Pre-estimate method for SOH (state of health) of power battery of electric car

Abstract

The invention relates to the technical field of power batteries of an electric car, and provides a pre-estimate method for the SOH (state of health) of a power battery of the electric car. The method comprises the steps: employing a Rint battery model and a dual-expansion Kalman filtering (Dual-EKF) to estimate the internal resistance of the battery in advance, and calculating the SOH through the internal resistance of the battery. The method is high in rate of convergence, is accurate in estimation, solves the problem that a conventional pre-estimate method for the SOH of the power battery of the electric car cannot accurately achieve real-time estimation, irons out the defects that the conventional pre-estimate method is not steady in discreet value at the initial stage of pre-estimate and calculation and is liable to cause oscillation, and is convenient for the real-time pre-estimate of the SOH of the power battery.

Description

technical field [0001] The invention relates to the technical field of electric vehicle power batteries, in particular to a method for estimating the state of health (SOH, State of Health) of electric vehicle power batteries, which is applicable to all vehicles that need to use power batteries, especially power batteries that need to be estimated in real time SOH vehicles. Background technique [0002] Due to its high specific energy and high power density, lithium-ion batteries have become a promising power battery, which is conducive to the rapid development of hybrid electric vehicles and pure electric vehicles. In order to keep the lithium-ion battery system working more efficiently and safely, control the battery pack in good health and prolong the life of the battery pack, it is necessary to study or improve the battery management system (BMS). [0003] SOH is directly understood as the state of health of the battery, which can reflect the service life of the battery....

AI Technical Summary

Problems solved by technology

[0005] The present invention solves the problem that the existing power battery SOH estimation method cannot accurately perform real-time estimation

To this end, the present invention provides a method for estimating the SOH of an electric vehicle power battery, which uses a Rint battery model and a dual-extended Kalman filter (Dual-EKF) algorithm for SOH estimation, which has the advantages of fast convergence speed, accurate estimation, etc. Advantages, and overcome the shortcomings of the existing SOH estimation methods, such as the initial estimated value is unstable and prone to oscillation, etc., which is convenient for real-time estimation of power battery SOH

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