CN115128494A - Battery state of health estimation method and device, electronic equipment and storage medium - Google Patents
Battery state of health estimation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN115128494A CN115128494A CN202110312826.1A CN202110312826A CN115128494A CN 115128494 A CN115128494 A CN 115128494A CN 202110312826 A CN202110312826 A CN 202110312826A CN 115128494 A CN115128494 A CN 115128494A
- Authority
- CN
- China
- Prior art keywords
- battery
- soh
- current
- determining
- prediction algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The application provides a method and a device for estimating the state of health of a battery, an electronic device and a storage medium, wherein the method comprises the following steps: at a first moment in a standby time of a battery, acquiring a first current and a first terminal voltage of the battery, wherein the first current and the first terminal voltage are generated based on a reference direct current pulse signal; determining a test state of health, SOH, of the battery based on the first current and the first terminal voltage; acquiring a second current and a second terminal voltage of the battery at a second moment in the battery running time; determining an estimated SOH of the battery based on the second current, the second terminal voltage, and a prediction algorithm; and under the condition that the condition of adjusting the prediction algorithm parameters is determined to be met based on the test SOH and the estimated SOH, adjusting the prediction algorithm parameters to obtain a target prediction algorithm so as to estimate the SOH of the battery based on the target prediction algorithm.
Description
Technical Field
The present disclosure relates to the field of battery technologies, and in particular, to a method and an apparatus for estimating a state of health of a battery, an electronic device, and a storage medium.
Background
The State Of Health (SOH) Of a battery refers to a percentage Of a charged capacity or a discharged capacity (a current capacity Of the battery) Of the battery to a nominal capacity Of the battery under a certain condition, and the SOH Of the battery reflects characteristic parameters Of battery aging and capacity attenuation, and according to the IEEE standard, when a capacity value Of a power battery is reduced to 80%, the battery is aged and cannot be used, and the battery should be replaced in time, so the SOH needs to be estimated.
Disclosure of Invention
In view of the foregoing problems, the present application provides a method and an apparatus for estimating a state of health of a battery, an electronic device, and a storage medium.
The application provides a method for estimating the state of health of a battery, which comprises the following steps:
acquiring a first current and a first terminal voltage of a battery at a first moment in the standby time of the battery, wherein the first current and the first terminal voltage are generated based on a reference direct current pulse signal;
determining a test SOH for the battery based on the first current and the first terminal voltage;
acquiring a second current and a second end voltage of the battery at a second moment in the battery running time, wherein the first moment and the second moment are in a preset time interval, and the second current and the second end voltage are generated when the battery runs;
determining an estimated SOH of the battery based on the second current, the second terminal voltage, and a prediction algorithm;
and under the condition that the condition of adjusting the prediction algorithm parameters is determined to be met based on the test SOH and the estimated SOH, adjusting the prediction algorithm parameters to obtain a target prediction algorithm so as to estimate the SOH of the battery based on the target prediction algorithm.
In some embodiments, when the battery is detected to be in a standby state, a control signal is generated, and the control signal is used for controlling the battery to generate a reference direct current pulse signal;
and sending the control signal to the battery to obtain a first current and a first terminal voltage of the battery.
In some embodiments, said determining a test SOH of said battery based on said first current and said first terminal voltage comprises:
determining a first internal resistance of the battery based on the first current and the first voltage;
determining a test SOH for the battery based on the first internal resistance.
In some embodiments, said determining a test SOH for said battery based on said first internal resistance comprises:
acquiring initial internal resistance and service life ending internal resistance;
determining a first difference value based on the initial internal resistance of the battery and the first internal resistance;
determining a second difference value based on the initial internal resistance and the end-of-life internal resistance of the battery;
determining a test SOH for the battery based on the first difference and the second difference.
In some embodiments, the method further comprises:
based on the test SOH and the estimated SOH, it is determined whether a condition for adjusting a parameter of a prediction algorithm is satisfied.
In some embodiments, said determining whether a condition for adjusting a parameter of a predictive algorithm is satisfied based on said test SOH and estimated SOH comprises:
determining a difference between the test SOH and the estimated SOH;
determining the size relation between the difference value and a preset threshold value;
and determining whether a condition for adjusting the budget algorithm parameters is met or not based on the magnitude relationship, wherein the prediction algorithm is determined to be the target algorithm under the condition that the condition for adjusting the prediction algorithm parameters is determined not to be met.
