CN-121995232-A - PNGV model and DEKF algorithm-based lithium battery state estimation method and system

CN121995232ACN 121995232 ACN121995232 ACN 121995232ACN-121995232-A

Abstract

The invention discloses a lithium battery state estimation method and system based on a PNGV model and DEKF algorithm, wherein the method comprises the steps of firstly, performing systematic offline test on a battery to obtain an open-circuit voltage-charge state curve of the battery and an equivalent capacitance of a second-order PNGV equivalent circuit model, secondly, discretizing a continuous state equation of the model into a differential equation through bilinear transformation, identifying other model parameters of the battery under different charge states by applying a recursive least square method, and finally, constructing a state filter and a parameter filter by adopting a double-expansion Kalman filtering algorithm, and realizing collaborative estimation of the charge state and the health state through data interaction and feedback of the two filters. According to the invention, through a progressive flow of parameter identification and collaborative state estimation, the problems of time-varying and state coupling of battery model parameters are effectively solved, and the combined estimation precision and robustness of the lithium battery state of charge and the lithium battery state of health under complex working conditions are remarkably improved.

Inventors

GENG XUEWEN
LI BIN
LI CHAO
ZHAO ZIANG
LU BAOHONG
WANG SHUAI
HE SI
ZHUO PING
LI GUANZHENG
Zeng Jiedi
ZHOU YANG

Assignees

新源智储能源发展(北京)有限公司
天津大学

Dates

Publication Date: 20260508
Application Date: 20260213

Claims (10)

