Real Time Monitoring System for Lithium-Ion Cell Using IoT

Shubha Rao K

, Nimisha T

, Sai Rashmi B J

and Sanjana Alladwar

BNM Institute of Technology, Bengaluru, Karnataka, India

Keywords: Battery Monitoring, State of Charge, Coulomb Counting, Internet of Things.

Abstract: With the increasing focus on green technology and electric vehicles, battery technology has gained

tremendous importance across the globe. The main concerns regarding electrochemical batteries are unsafe

charging/discharging operation, thermal run-away, range anxiety and lower lifespan. Hence, the need for real-

time battery monitoring systems is inevitable. This paper presents a real-time battery monitoring system

incorporating the Internet of Things (IoT) for lithium-ion cells using Raspberry Pi as its controller. The

developed system continuously monitors the essential data of the battery cell, such as current, voltage, and

State of Charge (SOC). It is visualised using Blynk IoT, and in case of overcharging/undercharging

conditions, a notification is sent to the user. The Historical data is stored in a Python database for analysis and

trend identification.

INTRODUCTION

Battery management systems are designed to ensure

the safe and efficient operation of batteries by

monitoring and controlling vital parameters such as

voltage, current, temperature, and state of charge

(SoC). Traditional BMS solutions, however, often

lack real-time monitoring capabilities and remote

accessibility. Emerging technologies such as the

Internet of Things (IoT) and cloud computing have

been rapidly adopted in various industries, including

energy management and battery monitoring systems.

The incorporation of IoT technology addresses the

limitations of traditional battery management by

enabling continuous, real-time data collection and

remote monitoring through cloud-based platforms.

Integrating IoT with battery management systems

(BMS) significantly improves monitoring,

controlling, and optimizing battery performance. This

paper explores the hardware implementation of an

IoT-based battery monitoring system, focusing on its

application in electric vehicles (EVs) and green

energy storage systems.

The research presents an innovative IoT-enhanced

battery monitoring system that leverages the

https://orcid.org/0000-0002-1160-7380

https://orcid.org/0009-0002-8815-7717

https://orcid.org/0009-0002-1906-0348

https://orcid.org/0009-0002-3614-4068

capability of the Raspberry PI microcomputer as the

central controller to transmit real-time data on battery

parameters to an online IoT platform named Blynk.

Integrating IoT technology with battery

monitoring enhances system accuracy and reliability.

It also provides data logging and remote monitoring

capability, thus making it a viable solution for EVs

and renewable energy systems. The developed system

utilizes an I2C communication protocol to interface

sensors to sense and monitor the battery status, such

as voltage, current, power, and state of charge on an

IoT platform. This standardized communication

protocol simplifies integration and ensures seamless

interoperability between different monitoring system

components.

The rest of the paper is organized as follows: A

brief literature survey is done in Literature Survey

section 2. The battery monitoring and State of Charge

(SoC) were discussed in section 3. Research

methodology and hardware implementation are

discussed in section 4. Results are presented in

section 5, and conclusions are drawn in section 6.

Rao K, S., T, N., Rashmi B J, S. and Alladwar, S.

Real Time Monitoring System for Lithium-Ion Cell Using IoT.

DOI: 10.5220/0013579500004639

In Proceedings of the 2nd International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2024), pages 119-123

ISBN: 978-989-758-756-6

119

LITERATURE REVIEW

The integration of Internet of Things (IoT)

technology with battery management systems (BMS)

has gathered significant attention in recent years to

monitor and control vital parameters of batteries in

real time. This section will provide a quick overview

of the available research papers on battery monitoring

and SOC and an overview of its applications. Paper

(Ahmed et al., 2021) demonstrates the coulomb

counting method to estimate the SoC of a lead-acid

battery used with a photo voltaic system. It also

monitors the charging and discharging process using

Blynk IoT and operates a relay in case of

overcharging/discharging by continuously

monitoring the SOC of the battery.

The research paper (Chen et al., 2024) by

Gozuoglu presents a low-cost electronic dummy load

integrated with IoT capabilities. The system

accurately monitors the state of charge (SOC) and

state of health (SOH) of lithium-ion batteries using

IoT technology, offering enhanced monitoring and

remote access. The paper (Gozuoglu, 2024) by Chen

et al. explores an IoT architecture for battery

monitoring in power substations. The system provides

real-time monitoring and intelligent maintenance

management, demonstrating its effectiveness in a 110

kV offshore substation.The paper's authors (Insia,

2023) focus on monitoring battery health at charging

stations. The system uses IoT to provide real-time data

on battery performance, improving the efficiency and

safety of charging processes.

