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Contents list available at CBIORE journal website International Journal of Renewable Energy Development Journal homepage: https://ijred.
Research Article Techno-economic assessment and strategic proposal for designing and optimizing the required powered battery for an electric motorcycle under varying driving cycle tests Tan-Thich Doa,b .
Tan-Ngoc Dinhc .
Vinh-Dat Lyc* Department of Electrical and Mechatronics.
Lac Hong University.
Vietnam Department of Mechanical Engineering and Advanced Institute of Manufacturing with High-tech Innovations.
National Chung Cheng University.
Taiwan Internal Combustion Engine Department.
Faculty of Vehicle and Energy Engineering.
Ho Chi Minh City University of Technology and Education.
Vietnam Abstract.
Recently, many countries have committed to achieving net-zero emissions by 2050, making the adoption of electric motorcycles increasingly significant.
The expansion of electric motorcycles has gained popularity due to their affordability, ease of use, and environmental benefits.
In the design of electric motorcycles, optimizing energy efficiency and economic viability both technologically and economically is a key This study focuses on developing a mathematical model and strategic proposal with the step-by-step calculation for determining the required power battery for electric motorcycles under various driving cycle tests, implemented using Matlab software.
The results analyze and discuss the effects of operating conditions on the electric motorcycleAos dynamic performance, average energy consumption, and battery cell and pack Ultimately, the battery pack optimization strategy was proposed and conducted using the Mixed-Integer Linear Programming (MILP) As a result, the Toshiba battery trademark was identified as the optimal choice for the required power battery in the electric motorcycle, considering both technological effectiveness and economic factors.
The Toshiba battery pack has a capacity of 39 Ah, 17 cells, a mass of 13.
94 kg, and a cost of $459, respectively.
After designing and optimizing the required battery pack for the electric motorcycle, the model was validated to ensure that the packAos energy exceeds the average energy consumption under varying driving cycle tests.
Therefore, the model demonstrates high This study provides valuable insights into designing and evaluating the dynamic performance and battery pack characteristics of electric Keywords: electric motorcycleAos dynamic performance, energy consumption, battery pack optimization strategy, technological effectiveness and economic factors, driving cycle test.
Mixed-Integer Linear Programming (MILP).
@ The author.
Published by CBIORE.
This is an open access article under the CC BY-SA license .
ttp://creativecommons.
org/licenses/by-sa/4.
0/).
Received: 10th July 2025.
Revised: 17th Sept 2025.
Accepted: 29th Sept 2025.
Available online: 11th Oct 2025 Introduction Nowadays, climate change, global warming, and air pollution are intensifying worldwide due to traditional emissions, particularly those from fossil fuels (Arshad et al.
Li et al.
Driga et al.
To protect the environment and human health, various solutions have been proposed, including the adoption of renewable energy sources.
Recently, the use of green energy in transportation has gained significant popularity.
In particular, electric motorcycles are increasingly used in many countries because they are affordable, convenient, and produce zero emissions (Guerra et al.
Huang et al.
Awirya et Murtiningrum et al.
However, the electric motorcycle has a challenged duration of the electric motorcycle operation because it depends on the required battery power (Morandin et al.
Therefore, the study of the battery's dynamics and power requirements is essential for balancing technological advancement with economic considerations.
To design a battery that meets the power requirements of an electric motorcycle, it is essential to consider the vehicleAos dynamic behavior.
In this context, (Sharp et al.
presented advanced modeling and simulation of electric motorcycle dynamics under various operating conditions.
Their results provided valuable insights into electric motorcycle behavior.
(Hanifah et al.
modeled and simulated motorcycle dynamics to estimate travel range and speed tracking under different driving cycles, demonstrating the modelAos capability to predict distance and track speed accurately.
(Du et al.
used Matlab/Simulink to model electric bike dynamics and evaluate ride comfort, revealing that the model suits its original design purpose.
Zaripov et al.
investigated electric bicycle dynamics under varying conditions, showing that energy efficiency is significantly influenced by dynamic parameters during operation.
(Thejasree et al.
developed a mathematical model to analyze the longitudinal dynamics of electric motorcycles, offering deeper insights into their More recently, (Salman et al.
developed and validated a dynamic model through experimental testing, showing strong agreement between the model and real-world (Qu et al.
studied electric bicycle dynamics under external forces and found the model to be highly reliable.
* Corresponding author Email: datlv@hcmute.
vn (V.
https://doi.
org/10.
61435/ijred.
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(Abagnale et al.
created a model to assess both the dynamics and environmental impact of electric bicycles, contributing significantly to performance evaluation and environmental analysis.
(Yuniarto et al.
conducted modeling, simulation, and validation of electric bicycle energy consumption in an Indonesian case study.
They concluded that the model showed high accuracy, with energy consumption errors of Oe4.
7% and Oe8.
63% during real-time driving and dynamometer testing, respectively.
(Zhu et al.
proposed a mathematical model using multibody dynamics to analyze bicycle motion, achieving good agreement with commercial simulation tools.
To further improve modeling accuracy, (Hima et al.
developed an 11-degree-of-freedom mathematical model based on the Lagrange formulation, offering a detailed analysis of dynamic behavior under various conditions.
(Moreno Giner et al.
presented a motorcycle dynamics model focusing on cornering analysis using Maple software, identifying key parameters that influence stability and control.
(Arricale et 2.
developed a nonlinear dynamic model of an electric bicycle using commercial software and introduced a control strategy to regulate motorcycle speed using a ProportionalIntegral-Derivative (PID) controller.
Their results demonstrated high modeling accuracy.
To make the significance of the dynamics performance, several researchers have investigated the dynamics and energy efficiency of hybrid electric (Asaei et al.
Asaei et al.
designed and implemented hybrid configurations to evaluate performance and fuel consumption, examining two different energy control strategies and demonstrating that hybridization enhances performance while reducing both fuel consumption and (Chen et al.
modeled and controlled a hybrid electric motorcycle equipped with an in-hub wheel motor, reporting an 11.
6% improvement in fuel consumption.
(Nguyen et al.
developed a computational model, revealing that hybrid electric motorcycles can achieve a 21.
1% reduction in fuel consumption.
Furthermore, (Nguyen et al.
analyzed energy consumption and operating costs, highlighting the economic as well as environmental advantages of hybrid electric motorcycles.
Recently, (Niccolai et al.
proposed and optimized a strategy of the powertrain system to achieve the minimum energy consumption for the electric motorcycle.
They revealed that the energy consumption is reduced by up to 36% when using their proposal strategy.
While (Li et al.
studied the fatigue behavior of the motorcycle frame to investigate the dynamics under complex working conditions.
