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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue VIII, August 2025

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Design and Testing of a Smart Battery Management System for
Swappable LiFePO4 for E-Rickshaws

Okorogu Benjamin A, Onyeyili Tochukwu I, Alagbu Ekene

Department of Electronics and Computer Engineering, Nnamdi Azikiwe University, Awka, Nigeria.

DOI: https://doi.org/10.51583/IJLTEMAS.2025.1408000157

Abstract: The global automotive industry is rapidly transitioning to electric vehicles (EVs), with e-rickshaws playing a crucial
role in developing nations due to their affordability and environmental benefits. The shift from traditional lead-acid batteries (LABs)
to Lithium Iron Phosphate (LiFePO₄) batteries is vital, but requires a sophisticated Battery Management System (BMS) for optimal
performance and safety. One of the major challenges faced by e-rickshaws is ensuring efficient, safe, and reliable battery
performance over extended periods. This research focused on designing and testing a smart BMS specifically for swappable
LiFePO₄ batteries in e-rickshaws. Objectives included optimizing the BMS for e-rickshaw operations, implementing real-time
monitoring and control, integrating IoT features for remote diagnostics, and testing its performance. The methodology involved
developing both hardware (LiFePO₄ cells, microcontroller, sensors) and software components (simulation tools like MPS and
Victron Connect, advanced algorithms for SoC and SoH). The system's architecture emphasized real-time monitoring and control,
ensuring a holistic and interconnected design. The designed smart BMS demonstrated robust performance, evidenced by a tightly
controlled output voltage ripple, oscillating minimally between approximately-3mV and +3mV, indicative of superior power
regulation. Efficiency analysis revealed a peak of over 97% at a 0.5A output current, which gradually decreased to approximately
93.5% at 3A, showcasing effective energy conversion and minimal power loss (increasing from near 0W to 1W at 3A) across the
operational range. Real-time monitoring via the BMV-700 system further validated the accuracy and reliability of the monitoring
and control algorithms, consistently showing stable operational parameters, including a constant power output of -191W, a steady
current draw of -7.5A, a stable State of Charge at 85%, and a constant battery voltage of 25.46V. This study's successful design and
testing of a smart BMS for swappable LiFePO₄ batteries in e-rickshaws significantly advances operational efficiency, enhances
safety, and accelerates the adoption of sustainable urban mobility solutions in developing regions.

Keywords: BMS, EVs, E-rickshaws, LiFePO4, Battery

I. Introduction

The global car industry is quickly moving towards electric vehicles (EVs) to cut down on fossil fuel use and carbon emissions, also
benefiting from lower running costs (Adekunle.O,2025).E-rickshaws are especially popular in developing countries because they're
affordable, good for the environment, and cheaper to operate than regular three-wheelers (Mordor intelligence, 2025). These
vehicles are essential for key transportation in both cities and rural areas, highlighting the importance of efficient EV technology,
especially in places like Nigeria and India, where cost-effectiveness is crucial especially in places like Nigeria and India where
being cost-effective is key (Garima. A and Lalit .M, 2025).

EVs are crucial for reducing pollution, greenhouse gases, and dependence on crude oil, leading to cleaner air and better public
health (Ken E.Z, 2022). However, getting people to use them in developing cities is tough due to poor infrastructure and economic
issues, such as unreliable power and high initial costs (Adekunle.O, 2025). For EVs to work well, solutions must be not only eco-
friendly but also affordable and practical, tackling these local problems.

Battery swapping offers a transformative solution for EV adoption, addressing limited range and long charging times crucial for
commercial e-rickshaws (EVreporter, 2023). This "Battery as a Service" (BaaS) model lowers upfront vehicle costs (Arthur D.
Little, n.d.). However, integrating swappable batteries introduces complex BMS challenges, including seamless communication,
accurate identification, and robust management across diverse packs in a dynamic ecosystem (Faraday Institution, n.d.). Key issues
involve ensuring interoperability, precise real-time SOC/SOH estimation, optimal charging, and effective thermal management
(MDPI, n.d.).

Existing BMS solutions often struggle with these complexities, particularly real-time data exchange and consistent performance
across swapped packs (Faraday Institution, n.d.). Thus, a "smart" BMS is essential to fully leverage swappable LiFePO4 batteries
in e-rickshaws. Such a system would integrate advanced algorithms, potentially AI/machine learning for precise estimations and
predictive maintenance, and enhanced wireless communication for seamless swapping infrastructure integration (AYAA
Technology, 2025; Monolithic Power Systems, n.d.). Consequently, this study aims to address these critical needs by designing and
testing a smart battery management system specifically tailored for swappable LiFePO4 batteries in e-rickshaw applications,
thereby contributing to improved operational efficiency, safety, and the accelerated adoption of sustainable urban mobility.

