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State of charge (SoC) estimation of LiFePO4 battery module using support vector regression
Haq I.N.a, Saputra R.H.a, Edison F.a, Kurniadi D.a, Leksono E.a, Yuliarto B.a
a Engineering Physics, Institut Teknologi Bandung, 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]© 2015 IEEE.In this research work, we demonstrate state-of-charge (SoC) estimation using support vector regression (SVR) approach for a high capacity Lithium Ferro Phosphate (LiFePO4) battery module. The proposed SoC estimator in this work is extracted from open circuit voltage (OCV)-SoC lookup table which is obtained from the battery module discharging and charging testing cycles, using voltage and current as independent variables. The SoC estimation based on SVR gives a perfectly linear curve fitting with its reference within the range of 37.5% to 90% while the rest hysteresis due to the discharging and charging process is compensated using OCV-SoC curve as the training data set. The SVR estimates the battery module SoC with RMSE of 2.3% over the whole test and the maximum positive and negative error is 4%, which means that it shows good accuracy.[/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 modules,Charging process,Coulomb Counting,Independent variables,Lithium ferro phosphates,State of charge,Support vector regression (SVR),Training data sets[/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]Coulomb Counting,Lithium Ferro Phosphate Battery Module,Open Circuit Voltage,State of Charge,Support Vector Regression[/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][/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/ICEVTIMECE.2015.7496640[/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]