Method for joint estimation of power battery system charge status and health status

Abstract

The invention relates to a method for joint estimation of power battery system charge status and health status. By means of a multiple time scale filtering algorithm, a macro time scale is used to obtain an estimate value of a power battery system parameter, and a micro time scale is used to estimate the system status, so that the health status and charge status of a power battery are assessed. The method based on multiple time scales for joint estimation of power battery parameter and status is formed and can achieve accurate joint estimation of available capacity and charge status of the power battery in an uncertain application environment. Not only is the estimation result more stable and reliable, but the calculation cost of the system is reduced as well.

Description

[0001] Technical field: The present invention relates to the technical field of power battery management, especially the estimation method of vehicle power battery system parameter identification and state estimation. Background technique: [0002] At present, most of the model-based SOC algorithms need to use the Kalman filter series algorithm to estimate the SOC of the battery. Although the theory shows that the Kalman filter is an optimal linear state estimation algorithm, it is only suitable for accurate models and statistical characteristics of input noise. Given the known situation, this obviously cannot meet the actual demand. [0003] Based on this, the H used in the present invention ∞ Filtering is an algorithm designed for robustness. The main method of the algorithm can be found in "Optimal State Estimation - Kalman, H ∞ and Nonlinear Filtering", National Defense Industry Press. Unlike the Kalman filter, the algorithm can still accurately complete the state estim...

AI Technical Summary

Problems solved by technology

[0004] The existing online estimation method of power battery SOC, due to the uncertainty of the maximum available capacity (hereinafter referred to as capacity), makes the estimation result of power battery SOC unreliable

And the existing relatively stable SOC-OCV curve (that is, the open circuit voltage curve) is used as the correction curve of the SOC estimation algorithm. When the battery temperature and aging degree are different, the curve will also change significantly.

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