Method for estimating SOC (State of Charge) of lithium ion battery based on gray extended Kalman filtering algorithm

Abstract

The invention discloses a method for estimating the SOC (State of Charge) of a lithium ion battery based on a gray extended Kalman filtering algorithm. The method comprises the steps of firstly predicting prior estimated values of polarization voltage and an SOC state variable of a battery model at the present moment through a gray prediction model and replacing a Jacobian matrix in the extended Kalman filtering algorithm, and then updating and correcting the prior estimated values through observed values by using the extended Kalman filtering algorithm so as to acquire an SOC estimated value of the lithium ion battery at the present moment. The invention provides a lithium ion battery SOC estimation method for an electric automobile battery management system, and the SOC estimation precision of the lithium ion battery can be improved.

Description

technical field [0001] The invention belongs to the technical field of lithium ion batteries, and more specifically relates to a method for estimating the SOC of lithium ion batteries. Background technique: [0002] As the main energy storage device of electric vehicles, power batteries are the core components of the development of electric vehicles. Lithium-ion batteries have become the main direction of electric vehicle power batteries because of their high energy density, long cycle life, and low self-discharge. [0003] The battery state of charge (State of Charge, SOC) is an important indicator of the battery state. Accurate online SOC estimation can effectively prevent battery overcharge and overdischarge, remind users to charge in time, replace batteries in time, etc. At the same time, it can also save battery costs, prolong battery life, and provide a basis for electric vehicle vehicle control. At present, researchers at home and abroad have provided SOC estimation...

AI Technical Summary

Problems solved by technology

The ampere-hour integration method is simple in principle and easy to calculate, but the accuracy of the ampere-hour method is restricted by accurate initial values, battery aging, and cumulative errors.

The open circuit voltage method can obtain relatively accurate SOC according to the OCV curve, but the open circuit voltage method needs to be left for a long time, which is difficult to meet the online estimation requirements

The extended Kalman filter method is a research hotspot in recent years. It has the characteristics of small amount of calculation and high estimation accuracy. However, the extended Kalman filter method requires that the noise statistics conform to the Gaussian model, which is difficult to meet in real vehicles, which may lead to non-convergence of the estimation results. , affecting the SOC estimation accuracy

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