They optimized the stiffness of the suspension system and indicated at 15 and 10 N mmOe1 for the front suspension and rear suspension system, respectively.
(Alagmy et al.
designed and developed the powertrain system for the electric motorcycle using the SolidWorks and Matlab/Simulink software, considering the cost factor.
Their method involved conducting experiments and analyzing the dynamic performances, and indicated cost-effectiveness.
To achieve long-distance operation of an electric motorcycle, the battery-powered system must be carefully considered during vehicle operation.
(Chen et al.
designed and tested a battery module for electric motorcycles, reporting a low voltage error between measurement and design 1% at 18 A and 0.
5% at 9 A.
(Fahma et al.
developed a battery model that accounted for the financial implications of using high-power batteries in electric motorcycles.
(LeBel et al.
proposed a model to evaluate battery pack sizing, providing a detailed analysis of how different cell configurations influence overall pack size.
(Brodsky et al.
designed, optimized, and validated a battery pack for electric motorcycles.
Their results confirmed the suitability of the design in terms of meeting power demands and minimizing heat generation.
More recently, (Kusumah et al.
determined the power requirements of an electric motorcycle using a 72 V, 21 Ah LiFePOCE battery.
They concluded that the battery pack could deliver 11.
51 HP at a speed of 86 km hOe1.
(Esparza et al.
developed a battery pack for an electric motorcycle prototype and validated it under various conditions, confirming its capability to supply sufficient propulsion power.
To analyze the thermal behavior of the battery, (Shahjalal et al.
developed a numerical battery model and concluded that a cooling system is essential to maintain optimal temperature during high-acceleration (Arifwardana et al.
compared the thermal performance of NMC and LiFePOCE batteries during operation and found that NMC cells exhibited higher temperatures.
(Elbeshbeshy et al.
used Matlab/Simulink to design a 48 V, 350 Wh battery pack, which was experimentally validated and found suitable for electric motorcycle propulsion.
(Nugraha et al.
analyzed power demands under various charge capacities, identifying a 48 V, 15 Ah Li-ion battery as (Aprillia et al.
designed and tested a 72 V, 40 Ah battery pack on a dynamometer, noting that it reached full charge at 71 V.
(Nguyen et al.
designed and optimized the battery pack characteristics and operating cost for a hybrid electric motorcycle converted from a Honda Lead 110 cc.
They concluded that a 48 V Ae 33 Ah battery pack is suitable for the model, and that adopting the hybrid configuration results in cost During the design of the electric motorcycle, the required battery pack system energy plays a crucial role in supplying the electricity for the auxiliary loads of the electric motorcycle, while the required battery pack energy depends on the energy consumption.
(Niccolai et al.
analyzed the energy consumption of the electric motorcycle, considering the recovery energy from the regenerative braking system.
Their results discussed the potential of regenerative energy for the energy consumption in the system.
(Syahrobi et al.
analyzed the characteristics of the Li-ion battery in the electric They tested it under various roads, such as uphill, flat road, and speed variations of 50, 30, and 10 km hOe1.
Their results indicated that the battery temperature increased when the electric motorcycle operated at high velocity, but the voltage during the charging and discharging process is stable, thus the Lion battery is suitable for the application of the electric (Izzaturrahman et al.
designed the required powered battery pack with the specifications of Li-ion 1865 cells battery type and capacity of 24 Ah, and voltage of 64 V for an electric motorcycle.
They revealed that the capacity and voltage were achieved at 23.
36 Ah and 69.
67 V, respectively, during the battery pack charging process.
Additionally, the battery pack can provide power for the electric motor up to 1.
54 HP and a maximum velocity of 34 km hOe1 in the dynamometer test.
Based on the reviewed literature, electric motorcycles play a crucial role in achieving net-zero emissions by 2050 in many Their growing popularity is attributed to ease of use, convenience, and affordability.
Therefore, enhancing electric motorcycle performance and understanding the dynamic behavior, required battery power, and energy consumption are In this study, a mathematical model was developed to simulate the dynamic performance and battery characteristics of an electric motorcycle under various driving cycle tests.
The model was implemented using Matlab/Simulink with a step-bystep calculation approach.
The basic blocks included Constant.
Function.
From.
To.
Display, and Mathematical.
Subsystems were created to simplify the model and facilitate management.
Data recording was performed using the Workspace platform in the software.
The effects of different operating conditions on the motorcycleAos dynamic performance and energy consumption were analyzed.
Furthermore, the required battery power and characteristics were evaluated for different battery types.
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Table 1 The NYCC characteristics of the driving cycle test Description Value Total duration / s Total distance / m Maximum speed / km hOe1 Average speed / km h Oe1 Percentage of time stopped (%) Number of stops Maximum acceleration / m s Oe2 Maximum deceleration / m sOe2 Oe3.
Idle time / s Acceleration phases Deceleration phases Table 2 The WLTP Class 1 characteristics of the driving cycle test Description Low Medium Total Duration / s Distance / m Stop time / s % of stop Highest acceleration / m sOe2 Oe Minimum acceleration / m sOe2 Oe1.
Oe0.
Oe Mean velocity without stops / km hOe1 Oe Highest velocity / km h Oe1 Mean velocity with stops / km h Oe1 Finally, a strategy was proposed to optimize the power requirements of the electric motorcycle.
This study is significant for guiding the design of battery systems in electric Additionally, the developed model can predict dynamic performance, estimate energy consumption, and determine cell and pack-level battery characteristics across diverse driving conditions.
Description of the mathematical model To facilitate and simplify the modeling and evaluation of the electric motorcycle dynamics, determination of the required powered battery, and optimization of the battery pack under driving cycle test in this study, several assumptions are made as The traction force of the electric motorcycle is considered with a longitudinal electric motorcycle, ignoring side forces effect with the horizontal and vertical planes of the electric motorcycle.
The tire-road adhesion and aerodynamic drag coefficient are constant values.
The tire of an electric motorcycle is rolling without slipping on the road.
The battery pack operates at near-optimal temperature.
aging or degradation, capacity fade, and thermal derating are neglected.
Battery pack layout .
eries/paralle.
is electrically uniform.
interconnect resistance and cell dispersion are neglected.
1 Driving cycle test description To evaluate the dynamic performance of the electric motorcycle, a driving cycle test was employed in this study.
this section, the characteristics of three driving cycle tests are described in detail in this section.
The New York City Cycle (NYCC) driving cycle test was conducted in New York City, reflecting typical stop-and-go traffic conditions.
The characteristics of the NYCC driving cycle test are summarized in Table 1, and the distance, velocity, and acceleration profiles are shown in Fig.
S1 .
n the Supplementary The Worldwide Harmonized Light Vehicle Test Procedure (WLTP) is classified into three categories: WLTP Class 1.
