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.

Remotely Controlled Self-balancing Mini Bike

Will Chen, Eric Tang, Jiaming Xu

Featured Project

# Remotely Controlled Self-balancing Mini Bike

Team Members:

- Will Chen hongyuc5

- Jiaming Xu jx30

- Eric Tang leweit2

# Problem

Bike Share and scooter share have become more popular all over the world these years. This mode of travel is gradually gaining recognition and support. Champaign also has a company that provides this service called Veo. Short-distance traveling with shared bikes between school buildings and bus stops is convenient. However, since they will be randomly parked around the entire city when we need to use them, we often need to look for where the bike is parked and walk to the bike's location. Some of the potential solutions are not ideal, for example: collecting and redistributing all of the bikes once in a while is going to be costly and inefficient; using enough bikes to saturate the region is also very cost inefficient.

# Solution

We think the best way to solve the above problem is to create a self-balancing and moving bike, which users can call bikes to self-drive to their location. To make this solution possible we first need to design a bike that can self-balance. After that, we will add a remote control feature to control the bike movement. Considering the possibilities for demonstration are complicated for a real bike, we will design a scaled-down mini bicycle to apply our self-balancing and remote control functions.

# Solution Components

## Subsystem 1: Self-balancing part

The self-balancing subsystem is the most important component of this project: it will use one reaction wheel with a Brushless DC motor to balance the bike based on reading from the accelerometer.

MPU-6050 Accelerometer gyroscope sensor: it will measure the velocity, acceleration, orientation, and displacement of the object it attaches to, and, with this information, we could implement the corresponding control algorithm on the reaction wheel to balance the bike.

Brushless DC motor: it will be used to rotate the reaction wheel. BLDC motors tend to have better efficiency and speed control than other motors.

Reaction wheel: we will design the reaction wheel by ourselves in Solidworks, and ask the ECE machine shop to help us machine the metal part.

Battery: it will be used to power the BLDC motor for the reaction wheel, the stepper motor for steering, and another BLDC motor for movement. We are considering using an 11.1 Volt LiPo battery.

Processor: we will use STM32F103C8T6 as the brain for this project to complete the application of control algorithms and the coordination between various subsystems.

## Subsystem 2: Bike movement, steering, and remote control

This subsystem will accomplish bike movement and steering with remote control.

Servo motor for movement: it will be used to rotate one of the wheels to achieve bike movement. Servo motors tend to have better efficiency and speed control than other motors.

Stepper motor for steering: in general, stepper motors have better precision and provide higher torque at low speeds than other motors, which makes them perfect for steering the handlebar.

ESP32 2.4GHz Dual-Core WiFi Bluetooth Processor: it has both WiFi and Bluetooth connectivity so it could be used for receiving messages from remote controllers such as Xbox controllers or mobile phones.

## Subsystem 3: Bike structure design

We plan to design the bike frame structure with Solidworks and have it printed out with a 3D printer. At least one of our team members has previous experience in Solidworks and 3D printing, and we have access to a 3D printer.

3D Printed parts: we plan to use PETG material to print all the bike structure parts. PETG is known to be stronger, more durable, and more heat resistant than PLA.

PCB: The PCB will contain several parts mentioned above such as ESP32, MPU6050, STM32, motor driver chips, and other electronic components

## Bonus Subsystem4: Collision check and obstacle avoidance

To detect the obstacles, we are considering using ultrasonic sensors HC-SR04

or cameras such as the OV7725 Camera function with stm32 with an obstacle detection algorithm. Based on the messages received from these sensors, the bicycle could turn left or right to avoid.

# Criterion For Success

The bike could be self-balanced.

The bike could recover from small external disturbances and maintain self-balancing.

The bike movement and steering could be remotely controlled by the user.

Project Videos