Indonesia Conference Directory

Corresponding Author
Irsyad Nashirul Haq

Institutions
(1)Graduate Student at Engineering Physics
Institut Teknologi Bandung
Bandung, Indonesia

(2,3,4) Department of Engineering Physics
Institut Teknologi Bandung
Bandung, Indonesia

(2)National Center for Sustainable Transportation Technology, Bandung, Indonesia

(1)christio.mege[at]gmail.com,
(2)irsyad[at]tf.itb.ac.id
(3)edi[at]tf.itb.ac.id,
(4)nugroho[at]tf.itb.ac.id,

Abstract
In this research the effect of temperature rising on battery performances such as depth of discharge and electricity generation efficiency had been conducted. After that temperature data acquired from data acquisition process is used as training data and test data to predict temperature using Holt-s Double Exponential Smoothing. The results show that at 0.7C, cells temperatures inside module reached 35.40C, rising about 4.90C. The temperature rising is greater than single cell that rose 30C to 29.70C. Then at 1.4C the module temperature reached 38.60C rising about 8.30C. Single cell temperature at 1.4C reached 35.70C, rising 9.40C. At 2.1C, single cell reached 45.10C with temperature increasing of 18.50C. Module temperature at 2.1C reached 480C with 190C increasing. Efficiency of electricity generation of single cell at 0.7C is 92.58%. The efficiency reduced to 84.48% at 1.4C rate. Then at 2.1C rate, single cell only capable of generated energy about 23.3Wh with 76.82% efficiency. Module at 0.7C has electricity generation efficiency of 91.58%. At 1.4C, the efficiency reduced to 83.38%. At 2.1C rate, the efficiency was getting smaller to 72.9%. Predictions conducted show that Holt-s Double Exponential Smoothing can predict the temperature rising in single cell. In module temperature predictions, training data was taken from one cell only to predict the rest of the cells. At 0.7C, Holt methods can predict six out of eight cells well. Five out of eight cells could also be predicted well at 1.4C. However at 2.1C, just four cells could be predicted well. The predictions accuracy of Holt-s Double Exponential Smoothing decreased when the temperature uniformity in module decreased as the C-rate increased

Keywords
Battery Performances, Temperature Predictions, Holt-s Double Exponential Smoothing, Discharging Process

Topic
Cooling System for Electric Vehicle

Link: https://ifory.id/abstract/demMZ3qNgKWy

Corresponding Author
Irsyad Nashirul Haq

Institutions
(1)Graduate Student at Engineering Physics
Institut Teknologi Bandung
Bandung, Indonesia

(2,3,4) Department of Engineering Physics
Institut Teknologi Bandung
Bandung, Indonesia

(2)National Center for Sustainable Transportation Technology, Bandung, Indonesia

(1)engly.nd[at]gmail.com,
(2)irsyad[at]tf.itb.ac.id
(3)edi[at]tf.itb.ac.id,
(4)brian[at]tf.itb.ac.id,

Abstract
In this research, the process of monitoring of the electric variable on a 14 Ah prismatic LiFePO4 battery has been carried out. The variables monitored include electric current, voltage, energy and internal resistance to be analysed for its effect on the temperature variable on the battery. An analysis of the relationship between the increase of temperature and the efficiency of energy has also been done. This process succeeded in getting the electrothermal value or heat arising from the electric variable in the battery. Electro thermal in the battery cell obtained the highest value 19.5 KJ and in the module obtained a value of 25.04 KJ, while the rate of electrothermal addition varies from 2.5 J/s to 22.5 J/s in a single cell and 20 J/s to 180 J/s on the battery module. Monitoring has also been implemented in the process of releasing battery energy both cells and modules. Monitoring of variable voltage, current, battery capacity, time and temperature has been done thus found that T of the battery was 20 0C when emptied with a discharge rate of 2.1 C and the temperature change of at least 3 0C at 0.7 C. While at 1.4 C, the temperature rises around 12 0C. In the battery module, the temperature rises around 6 0C when the battery module emptied at a rate of discharge 0.7 C, 15 0C at 1.4 C and around 20 0C at 2.1C. Machine learning can be used to estimate the increase of the temperature in a battery based on changes in voltage and electric current. This is done in order to determine the maximum electric current that can be supplied to the battery thus the thermal conditions of the battery can be maintained. The accuracy of estimating temperature value by using SVR on a single battery cell was 91.2% with RMSE was 1.107 0C while for the modules obtained 82.37% with RMSE is 1.18 0C. The accuracy value used RF for single cells was 97.28% with RMSE was 0 .625 0C and 98% with RMSE was 0.3 0C for battery modules.

