety-focused framework for identifying thermal runaway precursors before catastrophic failure, aligned with mass-production manufacturing constraints.
Multi-Sensor Evaluation & Validation
- Evaluated and experimentally validated four sensing modalities for early thermal runaway detection:
- Infrared pyrometers
- MEMS thermal sensors
- Hydrogen fluoride (HF) gas detectors
- Ultrasonic transducers
- Designed and executed controlled lab experiments simulating thermal events using hot plates, variable distances, and realistic manufacturing geometries.
Data Collection, Analysis & Interpretation
- Collected and analyzed real-time sensor data including temperature rise, gas concentration (HF ppm), detection latency, voltage fluctuations, and vibrational signatures.
- Quantified detection time vs. distance tradeoffs for HF gas sensing and identified performance limits relevant to factory floor deployment.
- Used Python and Jupyter Notebooks to process sensor outputs, visualize trends, and compare detection effectiveness across technologies.
SKILLS
Collaboration & Professional SkillsIndustry–academic collaboration (Honda)Multidisciplinary team environmentsTechnical presentations & poster designStakeholder communicationRequirements-driven engineeringEngineering & Design ToolsSolidWorks (CAD modeling)Engineering documentation & technical reportingSystems-level trade studiesManufacturing feasibility analysisRisk assessment & safety analysisInfrared pyrometers & thermal sensingMEMS thermal sensorsHydrogen fluoride (HF) gas detectionUltrasonic / acoustic sensingData acquisition (DAQ) & real-time monitoringExperimental design & lab validationCalibration & uncertainty considerationsLithium-ion battery fundamentalsThermal runaway mechanisms & mitigationIntelligent Power Units (IPUs)Battery safety & early-warning systemsBEV manufacturing environmentsSensor-based fault detection
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