Class 2, and Class 3.
The power-to-mass ratio (PMR), measured in W kgOe1, is used to determine which WLTP class the dynamic system belongs to.
The PMR is defined as follows:
ycEycAycI = ycEyaycA ycAyaycA Where: ycEyaycA and ycAyaycA represent the rated power output of the electric motorcycle (W) and the mass of the electric motorcycle .
without load person, respectively.
Additionally.
WLTP Class 1 is selected when the ycEycAycI O 22.
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Table 3 The FTP-75 characteristics of the driving cycle test Description Value Duration / s Distance / m % of stop Highest acceleration / m s Oe2 Minimum acceleration / m sOe2 Oe1.
Mean velocity without stops / km h Oe1 Highest velocity / km hOe1 Mean velocity with stops / km hOe1 when 22 < ycEycAycI O 34, and WLTP Class 3 is selected when ycEycAycI > 34.
In this study, the rated power of the electric motorcycle is 1800 W, and its mass is 130 kg.
Therefore, the PMR is calculated to be 15.
84 W kgOe1, which is less than 22 W kgOe1.
As a result.
WLTP Class 1 is suitable for modeling and simulating the electric motorcycle.
The driving cycle test is typically used for vehicles with low power and velocities under 70 km hOe1.
The driving cycle test includes velocity stages of low-medium-low.
Additionally, the characteristics of WLTP Class 1 are summarized in Table 2, and the distance, velocity, and acceleration profiles are shown in Fig.
S2 .
n the Supplementary The Federal Test Procedure 75 (FTP-.
driving cycle test was employed in this study.
This driving cycle test, which was developed by the United States Environmental Protection Agency (US-EPA), was conducted in urban conditions (Makarchuk et al.
The key characteristics of the FTP-75 driving cycle are summarized in Table 3, and the distance, velocity, and acceleration profiles are illustrated in Fig.
S3 .
n the Supplementary materia.
2 Electric motorcycle dynamic performance model During the operation of an electric motorcycle, various forces act upon it, including aerodynamic resistance force, acceleration force, climbing resistance force, and rolling resistance force, as described in Fig.
The electric motorcycle's stability and longitudinal forward dynamic were assumed in this According to Newton's Second Law, the relationship between these resistance forces and the traction force is described as follows:
yaycN = yayaycc yaya yaya yaycI Fig.
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Where: yaycN , yayaycc , yaya , yaya , yaycI represent the traction force, aerodynamic resistance force, acceleration force, climbing resistance force, and rolling resistance force.
All forces are units in N.
The aerodynamic resistance force is proportional to the square of the electric motorcycle's speed and is calculated using Eq.
yayaycc = yayce yaycc yuUycaycnyc ycO 2 Where: yayce is the front area of the electric motorcycle, in m2.
yaycc denotes the aerodynamic drag coefficient .
aycc = 0.
2 Oe 1.
yuUycaycnyc represents the air density .
uUycaycnyc = 1.
25 ycoyci ycoOe3 ).
and ycO is the speed of the electric motorcycle, in m sOe1.
Fig.
2 The electric motorcycle's dynamic performance under the driving cycle test .
The detailed function of the forces in the electric motorcycle .
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The acceleration force represents the electric motorcycle's ability to accelerate and depends on its mass and velocity.
It is defined by Eq.
yaya = ycAyaycA Where: ycAyaycA , and ycO denote the electric motorcycle mass in kg.
and the velocity of the electric motorcycle, in km hOe1.
When the electric motorcycle operates on a hill, a climbing resistance force is generated.
Conversely, when the motorcycle moves on a flat road, this resistance force is negligible.
The climbing resistance force is determined using the following yaya = yciycAyaycA ycycnycu.
Where: yci, ycAyaycA , and yu represent the gravitational acceleration .
ci = 9.
81 yco yc Oe2 ).
the mass of the electric motorcycle, in kg.
the slope angle .
, respectively.
The rolling resistance force opposes the motion of the wheel and is determined using Eq.
yaycI = ycAyaycA yciyaycI ycaycuyc.
Where: yaycI is the rolling coefficient.
It depends on the quality of the road.
In this study, the transmission system is an in-hub type, meaning the power from the electric motor is transferred directly to the wheel.
Consequently, the power and torque of the electric motorcycle are defined by Eq.
and Eq.
ycEyaycA = yaycN ycO ycNyaycA = yaycN ycIyc The input parameters of the electric motorcycle are listed in Table 4.
Indeed, the Evo200 Vinfast, which was manufactured in Vietnam, was selected for modeling and simulation in this study (VinFast, 2.
3 Modeling the average energy consumption for the propulsion system and auxiliary loads In this study, the average energy of the propulsion system, in Wh kmOe1, was calculated based on the driving cycle test:
yaycaycy = .
Where: yaycI is the total energy consumption in the driving cycle test, in Wh.
and ycc denotes the length of the driving cycle test, in km, respectively.
Additionally, the total energy consumption was estimated based on the driving cycle test.
It was shown in Eq.
yayc = O ycEyaycA yccyc During the electric motorcycle operation in the driving cycle test, auxiliary loads (Wh kmOe.
were consumed, including both permanent and intermittent loads.
Notably, intermittent loads account for 10% of the total auxiliary consumption.
Therefore, the average energy consumption of the auxiliary loads should be considered and is determined using Eq.
Additionally, the auxiliary power consumption of the electric motorcycle is presented in Table 5 (VinFast.
Where: ycIyc is the radius of the wheel of the electric motorcycle, in m.
From Eqs.
, the dynamic performance of the electric motorcycle was modeled and is illustrated in Fig.
Additionally, the detailed representation of the resistance forces in the electric motorcycle is shown in Fig.
yaycaycycu = ycEycaycycu OIyc Where: ycEycaycycu , and OIyc denote the auxiliary consumption power, in W.
and the duration of the driving cycle test, in s.
Ultimately, the total average energy consumption, in Wh kmOe1, comprises the average energy consumption of the propulsion system and the average energy consumption of the auxiliary loads.
It is defined by Eq.
Table 4 The specific parameters and input parameters of the electric motorcycle of Evo200 Vinfast Parameter Value Aerodynamic drag coefficient Gravitational acceleration Rolling resistance coefficient Front area of an electric motorcycle Transmission efficiency Trunk volume Dimension 1804 x 683 x 1127 (Length x Width x Heigh.
Wheelbase Electric motorcycle mass Ground clearance Electric motor type In-hub Table 5 The auxiliary power in the Evo200 Vinfast Auxiliary power type Description Headlight .
igh bea.
system Permanent auxiliary loads Battery management system Screen information light system Horn system Interrupt auxiliary loads Signal light system .
