Smart Battery Monitoring System Using ESP32-C6 & Blynk IoT

Modern electronics projects rely heavily on battery power — but most students  do not have professional tools to measure important battery parameters like real-time voltage, current, SoC (State of Charge), or health.
As a result:

• We never know how much capacity the cell actually has
• We don’t know how fast a project drains the battery
• Projects often fail unexpectedly because the battery dies without warning

To solve this real problem, I built a Smart BMS (Battery Monitoring System) using:

• ESP32-C6 (Wi-Fi + IoT)
• INA260 precision current/voltage sensor
• Kalman filtering for noise reduction
• Hybrid SOC estimation (Coulomb Counting + OCV Fusion)
• Blynk IoT dashboard for live monitoring

The result is a low-cost, accurate, real-time battery analyzer that works with any lithium cell.

What This Project Does

This Smart BMS continuously measures:

• Voltage and current of the battery
• State of Charge (%)
• Live performance graphs through Blynk IoT

This system updates values every second, and shows them on a mobile dashboard, allowing users to monitor a cell from anywhere.

It solves 3 real problems makers commonly face:

(1) No accurate way to measure true battery capacity
Using Coulomb counting, the system can calculate mAh used during a discharge cycle.

(2) No stable readings due to sensor noise
Kalman filtering smoothens voltage & current data.

(3) No way to see battery status remotely
Blynk dashboard shows V, I, SOC in real time.

Pin Out and Connections

How the System Works

1. INA260 Sensor Measures the Battery

• The INA260 provides millivolt-accurate voltage
• And milliamp-accurate current (both directions)
• ESP32-C6 reads these values through I²C pins (SDA=9, SCL=8)

These raw readings tend to be noisy, especially with small loads — that’s where filtering helps.

2. Noise Reduction Using a 2-State Kalman Filter

The code includes a lightweight bias-aware Kalman filter:

• Removes electrical noise
• Corrects drift
• Stabilises millivolt-level readings

So the dashboard always shows smooth, realistic data.

3. SOC Calculation Using Two Methods

To get accurate battery percentage, the system blends:

-Coulomb Counting

Measures current over time (Ah = ∫I dt)
→ Tracks how much charge is consumed

-OCV Estimation (Open Circuit Voltage)

A lookup table maps voltage to SOC based on 18650 cell characteristics.

-Fusion Algorithm

SOC = 97% Coulomb Counting + 3% OCV
This prevents drift and keeps percentage realistic.

4. IoT Dashboard Using Blynk

The ESP32-C6 connects to Wi-Fi and updates:

• Voltage → V0
• Current → V1
• SOC → V2

Data is sent once per second so the cloud stays stable.

The dashboard shows:

• Gauges for Voltage, Current, SOC
• Real-time streaming
• Works from any location

 Key Features of the Code

- Stable 1-second updates (cloud-safe)

- I²C protection + fast sampling

INA260 configured for reliability:

• 64-sample averaging
• 1.1ms conversion time

- Kalman filters for V & I :Smooths out micro-fluctuations.

- Hybrid SOC algorithm :Much more accurate than using only voltage.

- Remote monitoring :Fully functional IoT BMS using your phone.

Conclusion

This Smart BMS transforms a regular 18650 cell into a fully monitored IoT power source.
With precise sensing, noise filtering, battery modelling, and cloud connectivity,
this project brings professional-grade battery analytics into the hands of students and hobbyists.

It is simple to build, inexpensive, and extremely practical —
and solves a very real problem:

“We all use batteries in our projects, but we rarely understand them.
This project finally makes battery health measurable, visible, and smart.”

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