Lithium battery SOC and SOP estimation method based on second-order RC model

Abstract

The invention provides a lithium battery SOC and SOP estimation method based on a second-order RC model, and the method comprises the following steps: S1, selecting the type and model of a lithium battery, obtaining corresponding technical parameters, and building a battery equivalent circuit model; S2, performing an HPPC experiment on the battery at a specific temperature, obtaining battery characteristic parameters, establishing a relationship between battery open-circuit voltage and SOC, performing parameter identification by combining an optimization method, and feeding back the obtained parameters to the model; S3, carrying out SOC closed-loop estimation by combining an EKF algorithm; and S4, estimating the instantaneous power and the limited power of the battery according to P = U *I. The method is characterized in that an original battery model with current as input and voltage as output is improved into a model with voltage as input and current as output. As for SOP estimation, a known voltage and current calculation model is more suitable, and the problems of tedious steps and large calculation amount of an existing method can be solved.

Description

technical field [0001] The invention relates to the technical field of state of charge and power state prediction of lithium ion batteries, in particular to a method for estimating SOC and SOP of lithium batteries based on a second-order RC model. Background technique [0002] Although lithium-ion battery technology continues to improve, it still cannot fully meet the needs of use in terms of life and safety issues. SOC reflects the amount of remaining power in the battery. In the power battery management system, SOC plays an important role and is also the basis for other state estimation. SOP is the ability of the battery to output power. The accuracy of SOC and SOP prediction directly affects battery performance. The performance, the length of life and the safety of use. [0003] At present, there are many methods for SOC estimation, and the commonly used estimation methods can be divided into the following categories: ampere-hour integral method, open circuit voltage met...

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Problems solved by technology

[0003] At present, there are many methods for SOC estimation, and the commonly used estimation methods can be divided into the following categories: ampere-hour integral method, open circuit voltage method, estimation method based on equivalent circuit model, estimation method based on electrochemical model, and estimation method based on machine learning model. Estimation methods, among which the ampere-hour integration method is greatly affected by the initial SOC and current measurement errors, and the accuracy is poor; the open circuit voltage method is not suitable for dynamic estimation; the method model of the electrochemical model is too complicated and the calculation is large; the machine learning method requires a large amount of data training , the estimation accuracy outside the training range is not high; the closed-loop estimation algorithm based on the equivalent circuit model has reliable accuracy, but it is more dependent on the accuracy model, which is the most used method for battery SOC estimation

[0004] In recent years, there are mainly two kinds of SOP estimation methods commonly used: Map-based estimation method and model-based dynamic estimation method. Among them, the Map-based estimation method is simple and reliable, but requires a large number of offline calibration tests, and is only suitable for fully rested battery

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