In some embodiments, the method further comprises:
determining a first internal resistance based on the first current and the first terminal voltage;
determining the target equivalent circuit model based on the first internal resistance;
and updating the internal resistance parameter of the battery to be the internal resistance parameter corresponding to the target equivalent circuit model.
An embodiment of the present application provides an estimation device of a state of health SOH of a battery, including:
the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a first current and a first terminal voltage of a battery at a first moment in the standby time of the battery, and the first current and the first terminal voltage are generated based on a reference direct current pulse signal;
a first determination module to determine a test SOH for the battery based on the first current and the first terminal voltage;
a second obtaining module, configured to obtain a second current and a second end voltage of the battery at a second time in the battery operation time, where the first time and the second time are in a preset time interval, and the second current and the second end voltage are generated when the battery operates;
a second determination module to determine an estimated SOH of the battery based on the second current, the second terminal voltage, and a prediction algorithm;
and the adjusting module is used for adjusting the prediction algorithm parameters to obtain a target prediction algorithm under the condition that the condition of adjusting the prediction algorithm parameters is determined to be met based on the test SOH and the estimated SOH, so that the estimation of the SOH of the battery is carried out based on the target prediction algorithm.
An embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the method performs any one of the above estimation methods for a state of health, SOH, of a battery.
Embodiments of the present application provide a storage medium storing a computer program, which is executable by one or more processors, and is operable to implement any one of the above-described methods for estimating state of health, SOH, of a battery.
According to the estimation method, the estimation device, the electronic equipment and the storage medium for the state of health of the battery, a first voltage and a first current are generated through a direct current pulse signal, the first voltage and the first current are generated through a reference direct current pulse signal at a first moment of standby time of the battery, a test SOH is determined based on the first voltage and the first current, a second current and a second end voltage of the battery are obtained at a second moment of operation of the battery, the first moment and the second moment are within a preset time interval, an estimated SOH is determined based on the second current, the second voltage and the prediction algorithm, the prediction algorithm is optimized through the test SOH and the estimated SOH, a target budget algorithm is determined, further the estimation of the SOH is performed through the target algorithm, and the estimation accuracy of the SOH can be improved.
Drawings
The present application will be described in more detail below on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating an implementation of a method for estimating a state of health of a battery according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a system for estimating state of health of a battery according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of another battery state of health estimation system according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating a process for determining a test SOH of a battery according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a control instruction generated by referring to a dc pulse signal according to an embodiment of the present disclosure;
fig. 6 is a schematic flowchart of BMU signal detection provided in an embodiment of the present application;
FIG. 7 is a schematic flow chart illustrating another implementation of a SOH estimation method according to an embodiment of the present application;
fig. 8 is a schematic flow chart illustrating an implementation process of a parameter optimization method of a prediction algorithm according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an apparatus for estimating a state of health of a battery according to an embodiment of the present disclosure;
fig. 10 is a schematic structural diagram of a component of an electronic device according to an embodiment of the present application.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
The following description will be added if a similar description of "first \ second \ third" appears in the application file, and in the following description, the terms "first \ second \ third" merely distinguish similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances in a specific order or sequence, so that the embodiments of the application described herein can be implemented in an order other than that shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
Based on the problems in the related art, embodiments of the present application provide a method for estimating a state of health of a battery, which is applied to an electronic device. The functions implemented by the method for estimating the state of health of the battery provided by the embodiment of the application can be implemented by calling program codes by a processor of the electronic device, wherein the program codes can be stored in a computer storage medium.
An embodiment of the present application provides a method for estimating a state of health of a battery, and fig. 1 is a schematic flow chart illustrating an implementation of the method for estimating a state of health of a battery according to the embodiment of the present application, as shown in fig. 1, including:
step S101, at a first moment in a battery standby time, acquiring a first current and a first terminal voltage of the battery, wherein the first current and the first terminal voltage are generated based on a reference direct current pulse signal.
