Power battery parameter identification method suitable for sparse data

Abstract

The present invention provides a power battery parameter identification method suitable for sparse data. Based on a set membership identification algorithm, when it is difficult to determine statistical distribution features of actual system noise, it is not needed to perform assumption of the statistical distribution features of the system noise, the boundary of the system noise is only needed, asensor is configured to measure introduced errors, and round-off errors and model errors of the machine number can be taken as a form of boundary errors. The algorithm has a capacity of identification of redundant data and can ensure identification precision when the battery management system sample interval is increased, is specially suitable for a condition of sparse data, can observably improve the reliability of the power battery management system, etc.

Description

Technical field [0001] The invention relates to the field of power battery systems, and in particular to a parameter identification technology for vehicle-mounted power batteries. Background technique [0002] Power battery is an important part of new energy vehicles. The estimation of the remaining battery capacity and the assessment of battery aging can be realized through the battery management system, so as to keep the battery's SOC and SOH within the normal working level and prevent the damage caused by the battery. Overcharge, overdischarge, and overcurrent cause permanent damage to the battery, thereby increasing the service life of the battery and reducing the use cost of electric vehicles. Accurate identification and estimation of power battery parameters is the basis for ensuring accurate online estimation of internal conditions. [0003] On-line parameter identification can usually directly use information such as current and terminal voltage to obtain the parameter valu...

AI Technical Summary

Problems solved by technology

[0003] Online parameter identification can usually directly use current, terminal voltage and other information to obtain the corresponding parameter value under the SOC in real time, which is suitable for complex and changeable real vehicle systems. In this case, a large error will occur, and even divergence will occur, and with the increase of the sampling interval of the vehicle sensor, the accuracy of the identification result will decrease.

The commonly used recursive least squares identification method based on the forgetting factor, its random error must obey the normal distribution of zero mean and zero covariance, which is obviously difficult to meet in practical applications

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