II. Literature Review

According to, Prahal Bhagavath et al, 2023, A common substitute for traditional battery charging is battery swapping, which is
taking out a depleted EV battery and switching it out with a fully charged one. Any battery charging station allows EV users to

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change their batteries while they're on the road. This exchange is made easier by a strong data management system that offers a
platform and interface for finding new batteries. The battery's health is calculated and shown to the user by using data analytics to
measure the battery's temperature and voltage. This service is easier to use and takes less time in urban settings than the traditional
battery station charging process, which might take hours. The IoT-based real-time monitoring system for these swappable batteries
is described in this work. It allows users to look for nearby battery swapping stations and transmits the battery health and state of
charge for each station. The difficult chore of manually monitoring the station's batteries is made easier by the system, which also
notifies the management of the station's operating status and battery condition.

The study by Bhuvaneswari et.al., 2024 presents a Smart Electric Vehicle Design featuring an Intelligent Battery Management
System (IBMS) empowered by a Smart Battery Management System (SBMS). The SBMS integrates advanced components such
as a Microcontroller (MCU), Alarm System, Current Sensor, Voltage Sensor, and Temperature Sensor. The comprehensive
integration of the SBMS components enhances the reliability, safety, and performance of the electric vehicle, addressing crucial
aspects of battery management.

According to Singh et.al. 2023 the battery management system (BMS) is essential for preventing accidents and for regulating the
thermal and electrical operating limitations of the battery packs. Additionally, the BMS aids in the battery pack's optimal
performance, extending battery life and saving money and time. The future issues in BMS are discussed in this study along with
potential solutions. This article covers SoC estimation, which calculates the estimated driving range of the electric car, and cell
balancing automation, which equalizes the voltage and SoC among the cells when the cells are at different SoC hence optimizing
the battery life. The control of the battery management system's cell balancing function is the main topic of this paper.

III. Methodology

Mathematical Model

State Of Charge (SOC) Estimation

SOC is an important variable in the context of the BMS. SOC is the battery's remaining or available capacity expressed as a
percentage of its rated capacity. To put it simply, SOC is a battery fuel indicator that indicates how much energy is left in the battery
before it has to be recharged.

SOC(t) =
Qremaining(t)

Qmax(t)
× 100%-------------------------------------------- (3.1)

State Of Health (SOH) Estimation

SOH is an estimate that shows the typical state of a battery and its performance capacity in relation to its nominal performance
when it was brand-new. It is sometimes referred to as a performance where 100% represents a battery in perfect health with no
capacity decrease. A battery's SOH will inevitably decrease over time due to factors like aging, usage, ambient conditions, and
charging cycles.

SoH(t) =
Qmax(t)

Qnominal(t)
× 100[%]------------------------------------------------------ (3.2)

State of Power (SOP) Estimation

The State of electricity (SOP) is the battery's capacity to supply or absorb a specific amount of electricity at a given moment. It
depends on the battery's state of charge (SOC), state of health (SOH), and operational parameters (e.g., temperature and current). It
may be described as the instantaneous power availability represented by the ratio of peak power to nominal power. Understanding
SOP is essential for power management in applications that need quick power, such grid storage systems or electric cars.

SOP(t) =
Pmax(t)

Pnominal(t)
× 100[%]------------------------------------------------------- (3.3)

Designing and testing a Smart Battery Management System (BMS) for Swappable LiFePO4 Batteries for E-Rickshaws involves
multiple layers of complexity, including electrical modeling, control algorithms, and real-time monitoring. The mathematical model
for such a system would typically include the following aspects:

1. Battery Modeling (LiFePO4 chemistry)

2. State of Charge (SOC) Estimation

3. State of Health (SOH) Estimation

4. Temperature and Thermal Management

5. Charging and Discharging Algorithms

We can break down the model into mathematical formulations. Below is a comprehensive structure:

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1. Battery Electrical Model

A common approach is to use an equivalent circuit model (ECM), such as the Thevenin model which includes

 Open-circuit voltage ��(��) as a function of State of Charge (SoC)

 Internal resistance ��0.

 RC networks to model transient voltage behavior.

