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29 EV Battery Thermal Fault Early Detection & Safety Module
RJ Schneider
Skyler Yoon
Troy Edwards
Wenjing Song
# Team Members
- RJ Schneider (rs49)
- Skyler Yoon (yy30)
- Troy Edwards (troyre2)
# Problem
Lithium-ion batteries used in electric vehicles can experience abnormal heating due to internal
faults, charging stress, or cooling failure. These thermal issues often begin with localized hot
spots or an unusually fast increase in temperature before visible failure occurs. While vehicle
battery management systems handle internal protection, there is a need for an external, lowvoltage monitoring and diagnostic module that can provide early warning and a hardware-level
safety output for laboratory testing, validation, and educational demonstration environments.
# Solution
We propose a battery thermal fault monitoring module that detects early thermal fault indicators
using multiple temperature sensors and simple decision logic. The system will use two
independent detection paths: a microcontroller-based path for data logging and trend analysis,
and a hardware comparator path for fast threshold-based fault detection. A custom PCB will
integrate sensor interfaces, signal conditioning, control logic, and alert outputs. The system will
be demonstrated using a low-voltage heating element to safely simulate abnormal battery heating
behavior.
# Solution Components
## Subsystem 1 (Thermal Sensing Front-End)
Components:
- 10k NTC Thermistors (x3)
- 1% Precision Resistors (voltage divider networks)
- MCP6002 Rail-to-Rail Op-Amp (or equivalent)
Function:
This subsystem converts temperature changes into analog voltage signals using thermistor
voltage dividers. A simple active low-pass filter is implemented on the PCB to reduce noise from
the heating element and power supply. Multiple sensors allow detection of uneven heating across
the simulated battery surface.
## Subsystem 2 (Dual-Logic Decision Unit)
Components:
- ESP32-WROOM-32 Microcontroller
- LM311 Voltage Comparator
Function:
The ESP32 samples temperature data using its ADC and calculates temperature rate-of-rise to
generate early warning alerts. In parallel, the LM311 comparator directly monitors one sensor
voltage and triggers a fault output when a fixed temperature threshold is exceeded. This provides
a simple hardware backup path that does not rely on firmware execution.
## Subsystem 3 (Power Regulation and Safety Output)
Components:
- 5V to 3.3V LDO Regulator (e.g., AMS1117-3.3)
- SPDT 5V Relay Module
- Logic-Level MOSFET (IRLZ44N or equivalent)
Function:
This subsystem regulates input power for the PCB and provides output signaling. The relay
represents a low-voltage safety cutoff output that simulates a charger-disable or contactor-enable
signal. The MOSFET is used to control the heating element during demonstration and testing.
# Criterion For Success
1. Hardware Fault Trigger:
The comparator-based protection path must activate the relay output within 200 ms of
exceeding a preset temperature threshold.
2. Early Warning Detection:
The ESP32 must trigger a warning alert when the measured temperature rise exceeds a
configured rate-of-rise threshold for at least 3 seconds.
3. Temperature Accuracy:
PCB sensor readings must be within ±1.5°C of a calibrated reference thermometer.
4. Noise Reduction Performance:
The PCB filtering stage must demonstrate reduced ADC signal noise compared to an
unfiltered measurement when the heating element is active.
5. Fail-Safe Behavior:
The relay output must default to an open (safe) state when system power is removed.

Microcontroller-based Occupancy Monitoring (MOM)

Vish Gopal Sekar, John Li, Franklin Moy

Microcontroller-based Occupancy Monitoring (MOM)

Featured Project

# Microcontroller-based Occupancy Monitoring (MOM)

Team Members:

- Franklin Moy (fmoy3)

- Vish Gopal Sekar (vg12)

- John Li (johnwl2)

# Problem

With the campus returning to normalcy from the pandemic, most, if not all, students have returned to campus for the school year. This means that more and more students will be going to the libraries to study, which in turn means that the limited space at the libraries will be filled up with the many students who are now back on campus. Even in the semesters during the pandemic, many students have entered libraries such as Grainger to find study space, only to leave 5 minutes later because all of the seats are taken. This is definitely a loss not only to someone's study time, but maybe also their motivation to study at that point in time.

