2021Advances in science and technologyOpen access

Prediction of State of Charge (SOC) of Battery Electric Vehicle

Illa Rizianiza, Dian Mart Shoodiqin

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Abstract

Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.

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What this paper is about

Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.

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Available abstract

Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.

Key concepts: Battery (electricity), State of charge, Voltage, Capacitance, Mean squared error, Automotive engineering, Charge (physics), Fuzzy logic

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