The terminal voltage ��(��) is modeled as: ��(��) = ��(��) − ��(��)��0 − ��(��)

Where ��(��) is the voltage across the RC network(s), governed by:
��V��

��t
= −

1
��1��1

V�� +
1

��1
��(��) ------------------------------- (3.4)

2. State of Charge Estimation:

SoC is the remaining capacity relative to full charge, often estimated by Coulomb counting:
��(t) = SoC(t0) −

1
Qnom

∫ ��(τ)��τ
t

t0
---------------------------- (3.5)

Where ��nomis the nominal battery capacity.

3. State of Health (SoH) Modeling:

SoH reflects battery aging and capacity fade, modeled as:

��(��) =
Qavailable(��)

Qnom
-------------------------------------------------- (3.6)

Where:

 Qavailable(��): is the available capacity of the battery

 Qnom: is the battery’s original capacity

4. Temperature/Thermal Model:

Battery temperature T affects performance and safety:

��ℎ
��
��

= �� − �� -------------------------------- (3.7)

Where ��ℎ is thermal capacity, �� = I2�� is heat generated, and �� is heat dissipated.

5. Control Algorithms:

The BMS uses control laws to manage charging/discharging, e.g.

 Charge current limit ��ℎ�� ≤ ��(��, ��)

 Voltage limits �� ≤ ��(��) ≤ ��

 Temperature limits for safety.

Thermal management algorithms would aim to keep the temperature within safe limits by using active cooling or heating strategies.

6. Charging and Discharging Algorithms

The charging/discharging algorithm is central to the BMS. Charging can follow a CC-CV (Constant Current-Constant Voltage)
approach. For example:

 Constant Current (CC) Phase: Ι��ℎ��(��) = Ι��(constant) until the voltage reaches a predefined threshold.

 Constant Voltage (CV) Phase: ��(��) = V��(constant) During which the current is reduced as the battery approaches
full charge.

For discharging:

P�� = Ι��ℎ��(��) ∙ ��(��)------------------------ (3.8)

Where:

 (Ι��ℎ��(��)) is the current during discharging.

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 (��(��)) is the battery voltage during the discharge phase.

5. Power Management for Swapping System

For battery swapping, the system must manage the power flow to/from the battery to match the load demands of the E-Rickshaw
while optimizing battery health and charging efficiency. The power management model may involve:

 A control strategy that decides when to swap batteries, using a threshold-based approach based on SOC and SOH.

 An optimization problem where the objective is to minimize downtime and ensure fast, efficient battery swaps while
maintaining battery health.

Objective function for optimal swapping strategy:

�� ∑ ��(��)��
��=1 ---------------------------------- (3.9)

Where (��(��)) is the time required for each swap, subject to constraints like SOC, battery health, and voltage levels.

Typical parameters used in the circuit diagram above are;

The Output Voltage Divider (R1 and R2):

For the Output Voltage (Vout) the calculation is as follows:

�� = �� × (1 +
��1
��2

) ------------------------------------------ (3.10)

Where

�� is the Reference Voltage = 0.8V (adjusted slightly for exact 5V output matching)

R1 and R2 are the feedback voltage divider

To find R1, we use;

��1 = ��2 (
��
��

− 1) ----------------------------------------- (3.11)

Where R2 is = 7.68kΩ(chosen for low current)

The Inductor (L):

The inductor value (L) is calculated as:

�� =
�� × (�� − ��)

�� × ∆�� × ��
---------------------------- (3.12)

Where,

�� is the output voltage which is = 5V

�� is the input voltage which is = 12V

�� is the switching frequency, which is = 500��

∆�� is the desired inductor ripple current, which is = 0.583A

∴ �� = 10µH
Output Voltage Ripple

To calculate for the output voltage ripple, we use;

∆�� =
∆ ��

8 × �� × ��
------------------------------------------------- (3.13)

Where,

�� is value of the output capacitance (C3 and C4) which is = 44�� (this corresponds to the output voltage ripple of 3.31mV )

The input capacitors C1 and C2 (2 x 10µF and 0.1µF) are used to filter the high-frequency noise from the input supply and provide
a stable input voltage to the converter's switching stage. The bulk capacitor (10µF) handles the large, low-frequency input current
pulses, while the small ceramic capacitor (0.1µF) is placed very close to the IC to filter out high-frequency noise.

Other Components (R9, C6, R4, C10, C5)

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 C5 (Bootstrap Capacitor): The 0.1µF bootstrap capacitor powers the high-side MOSFET driver. Its value is generally a
small, standard value recommended in the IC's datasheet.

 C6 and R4: These components form a compensation network connected to the feedback pin (FB) and a compensation pin
(COMP) on the IC. They are part of the control loop, ensuring the converter is stable and responds properly to changes in
load. The values are calculated using control loop theory to set the converter's bandwidth and phase margin.