In this embodiment, the battery may be a lithium battery, the battery includes a battery body and a converter battery management unit BMU, and in some embodiments, the battery may become an energy storage system, and the converter may include: in this embodiment of the application, the electronic device may generate a control signal, and the control signal may enable the battery to generate a reference DC pulse signal, and the BMU system may detect a first current and a first terminal voltage generated on the battery body, and then the BMU system may send the first current and the first terminal voltage to the electronic device. Illustratively, the first current and the first terminal voltage are transmitted to a controller of the electronic device.
Step S102, determining the SOH of the battery based on the first current and the first terminal voltage.
In the embodiment of the application, determining the test SOH of the battery may be implemented by determining a first internal resistance of the battery based on a first current and the first voltage; determining a test SOH for the battery based on the first internal resistance. In the embodiment of the application, the initial internal resistance and the service life ending internal resistance can be obtained; determining a first difference value based on the initial internal resistance of the battery and the first internal resistance; determining a second difference value based on the initial internal resistance and the end-of-life internal resistance of the battery; and determining a test SOH corresponding to the target equivalent circuit model based on the first difference and the second difference.
Step S103, obtaining a second current and a second terminal voltage of the battery at a second time within the battery operation time.
In an embodiment of the present application, the second current and the second terminal voltage are generated when the battery operates. In this embodiment of the application, the battery running time may be a charging and discharging time of the battery, and the BMU system of the battery may send the generated second current and the second terminal voltage to the electronic device, so that the electronic device obtains the second current and the second terminal voltage of the battery. In the embodiment of the application, the first time and the second time are in the preset time interval, and the preset time interval can be set based on experience, so that the calculation accuracy can be improved.
Step S104, determining an estimated SOH of the battery based on the second current, the second terminal voltage and a prediction algorithm.
In the embodiment of the application, the prediction algorithm may be a kalman filter algorithm model, and the estimated SOH during the operation of the battery may be estimated through the second current, the second terminal voltage and the kalman filter algorithm model.
And S105, under the condition that the condition of adjusting the prediction algorithm parameters is determined to be met based on the test SOH and the estimated SOH, adjusting the prediction algorithm parameters to obtain a target prediction algorithm, and estimating the SOH of the battery based on the target prediction algorithm.
In the embodiment of the application, the difference value between the test SOH and the estimated SOH can be determined; determining the size relation between the difference value and a preset threshold value; and under the condition that the difference value represented by the size relationship is smaller than the preset threshold value, determining that the condition for adjusting the parameters of the prediction algorithm is not met, wherein the prediction algorithm is a target prediction algorithm. Determining that a condition for adjusting a prediction algorithm parameter is met and adjusting the prediction algorithm parameter under the condition that the difference value represented by the magnitude relation is greater than or equal to the preset threshold value; a target prediction algorithm is determined based on the adjusted parameters.
According to the estimation method for the state of health of the battery, the first voltage and the first current are generated through the direct current pulse signal in the first time of the standby time of the battery, the test SOH is determined based on the first voltage and the first current, the second current and the second end voltage of the battery are obtained in the second time when the battery runs, the first time and the second time are within the preset time interval, the estimated SOH is determined based on the second current, the second voltage and the prediction algorithm, the prediction algorithm is optimized through the test SOH and the estimated SOH, the target prediction algorithm is determined, the estimation of the SOH is further performed through the target prediction algorithm, and the estimation accuracy of the SOH can be improved.
Based on the foregoing embodiments, an embodiment of the present application further provides a method for estimating a state of health of a battery, where the method includes:
step S201, when the battery is detected to be in a standby state, a control signal is generated.
In the embodiment of the application, whether the battery is in a standby state or not can be determined by acquiring the current or the voltage of the battery, and when the battery is in the standby state, the electronic equipment can generate a control signal and send the control signal to the converter, wherein the converter can be AC/DC or DC/DC. In the embodiment of the application, a current control loop can be adopted in the converter, and a control function is added, so that the battery is subjected to pulse charging and discharging operation. Fig. 2 is a schematic diagram of a battery state of health estimation system according to an embodiment of the present disclosure, as shown in fig. 2, a controller of an electronic device is connected to a BMS system of a battery, the controller of the electronic device is connected to DC/DC, and the controller is connected to AC/DC. Fig. 3 is a schematic diagram of another battery state of health estimation system according to an embodiment of the present disclosure, as shown in fig. 3, a controller of an electronic device is connected to AC/DC.
