Project :: ECE 445 - Senior Design Laboratory

#	Title	Team Members	TA	Documents	Sponsor
35	Electric Scooter Battery Management System with Integrated SOC and SOH Estimation	Edward Chow Jay Goenka Samar Kumar	Xiaodong Ye	design_document1.pdf final_paper1.pdf proposal1.pdf video
	# Title UAV Battery Management System with Integrated SOC and SOH Estimation # Team Members: - Edward Chow (ec34) - Jay Sunil Goenka (jgoenka2) - Samar Kumar (sk127) # Problem UAV batteries are safety-critical and performance-critical as a weak or degraded pack can cause sudden voltage drop, shutdown, reduced flight time, or unsafe thermal behavior. The usual BMS implementations primarily rely on fixed thresholds for voltage, temperature or current to prevent immediate failures. However, threshold-only systems do not provide predictive insight into battery degradation. Battery health issues are often discovered only after runtime loss or unsafe behavior. Additionally high discharge currents and fluctuating temperatures are common in UAV operations, which fastens degradation. A lightweight BMS that not only protects the pack in real time but also estimates battery health and degradation risk would improve reliability, reduce unexpected failures, and enable better operational decisions such as deciding if the battery is safe to use or needs to be retired. # Solution To address the delicate nature of UAV batteries we decided to undertake a project with the aim to design and construct a compact and efficient battery management system that seamlessly integrates reliable real-time protection with intelligent prediction. Our primary algorithm for estimating the battery’s State of Charge (SOC) will be coulomb counting, which relies on continuous current measurement. We are researching the Kalman filter method as a second algorithm for more accurate calculation. The BMS will also monitor cell voltages and temperatures to ensure safe operation and provide valuable data for battery condition assessment. By analyzing SOC history, voltage behavior, current profiles, and temperature data, the system should be able to estimate the State of Health (SOH) of the battery. SOH over time will help us understand the capacity fade and degradation trends over time. We also plan to log all measurements and stream it to an external dashboard for visualization and analysis. As an extension, the project could also incorporate a lightweight AI-driven model to assist in SOH estimation and degradation assessment. # Solution Components ## Slave Board The slave board will be responsible for monitoring individual cell voltages and temperatures and supporting passive cell balancing. It will report accurate measurement data to the master board, ensuring safe operation of the battery pack at the cell level. The HW components and sensors include: Cell monitoring IC: Analog Devices LTC6811 or LTC6813s (multi-cell voltage sensing with built-in diagnostics and balance control) isoSPI communication interface: Analog Devices LTC6820 Temperature sensors: 10 kΩ NTC thermistors (e.g., Murata NCP18XH103F03RB) Passive balancing: bleed resistors (33–100 Ω) and N-MOSFETs per cell Cell sense connectors and basic RC filtering/ESD protection Power regulation: buck converter (e.g., TPS62130) and 3.3 V LDO ## Master Board The master board is responsible for actually performing pack-level protection, SOC and SOH estimation, data logging, and external communication. It makes sure safety limits are enforced by aggregating data from the slave board. The HW components and sensors include: Microcontroller: STM32H7 series Current sensing: shunt resistor with TI INA240 current-sense amplifier Protection switching: back-to-back N-channel MOSFETs with gate driver (e.g., BQ76200) Power regulation: buck converter (e.g., TPS62130) and 3.3 V LDO Communication: isoSPI (LTC6820), CAN Data logging: microSD card or onboard flash memory ## BMS Viewer The BMS Viewer will be a software dashboard used to visualize real-time and logged battery data and assess battery health. Potential features: Live display of SOC, SOH, pack voltage, pack current, and temperature Time-series plots of voltage, current, temperature, and SOC Data ingestion via USB, CAN, or wireless telemetry Backend implemented in Python or Node.js with a web-based dashboard # Criterion For Success - BMS detects and mitigates fault conditions within a bounded response time (≤100 ms). - Cell voltage within ±50 mV per cell, pack current within ±10%, temperature within ±5°C after calibration. - SOC remains within ±10% of a reference SOC over a full UAV-like discharge cycle. - SOH estimate is within ±15% of a capacity-based reference and shows consistent degradation trends. - BMS Viewer displays and logs SOC, SOH, pack voltage/current, and temperature in real time.

Featured Project

# Oxygen Delivery Robot

Team Members:

- Rutvik Sayankar (rutviks2)

- Aidan Dunican (dunican2)

- Nazar Kalyniouk (nazark2)

# Problem

Children's interstitial and diffuse lung disease (ChILD) is a collection of diseases or disorders. These diseases cause a thickening of the interstitium (the tissue that extends throughout the lungs) due to scarring, inflammation, or fluid buildup. This eventually affects a patient’s ability to breathe and distribute enough oxygen to the blood.

Numerous children experience the impact of this situation, requiring supplemental oxygen for their daily activities. It hampers the mobility and freedom of young infants, diminishing their growth and confidence. Moreover, parents face an increased burden, not only caring for their child but also having to be directly involved in managing the oxygen tank as their child moves around.