 R9 and C10: These are typically part of a soft-start circuit or power good (PG) function. They control how the output
voltage ramps up at startup, preventing a large inrush current that could damage the converter or the input power supply.

The most general and fundamental formula for inductor current is:

��(��) =
1
L

∫ ��(τ)��τ +
t

t0
��(��0) -------------------------------- (3.14)

Where:

 ��(��) is the inductor current at time (t)

 L is the inductance in Henries (H).

 v(τ) is the instantaneous voltage across the inductor.

 ��0 This is the initial time.

 ��(��0) is the initial current flowing through the inductor at time (��0)

This relationship is a direct consequence of the fundamental inductor equation, which states that the voltage across an inductor is
proportional to the rate of change of current. This is a crucial term because the current in an inductor cannot change instantaneously;
it depends on the past state of the circuit.

The efficiency (η) is calculated as the ratio of the output power (P��) to the input power (P��). This value is often expressed as a
percentage.

η =
P��
P��

× 100% ------------------------------- (3.15)

Since power is the product of voltage and current (P =V × I)

so, the formula can also be written as:

η =
�� × ��

V�� × ��
× 100% ------------------------ (3.16)

Power loss (P��) is a key component of a converter's efficiency and is calculated using this simple formula:

P�� = P�� − P�� ------------------------- (3.17)

Where:

 P�� is the input power (the total power supplied to the converter).

 P�� is the output power (the power delivered to the load).

Flow Chart

Complete Detect

Check

YES

Swap

Initialize BMS Start Monitor Battery
Status

Battery
overhe
ating

Cool Battery

Charge
level
low?

Swap Battery

Check for faults
Perform
diagnostic
s

Monitor new
battery
status

E-rickshaw

ready

YES

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Circuit Diagram

In Battery Management Systems (BMS) applications, the MP9943 is a synchronous step-down converter integrated circuit that may
be utilized for power transmission and battery charging. It is a switch-mode, rectified, synchronous, high-frequency converter with
integrated power MOSFETs. Over a broad input supply range, it provides a small way to deliver a 3A peak output current with
good load and line regulation.

IV. Results and Analysis

Figure 4.1: Output voltage ripple waveform

This gragh displays the ripple component present in the output voltage. Ripple is the small, unwanted AC voltage superimposed on
a DC voltage. Here, it is shown in millivolts (mV) over time in microseconds (��). A lower ripple amplitude generally indicates
better regulation of the output voltage. The ripple voltage oscillates between approximately -3mV and +3mV, indicating the small
AC component present in the DC output voltage of a power supply.

Figure 4.2: voltage switching waveform

This graph shows the voltage of a switching signal, labeled Vsw(V), over time in microseconds (��).

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 Output Ripple: the graph shows the output ripple voltage (Vripple(mV)) over a longer time scale in microseconds (��)
during transient load conditions. It shows the output voltage’s response when the load changes suddenly. There are visible
overshoots and undershoots in the ripple when the load changes.

 Load Current: this graph shows the load current (lo(A)) over the same time scale as the output ripple. It shows the stepped
changes in the load current, indicating a transient event where the current shifts between a lower and higher value. It’s
changing between approximately 1.5A and 3A.

Figure 4.6: efficiency vs output current

This graph plots the efficiency (��(%)) of a power converter against the output current (lo(A)). The efficiency initially rises to a
peak of over 97% around 0.5A output current, then gradually decreases as the output current increases, reaching about 93.5% at
3A. This curve illustrates how the converter's efficiency varies with the load. The efficiency decreases as the output current
increases.

Figure 4.7: power loss vs output current

This graph shows the power loss (Ploss(W)) of the converter as a function of the output current (lo(A)). As expected, the power
loss increases significantly as the output current increases from near 0W at low currents to 1W at 3A.

Figure 4.8: BMV-700 status screen

This screen shows the real-time status of the battery.

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Figure 4.9: BMV-700 status screen

V. Conclusion

The research successfully designed and implemented a smart BMS that integrated comprehensive monitoring, control, and
protection functionalities for LiFePO₄ battery packs. The system's architecture effectively linked individual cell monitoring to
centralized analysis and control, ensuring optimal battery performance and safety within the e-rickshaw. The electrical performance
of the developed BMS, particularly its power conversion efficiency, was highly impressive. This research made significant
contributions to the field of electric mobility and sustainable transportation by accelerating EV adoption and advancing battery
management technology.

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