And step S202, sending the control signal to the battery.
In the embodiment of the application, after the battery receives the control signal, the reference direct current pulse signal can be generated based on the control signal.
Step S203, obtaining a first current and a first terminal voltage of the battery, where the first current and the first terminal voltage are generated based on a reference dc pulse signal.
Step S204, determining the SOH of the battery based on the first current and the first terminal voltage.
Step S205, obtaining a second current and a second terminal voltage of the battery at a second time in the battery running time, where the first time and the second time are in a preset time interval, and the second current and the second terminal voltage are generated when the battery runs.
In step S206, an estimated SOH of the battery is determined based on the second current, the second terminal voltage and a prediction algorithm.
Step S207, under the condition that the condition of adjusting the prediction algorithm parameters is determined to be met based on the test SOH and the estimated SOH, adjusting the prediction algorithm parameters to obtain a target prediction algorithm, and estimating the SOH of the battery based on the target prediction algorithm.
According to the SOH estimation method provided by the embodiment of the application, when the battery is detected to be in a standby state, the pulse test function is added in the converter by generating the control signal, so that the battery internal resistance test is more convenient, the operation clearance of the system can be automatically completed, a test device is not required to be additionally used, and the requirements on cost and manpower are reduced.
In some embodiments, the step S102 "determining the test SOH of the battery based on the first internal resistance" may be implemented by the following steps, and fig. 4 is a schematic flowchart of a process for determining the test SOH of the battery according to an embodiment of the present application, as shown in fig. 4:
step S1021, the initial internal resistance and the service life ending internal resistance of the battery are obtained.
In the embodiment of the application, the initial internal resistance and the end-of-life internal resistance can be predetermined, and the initial internal resistance can be R NEW Indicating that the end-of-life internal resistance can be represented by R EOL And (4) showing.
In step S1022, a first difference value is determined based on the initial internal resistance of the battery and the first internal resistance.
In the embodiment of the present application, the first internal resistance may be represented by R0 (Ti).
Step S1023, a second difference value is determined based on the initial internal resistance and the end internal resistance of the battery life;
step S1024, determining a test SOH of the battery based on the first difference and the second difference.
In the embodiment of the present application, the SOH under test can be represented by SOH (ti), and the calculation formula of SOH (ti) is shown in formula (1):
SOH(Ti)＝R EOL -R0(Ti)/R EOL -R NEW (1)；
according to the SOH estimation method provided by the embodiment of the application, the first internal resistance of the battery can be detected in the standby time, the testing SOH is determined through the first internal resistance, so that the parameters of the prediction algorithm are adjusted according to the testing SOH and the estimated SOH to obtain the target prediction algorithm, and the SOH estimation is further performed based on the target prediction algorithm.
In some embodiments, before step S105 "in the case where it is determined that a condition for adjusting a prediction algorithm parameter is satisfied based on the test SOH and the estimated SOH, adjusting the prediction algorithm parameter to obtain a target prediction algorithm for estimating the SOH of the battery based on the target prediction algorithm", the method further comprises:
and S106, determining whether the condition for adjusting the parameters of the prediction algorithm is met or not based on the test SOH and the estimated SOH.
In the embodiment of the present application, the step S106 "determining whether the condition for adjusting the prediction algorithm parameter is satisfied based on the test SOH and the estimated SOH" may be implemented by:
in step S1061, the difference between the test SOH and the estimated SOH is determined.
In the embodiment of the present application, the estimated SOH may be represented by SOH (ti).
Step S1062, determining the size relationship between the difference value and a preset threshold value.
In the embodiment of the application, the preset threshold can be represented by xi, and xi and SOH (t) can be compared i )-SOH(T j ) To determine the size relationship.
In the embodiment of the application, xi is largeIn SOH (t) i )-SOH(T j ) Then, the estimation accuracy of the prediction algorithm for estimating the SOH can be considered to meet the requirement, and step S1063 is performed when ξ is less than or equal to the SOH (t) i )-SOH(T j ) Then, step S1064 is performed.
And step S1063, determining that the condition for adjusting the prediction algorithm parameters is not met.
The method and the device try two ways, and when the condition that the parameter of the prediction algorithm is not adjusted is determined, the prediction algorithm is determined to be the target prediction algorithm.