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Fig.
3 Modeling the average energy consumption: .
for the propulsion system, .
auxiliary loads, and .
total average energy consumption yaycayceyca = .
aycaycycu yaycaycy ) O .
Oe yuCycy ) .
Where: yuCycy is the efficiency of the transmission.
From Eqs.
, the electric motorcycle dynamic performance model was modeled and described in Fig.
4 Mathematical model of the battery power characteristics 1 Modeling the basic parameters of the cell battery In this section, the dimensional parameters and characteristics of the battery were analyzed in detail under different battery configurations.
Nowadays, the configuration of the pouch and cylindrical batteries are widely used in vehicles.
To achieve high voltage, a large number of battery cells are connected in series.
Additionally, multiple series-connected battery packs are arranged in parallel to increase capacity.
The assembled battery pack is then enclosed in a protective casing.
The battery connection structure is illustrated in Fig.
The cell battery energy .
aycayca ), in Wh, can be determined based on the cell voltage and cell battery capacity.
It was defined by Eq.
yaycayca = ycOycayca yaycayca Where: ycOycayca and yaycayca corresponding to the cell voltage of the battery .
n V) and cell capacity .
n A.
Additionally, the volume energy density .
ayc ), in Wh mOe3, or weight energy density .
ayci ), in Wh kgOe3, was calculated by the cell battery energy by its volume or mass, respectively.
These are defined in Eq.
and Eq.
yayc = yaycayca ycOycayc.
yayci = yaycayca Where: ycOycayc.
represents the volume of the cell battery for the cylindrical configuration or pouch configuration, in mOe3.
ycoycayca denotes the mass of the cell battery, in kg.
Since the dimensional parameters of the cell battery different between cylindrical and pouch configurations, the determination of cell volume also varies.
The volume of the cell battery for the cylindrical and pouch configurations is determined using Eq.
and Eq.
, respectively.
yuUyayca2 ya 4 yca ycOycayc.
= ycNycy ycOycy yaycy .
ycOycayc.
= Where: Dyca represents the diameter of the cell battery for a cylindrical configuration.
yayca is the height of the cell battery for the cylindrical configuration.
For the pouch configuration, ycNycy , ycOycy , and yaycy denotes the thickness, width, and height of the cell battery, respectively, as shown in Fig.
All the dimension parameters are in units of m.
From Eq.
, the cell battery characteristics were modeled and shown in Fig.
S4 .
n the Supplementary materia.
It consists of the basic dimensions, volume, and mass of the cell During the design of the battery pack energy .
aycaycy ), in Wh, the average energy consumption in the driving cycle test must be carefully considered to ensure sufficient energy supply for ISSN: 2252-4940/A 2025.
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Fig.
Arrangement of the cell battery.
Dimension parameters of the cell battery both the propulsion system and auxiliary loads of the electric To ensure sufficient energy for the electric motorcycle to complete the travel distance, the requirement is established by the following conditions:
yaycaycy Ou yaycayceyca y ycc The minimum required battery pack energy must balance the average energy consumption and is dependent on the length of the driving cycle test, as expressed in Eq.
yaycaycy = yaycayceyca y ycc When cell batteries are connected in series, they form a series battery string, which determines the battery pack voltage .
cOycaycy ).
The total voltage of the series battery string is the sum of the individual cell voltages .
cOycayca ).
Therefore, the number of cells in the series battery string is estimated using Eq.
and must be an integer value:
ycAycayc = ycOycaycy ycOycayca yaycaycy yaycayc Additionally, the total number of cells in the battery pack .
cAycaycy ) can be estimated by multiplying the number of cells in the series battery string by the number of parallel-connected series battery strings, as described by Eq.
ycAycaycy = ycAycayc ycAycycy .
Eq.
and Eq.
are used to determine the battery pack volume .
cOycaycy ), in mOe3, and the mass of the battery pack .
coycaycy ), in These are calculated as follows:
ycOycaycy = ycAycaycy ycOycayc.
To increase the capacity of the battery pack, a series of battery strings are connected in parallel.
The number of parallelconnected series battery strings is determined and must be an integer, as shown in Eq.
ycAycycy = yaycaycy = ycAycycy yaycayca The series battery string energy .
aycayc ) is determined by multiplying the number of cells in the series battery string by the energy of an individual cell:
yaycayc = ycAycayc yaycayca The battery pack capacity, in Ah, is calculated by multiplying the number of parallel-connected series battery strings by the capacity of an individual cell.
This is defined by Eq.
as follows:
ycoycaycy = ycAycaycy ycoycayca From Eq.
to Eq.
, the battery pack was modeled and described in Fig.
S5 .
n the Supplementary materia.
2 Modeling the designed battery pack with the high-voltage Fig.
S6 .
n the Supplementary materia.
shows the modeling of the designed battery pack with a high-voltage system in this The maximum current in series battery string .
aycoycaycyc ), in A, is calculated based on the maximum the ratio of the charge- ISSN: 2252-4940/A 2025.
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Table 6 Specification of the cell battery with the cylindrical and pouch configuration under the FTP-75 Manufacturer Model Geometry type Costcb Ccb Ucb Cmax Crate Mcb Wcb Tcb Hcb Dcb Cycle life Chemistry LG Chem (DNK Power.
INR21700M50L
Cylindrical Li-ion
0 to 45
Oe20 to 55
Group 1
Samsung
(DNK
Power.
INR21700
Cylindrical Li-ion
0 to 45
Oe20 to 60
Group 2
DNK
(DNK
Power.
DNK Cylindrical LiNCA 0 to 45 Oe20 to 60 discharge .
aycycaycyce_ycoycaycu ), in hOe1, and cell battery capacity .
aycayca ), in Ah, as described in Eq.
yaycoycaycyc = yaycycaycyce_ycoycaycu yaycayca The maximum current in the battery pack .
aycoycaycaycy ), in A, depends on the maximum current in the series battery string .
aycoycaycyc ), in A, and the number of parallel-connected series battery strings .
cAycycy ).
The relationship is defined by Eq.
yaycoycaycaycy = ycAycycy yaycoycaycyc The maximum power of the battery pack .
cEycoycaycy ), in W, is calculated based on the maximum current in the battery pack .
aycoycaycaycy ), in A, and battery pack voltage .
cOycaycy ), in V, using the following formula:
ycEycoycaycy = ycOycaycy yaycoycaycaycy The continuous current in a string .
aycaycayc ), in A, is estimated using the equation Eq.
