Projects / Electrical / Electrical project 76

Battery State-of-Charge Estimation: Coulomb Counting plus Kalman Filter

Approx. component budget: Rs. 5,000 - 9,000

Objective

Estimate how much charge remains in a battery more accurately than voltage alone.

What you will build

A system that measures current, voltage and temperature of a Li-ion cell and estimates its state of charge using Coulomb counting and a Kalman filter.

Results you can expect

• SoC error vs a full-discharge reference for 3 test profiles
• Drift of Coulomb counting vs Kalman
• Effect of temperature
• And capacity fade across cycles

What you will learn

Battery modelling, state estimation and the maths behind a BMS.

Tools and notes

Li-ion cell or 18650 pack, INA226, ESP32, Python (NumPy). Charge only with a proper protected charger and keep cells in a fire-safe box.

Resume points from this project

• Built and tested Battery State-of-Charge Estimation: Coulomb Counting plus Kalman Filter; objective: estimate how much charge remains in a battery more accurately than voltage alone.
• Measured and analysed soC error vs a full-discharge reference for 3 test profiles and drift of Coulomb counting vs Kalman; wrote the report and presented the results in a viva. Add your own numbers: [x].

Replace [x] with your own measured results. Write only what you really did.

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