Self-adaptive kalman filter estimation algorithm for power battery

Abstract

The invention discloses a self-adaptive kalman filter estimation algorithm for state of charger (SOC) for a power battery. According to the algorithm, the weighted sum of residual errors between output values measured each time and output values obtained by estimation and residual errors of the output values obtained by sigma points under various states can be obtained, then the weighted sum is regarded as an innovation to estimate the current noise covariance, the current noise covariance can be reduced along with the time, and the real-time feedback can be performed. Proved by theoretic and actual data validation, the algorithm can greatly improve the estimated accuracy of SOC.

Description

technical field [0001] The invention relates to an adaptive Kalman filter estimation algorithm for a power battery, in particular to an adaptive noise covariance, aiming at the error caused by the constant noise covariance in the square root infinite Kalman filter algorithm that affects the estimation accuracy An estimation algorithm of the remaining battery charge (SOC) from the adaptive Kalman filter is proposed. Background technique [0002] Estimating the state of charge (SOC) of the power battery is of great significance to the effective use of the battery and is also one of the key technologies of the battery management system. Since SOC is not directly measurable and is a nonlinear quantity, it is also affected by many other factors, so it is very difficult to estimate, and it is difficult to improve the estimation accuracy. At present, the commonly used method in engineering is the current integration method (ampere-hour method), but this method is sensitive to the ...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is: the constant noise covariance in the square root infinite Kalman filter algorithm brings a certain estimation error

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