It depends on the cell battery capacity .
aycayca ), in Ah, and the ratio of charge-discharge .
aycycaycyce ), in hOe1, as follows:
yaycaycayc = yaycayca yaycycaycyce The battery pack's continuous current .
aycaycyycayca ), in A, and the battery pack's continuous power .
cEycaycyycaycy ), in W, are determined corresponding to Eq.
and Eq.
, as follows:
yaycaycyycayca = yaycaycayc ycAycycy .
ycEycaycyycaycy = ycOycaycy yaycaycyycayca In addition, the cost of the battery pack was calculated to evaluate the effectiveness of economics by varying the batteries in this study.
The cost of the battery pack, in $, was determined based on the manufacturer and shown as follows:
yaycuycycycaycy = ycAycayc ycAycycy yaycuycycycayca Where: yaycuycycycayca is the cost of a cell battery, in $.
Evlithum (EVLithium.
SLPB
Pouch
LiNixCoyMnzO2
Oe20 to 55
Oe20 to 55
A123
(Buy A123 Products, 2.
AMP20
M1HD
Pouch LiFePO4 Oe30 to 55 Oe40 to 60 Toshiba (EVLithium.
A123 39AH NCM
Pouch LiFePO4 0 to 45 Oe20 to 50 From the datasheet of the manufacturer, the specifications of the cell battery and the cost of the cell battery with the configuration of cylindrical and pouch are listed in Table 6.
Group 1 included the cell battery with the cylindrical configuration, and Group 2 consisted of the cell battery with the pouch configuration.
5 Proposal strategy to optimize the battery pack In this study, the calculation and design of the required powered battery for an electric motorcycle were thoroughly analyzed in the previous section.
However, economic and technological effectiveness considering factors such as cost, performance, and the weight or volume of the battery pack, was also critically evaluated.
In other words, the two key factors of energy and economy .
EE) were emphasized in this section.
Mixed-Integer Linear Programming (MILP) is a mathematical optimization technique that can be used to optimize various parameters in battery pack design.
In this context.
MILP helps minimize costs, maximize performance, and enforce constraints such as weight and volume.
The optimization process was conducted in a Matlab software environment.
The following equations define the constraint conditions for this proposed strategy: the general objective function is presented in Eq.
while the constraint conditions are described in Eqs.
= ycoycnycu Oc yaycn ycuycn ycycnycEa ycuycn OO .
, .
OAycn ycn Where: yaycn represents the input parameters of the i-th battery pack, which are related to the economy and technology parameters, ycuycn is the binary decision variable of the ith battery For technological effectiveness, the total required power battery pack must meet or exceed the estimated energy consumption, which was derived from the driving cycle test.
This relationship is expressed in Eq.
Additionally, the capacity of the battery pack should be higher than 30 Ah (Zahedi et al.
, as depicted in Eq.
Oc yaycaycyycn ycuycn Ou yaycayceyca ycn ISSN: 2252-4940/A 2025.
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Oc yaycaycyycn ycuycn Ou yaycoycnycu Oc ycOycaycyycn ycuycn O ycOycoycaycu ycn Where: yaycoycnycu is the minimum capacity of the battery pack, in Ah.
For the dimensional factor, the total volume and mass of the battery pack should not exceed the available space and predefined limits.
The volume and mass constraints are described in Eq.
and Eq.
, respectively:
Oc ycoycaycyycn ycuycn O ycoycoycaycu ycn Where: ycOycoycaycu and ycoycoycaycu denote the expected maximum volume limit, in L, and the expected maximum volume limit, in kg.
Fig.
5 The flowchart of the proposal strategy to optimize the battery pack for an electric motorcycle, using MILP ISSN: 2252-4940/A 2025.
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Fig.
6 Electric motorcycle dynamic performance under NYCC driving cycle test: .
Traction force.
Power.
Torque For economic effectiveness, the cost of the battery pack should be minimized while remaining feasible for purchasing the electric motorcycle.
The cost constraint is established in Eq.
Oc yaycuycycycaycyycn ycuycn O yaycuycycycoycnycu ycn Where: yaycuycycycaycyycn represents the total cost of battery pack in the ith battery pack, in $.
yaycuycycycoycnycu denotes the expected minimum cost limit, in $.
From Eqs.
, the relationship between technological effectiveness, dimensional constraints, and economic constraints is expressed as a system of equations, as Ocycn yaycaycyycn ycuycn Ou yaycayceyca_ycn Ocycn yaycaycyycn ycuycn Ou yaycoycnycu Ocycn ycOycaycyycn ycuycn O ycOycoycaycu Ocycn ycoycaycyycn ycuycn O ycoycoycaycu Ocycn yaycuycycycaycyycn ycuycn O yaycuycycycoycnycu ycuycn OO .
, .
OAycn In this study, the MILP approach was used to determine the optimal battery pack and implemented in the Matlab software environment using the AuintlinprogAy function solver, owing to its capability of efficiently solving minimization problems in MILP problems, which enables efficient and accurate solutions (Willis et al.
MathWorks, 2.
The flowchart of the MILP solver process in this study is shown in Fig.
6 Validation of battery pack energy at the best choice of the trademark battery To evaluate the required battery pack energy and ensure that the required battery pack energy is greater than the energy consumption in this study, which is to ensure sufficient energy for the vehicle to complete the travel distance.
The model should be validated in this context.
In fact, the battery pack energy, average energy consumption, and remaining battery pack energy can be described using the energy balance law, as Remaining energy = Energy of pack Ae Energy consumed .
yaycaycy yaycyceycaycy = Oe yaycayceyca Where: yaycyceycaycy is the remaining battery pack energy, in Wh km Oe1 yaycaycy represents the battery pack energy, in Wh.
ycc is the distance of the driving cycle test, in km.
and yaycayceyca denotes the average energy consumption, in Wh km Oe1.
The state of charge (SOC) represents the percentage of the remaining battery pack energy during electric motorcycle operation under a driving cycle.
The SOC can be calculated using Eq.
(Wenzl, 2.
ycIycCya.
= ycIycCya.
c0 ) Oe yc ya.
yuCycaycaycyc O yccyc yaycaycy .
Where: ycIycCya.
c0 ) represents the fully charged battery pack with a value of 1.
denotes the current of discharging, in A.
yuCycaycaycyc is the coulombic efficiency.
yc represents the duration of time, in and yaycaycy is the battery pack capacity, in Ah.
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Therefore, the remaining battery pack energy is calculated based on the SOC, battery pack capacity, and cell battery voltage, as expressed in the following equation:
yaycyceycaycy = ycIycCya.
yaycaycy ycOycayca ycc y ycAycaycy .
Results and Discussion 1 Analysis of the electric motorcycle dynamic performance under varying driving cycle tests During electric motorcycle operation, various resistance forces influence its performance.
Therefore, a detailed analysis of the electric motorcycle's dynamic performance under different driving cycle tests is essential.
