<![CDATA[This work aim to evaluate effectiveness of Kalman Filter in increase State of Charge (SoC) estimation on the system management battery For Street Lighting based on ESP32 microcontroller. SoC is a crucial parameter in determine level filling battery on time certain . In study Here , we use random data from the INA219 voltage and current sensor to simulate various condition operational . We do analysis to use of Kalman Filter on this data use evaluate improvement accuracy estimate SoC. Studies literature show that the Kalman Filter has succeed applied in various application For repair estimate system based on measurement data that is not perfect . Through modeling and simulation , we compare SoC estimation obtained from Kalman Filter with estimate direct from sensor data. Experimental results show that the Kalman Filter is significant reduce variation and increase accuracy battery SoC estimation , with average error not enough from 1.5%. Findings This support that use of Kalman Filter in system management PJU batteries have potential big For increased reliability operational and efficiency use energy . Research This give donation important in development monitoring and management technology more battery sophisticated and accurate For applications in the field.]]>
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