Project

# 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
proposal1.pdf
# 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.

Economic Overnight Outlet

Chester Hall, Sabrina Moheydeen, Jarad Prill

Featured Project

**Team**

- Chester Hall (chall28), Sabrina Moheydeen (sabrina7), Jarad Prill (jaradjp2)

**Title**

- Economic Overnight Outlet

**Problem**

- Real-time pricing in ISOs, such as the Midwest, California, New England, and New York, provides differentials in electricity prices throughout the day that can be taken advantage of. The peak price of electricity compared to the minimum prices can feature variations of up to 70%. With price agnostic charging, this results in unnecessary costs for those who charge devices (see attached spreadsheet). This same principle can thus be scaled for large commercialized applications requiring high-capacity batteries, resulting in a higher savings potential to be taken advantage of.

- Calcs: https://docs.google.com/spreadsheets/d/1JBzt2xm0Ue4a_teosdak623h0zSP5nHRKi7Wi8rMcPo/edit?usp=sharing

**Solution Overview**

- We will create a device that can fetch real-time prices from regional ISOs and enable charging when prices are lowest. Our primary application will be centered towards warehouse electric vehicles using high-capacity, fast-charging lithium ion batteries. Such vehicles include forklifts, cleaning machines, and golf carts.

**Solution Components**

- [ISO LMP API] - Through use of a WiFi-enabled microcontroller we can fetch real-time prices and build our control system around these values.

- [Passive High Performance Protection] - In order to provide downstream safety to the loads, we will ensure the device features surge protection and is rated for the high current of fast charging. The switching of the connection will be done with a contactor whose coil is energized according to the microcontroller.

- [Device Display] - LCD display to show information about the current energy price and the current day’s savings.

- [Manual User Override] - The device will feature a manual toggle switch to either enable or disable the cost-optimized charging feature allowing users to charge loads at any time, not necessarily the cheapest.

- [User Interface] - Software application to allow for user input regarding the time of day the device must be charged by. The application will also display information about total savings per week, month, or year and savings over the device’s lifetime.

- [Control Power Converter] - In order to run the low voltage control systems from the outlet, either 120VAC or 3-phase 480VAC, we will need to step this down to a low DC voltage of around 3.3VDC.

- [Memory System] - Microcontroller capable of performing control function within user specified parameters.

- [Device Connection] - Connectivity to the battery of the device being charged so that current state of charge (SoC) information can be used. Potential experimental filter algorithms will be used in order to estimate the SoC automatically, without requiring the user to input the specific data of the device being used.

**Criterion for Success**

- Able to charge devices at lowest cost times of the day and display current pricing and savings information. The upfront cost of a large-scale reproducible product must be less than the lifetime savings incurred by purchasing the product. Users without an engineering background can easily analyze their savings to visually recognize the device’s benefit.