The technical paper (Lee et al., 2022) investigates

the application of IoT in renewable energy storage

systems. The study highlights the benefits of real-time

monitoring and data analytics for optimizing battery

performance and lifespan. The authors of the paper

(Patel et al., 2021) presented a system designed for

industrial battery monitoring. The IoT-based solution

offers continuous monitoring and predictive

maintenance, reducing downtime and maintenance

costs. The paper (Syafii et al., 2024) by Ahmed et al.

explores the integration of IoT with smart grids for

battery monitoring. The system provides real-time

data on battery status, enhancing grid reliability. The

research work presented in the above papers mainly

uses low-cost microcontrollers such as

Arduino/ESP32 microcontrollers to perform real-time

monitoring, which has limited data storage and control

capability. Hence, the research work presented in this

paper utilizes an advanced microcomputer, Raspberry

4, which can easily integrate with IoT technology and

log real-time data using the Excel tool.

BATTERY MANAGEMENT

AND STATE OF CHARGE

ESTIMATION

A Battery Management System (BMS) is an

embedded system that supervises the operation of a

rechargeable battery, ensuring its safe and efficient

use. It monitors various parameters such as the state

of charge, State of health, voltage, and temperature of

individual cells within the battery pack. It also

balances the charge across cells and protects against

overcharging, overheating, and short circuits. A BMS

also extends the battery's lifespan and enhances its

performance. Additionally, it provides critical data

for optimizing battery usage and maintaining overall

system reliability.

A crucial parameter in BMS is the State of Charge

(SOC), representing the remaining capacity of a

battery as a percentage of its total capacity. It

indicates how much charge is left in the battery, with

0% meaning fully discharged and 100% meaning

fully charged1. SoC is essential for predicting battery

performance and lifespan and helps manage energy

usage efficiently. Various methods, such as voltage-

based measurements and coulomb counting, estimate

SOC. Accurate SoC measurement is vital for

applications like electric vehicles, which function

similarly to a fuel gauge, providing users with real-

time information about their battery's status.

The following subsections briefly discuss the

battery management system and State of Charge

estimation.

Figure 1: Functionalities of Battery Management System.

ISPES 2024 - International Conference on Intelligent and Sustainable Power and Energy Systems

120

3.1 Battery Management Systems

To ensure the efficient and safe operation of battery

systems, it is essential to have a reliable battery

management system in place. Figure 1 shows the

functionalities of the battery management system.

The traditional methods of monitoring battery status

often lack real-time capabilities and require manual

intervention.

This paper aims to develop a battery monitoring

system using Raspberry Pi that addresses the

limitations of existing solutions. The system should

be capable of continuously monitoring key

parameters such as voltage, current, power and state

of charge for the battery in real-time. It should

provide accurate and timely information about battery

health and performance.

3.2 State of Charge (SoC) Estimation

by Coulomb Counting Method

The Coulomb counting method is an extensively used

method for estimating the State of Charge (SoC) of a

battery. This method involves measuring the charging

or discharging current of the battery and integrating

this current over time to determine the net charge

transferred, which is given in equation (1).

𝑆𝑂𝐶



𝑡



𝑆𝑜𝐶



𝑡







𝑄



𝑖







𝑑𝑡 1

Where:

SoC(t) is the state of charge at time t,

SoC(t

) is the initial state of charge,

Q0 is the rated capacity of the battery,

batt

is the battery current.

In this work, SoC is estimated in real-time, and the

steps involved are as follows:

Initialization: Start with a known initial SoC,

typically determined by fully charging the battery.

Current Measurement: Continuously measure the

charging/discharging current of the battery.

Integration: Integrate the current over time to

calculate the total charge transferred.

SoC Update: Update the SoC based on the integrated

current and the initial SOC.

The advantage of SoC estimation using the

coulomb counting method is its simplicity and ease of

implementation

DESIGN METHODOLOGY AND

SPECIFICATIONS

Figure 2 depicts the block diagram of the Raspberry

Pi-based battery monitoring system. The battery

being monitored is a lithium-ion pouch-type battery

cell. Its specifications are given in Table 1.

Figure 2: Block diagram representation of battery

monitoring system through IoT.

The current, voltage and power of the battery cell

are sensed using Adafruit's INA219 current sensor,

whose basic working principle is based on Ohm’s

law. The current to be measured is passed through a

shunt resistor of 0.1Ω. The current is determined by

measuring shunt voltage. INA219 can also measure

bus voltage, which measures power consumed by

load. It uses I2C technology to interface with the

controller circuit, is compact, and is designed to

measure current and voltage with high accuracy. This

sensor provides precise readings, making it ideal for

monitoring power usage and battery charging. Table

2 lists the essential specifications of INA 219 used in

the research work. Another advantage of using

INA219 is that there is no need for a separate ADC

chip to convert current and voltage values.

