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State of Charge (SoC) and State of Health (SoH) Estimation of Lithium-Ion Battery Using Dual Extended Kalman Filter Based on Polynomial Battery Model
Azis N.A.a, Joelianto E.a, Widyotriatmo A.a
a Instrumentation and Control Master Program, Instrumentation and Control Research Group, Faculty of Industrial Technology, 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]© 2019 IEEE.The source of electricity storage that is widely used in electric vehicles is batteries. The important parameters for batteries are State of Charge (SOC) and State of Health (SOH). These parameters are important to help protecting the battery, increase battery life and for the safety of the user’s operation. Since SOC SOH from the battery cannot be measured directly, the SOC SOH parameters need to be estimated. In this paper, the battery is modeled using the polynomial model. The parameter constant is then predicted using Generalized Reduced Gradient (GRG) optimization method. In order to estimate the SOC and SOH simultaneously, Dual Extended Kalman Filter (DEKF) method will be carried out. The first EKF is used to estimate the SOC while the second EKF estimates the internal resistance and the actual capacity of the battery. Futhermore, the results from the second EKF is used to determine the SOH by means of the capacity fade method and resistancy method. The use of DEKF provides more accurate results because it can compensate noise measurements and models and without requiring initial SOC value. It also gives less computational effort and real time results. Lastly, the results of DEKF method are compared with the EKF method to see the performance of the estimator. Simulation results show that DEKF method successfully estimates the SOC, internal resistance and capacity of the battery. DEKF also gives better result in estimating the SOC of battery. The SOH of battery can also be known from the results of battery parameters 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]Computational effort,Dual extended Kalman filter,Electricity storages,Generalized reduced gradient,Parameters estimation,Polynomial modeling,State of charge,State of health[/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]Dual Extended Kalman Filter,Extended Kalman Filter,Lithium-ion battery,State of Charge,State of Health[/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 paper is supported by USAID through Sustainable Higher Education Research Alliances (SHERA) Program – Centre for Collaborative (CCR) National Center for Sustainable Transportation Technology (NCSTT).[/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/ICA.2019.8916734[/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]