Fig.
6 illustrates the electric motorcycleAos dynamic performance, including traction force, power, and torque under the NYCC driving cycle test.
Additionally.
Figs S7 and S8 depict the dynamic performance of the electric motorcycle under the WLTP Class 1 and FTP-75 driving cycles, respectively .
n the Supplementary materia.
Fig.
6 shows the variation in traction force, power, and torque of the electric motorcycle during the NYCC driving cycle It can be observed that these parameters change in response to velocity variations throughout the test.
At 198th seconds, the traction force and torque reach their maximum values of 550.
69 N and 110 N.
m, respectively.
Additionally, the maximum power was achieved at 3853.
91 W at 210th seconds.
Determining the maximum power and torque is crucial for selecting a suitable electric motor for the motorcycle.
In other words, if the selected motor is inadequate, such as having insufficient power compared to the motorcycleAos requirements, it may fail to generate enough traction force to sustain the motorcycleAos motion effectively.
Similarly, the electric motorcycle's dynamic performance varied according to the WLTP Class 1 driving cycle test, as shown in Fig.
S7 .
n the Supplementary materia.
At the initial stage of the WLTP Class 1 driving cycle test, the traction force and torque reached high values of 170.
25 N and 40.
86 N.
m at the 13th second.
This can be attributed to the acceleration of the electric motorcycle at the start of the test.
Additionally, the traction force in this driving cycle is lower than in the NYCC driving cycle test, reaching a maximum of 188.
08 N when the motorcycle operates at high velocity phase.
This occurs because the acceleration in the WLTP Class 1 cycle is lower than in the NYCC driving cycle test.
The electric motorcycleAos dynamic performance reaches its peak at the high-speed phase, between the 589th and 1022nd seconds.
For the FTP-75 driving cycle test, the variation in the electric motorcycleAos dynamic performance is shown in Fig.
S8 .
n the Supplementary materia.
It can be observed that the traction force, power, and torque fluctuate frequently throughout the driving cycle due to changes in the motorcycleAos acceleration.
The highest values recorded for traction force, power, and torque are 354.
02 N, and 84.
97 N.
m, respectively, at 195th Fig.
7 The variation of the forces during the electric motorcycle operation under varying driving cycle tests: .
NYCC.
WLTP Class 1.
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The maximum power reached 7009.
89 W at 226th From the 1368th seconds to the 1990th seconds, the dynamic performance drops to zero due to the time-off stage of the driving cycle test.
The variation in forces acting on the electric motorcycle is shown in Fig.
These forces include the acceleration force, rolling resistance force, aerodynamic drag resistance force, and climbing resistance force under different driving cycles.
It can be observed that the acceleration force is the dominant force compared to the rolling resistance, aerodynamic drag resistance, and climbing resistance forces, as it primarily drives the motorcycle forward.
The aerodynamic drag resistance force fluctuates depending largely on the motorcycleAos velocity.
Meanwhile, the climbing resistance force remains zero since the motorcycle operates in a city environment with no slope angles.
The rolling resistance force remains constant at 21.
because the electric motorcycle operates on a well-maintained road, as shown in Fig.
, corresponding to the NYCC and FTP-75 driving cycle tests.
However, when the electric motorcycle operates on urban roads between the 589th and 1022nd seconds in the WLTP Class 1, the road quality Consequently, the rolling resistance force increases 0 N, as depicted in Fig.
2 Effect of operating conditions on the electric motorcycle dynamic During electric motorcycle operation, its performance is influenced by various operating conditions.
In this section, the effects of motorcycle mass and aerodynamic area are analyzed to evaluate dynamic performance under driving cycle tests.
ensure significance, the NYCC driving cycle test was chosen to assess the impact of motorcycle mass on dynamic performance.
Additionally, the WLTP Class 1 driving cycle test was selected to evaluate the effect of aerodynamic area on the motorcycle's dynamic performance.
Fig.
8 illustrates the effect of electric motorcycle mass on dynamic performance under the NYCC driving cycle test, including traction force, acceleration force, power, and torque.
It can be observed that as the motorcycle mass increases, the traction force, acceleration force, and rolling resistance force also increase.
Specifically, the rolling resistance force reached 03 N, 21.
04 N, and 28.
06 N when the motorcycle mass increased by 130 kg, 195 kg, and 260 kg, respectively, as shown in Fig.
Similarly, the power and torque of the electric motorcycle increased with higher mass, as depicted in Figs.
and 8 .
In other words, increasing dynamic power is Fig.
8 Effect of electric motorcycle mass on dynamic performance under the NYCC driving cycle test Table 7 The rolling resistance coefficient under different types of roads No.
Type of road Pressed dirty .
uring rai.
Pressed dirty .
ith dr.
Snow Gravel Iced Good asphalt or concrete Wet sand Pebble with having potholes Muddy dirty ycyc (%) 0 Ae 15 5 Ae 3.
0 Ae 5.
0 Ae 2.
5 Ae 3.
0 Ae 1.
0 Ae 15 5 Ae 5.
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necessary to overcome resistance forces, thereby enhancing performance and enabling the motorcycle to move forward The effect of aerodynamic area on dynamic performance under the WLTP Class 1 test is demonstrated in Fig.
It can be observed that as the aerodynamic area increases, both the Fig.
9 Effect of aerodynamic area on dynamic performance under WLTP Class 1 Fig.
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traction force and aerodynamic resistance force also increase.
Notably, the aerodynamic resistance force shows a significant difference between 600th seconds and 1000th seconds when the aerodynamic area increases from 0.
6 mA to 1.
2 mA in increments of 0.
3 mA, achieving a maximum value of 144 N.
This phenomenon can be explained by the fact that the electric motorcycle operates in the high-speed phase during this period.
As stated in Eq.
, the aerodynamic resistance force is proportional to the square of the motorcycle's speed, leading to a more pronounced effect at higher speeds.
To emphasize the importance of surface road quality, which are represented the rolling resistance coefficient, this study analyzed the effect of the rolling resistance coefficient under various road conditions.
The rolling resistance coefficients for different road types are listed in Table 7 (Chen et al.
, 2.
Fig.
10 illustrates the dynamics of the electric motorcycle in the NYCC driving cycle across nine road conditions: good asphalt or concrete, snow, wet dirt .
uring rai.
, dry dirt, gravel, wet sand, pebbles with potholes, muddy dirt, and ice.
The results show that vehicle dynamics vary significantly depending on the rolling resistance coefficient and the driving cycle.
When the motorcycle operates on poor road surfaces, the required traction force, power, and torque increase substantially to adapt to the worst-case scenario.
