Multi-innovation hybrid Kalman filtering and H infinity filtering algorithm

Abstract

The invention discloses a multi-innovation hybrid Kalman filtering and H infinity filtering algorithm, which comprises the following steps of: 1) establishing a first-order RC circuit model of a lithium battery, writing a state space expression for describing a lithium battery system according to the model, taking current and voltage as input, and performing parameter identification by using a recursive least square method; 2) respectively establishing a multi-innovation extended Kalman filter and a multi-innovation filter, defining a hybrid filtering performance evaluation index to achieve better weight distribution, and establishing a multi-innovation-based hybrid Kalman / H infinity filter; and 3) taking different values of parameters in the weight expression to verify the advantages of high convergence precision and good robustness of the multi-innovation hybrid Kalman / H-infinity filtering algorithm. By establishing the hybrid Kalman / H infinity filter based on multiple innovations, the problem that the estimation error is increased due to the fact that the current innovation and historical information are not fully utilized in an existing SOC estimation method is solved, and theSOC estimation precision and the robustness of the filter are improved by reasonably setting the weight.

Description

technical field [0001] The invention relates to a lithium battery SOC estimation method, in particular to a multi-innovation mixed Kalman filter and H infinite filter algorithm. Background technique [0002] At present, energy storage batteries are developing in the direction of lithium-ion batteries with lithium iron phosphate as the positive electrode material. The most important reason is that lithium batteries have high energy density and long cycle life, so they have attracted the attention of experts and researchers in this field. . Lithium battery SOC, as a key estimator of the battery management system, plays an important role in battery life, work efficiency and protection. Therefore, accurate estimation of the SOC of lithium batteries is of great significance for the efficient use of lithium batteries and the entire energy management system. [0003] In recent years, commonly used lithium battery SOC state estimation methods are mainly divided into two categories...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the defects of the prior art, provide a multi-innovation mixed Kalman filter and H infinite filter algorithm, and solve the problem that the existing SOC estimation method fails to make full use of the current innovation and historical information, resulting in an increase in the estimation error question

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