Table 1: Lithium-ion cell specifications.

SI.No Paramete

ical value

1 Nominal Volta

e 3.7 V

2 Max.Voltage 4.1V

3 Capacit

1500mA

4 Discharge rate 0.5C

eratin

Tem

erature 0-50°C

The DHT-11 temperature humidity sensor was

used to measure battery cell temperature

continuously. A high-quality metal LED rated 3-

9V,10mA, is used as load.

Raspberry Pi micro-computer is the central

control unit used to interface sensors to collect battery

data and process it to estimate SOC. It also connects

with the Blynk IoT platform through Wi-fi for real-

Real Time Monitoring System for Lithium-Ion Cell Using IoT

121

time monitoring.

For real-time monitoring and sending notifications in

case of undercharging/overcharging, the Blynk IoT

platform is incorporated, which allows users to create

mobile applications for controlling and monitoring

devices connected to the internet.

Table 2: INA 219 current sensor specifications.

Figure 3: Implementation Flowchart.

The Raspberry Pi is programmed using Python

programming language through Thonny IDE. The

Raspberry Pi is interfaced with INA219 using the I2C

protocol. It is also interfaced with the DHT11

temperature sensor and LCD. The sensor values are

read continuously every 1Sec and are used to estimate

SoC using the coulomb counting method. Using Wi-

fi, lithium-ion cells' current, voltage, and SoC are sent

to Blynk IoT for real-time monitoring. Figure 3 shows

a flowchart indicating programming steps.

The SoC parameters are continuously monitored to

check whether they are within range, and if not, a

notification is sent to the registered user.

IMPLEMENTATION RESULTS

Figure 4 shows the hardware implementation of

battery monitoring. The figure.5 shows the battery

parameters displayed on LCD.

Figure 4: Hardware setup of battery monitoring

Figure 5: LCD displaying battery parameters.

The central aspect of this research work is the

online monitoring of battery parameters using IoT

technology. The Blynk IoT is configured to display

the vital parameters of the battery cell in the

dashboard and update it regularly every second.

Figure 6 shows a snapshot of battery values displayed

in the Blynk IoT platform.

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122

Figure 6: Blynk dashboard to display battery cell

parameters.

The Raspberry Pi continuously logs the battery

parameters (such as voltage, current, power, shunt

voltage and SoC) in an Excel sheet. Table 3 shows a

part of data logging.

Table 3: Data Logging of Battery Status

Figure 7 shows a visualization of voltage, current, and SoC

monitored continuously.

Figure 7: Battery cell visualization

CONCLUSIONS

A battery monitoring system utilizing a Raspberry Pi

has been implemented using IoT for real-time

monitoring. The implemented system provides

accurate and timely information about the battery's

status, including SoC, estimated using the coulomb

counting method. The battery data is then processed

and visualized through a user-friendly interface LCD

and logged into the Excel software tool. The data is

also continuously and remotely monitored on Blynk

IoT.

REFERENCES

Ahmed, M., Khan, A., & Ali, S. (2021). Real-Time Battery

Monitoring System Using IoT for Smart Grids. IEEE

Access.

Chen K, Luo L, Lei W, Lv P and Zhang L. (2024). Design

and implementation of online battery monitoring and

management system based on the internet of things.

Front.EnergyRes.12:1454398. doi:

10.3389/fenrg.2024.1454398.

Gozuoglu, Abdulkadir. (2024). Iot-Enhanced Battery

Management System for Real-Time SOC and SOH

Monitoring Using Stm32-Based Programmable

Electronic Load. Available at SSRN:

https://ssrn.com/abstract=4981426.PP:1-32.

K. Insia,2023. Design and Analysis of IoT-Based Battery

Management and Monitoring System for Electric

Vehicle, AJSE, vol. 22, Issue no. 2, pp. 181 – 188.

Lee, J., Kim, H., & Park, S. (2022). Advanced Battery

Monitoring System Using IoT for Renewable Energy

Storage . Journal of Renewable Energy.

Patel, M., Desai, K., & Shah, R. (2021). IoT-Based Battery

Health Monitoring System for Industrial Applications.

IEEE Transactions on Industrial Electronics.

Syafii, Irfan El Fakhri, Thoriq Kurnia Agung, Farah

Azizah. (2024). Design of battery state of charge

monitoring and control system using coulomb counting

method. Indonesian Journal of Electrical Engineering

and Computer Science. Vol. 33, No. 2, pp. 736-745

ISSN: 2502-4752, DOI:10.11591/ijeecs.v33.i2.pp736-

745

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