Keywords
Prismatic LiFePO4 Battery, Support Vector Machine, Electrothermal, Random Forest

Topic
Cooling System for Electric Vehicle

Link: https://ifory.id/abstract/dpyUcRXZY3E7

Corresponding Author
Irsyad Nashirul Haq

Institutions
(1)Graduate Students, Department of Engineering Physics, Institut Teknologi Bandung
(2,3,4) Department of Engineering Physics,
Institut Teknologi Bandung
(2) National Center for Sustainable Transportation Technology, Bandung, Indonesia

(1)fadhlin_nugraha[at]yahoo.com,
(2)irsyad[at]tf.itb.ac.id
(3)edi[at]tf.itb.ac.id,
(4)nugroho[at]tf.itb.ac.id

Abstract
In this study, an investigation of thermal characteristics was carried out at two stages namely, the experimental stage and the simulation and modeling stage. In the experimental stage, the battery consists of 1 cell with a capacity of 14Ah. At the experimental stage, the battery under investigation works at the discharge currents C1, C2, and C4, with natural convection studies in insulation and non-insulation systems. The assumptions used in this study are the battery used have experienced more than 10 cycles, the heat radiation from the battery is ignored, the parameters and thermal constants are considered constant. The ambient temperature range for operation is at 24oC – 28,5oC. Experimental results show that the battery system under insulation conditions has more stable thermal characteristics compared to non-insulation systems. As well as the results of the simulation stage 1 battery cell under conditions of insulation and non-insulation. In addition, estiomation were also made for the 10 battery insulation system. The temperature rise characteristic shows an exponential graph on all simulations performed. By evaluating the measurement values in the experimental and simulation stages, the results of the non-insulation conditions show an error for the C1 discharging current of 1,77%, C2 of 1,97%, and C4 of 0,38%. The results of insulation conditions show an error for the C1 emptying current of 1,10%, C2 of 0,53%, and C4 of 0,05%.

Keywords
Thermal Characteristics Estimation, Battery Module, Finite Element Method, Temperature Distribution

Topic
Cooling System for Electric Vehicle

Link: https://ifory.id/abstract/8tyQhfd7MEac

Corresponding Author
Irsyad Nashirul Haq

Institutions
(1,2)Graduate Student at Department of Engineering Physics. Institut Teknologi Bandung
(1)ariwibawak[at]gmail.com (2)handre.kerthautama[at]gmail.com

(3,4,5) Department of Engineering Physics. Institut Teknologi Bandung
(3)edi[at]tf.itb.ac.id,
(4)irsyad[at]tf.itb.ac.id
(5)supri[at]tf.itb.ac.id,

(4) National Center for Sustainable Transportation Technology. Bandung, Indonesia

Abstract
The lithium-ion batteries (LIB) frequently being used in the electric vehicle due to high energy density, high power density, and fairly long-life cycles. In this paper, LIB applied to electric trike which becoming very popular recently. Performance and life cycles of LIB sensitive to temperature, so it is necessary to maintain the temperature condition in the range of -20oC to 40oC. Temperature significantly affects the performance of the LIB battery and also limits the applications of the LIB battery. To be able to maintain the temperature of LIB at optimum temperature, the temperature distribution inside the LIB pack should be investigated first. This paper study the temperature distribution of the LIB pack used in the electric trike. Pack geometry, as well as the surrounding condition, first build in Solidworks. The geometry model then imported to Comsol Multiphysics to study phenomena of mass transport and heat transfer within the LIB pack and surrounding area. The main objective of this study is to lay the foundation to design the battery thermal management system (BTMS) in the LIB pack for the electric trike.

Keywords
lithium-ion batteries, thermal characteristic, temperature distribution, electric trike

Topic
Cooling System for Electric Vehicle

Link: https://ifory.id/abstract/RVK97qjQYprN