In the embodiment of the present application, the parameters of the prediction algorithm are denoted by f (k), and f (k) may include k1, k2, k3, k4, and the like.
Step S1064, determining that the condition for adjusting the prediction algorithm parameters is satisfied.
In the embodiment of the present application, when adjusting the parameters of the prediction algorithm, one or more of them may be adjusted.
After step S1064 is performed, step S105 is performed.
In the embodiment of the application, during the adjustment, the calculated SOH (t) i )-SOH(T j )<And xi, completing the adjustment, and further determining a target prediction algorithm.
According to the SOH estimation method provided by the embodiment of the application, the parameters of the prediction algorithm are optimized by taking the tested SOH determined by the first current and the first terminal voltage as a standard to obtain the target prediction algorithm, so that the SOH is estimated in real time based on the target prediction algorithm, and the estimation precision can be improved.
In some embodiments, after step S105, the method further comprises:
and step S107, acquiring a third current and a third terminal voltage of the battery.
And step S108, calculating the SOH of the battery based on the third current, the third terminal voltage and the target prediction algorithm.
In some embodiments, when step S102 is executed, the following steps may also be executed:
step S106, determining a first internal resistance based on the first current and the first terminal voltage.
In this embodiment of the application, the first internal resistance may be calculated based on the first current and the first terminal voltage.
And S107, determining a target equivalent circuit model based on the first internal resistance.
In the embodiment of the application, a plurality of equivalent circuit models can be established in advance, and the internal resistances of the equivalent circuit models are different. A target equivalent circuit model may be determined from the equivalent circuit model based on the second internal resistance.
And step S108, updating the internal resistance parameter of the battery to be the internal resistance parameter corresponding to the target equivalent circuit model.
According to the SOH estimation method provided by the embodiment of the application, the equivalent circuit model is set, the corresponding target equivalent circuit model is determined after the first internal resistance is determined, the internal resistance parameters are updated through the target equivalent circuit model, the internal resistance parameters of the battery can be updated at irregular time in the operation process, and the latest internal resistance parameters are adopted for calculation when the SOH estimation is performed next time, so that the SOH estimation precision can be improved.
Based on the foregoing embodiments, an embodiment of the present application further provides a method for estimating an SOH, where the method includes:
step S301, a battery internal resistance test function is added to a converter of the energy storage system (similar to the battery in the above embodiment).
In the embodiment of the present application, a current transformer used by an energy storage battery includes two types, namely DC/AC and DC/DC, on the premise of not changing hardware configuration, a direct current pulse discharge function is increased by control, that is, constant current control of the battery is adopted, and the time of the constant current control is Δ T, fig. 5 is a schematic flow diagram of a control instruction generated by referring to a direct current pulse signal provided in the embodiment of the present application, as shown in fig. 5, a controller sends a reference pulse current to a current control loop, and a PWM generates a switch control instruction to send to the current transformer.
In this embodiment, a BMU collects changes in current and terminal voltage flowing through a battery module, calculates battery internal resistance parameters through a BMU control chip (the same as the electronic device in the above embodiment) to obtain battery internal resistance through the battery internal resistance parameters, and fig. 6 is a schematic flow diagram of BMU signal detection provided in this embodiment of the present application, where, as shown in fig. 6, the current and voltage changes are obtained through reference to each battery module input to the battery by a dc pulse, and through a BMU corresponding to each battery module, so as to calculate the battery internal resistance parameters.
Step S302, adding a timing battery parameter detection process to the operation control logic of the energy storage converter, and performing the battery parameter detection in step 301 at the system standby time (in the same battery standby time as in the above embodiment) to obtain a battery internal resistance parameter R0 (T). According to different system standby time, battery internal resistances R0(Ti) of a series of times T1, T2 and T3 … … Tn are obtained. And calculating SOH _ test of the battery based on the measured internal resistance parameter of the battery (same as the test SOH in the above embodiment), wherein the SOH _ test is calculated according to the following formula:
SOH(Ti)＝R EOL -R0(Ti)/R EOL -R NEW 。
step S303, when the energy storage system runs, the battery carries out SOH estimation, and the SOH estimation of the battery based on the Kalman filtering algorithm is taken as an example in a mode based on an equivalent circuit model. After the system is standby each time, updating the equivalent circuit model parameters according to the battery internal resistance parameters obtained in step 302, and performing battery SOH evaluation, wherein the evaluation can be performed in real time during the operation of the energy storage system, so as to obtain a series of battery SOH (ti) (the same as the estimated SOH in the above embodiment) at times t1, t2, t3, t4 … …, and tn.
