Lithium battery health state estimation method based on multi-factor evaluation model

Abstract

The invention discloses a lithium battery health state estimation method based on a multi-factor evaluation model. The method comprises the following steps: firstly, establishing a first-order RC equivalent circuit model of a lithium ion battery; obtaining a specific function relationship between the open-circuit voltage and the state of charge, and constructing an SOC-OCV relationship table between the open-circuit voltage OCV and SOC values of different states of charge; then respectively establishing state observation equations taking ohm internal resistance, polarization internal resistance and polarization time constant as state variables; obtaining ohmic internal resistance, polarization internal resistance and polarization capacitance of the lithium battery in real time through iterative computation; and evaluating the health state of the brand-new battery of the same type by using the weight coefficients respectively corresponding to the ohmic internal resistance, the polarization internal resistance and the polarization capacitance obtained by training. According to the method, the problem that in the prior art, the SOH accuracy is estimated only through the ohmic internalresistance, and large defects exist is solved; by means of the Kalman filtering algorithm, the ohmic internal resistance, the polarization internal resistance and the polarization capacitance of thebattery are estimated online, and the estimation precision of the health state is improved.

Description

technical field [0001] The invention belongs to the technical field of estimating the state of health of a lithium battery, in particular to a method for estimating the state of health of a lithium battery based on a multi-factor evaluation model. Background technique [0002] With the development of clean energy, lithium batteries have gained more and more applications in wind, solar energy storage, electric vehicles and other fields. In order to ensure the safe and effective operation of the battery, a battery management system needs to be established to monitor the battery’s voltage, current, temperature and other parameters in real time, and accurately estimate the battery’s state of charge, state of health (SOH) and other status information. Among them, accurately estimating the SOH can grasp the usage information of the battery in real time, reduce the abuse of electric energy, and replace the battery with a low utilization rate of electric energy in time. For a batte...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method for estimating the state of health of a lithium battery based on a multi-factor evaluation model, which solves the problem of relatively large defects in the accuracy of SOH estimation using only ohmic internal resistance in the traditional technology. The present invention utilizes the Kalman filter algorithm, Online estimation of the battery's ohmic internal resistance, polarization internal resistance, and polarization capacitance improves the estimation accuracy of the state of health

Images