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• W4285224403 abstract "Electric vehicles are currently being developed where the battery is the main driver. SOC (State of Charge) is one of the determinants of the success of electric vehicles. It is the current battery capacity expressed in per cent and provides information about the battery’s state. One method to measure SOC is to use Coulomb Counting; the integration of current against time. Kalman Filter is also a very suitable method for estimating SOC to obtain an effective and efficient BMS. SOC estimation requires accurate battery modelling to create a reliable BMS enabling the battery to be able to operate optimally. The important thing in electric vehicles is to optimize BMS performance with an accurate SOC estimation algorithm. This research is oriented to the estimation of the state of charge on the BMS to optimize the BMS performance with an accurate SOC estimation algorithm. In this research, 2 battery models were proposed, i.e., the simple and Thevenin battery models. SOC estimation based on those battery models was done with coulomb counting, open-circuit voltage, and Kalman Filter. The results showed that the estimated final SOC value with Model 1 was 20.04%, while the estimated final SOC value with Model 2 was 19.42%. The direct impact or consequence of the findings in this study is that the coulomb counting method is easier to use in calculating battery capacity." @default.
• W4285224403 created "2022-07-14" @default.
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• W4285224403 date "2022-01-01" @default.
• W4285224403 modified "2023-09-25" @default.
• W4285224403 title "Optimization of Battery Management System with SOC Estimation by Comparing Two Methods" @default.
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• W4285224403 doi "https://doi.org/10.1007/978-981-19-1804-9_34" @default.
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