CN-121978564-A - Internal resistance estimation method, device, computer program product, and computer-readable storage medium

CN121978564ACN 121978564 ACN121978564 ACN 121978564ACN-121978564-A

Abstract

The application provides an internal resistance estimation method, device, equipment, a computer program product and a computer readable storage medium, wherein the method comprises the steps of synchronously collecting voltages and currents of a plurality of batteries according to a target sampling period, converting a second-order equivalent circuit model of each battery based on terminal voltages and terminal currents collected at the current sampling time and the historical sampling time to obtain a linear regression model, carrying out iterative estimation on a parameter vector to be estimated in the linear regression model by adopting a recursive least square method with forgetting factors to obtain a target parameter vector at the current sampling time, determining the direct current internal resistance to be processed of the battery at the current sampling time based on the target parameter vector and a determined target mapping relation, wherein the target mapping relation is determined based on the second-order equivalent circuit model and the linear regression model, and represents the corresponding relation between the parameter vector and the direct current internal resistance.

Inventors

DONG GUANGZHONG
WANG JINGDE
ZHANG YUXIN
LI HAO
Zhu Chaoji
CHEN DAREN
CHEN JIAN
LUO XIAOJIA

Assignees

深圳拓邦股份有限公司
哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院)

Dates

Publication Date: 20260505
Application Date: 20260130

Claims (10)

1. A method of estimating internal resistance, the method comprising: Synchronously collecting the voltages and currents of a plurality of batteries according to a target sampling period; converting a second-order equivalent circuit model of each battery based on terminal voltage and terminal current acquired at the current sampling moment and the historical sampling moment to obtain a linear regression model; Performing iterative estimation on the parameter vector to be estimated in the linear regression model by adopting a recursive least square method with forgetting factors to obtain a target parameter vector at the current sampling moment; and determining the direct current internal resistance of the battery to be processed at the current sampling moment based on the target parameter vector and the determined target mapping relation, wherein the target mapping relation is determined based on the second-order equivalent circuit model and the linear regression model and represents the corresponding relation between the parameter vector and the direct current internal resistance.
2. The method of claim 1, wherein converting the second-order equivalent circuit model of the battery based on the voltages and currents acquired at the current sampling time and the historical sampling time to obtain a linear regression model, comprises: discretizing the second-order equivalent circuit model based on the voltage and the current to obtain a first equation; performing differential operation on the first equation of adjacent sampling moments to obtain a second equation, wherein the second equation comprises the voltage, the current and the parameter vector to be estimated; and finishing the second equation to obtain the linear regression model.
3. The method of claim 1, wherein the iterative estimation of the parameter vector to be identified in the linear regression model by using a recursive least square method with a forgetting factor to obtain the target parameter vector at the current sampling time comprises: initializing a covariance matrix corresponding to a parameter vector in the linear regression model and the linear regression model; And for each sampling moment, adopting the recursive least square method with the forgetting factor, iteratively updating the parameter vector and the covariance matrix based on the regression vector and the output value in the linear regression model, and determining the updated parameter vector as the target parameter vector.
4. The method of claim 2, wherein determining the target mapping relationship comprises: And obtaining the target mapping relation based on the second equation and coefficients of corresponding items of terminal voltage and terminal current in the linear regression model.
5. The method of claim 4, wherein the obtaining the target mapping relationship based on coefficients of the second equation and corresponding terms of terminal voltage and terminal current in the linear regression model includes: comparing the second equation with coefficients of corresponding items of terminal voltage and terminal current in the linear regression model, and establishing an equation set between a parameter vector of the linear regression model and physical parameters of the second-order equivalent circuit model; and analyzing a functional relation of the direct current internal resistance with respect to at least one parameter in the parameter vector from the equation set, and determining the functional relation as the target mapping relation.
6. The method of claim 1, wherein the determining the internal resistance of the battery to be processed at the current sampling time based on the target parameter vector and the determined target mapping relationship further comprises: And carrying out post-processing on the initial direct current internal resistance sequence corresponding to each battery to obtain a target direct current internal resistance sequence, wherein the post-processing comprises exception processing and smooth filtering, and the initial direct current internal resistance sequence is formed by the direct current internal resistances to be processed at each sampling moment.
7. The method of claim 6, wherein the post-processing the initial dc internal resistance sequence corresponding to the battery to obtain a target dc internal resistance sequence comprises: Determining the average value and standard deviation of the initial direct current internal resistance sequence in a sliding window; invalidating abnormal direct current internal resistance in the initial direct current internal resistance sequence to obtain a processed direct current internal resistance sequence; and carrying out smooth filtering on the processed direct current internal resistance sequence to obtain the target direct current internal resistance sequence.
8. An internal resistance estimation apparatus, characterized by comprising: a memory for storing computer executable instructions or computer programs; a processor for implementing the method of any of claims 1 to 7 when executing computer executable instructions or computer programs stored in the memory.
9. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores one or more programs, the one or more programs may be executed by one or more processors to implement the methods of any of claims 1-7.

