Electric vehicle power battery SOC estimation method based on multi-innovation theory

Abstract

The invention provides an electric vehicle power battery SOC estimation method based on multi-innovation theory, and belongs to the technical field of the electric vehicle power battery management. The technical scheme is an electric vehicle power battery SOC estimation method based on the multi-innovation theory. The method comprises the following steps: determining a function relation formula of a OCV-SOC of a power battery through intermittent constant current discharge method; establishing a power battery second-order RC equivalent circuit model, and deducing identification model thereof; constructing a multi-innovation extended Kalman filtering algorithm according to the multi-innovation identification theory, and combining a joint estimation algorithm with a multi-innovation forgetting factor recursive least square identification algorithm. The method has the advantages of being accurate in computation and suitable for electric vehicle power battery SOC estimation.

Description

technical field [0001] The invention relates to the technical field of electric vehicle power battery management, in particular to a method for estimating the SOC of an electric vehicle power battery based on multiple innovation theory. Background technique [0002] While fuel vehicles bring convenience to people, they also cause unavoidable problems such as environmental pollution and energy crisis. The use of efficient and clean energy to replace traditional power sources will become the focus of the reform of the automobile industry. Compared with traditional internal combustion engine vehicles, electric vehicles have the advantages of energy saving, environmental protection, low noise and pollution-free, and high energy conversion rate. They can not only protect the environment, but also alleviate energy shortages. And other issues. Therefore, new energy electric vehicles have gradually become the focus of the transportation industry in various countries, and their futu...

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Problems solved by technology

[0004] At present, the model of the ampere-hour integration method is relatively simple and widely used, but the initial SOC cannot be determined, and the estimation error will gradually accumulate; although the open circuit voltage method is simple, it needs to stand the battery for a long time, which cannot meet the requirements of real-time detection; The estimation error of the neural network method is greatly affected by the sample data and training methods, and there are shortcomings such as local optimum and long training period, so it needs more in-depth research to be applied in practice; the linear model method can be applied theoretically to various different types of batteries, but because the function of changing SOC and current and voltage is not universal, it is not suitable for actual SOC estimation

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