# Solution

We plan on utilizing a fleet of microcontrollers that will scan for nearby Wi-Fi and Bluetooth network signals in different areas of a building. Since students nowadays will be using phones and/or laptops that emit Wi-Fi and Bluetooth signals, scanning for Wi-Fi and Bluetooth signals is a good way to estimate the fullness of a building. Our microcontrollers, which will be deployed in numerous dedicated areas of a building (called sectors), will be able to detect these connections. The microcontrollers will then conduct some light processing to compile the fullness data for its sector. We will then feed this data into an IoT core in the cloud which will process and interpret the data and send it to a web app that will display this information in a user-friendly format.

# Solution Components

## Microcontrollers with Radio Antenna Suite

Each microcontroller will scan for Wi-Fi and Bluetooth packets in its vicinity, then it will compile this data for a set timeframe and send its findings to the IoT Core in the Cloud subsystem. Each microcontroller will be programmed with custom software that will interface with its different radio antennas, compile the data of detected signals, and send this data to the IoT Core in the Cloud subsystem.

The microcontroller that would suit the job would be the ESP32. It can be programmed to run a suite of real-time operating systems, which are perfect for IoT applications such as this one. This enables straightforward software development and easy connectivity with our IoT Core in the Cloud. The ESP32 also comes equipped with a 2.4 GHz Wi-Fi transceiver, which will be used to connect to the IoT Core, and a Bluetooth Low Energy transceiver, which will be part of the radio antenna suite.

Most UIUC Wi-Fi access points are dual-band, meaning that they communicate using both the 2.4 GHz and 5 GHz frequencies. Because of this, we will need to connect a separate dual-band antenna to the ESP32. The simplest solution is to get a USB dual-band Wi-Fi transceiver, such as the TP-Link Nano AC600, and plug it into a USB Type-A breakout board that we will connect to each ESP32's GPIO pins. Our custom software will interface with the USB Wi-Fi transceiver to scan for Wi-Fi activity, while it will use the ESP32's own Bluetooth Low Energy transceiver to scan for Bluetooth activity.

## Battery Backup

It is possible that the power supply to a microcontroller could fail, either due to a faulty power supply or by human interference, such as pulling the plug. To mitigate the effects that this would have on the system, we plan on including a battery backup subsystem to each microcontroller. The battery backup subsystem will be able to not only power the microcontroller when it is unplugged, but it will also be able to charge the battery when it is plugged in.

Most ESP32 development boards, like the Adafruit HUZZAH32, have this subsystem built in. Should we decide to build this subsystem ourselves, we would use the following parts. Most, if not all, ESP32 microcontrollers use 3.3 volts as its operating voltage, so utilizing a 3.7 volt battery (in either an 18650 or LiPo form factor) with a voltage regulator would supply the necessary voltage for the microcontroller to operate. A battery charging circuit consisting of a charge management controller would also be needed to maintain battery safety and health.

## IoT Core in the Cloud

The IoT Core in the Cloud will handle the main processing of the data sent by the microcontrollers. Each microcontroller is connected to the IoT Core, which will likely be hosted on AWS, through the ESP32's included 2.4GHz Wi-Fi transceiver. We will also host on AWS the web app that interfaces with the IoT Core to display the fullness of the different sectors. This web app will initially be very simple and display only the estimated fullness. The web app will likely be built using a Python web framework such as Flask or Django.

# Criterion For Success

- Identify Wi-Fi and Bluetooth packets from a device and distinguish them from packets sent by different devices.

- Be able to estimate the occupancy of a sector within a reasonable margin of error (15%), as well as being able to compute its fullness relative to that sector's size.

- Display sector capacity information on the web app that is accurate within 5 minutes of a user accessing the page.

- Battery backup system keeps the microcontroller powered for at least 3 hours when the wall outlet is unplugged.

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