A Lithium Battery State of Charge Estimation Method Based on Characteristic Curve Optimization

Abstract

The invention discloses a method for estimating the state of charge of a lithium battery based on characteristic curve optimization, which includes the following: every 10% of the SOC of the battery state of charge is a sampling point, and the characteristic curve L of discrete OCV‑SOC is obtained 1 , to obtain the characteristic curve L of the continuous OCV‑SOC 2 ;Based on the differential evolution DE method, the characteristic curve L obtained by fusion 1 and the characteristic curve L 2 ; Finally, based on the extended Kalman EKF to introduce the weight term factor of the historical SOC estimation result error, the W‑EKF estimation model is established, combined with the second-order RC battery equivalent circuit model, and the real-time measured voltage, current and temperature of the lithium battery under different working conditions are input Data to accurately estimate the SOC value at the current moment. The method of the present invention is used for accurate estimation of the state of charge (SOC) of the battery.

Description

technical field [0001] The invention relates to the technical field of lithium batteries, in particular to a method for estimating state of charge of lithium batteries based on characteristic curve optimization. Background technique [0002] As the power source of the vehicle, the lithium battery plays a key role in the operation of the vehicle under different working conditions. The state of charge (SOC) of the battery is an important parameter reflecting the remaining energy of the lithium battery. Accurate estimation of the SOC of the power lithium battery can effectively improve the The service life of the battery can avoid overcharging / overdischarging of the battery and ensure the safe and stable operation of the vehicle system. Since SOC is an electrochemical parameter inside the battery and cannot be directly measured from the outside, and SOC has an inseparable nonlinear relationship with the voltage, current, temperature and other characteristics of the battery duri...

AI Technical Summary

Problems solved by technology

The ampere-hour integration method can calculate the SOC value at any time by determining the initial value of the SOC and using the cumulative summation of the current combined with the rated capacity of the battery. However, there is a cumulative error in the integration process. When the current fluctuates greatly, the cumulative error will affect the SOC. Estimation accuracy leads to erroneous estimates, and the initial value of SOC is usually difficult to obtain; the open circuit voltage method estimates the relationship between the open circuit voltage of the battery and the SOC, but the open circuit voltage method needs to be used after the battery is left to stand until the polarization effect disappears. Estimated in the working state of the battery; the neural network method conducts network training on a large number of battery operating data to form a network with nonlinear expression ability to estimate SOC, but the coverage of battery operating data is not comprehensive, resulting in nonlinear representation ability Not enough, and the constraints of the computing power of embedded devices for vehicles are also limited; Kalman filtering (KF) can model and estimate linear systems, and in order to apply to nonlinear battery systems, Extended Kalman (EKF) has been developed, and EKF estimates SOC It is necessary to establish a battery model and perform parameter identification on the characteristic curve of model parameters combined with open circuit voltage (OCV) and SOC. The static interval method is often used to take sampling points (every 10% SOC is a sampling point) to obtain the relationship between OCV and SOC experimentally, and then Use linear interpolation to obtain the complete OCV-SOC characteristic curve, but the linear interpolation method will bring errors, and EKF only uses the SOC estimation state at the previous moment to correct the SOC estimation at the current moment, ignoring the influence of historical estimation results, and will also Introduce error to SOC estimation

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