An Extended Kalman Filter SOC Estimation Method

Abstract

The invention discloses a battery hysteresis voltage characteristic and rebound voltage characteristic-based extended Kalman filter SOC estimation method. The method comprises the following steps of (1), collecting parameter information such as a voltage, a current, a historical SOC, an accumulated charging / discharging frequency N and the like in a current state; (2), building a three-order RC equivalent circuit model of a battery based on a hysteresis voltage characteristic and a rebound voltage characteristic of the battery; (3), establishing an SOC state equation based on an improved ampere-hour integration method; (4), based on the established state equation and an observation equation, continuously correcting an SOC value at a current moment according to an extended Kalman recursion principle, thereby enabling an estimated value to be gradually close to a real value; and (5), when an SOC error is smaller than 2%, ending an extended Kalman recursion process of the SOC. According to the scheme, the shortcomings that an open circuit voltage method cannot perform online estimation and an ampere-hour integration method has current error accumulation are overcome; the estimation precision of the battery SOC is improved; and online estimation of the battery SOC is realized.

Description

technical field [0001] The invention relates to the technical field of lithium iron phosphate power batteries, and more specifically, relates to an extended Kalman filter SOC estimation method. Background technique [0002] In the lithium-powered battery management system for electric vehicles, the estimation of the battery state of charge (SOC) is the core content of the battery management system (BMS), and the balance control of the battery and the control of the state of health (SOH) are based on the estimation of the SOC. Basically, whether the estimation of SOC is accurate or not will directly affect the decision-making of the battery management system. Therefore, accurate SOC estimation is of great significance for improving battery performance, improving battery reliability, and prolonging battery life. [0003] Estimation of battery SOC and battery open circuit voltage, charge and discharge current, battery internal resistance. It is related to parameters such as se...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art. An extended Kalman filter SOC estimation method overcomes the shortcomings of the open-circuit voltage method that cannot be estimated online and the current error accumulation of the ampere-hour integration method, and improves the estimation of the battery SOC. Accuracy, realizes online estimation of battery SOC

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