# Solution

Given the absence of relevant solutions in the current market, our project aims to ease the challenges faced by parents and provide the freedom for young children to explore their surroundings. As a proof of concept for an affordable solution, we propose a three-wheeled omnidirectional mobile robot capable of supporting filled oxygen tanks in the size range of M-2 to M-9, weighing 1 - 6kg (2.2 - 13.2 lbs) respectively (when full). Due to time constraints in the class and the objective to demonstrate the feasibility of a low-cost device, we plan to construct a robot at a ~50% scale of the proposed solution. Consequently, our robot will handle simulated weights/tanks with weights ranging from 0.5 - 3 kg (1.1 - 6.6 lbs).

The robot will have a three-wheeled omni-wheel drive train, incorporating two localization subsystems to ensure redundancy and enhance child safety. The first subsystem focuses on the drivetrain and chassis of the robot, while the second subsystem utilizes ultra-wideband (UWB) transceivers for triangulating the child's location relative to the robot in indoor environments. As for the final subsystem, we intend to use a camera connected to a Raspberry Pi and leverage OpenCV to improve directional accuracy in tracking the child.

As part of the design, we intend to create a PCB in the form of a Raspberry Pi hat, facilitating convenient access to information generated by our computer vision system. The PCB will incorporate essential components for motor control, with an STM microcontroller serving as the project's central processing unit. This microcontroller will manage the drivetrain, analyze UWB localization data, and execute corresponding actions based on the information obtained.

# Solution Components

## Subsystem 1: Drivetrain and Chassis

This subsystem encompasses the drive train for the 3 omni-wheel robot, featuring the use of 3 H-Bridges (L298N - each IC has two H-bridges therefore we plan to incorporate all the hardware such that we may switch to a 4 omni-wheel based drive train if need be) and 3 AndyMark 245 RPM 12V Gearmotors equipped with 2 Channel Encoders. The microcontroller will control the H-bridges. The 3 omni-wheel drive system facilitates zero-degree turning, simplifying the robot's design and reducing costs by minimizing the number of wheels. An omni-wheel is characterized by outer rollers that spin freely about axes in the plane of the wheel, enabling sideways sliding while the wheel propels forward or backward without slip. Alongside the drivetrain, the chassis will incorporate 3 HC-SR04 Ultrasonic sensors (or three bumper-style limit switches - like a Roomba), providing a redundant system to detect potential obstacles in the robot's path.

## Subsystem 2: UWB Localization

This subsystem suggests implementing a module based on the DW1000 Ultra-Wideband (UWB) transceiver IC, similar to the technology found in Apple AirTags. We opt for UWB over Bluetooth due to its significantly superior accuracy, attributed to UWB's precise distance-based approach using time-of-flight (ToF) rather than meer signal strength as in Bluetooth.

This project will require three transceiver ICs, with two acting as "anchors" fixed on the robot. The distance to the third transceiver (referred to as the "tag") will always be calculated relative to the anchors. With the transceivers we are currently considering, at full transmit power, they have to be at least 18" apart to report the range. At minimum power, they work when they are at least 10 inches. For the "tag," we plan to create a compact PCB containing the transceiver, a small coin battery, and other essential components to ensure proper transceiver operation. This device can be attached to a child's shirt using Velcro.

## Subsystem 3: Computer Vision

This subsystem involves using the OpenCV library on a Raspberry Pi equipped with a camera. By employing pre-trained models, we aim to enhance the reliability and directional accuracy of tracking a young child. The plan is to perform all camera-related processing on the Raspberry Pi and subsequently translate the information into a directional command for the robot if necessary. Given that most common STM chips feature I2C buses, we plan to communicate between the Raspberry Pi and our microcontroller through this bus.

## Division of Work:

Given that we already have a 3 omni wheel robot, it is a little bit smaller than our 50% scale but it allows us to immediately begin work on UWB localization and computer vision until a new iteration can be made. Simultaneously, we'll reconfigure the drive train to ensure compatibility with the additional systems we plan to implement, and the ability to move the desired weight. To streamline the process, we'll allocate specific tasks to individual group members – one focusing on UWB, another on Computer Vision, and the third on the drivetrain. This division of work will allow parallel progress on the different aspects of the project.

# Criterion For Success

Omni-wheel drivetrain that can drive in a specified direction.

Close-range object detection system working (can detect objects inside the path of travel).

UWB Localization down to an accuracy of < 1m.

## Current considerations

We are currently in discussion with Greg at the machine shop about switching to a four-wheeled omni-wheel drivetrain due to the increased weight capacity and integrity of the chassis. To address the safety concerns of this particular project, we are planning to implement the following safety measures:

- Limit robot max speed to <5 MPH

- Using Empty Tanks/ simulated weights. At NO point ever will we be working with compressed oxygen. Our goal is just to prove that we can build a robot that can follow a small human.

- We are planning to work extensively to design the base of the robot to be bottom-heavy & wide to prevent the tipping hazard.