TY - GEN
T1 - Battery Monitoring System to Obtain State of Charge and State of Health on Electric Vehicles
AU - Mujiyanti, Safira Firdaus
AU - Lestari, Ayu Nur
AU - Tjandra, Nabiilah Aziizah
AU - Utami, Riski Mulyaning
AU - Kaltsum, Zalfaa Shaafia
AU - Kurniawan, Izef Aulia
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Battery is the main component used in electrical vehicle. However, various issues often arise, such as sudden vehicle failure due to the absence of available battery monitoring devices. In this research a battery monitoring system is made, the Li-Po 3300mAh battery is monitored to determine its State of Charge (SOC) and State of Health (SOH). To achieve this, voltage, current, and temperature sensors are employed. In the research of the battery monitoring system for charge status and health status, remote monitoring is also implemented using the Internet of Things with a real-time Android interface via android mobile phones. Each sensor is validated to ensure accuracy. The voltage sensor has an error correction value of 2.74% and an accuracy of 97.02%. The current sensor has an error correction value of 0.56% and an accuracy of 99.44%. The DS18B20 temperature sensor has an error correction value of 4.74% and an accuracy of 95.26%, indicating its suitability for the system.
AB - Battery is the main component used in electrical vehicle. However, various issues often arise, such as sudden vehicle failure due to the absence of available battery monitoring devices. In this research a battery monitoring system is made, the Li-Po 3300mAh battery is monitored to determine its State of Charge (SOC) and State of Health (SOH). To achieve this, voltage, current, and temperature sensors are employed. In the research of the battery monitoring system for charge status and health status, remote monitoring is also implemented using the Internet of Things with a real-time Android interface via android mobile phones. Each sensor is validated to ensure accuracy. The voltage sensor has an error correction value of 2.74% and an accuracy of 97.02%. The current sensor has an error correction value of 0.56% and an accuracy of 99.44%. The DS18B20 temperature sensor has an error correction value of 4.74% and an accuracy of 95.26%, indicating its suitability for the system.
KW - IoT
KW - battery monitoring system
KW - electric vehicle
UR - http://www.scopus.com/inward/record.url?scp=85182732081&partnerID=8YFLogxK
U2 - 10.1109/ICT-PEP60152.2023.10351183
DO - 10.1109/ICT-PEP60152.2023.10351183
M3 - Conference contribution
AN - SCOPUS:85182732081
T3 - ICT-PEP 2023 - 2023 International Conference on Technology and Policy in Energy and Electric Power: Decarbonizing the Power Sector: Opportunities and Challenges for Renewable Energy Integration, Proceedings
SP - 88
EP - 92
BT - ICT-PEP 2023 - 2023 International Conference on Technology and Policy in Energy and Electric Power
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2023
Y2 - 2 October 2023 through 3 October 2023
ER -