Conversely, under good road conditions, the rolling resistance coefficient is low, resulting in a lower power demand.
In this context, the best performance occurs on good asphalt or concrete, while the most demanding case is observed on muddy dirt roads.
3 Analysis of the average energy consumption under various driving cycle tests Fig.
11 shows the average energy consumption of the electric motorcycle, including the average energy consumption of the propulsion system and auxiliary loads, under varying driving cycles of the NYCC.
WLTP Class 1, and FTP-75.
It can be seen that the average energy consumption gradually increased during the electric motorcycle duration operation in the driving cycle test.
Especially, the average energy consumption of the auxiliary is constant at 5.
47, 2.
19, and 2.
Wh kmOe1.
Because the average energy consumption was calculated under the length of the driving cycle, leading to the average energy consumption of the NYCC is higher than WLTP Class 1.
WLTP Class 1 is superior to the FTP-75.
The determination of the average energy consumption of the electric motorcycle under the driving cycle test aims to establish the conditions for designing the required battery pack.
Obviously, the design of the battery pack energy is higher than the average energy consumption of the propulsion system and auxiliary loads.
In other words, to sufficient energy supply for the propulsion system and auxiliary load, the average energy of the battery pack should be higher than the average energy 4 Discussion on the required powered battery under the driving cycle test The characteristics of the cell battery and battery pack under different configurations, including cylindrical and pouch designs, are listed in detail in Table 8.
It can be observed that the volume of both the cell battery and battery pack in Group 2 is larger than in Group 1.
Consequently, the dimensions of the battery pack box in Group 2 are greater than those in Group 1, though the difference is not significant.
Additionally, the maximum current in a series battery string, the maximum current in the battery pack, the continuous current in a string, the battery pack's continuous current, and the battery pack's continuous power are all higher in Group 2 compared to Group 1.
As a result, the required power for the battery pack in Group 2 is greater than that of Group 1.
This explains why the cost of the battery pack for Group 2 is higher than that of Group 1.
From the electric motorcycle dynamic performance analysis, the maximum power required was 3853.
91 W, 3289.
W, and 7009.
89 W for operation under the NYCC.
WLTP Class 1, and FTP-75 driving cycles, respectively.
This indicates that the maximum power of the battery pack in Group 1 is insufficient to supply the necessary energy for both the propulsion system and auxiliary loads.
In contrast, the Table 8
Determination of the cell battery and battery pack characteristics Group 1
Manufacturer LG Chem
Samsung
DNK
INR21700
DNK
Model
INR21700M50LT
Geometry type Cylindrical Cylindrical Cylindrical Eaec Vcv.
, .
x 106 L
Ecb
Ncs
Ebs
Msp
Cbp
Ncp
Vbp
Imcss
yaycoycaycaycy ycEycoycaycy yaycaycayc
ycEycaycyycaycy Costbp
Evlithum
SLPB
Pouch ISSN: 2252-4940/A 2025.
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Group 2
A123
AMP20
M1HD
Pouch
Toshiba
A123 39AH NCM
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Fig.
11 The variation of the average energy consumption during the electric motorcycle operation under varying driving cycle tests: .
NYCC.
WLTP Class 1.
FTP-75 maximum power of the battery pack in Group 2 exceeds the required power for the electric motorcycle by approximately 61, 3.
72, and 4.
71 times for the Evlithium.
A123, and Toshiba battery trademarks, respectively.
Therefore.
Group 2 is the more suitable choice for designing the required battery pack.
However, the best battery pack option will be revealed and analyzed in the next section.
Analysis of the proposal strategy to optimize the battery pack and validation of the model.
As a result, priority was given to batteries in Group 2, as their maximum power exceeds the electric motorcycleAos dynamic performance requirements.
In other words, the selected battery pack is capable of supplying sufficient electrical power for both the propulsion system and auxiliary loads during operation.
Moreover, both technological effectiveness and economic feasibility must be considered.
Based on the proposed MILPbased optimization strategy, the battery pack with the Toshiba trademark emerges as the best choice.
This is because it balances both technological performance and costeffectiveness, making it an affordable and practical option.
this study, the detailed parameter settings, convergence criteria for this solver configuration, and the brief justification for the choice of constraints are listed in Table 9, which is presented in detail in Section 2.
After optimizing the battery pack for the electric motorcycle under various driving cycle tests, the proposed strategy identified the Toshiba battery pack as the best choice, balancing both technological and economic factors through the MILP The validation of the battery pack energy under driving cycle tests is therefore essential to support and clarify this study.
The validation of battery pack energy at the best choice of the trademark battery is carried out based on equations .
, which are described in detail in Section In addition, according to the design constraints in Eq.
the battery pack energy over the driving cycle distance must exceed the average energy consumption, and this was validated against the minimum required pack energy.
Furthermore, the safety coefficient in Eq.
was considered to ensure that the designed pack could reliably supply energy to the electric motorcycle under worst-case conditions.
Therefore, the model is designed and considered under worst-case conditions.
This aims to make sure that the electric motorcycle can operate in all realistic conditions.
Fig.
12 illustrates the average energy consumption, total battery pack energy, and remaining battery pack energy during electric motorcycle operation across different driving cycles.
The results show that average energy consumption gradually increased, while the battery pack energy also increased and consistently remained above the consumption level to meet demand all the driving cycle test.
Meanwhile, the remaining battery pack energy decreased due to auxiliary load power A slight discrepancy between battery pack energy and average energy consumption was observed in the NYCC and WLTP Class 1 cycles, as shown in Fig.
In contrast.
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Table 9 Hyperparameter setting of the MILP in this study Parameter Description Function AuIntlprogAy function was used to solve the problem.
The solve the minimization problem in this Maxtime This is the runtime limit for solving the problem in this study.
TolGapRel TolGapRel stands for Relative Optimality Tolerance (Relative MIP Ga.
It defines the stopping criterion based on how close the current solution is to the theoretical optimum .
onvergence Heuristics It is algorithms that quickly find good feasible solutions to guide the solver and speed up the CutGeneration This process adds linear constraints, or cuts, to eliminate infeasible regions without removing feasible integer solutions, tightening the relaxation and accelerating convergence to the optimal solution.
yaycayceycaycn During the design of the required powered battery for an electric motorcycle, the average energy consumption of the electric motorcycle is considered to ensure sufficient energy for the vehicle to complete the travel distance.
yaycoycnycu The parameter is the minimum capacity of the battery pack in an electric motorcycle.
The minimum capacity constraint ensures that the selected battery cells can provide enough energy storage for the electric motorcycle to operate over its required driving range.
ycOycoycaycu This is the maximum battery pack volume in an electric motorcycle because electric motorcycles have very limited space for battery placement, ensuring the selected cells are compact enough to physically fit.
ycoycoycaycu The parameter is the maximum mass of the battery pack.
