A BMS engineer uses a Kalman filter for real-time SOC estimation. The filter has two key parameters: process noise covariance (Q) and measurement noise covariance (R). If the current sensor is highly accurate but the battery model has significant uncertainty, how should Q and R be tuned?
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A
High Q, low R โ trust the sensor measurement more than the model prediction
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B
Low Q, high R โ trust the model prediction more than the sensor measurement
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C
Both set equal to minimize computation time
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D
High Q, high R โ reject both sources equally