Power battery SOC estimation method

Abstract

The invention provides a power battery SOC estimation method, wherein power battery SOC estimation is performed based on a BP neural network assisted extended Kalman filter. According to the power battery SOC estimation method, a state estimation updated value is used as the input value of the BP neural network, and the estimated value of an observation noise variance-covariance matrix is used as an objective output value of the BP neural network, thereby performing online training on a constructed BP neural network. The observation noise variance-covariance matrix which is output by the BP neural network is supplied to an error variance-covariance prediction equation and a filtering gain equation of the extended Kalman filter, thereby realizing recursive calculation of the BP neural network assisted extended Kalman filter. The power battery SOC estimation method can settle the problems such as incapability of satisfying a requirement for online estimation, large accumulative error, easy diffusion, and easy influence by a noise in an existing estimation method. Furthermore the power battery SOC estimation method has high estimation precision.

Description

technical field [0001] Invention and design of the lithium-ion battery state of charge prediction field, especially a power battery SOC estimation method, which uses a neural network-assisted extended Kalman filter algorithm to estimate the power battery SOC. Background technique [0002] As the main means of transportation in the future, electric vehicles have certain requirements for their start-up, acceleration, climbing performance and cruising range, and these performances largely depend on the performance of the power battery. Battery state of charge (SOC) is a very important parameter in electric vehicles. Only by accurately estimating battery SOC can the utilization efficiency of electric vehicles be effectively improved, driving optimized, and battery life extended. However, SOC is an implicit state quantity of the power battery, which is difficult to directly test and calculate. Because of this, only by establishing an accurate and reliable SOC estimation algorith...

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: In order to solve the problems existing in the existing estimation methods that cannot satisfy the online estimation, the cumulative error is large, diverges, and is easily affected by noise, etc., the present invention proposes a power battery SOC estimation method

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