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Battery Temperature Rate of Change Estimation by Using Machine Learning
Ndaomanu E.H.a, Nashirul Haq I.a,b, Leksono E.a, Yuliarto B.a
a Institut Teknologi Bandung, Department of Engineering Physics, Bandung, Indonesia
b National Center for Sustainable Transportation Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.In work, 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 analyzed 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. In the end, the values obtained would be processed using machine learning with SVM and Random Forest methods.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Battery temperature,Electric variables,electro thermal,Internal resistance,LiFePO4,Random forest methods,Rate of change,Temperature variables[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]electro thermal,machine learning,prismatic LiFePO4 battery,random forest,SVR[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This research was partially funded by Lembaga Pengelola Dana Pendidikan (LPDP), the Indonesian Ministry of Research, Technology and Higher Education through National Centre for Sustainable Transportation Technology (NCSTT) under USAID-SHERA Program, and World Class University (WCU) Program Institut Teknologi Bandung.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT48285.2019.8994018[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]