1. The lithium battery state estimation method based on the PNGV model and DEKF algorithm is characterized by comprising the following steps of: S1, acquiring battery reference characteristic parameters, namely performing systematic off-line test on a lithium ion battery, wherein the off-line test comprises a maximum capacity test, a low-current constant-current charge-discharge test and a mixed power pulse characteristic test so as to calibrate an open-circuit voltage-state-of-charge relationship curve of the battery, and calculating an equivalent capacitance parameter of an open-circuit voltage change generated by current accumulation represented by a second-order PNGV equivalent circuit model through a formula; S2, identifying and updating model parameters, namely constructing a discretization differential equation of the second-order PNGV equivalent circuit model, and identifying the model parameters of the battery in different charge states based on current and voltage data of the battery under a mixed power pulse characteristic test by using a recursive least square method, wherein the model parameters comprise ohmic internal resistance, electrochemical polarization capacitance, concentration polarization resistance and concentration polarization capacitance; S3, a state and parameter collaborative estimation step is carried out, wherein a system state space equation is built based on a second-order PNGV equivalent circuit model, a double-expansion Kalman filtering algorithm is adopted, a first state expansion Kalman filter and a second parameter expansion Kalman filter are synchronously built and operated, the first state expansion Kalman filter estimates a system state vector comprising a charge state, an electrochemical polarization voltage, a concentration polarization voltage and an equivalent capacitance voltage according to the system state space equation, the second parameter expansion Kalman filter estimates a model parameter vector comprising the ohmic internal resistance, the electrochemical polarization capacitance, the concentration polarization resistance, the concentration polarization capacitance, the equivalent capacitance and the maximum battery capacity, the first state expansion Kalman filter and the second parameter expansion Kalman filter share an output equation of a system, and collaborative estimation of the charge state and the health state of the lithium battery is achieved through an interactive state and parameter estimation value.
2. The method for estimating the state of a lithium battery based on the PNGV model and DEKF algorithm according to claim 1, wherein in S2, the performing parameter identification by applying the recursive least square method specifically includes: constructing a differential equation: , wherein, N is the discrete point in time of which, Represents an open circuit voltage at time n, Represents the battery terminal voltage at time n, The voltage at two ends of the equivalent capacitor at the moment n is represented, I (n) is the discharge current at the moment n, and k 1 to k 5 are coefficients to be identified; defining an observation column vector: and intermediate parameter column vectors ; Based on a recurrence formula: updating an estimate of an intermediate parameter column vector And a covariance matrix P (n), wherein K (n) is a gain matrix and I is a unit matrix; And calculating the values of the ohmic internal resistance, the electrochemical polarization capacitance, the concentration polarization resistance and the concentration polarization capacitance according to the updated coefficients k 1 to k 5 .
3. The method for estimating the state of a lithium battery based on the PNGV model and DEKF algorithm according to claim 1, wherein in S3, the specific implementation of the dual extended kalman filter algorithm in the collaborative state estimation stage includes: The state equation of the first state filter is obtained based on second-order PNGV model discretization, and the state vector comprises a charge state, an electrochemical polarization voltage, a concentration polarization voltage and an equivalent capacitance voltage; the state vector of the second parameter filter comprises time-varying model parameters , wherein, Ohmic internal resistance of the battery at time n; The electrochemical polarization resistance of the battery at the moment n; the concentration polarization resistance of the battery at the moment n; Electrochemical polarization capacitance of the battery at time n; the concentration polarization capacitance of the battery at the moment n; q (n) is the maximum available capacity of the battery at the moment n; interaction of the two filters based on coupled observation equations, the parametric filter calculates the jacobian matrix using the state estimate at the last time And updating the parameters, the updated parameters being passed in real time to a state filter for computing a jacobian matrix And completing state estimation, wherein the state estimation and the state estimation are alternately operated to realize cooperative estimation of the state and the parameters.
4. The method for estimating the state of a lithium battery based on the PNGV model and DEKF algorithm according to claim 1, wherein in the step of obtaining the battery reference characteristic parameter, calibration of the equivalent capacitance C b is achieved by: Based on the test data of the mixed power pulse characteristics, the charge variation is calculated And the corresponding open circuit voltage variation , wherein, For a terminal voltage immediately before the application of a pulsed load, The end voltage is the end voltage of the standing recovery period after the pulse is ended; Equation based on conservation of energy The value of the equivalent capacitance C b is calculated by inversion.
5. A PNGV model and DEKF algorithm based lithium battery state estimation method according to claim 3, wherein the interaction of the two filters is achieved by: jacobian matrix of observation equations versus state vectors for the first state expansion Kalman filter Jacobian matrix of observation equations versus parameter vectors for the second parametric extended kalman filter Coupled by the following relationship: ; Wherein the method comprises the steps of By recursion of relations Updating, thereby realizing feedback correction of the state estimation result to parameter estimation.
6. A PNGV model and DEKF algorithm-based lithium battery state estimation system for implementing the method of any of claims 1-5, comprising a hardware module for progressive processing: the battery unit is a lithium ion battery; The system comprises a sensor module, a battery reference characteristic parameter acquisition step, a battery control module and a control module, wherein the sensor module is configured to acquire current and voltage data of a battery in real time; A processing module, coupled to the sensor module, comprising: The parameter identification unit is used for executing a recursive least square method to identify model parameters of a second-order PNGV equivalent circuit model changing along with the state of charge, wherein the model parameters are identified and updated by constructing a discretization differential equation of the second-order PNGV equivalent circuit model, and identifying model parameters of the battery under different states of charge by applying the recursive least square method based on current and voltage data of the battery under a mixed power pulse characteristic test, wherein the model parameters comprise ohmic internal resistance, electrochemical polarization capacitance, concentration polarization resistance and concentration polarization capacitance; The state estimation unit is used for executing a double-extended Kalman filtering algorithm to cooperatively estimate the state of charge and the state of health, wherein the state and parameter cooperative estimation step is used for constructing a system state space equation based on a second-order PNGV equivalent circuit model, synchronously constructing and operating a first state extended Kalman filter and a second parameter extended Kalman filter by adopting the double-extended Kalman filtering algorithm, wherein the first state extended Kalman filter estimates a system state vector comprising the state of charge, electrochemical polarization voltage, concentration polarization voltage and equivalent capacitance voltage according to the system state space equation, and the second parameter extended Kalman filter estimates a model parameter vector comprising the ohmic internal resistance, electrochemical polarization capacitance, concentration polarization resistance, equivalent capacitance and the maximum capacity of a battery; and the output module is used for displaying or transmitting the state estimation result.
7. A PNGV model and DEKF algorithm based lithium battery state estimation system according to claim 6 wherein the processing module includes a processor and memory, the memory storing a computer program which when executed by the processor implements the method steps of any of claims 1-5.
8. The PNGV model and DEKF algorithm based lithium battery state estimation system of claim 6, integrated into a battery management system, for use in an electric vehicle, an electrochemical energy storage station, or a distributed energy system.
9. A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a PNGV model and DEKF algorithm-based lithium battery state estimation method as claimed in any one of claims 1 to 5.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a PNGV model and DEKF algorithm-based lithium battery state estimation method as claimed in any one of claims 1 to 5 when the computer program is executed.

