Project

# Title Team Members TA Documents Sponsor
29 EV Battery Thermal Fault Early Detection & Safety Module
RJ Schneider
Skyler Yoon
Troy Edwards
# 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.

BusPlan

Aashish Kapur, Connor Lake, Scott Liu

BusPlan

Featured Project

# People

Scott Liu - sliu125

Connor Lake - crlake2

Aashish Kapur - askapur2

# Problem

Buses are scheduled inefficiently. Traditionally buses are scheduled in 10-30 minute intervals with no regard the the actual load of people at any given stop at a given time. This results in some buses being packed, and others empty.

# Solution Overview

Introducing the _BusPlan_: A network of smart detectors that actively survey the amount of people waiting at a bus stop to determine the ideal amount of buses at any given time and location.

To technically achieve this, the device will use a wifi chip to listen for probe requests from nearby wifi-devices (we assume to be closely correlated with the number of people). It will use a radio chip to mesh network with other nearby devices at other bus stops. For power the device will use a solar cell and Li-Ion battery.

With the existing mesh network, we also are considering hosting wifi at each deployed location. This might include media, advertisements, localized wifi (restricted to bus stops), weather forecasts, and much more.

# Solution Components

## Wifi Chip

- esp8266 to wake periodically and listen for wifi probe requests.

## Radio chip

- NRF24L01 chip to connect to nearby devices and send/receive data.

## Microcontroller

- Microcontroller (Atmel atmega328) to control the RF chip and the wifi chip. It also manages the caching and sending of data. After further research we may not need this microcontroller. We will attempt to use just the ens86606 chip and if we cannot successfully use the SPI interface, we will use the atmega as a middleman.

## Power Subsystem

- Solar panel that will convert solar power to electrical power

- Power regulator chip in charge of taking the power from the solar panel and charging a small battery with it

- Small Li-Ion battery to act as a buffer for shady moments and rainy days

## Software and Server

- Backend api to receive and store data in mongodb or mysql database

- Data visualization frontend

- Machine learning predictions (using LSTM model)

# Criteria for Success

- Successfully collect an accurate measurement of number of people at bus stops

- Use data to determine optimized bus deployment schedules.

- Use data to provide useful visualizations.

# Ethics and Safety

It is important to take into consideration the privacy aspect of users when collecting unique device tokens. We will make sure to follow the existing ethics guidelines established by IEEE and ACM.

There are several potential issues that might arise under very specific conditions: High temperature and harsh environment factors may make the Li-Ion batteries explode. Rainy or moist environments may lead to short-circuiting of the device.

We plan to address all these issues upon our project proposal.

# Competitors

https://www.accuware.com/products/locate-wifi-devices/

Accuware currently has a device that helps locate wifi devices. However our devices will be tailored for bus stops and the data will be formatted in a the most productive ways from the perspective of bus companies.