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[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]© 2017 IEEE.In this work, we developed the state of energy (SOE) estimation method for battery module while taking into account the unbalance voltage between cells and energy efficiency by using energy counting and support vector machine (SVM). The energy counting is performed by accumulating power coming in and out of the battery module when discharging or charging process occurs. The power is accumulated over time and compared to its nominal energy to obtain the SOE of the battery module. The experiment was conducted in two stages by utilizing Lithium Nickel Cobalt Aluminium Oxide (LiNiCoAlO2) batteries with the nominal voltage 3.6 V and the nominal capacity 3350 mAh, a DC power supply model, a programmable DC electronic load, and sensors for battery monitoring and protection system. In the first experiment, the energy counting is performed on a single cell to obtain individual battery cell characteristics based on C5, C10, and C20 discharging methods. The analytical results show that the relationship energy efficiency of charging and discharging process to discharge rate was 0.8974 × C(exp(-0.033)). The dataset from one battery cell characteristics is arranged into a lookup table that expresses the relationship between voltage, current and SOE of the battery. The lookup table was used as training datasets for SVM to generate model for estimating battery cell and module SOE. The second experiment was conducted to estimate the SOE of battery module consisting of 10 battery cells in series connection with the same discharging method as the single cell experiment. The SOE estimation results by using radial basis function based support vector regression model, with cost function value of 30, and an epsilon value of 0.04, and with kernel parameter value of 1, give the average of root-mean-squared-error value was below 5% which show an acceptable result. Because of cell unbalance will occur continuously during the battery charging and discharging process, the lowest SOE value from battery cells is selected as the reference value for the next cycle’s process estimation.[/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]Analytical results,Battery charging and discharging,Discharging process,Lithium ions,Radial basis functions,Root mean squared errors,state of energy,Support vector regression models[/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]cells unbalance,energy efficiency,lithium-ion,state of energy,support vector machine[/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]ACKNOWLEDGMENT This research is partially supported by the Research Grant of Institut Teknologi Bandung, Ministry of Research, Technology, and Higher Education, Indonesia, and Research Grant from the USAID under the SHERA program.[/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/ICEVT.2017.8323542[/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]