Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158839
Title: Artificial intelligence approach for battery modelling and simulation
Authors: Haziq Hulaif
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Haziq Hulaif (2022). Artificial intelligence approach for battery modelling and simulation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158839
Abstract: Lithium-Ion Batteries (LIBs) are widely used as energy storage for various applications. Battery temperatures increase due to high current, insufficient cooling, and high ambient temperature. Hence, there should be proper thermal management of batteries so that there would not be any risks such as fires or explosions and that health is maintained. A battery thermal management system requires modeling and analyzing the relationship between battery temperature and variables that affect the temperature. In this report, a battery thermal model was created to investigate the thermal behavior of the Lithium-Ion batteries and the effect of the Heating, Ventilation, and Air Conditioning (HVAC) system. Further, a linear regression-based machine learning model was built to estimate battery temperatures. The samples obtained from the battery thermal model were used to train the machine learning model. A hypothetical test system consisting of a battery kept inside a room with a connected HVAC system was used to validate models. The result from the model helps to determine at which cooling power and airflow rate are produced by the HVAC for which battery temperature cools down faster and efficiently.
URI: https://hdl.handle.net/10356/158839
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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