Description

Internal resistance estimation method, device, computer program product, and computer-readable storage medium Technical Field The present application relates to battery management and state estimation techniques, and more particularly, to an internal resistance estimation method, apparatus, device, computer program product, and computer readable storage medium. Background At present, on-line estimation of the direct current internal resistance (Direct Current Resistance, DCR) of a battery mainly faces the technical problems that firstly, a traditional accurate measurement method (such as HPPC test) depends on application of special excitation pulse under specific working conditions, cannot adapt to real-time estimation requirements of the battery in a complex dynamic operation process and has invasive and offline limitations, and secondly, although an on-line parameter identification method (such as a scheme of combining an ARX model with an RLS and embedding a double extended Kalman filter) based on a battery model and a recursive algorithm exists, the methods are generally coupled in a complex joint state estimation framework, so that calculation cost is huge, efficient and real-time independent internal resistance estimation is difficult to realize in a vehicle-mounted battery management system with limited resources, and thirdly, the internal resistance estimation result is easily confused with other polarization parameters and the accuracy and physical clarity are insufficient. Therefore, an online estimation method capable of directly and accurately analyzing the internal resistance of direct current by only using the conventional operation data of the battery management system and having light calculation is needed. Disclosure of Invention Embodiments of the present application provide a method, an apparatus, a computer program product, and a computer readable storage medium for estimating internal resistance, which can reduce the computational complexity and improve the accuracy of internal resistance estimation. The technical scheme of the embodiment of the application is realized as follows: The embodiment of the application provides an internal resistance estimation method, which comprises the following steps: Synchronously collecting the voltages and currents of a plurality of batteries according to a target sampling period; converting a second-order equivalent circuit model of each battery based on terminal voltage and terminal current acquired at the current sampling moment and the historical sampling moment to obtain a linear regression model; Performing iterative estimation on the parameter vector to be estimated in the linear regression model by adopting a recursive least square method with forgetting factors to obtain a target parameter vector at the current sampling moment; and calculating to obtain the direct current internal resistance of the battery to be processed at the current sampling moment based on the target parameter vector and the determined target mapping relation, wherein the target mapping relation is determined based on the second-order equivalent circuit model and the linear regression model and represents the corresponding relation between the parameter vector and the direct current internal resistance. In the above scheme, the converting the second-order equivalent circuit model of the battery based on the voltage and the current acquired at the current sampling time and the historical sampling time to obtain the linear regression model includes: discretizing the second-order equivalent circuit model based on the voltage and the current to obtain a first equation; performing differential operation on the first equation of adjacent sampling moments to obtain a second equation, wherein the second equation comprises the voltage, the current and the parameter vector to be estimated; and finishing the second equation to obtain the linear regression model. In the above scheme, the iterative estimation is performed on the parameter vector to be identified in the linear regression model by adopting a recursive least square method with forgetting factors to obtain the target parameter vector at the current sampling time, including: initializing a covariance matrix corresponding to a parameter vector in the linear regression model and the linear regression model; And for each sampling moment, adopting the recursive least square method with the forgetting factor, iteratively updating the parameter vector and the covariance matrix based on the regression vector and the output value in the linear regression model, and determining the updated parameter vector as the target parameter vector. In the above scheme, determining the target mapping relationship includes: And obtaining the target mapping relation based on the second equation and coefficients of corresponding items of terminal voltage and terminal current in the linear regression model. In the above solution, the obtaining th