Description

PNGV model and DEKF algorithm-based lithium battery state estimation method and system Technical Field The invention belongs to the field of battery management technology and state estimation, and particularly relates to a method and a system for jointly estimating the state of charge (SOC) and the state of health (SOH) of a lithium ion battery based on a second-order PNGV equivalent circuit model and a double-expansion Kalman filtering (DEKF) algorithm. Background With the continuous expansion of the application scale of lithium ion batteries, the problems of operation safety, reliability and service life management of electrochemical energy storage systems are increasingly prominent, which puts higher demands on the state estimation capability of Battery Management Systems (BMS). The battery state of charge (SOC) and state of health (SOH) serve as key indexes for reflecting the residual energy and the aging degree of the battery, and are core bases for realizing safe operation, energy optimization scheduling and life management of the battery. In the actual operation process, the lithium ion battery is in a complex and changeable working condition for a long time, and is influenced by factors such as charge and discharge multiplying power change, environmental temperature fluctuation, aging effect and the like, and the internal parameters of the lithium ion battery show obvious nonlinearity and time-varying characteristics. If the estimation accuracy of the SOC and SOH is insufficient, the battery is liable to be overcharged, overdischarged or reduced in capacity utilization, which not only reduces the system operation efficiency, but also may cause safety risk. Therefore, the realization of high-precision estimation of SOC and SOH under complex working conditions and battery aging is a key technical problem to be solved in the current battery management field. The current mainstream battery state of charge estimation method mainly comprises an ampere-hour integration method, an equivalent circuit model-based method and a data driving method. The method based on the equivalent circuit model is widely applied to engineering practice because of being capable of considering good balance between physical meaning and computational complexity of the model. The common second-order RC equivalent circuit model has the advantages of simple structure and small calculation amount, but has limitation in describing the terminal voltage drift characteristic of the battery under long-time load, and is difficult to accurately reflect the open-circuit voltage dynamic change caused by load current accumulation. In contrast, the second-order PNGV equivalent circuit model is connected with a high-capacity capacitor C b in series on the basis of the second-order RC model, and the capacitor can represent the change of open-circuit voltage along with load current integration, so that the defect of the traditional model in direct-current response and accumulated error processing is overcome, and the nonlinear voltage response of the battery under a complex working condition can be simulated more accurately. However, for parameter identification of a second-order PNGV equivalent circuit model, the existing research often adopts a curve fitting method, and the method has the problems of large calculation error and low identification efficiency. In addition, the current SOC estimation based on PNGV equivalent circuit model mostly adopts a single extended Kalman filtering algorithm. Because the lithium battery has a high nonlinear characteristic, parameters such as internal resistance, capacitance and the like of the lithium battery can dynamically change along with the SOC, the ambient temperature and the SOH of the health state, a strong coupling relation exists between the SOC and the SOH, and if the SOC estimation is carried out only through the fixed model parameters determined offline, the estimation precision can be obviously reduced due to the aging of the battery. Disclosure of Invention Aiming at the defects in the prior art, the invention aims to provide a lithium battery state estimation method and system based on a PNGV model and DEKF algorithm, which utilize time-varying model parameters of a battery and realize collaborative estimation of a state of charge and a health state through data interaction and feedback of a state filter and a parameter filter. In order to achieve the above object, the present invention proposes the following technical solutions: in a first aspect, a method for estimating a state of a lithium battery based on a PNGV model and DEKF algorithm, includes the steps of: S1, acquiring battery reference characteristic parameters, namely performing systematic off-line test on a lithium ion battery, wherein the off-line test comprises a maximum capacity test, a low-current constant-current charge-discharge test and a mixed power pulse characteristic test so as to calibrate an open-circuit voltage-state-