Limits total battery mass so the pack doesnAot make the motorcycle too heavy.
yaycuycycycoycnycu The cost of the battery pack should be as low as possible while remaining affordable, taking economic factors into consideration.
during the FTP-75 cycle, the two values were much closer due to the longer cycle distance and higher maximum power requirement, which were used to determine the battery pack specifications in the worst case.
Overall, the model demonstrates high accuracy and reliability.
Value Intlprog Unit Ae 10Oe6 Ae Ae Ae Conclusions During the design and calculation of the required power battery pack for the electric motorcycle under varying driving cycle tests, the optimization of both technological and economic effectiveness was considered.
Several significant conclusions Fig.
12 Validation of battery pack energy under driving cycle test .
NYCC.
WLTP Class 1.
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were drawn in this study.
Indeed, the effect of operating conditions on the electric motorcycle's dynamic performance was analyzed under various driving cycle tests, including NYCC.
WLTP Class 1, and FTP-75.
Additionally, estimation of the average energy consumption from various driving cycle tests to aid in the design of the required power battery pack.
Furthermore, development of a mathematical model to analyze the characteristics of individual battery cells and the overall battery pack.
The proposed strategy for optimizing technological and economic effectiveness in this study utilizes the MILP approach, considering the total required power battery pack, volume, mass, and cost.
As a result, the Toshiba battery trademark was identified as the optimal choice for the electric motorcycle's battery pack.
After designing and optimizing the required powered battery pack for the electric motorcycle, the model is validated and ensure that the battery pack energy exceeds the average energy consumption under varying driving cycle tests.
Therefore, the model achieves high In addition, the model can be applied to other electric motorcycles and battery types, provided their specifications are Though the electric motorcycle dynamics, required battery pack, and proposal strategy to optimize the battery pack considering the technology and economic factors under varying driving cycle tests were performed in this study.
However, several future works are provided.
Indeed, the control strategy is being considered to optimize the propulsion system of the electric motorcycle.
Design and validate control algorithms .
, model predictive control, optimal torque/speed schedulin.
to improve energy efficiency and transient performance across driving cycles.
Furthermore, the optimization of the battery pack operation and management needs to be conducted by an intelligent control algorithm.
Implement AI-based optimization-based management system (BMS) strategies .
einforcement learning, model predictive control, genetic algorithm.
to optimize charge/discharge scheduling, extend usable energy, and reduce degradation while respecting safety limits.
yaycyceycaycy yaycaycycu
FTP-75
ycOycoycaycu
WLTP
yuUycaycnyc OIyc yuCycy yuCycaycaycyc Aerodynamic resistance force (N) Acceleration force (N) Climbing resistance force (N) Rolling resistance force (N) Gravitational acceleration .
ci = 9.
81 yco yc Oe2 ).
Height of the cell battery .
Maximum current in the battery pack (A) Maximum current in series battery string (A) Battery pack's continuous current (A) Continuous current in a string (A) Expected maximum volume limit .
Mass of the cell battery .
Mass of the battery pack .
Weight of cell battery .
Number of parallel-connected series battery strings Electric motorcycle mass .
Mixed-Integer Linear Programming Total number of cells in the battery pack Number of cells in the series battery string New York City Cycle State of charge Power of an electric motorcycle (W) The power-to-mass ratio Proportional-Integral-Derivative Maximum power of the battery pack (W) Battery pack's continuous power (W) Auxiliary consumption power (W) Radius of the wheel .
Thickness of the cell battery .
Operating temperature at charging .
C) Operating temperature at discharging .
C) Duration of the time .
Width of the cell battery .
Potential of the cell battery (V) Battery pack voltage (V) Binary decision variable of the ith battery pack Speed of the electric motorcycle .
sOe.
Battery pack volume .
Volume of the cell battery for the cylindrical configuration or pouch configuration .
Expected maximum volume limit (L) Worldwide Harmonized Light Vehicle Test Procedure Air density .
uUycaycnyc = 1.
25 ycoyci ycoOe3 ) Duration of the driving cycle test .
Efficiency of the transmission Coulombic efficiency Energy and economy Acknowledgments Abbreviations and nomenclatures yaycn
yayce BMS
Costcb
Ccb
Cmax
yaycycaycyce_ycoycaycu yaycycaycyce yaycuycycycaycyycn
yaycuycycycaycy Crate
Dcb
yaycaycy yayc
yaycaycy yaycI
Hcb
yaycoycaycaycy yaycoycaycyc
ycoycaycy Mcb
ycAycycy ycAyaycA
MILP
ycAycaycy ycAycayc
NYCC
SOC
ycEyaycA
PMR
PID
ycEycoycaycy ycEycaycyycaycy ycEycaycycu ycIyc Tcb yc Wcb Ucb ycOycaycy ycuycn ycO ycOycaycy ycOycayc.
Artificial Intelligence Input parameters of the i-th battery pack Front area of the electric motorcycle .
Battery management system Cost of the cell battery ($) Aerodynamic drag coefficient .
aycc = 0.
2 Oe 1.
Capacity of the cell battery (A.
Rolling coefficient Maximum capacity of the cell battery (A.
Maximum the ratio of the charge-discharge .
Oe.
Ratio of charge-discharge .
Oe.
Total cost of battery pack in the ith battery pack ($) Expected minimum cost limit ($) Minimum capacity of the battery pack (A.
Cost of the battery pack ( $) Charge-discharge ratio of a cell battery Length of the driving cycle test .
Diameter of the cell battery .
Cell battery energy (W.
Battery pack energy (W.
Volume energy density (Wh mOe.
Weight energy density (Wh kgOe.
Average energy of the propulsion system (Wh kmOe.
Total energy consumption in the driving cycle test (W.
Average energy consumption of the auxiliary loads (Wh kmOe.
Remaining battery pack energy (Wh km Oe.
Auxiliary power consumption (Wh kmOe.
Series battery string energy (W.
Federal Test Procedure 75 Traction force (N) This study was conducted with financial support from CCU.
LHU, and HCMUTE.
The author would like to express sincere gratitude for this support.
Author Contributions: T.
: Conceptualisation.
Methodology.
Validation.
Investigation.
Formal analysis.
Data curation.
Software.
Writing - original draft.
Investigation.
Resources.
Software.
Visualisation.
Writing - review & editing.
Conceptualisation.
Writing - review & editing.
Funding acquisition.
Project administration.
Supervision.
All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Supplementary material: Can be found at the following link:
https://ijred.
id/index.
php/ijred/article/downloadSuppFi le/61561/15931 Data availability: Data will be made available on request.
References