Step S304, optimizing the precision of the real-time battery state of health (SOH) (ti) based on the SOH _ test. When ti-Tj<When epsilon, epsilon is an acceptable time interval during which SOH estimation method optimization is performed. When SOH (t) i )-SOH(T j )<ξ, the real-time SOH evaluation accuracy in step S303 is acceptable; when SOH (t) i )-SOH(T j )>ξ, relevant parameters of the SOH estimation method are adjusted in the step S303 until the accuracy requirement is met, and then the SOH estimation is carried out based on a target prediction algorithm.
According to the SOH estimation method provided by the embodiment of the application, the pulse test function is added in the converter, so that the battery internal resistance test is more convenient, and the operation clearance of the system can be automatically finished. And a test device is not required to be additionally used, so that the requirements on cost and labor are reduced. Parameters of the battery equivalent circuit model are updated at irregular time in the system operation process, and the latest battery internal resistance parameter is adopted for estimating the SOH of the battery, so that the estimation precision can be improved. According to the standard of SOH obtained by battery parameter test, parameters in the SOH real-time evaluation algorithm are optimized, and evaluation precision is improved.
Based on the foregoing embodiments, an SOH estimation method is further provided in an embodiment of the present application, and fig. 7 is a schematic flow chart illustrating an implementation of another SOH estimation method provided in the embodiment of the present application, as shown in fig. 7, including:
step S701, determining whether the battery equivalent circuit parameter is empty.
In this embodiment of the application, when the battery equivalent circuit parameter is not empty, step S705 is executed. When the battery equivalent circuit parameter is empty, step S702 is executed.
Step S702, examine the dc pulse test procedure.
Step S703, determine the battery SOH (Tj)
Step S704, the battery parameter update flag.
Step S705, an energy storage control program is called.
And step S706, judging whether the energy storage system is started to operate.
In the embodiment of the present application, step S707 is executed when the energy storage system starts to operate, and step S702 is executed when the energy storage system does not start to operate.
In step S707, battery soh (ti) is determined.
In step S708, it is determined whether the battery parameter is updated.
In the embodiment of the present application, when the battery parameter is not updated, the process is ended. When the battery parameter is updated, step S709 is executed.
Step S709, determine if SOH (t) i )-SOH(T j )<ξ。
In the examples of the present application, when SOH (t) i )-SOH(T j )<ξ, step S411 is executed when it is not SOH (t) i )-SOH(T j )<ξ, step S410 is executed.
Step S710, adjusting parameters of the battery monitoring state evaluation method.
In step S711, the battery parameter update flag is cleared.
Based on the foregoing embodiments, an embodiment of the present application further provides a parameter optimization method for a prediction algorithm, and fig. 8 is a schematic implementation flow diagram of the parameter optimization method for the prediction algorithm provided in the embodiment of the present application, as shown in fig. 8, including:
in step S801, battery system measurement data is acquired.
Step S802, evaluation is performed by an SOH evaluation algorithm.
In the embodiment of the application, the SOH _ estimate (t) is obtained by evaluating through an SOH evaluation algorithm.
In step S803, SOH test is obtained by pulse test and calculation.
Step S804, determining that the SOH deviation is greater than the set value.
In the embodiment of the present application, when not greater than the set value, step S801 is executed. When greater than the set value, step S805 is performed.
In step S805, it is determined that the time interval between the two sides is smaller than the set value.
When the time interval is smaller than the set value, step S806 is performed. When the time interval is not less than the set value twice, step S807 is performed.
Step S806, adjust algorithm parameters f (K) K1, K2, K3 … ….
In step S807, an abnormality is recorded.
Based on the foregoing embodiments, the present application provides a method and an apparatus for estimating a state of health of a battery, where the apparatus includes modules and units included in the modules, and the modules and the units may be implemented by a processor in a computer device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
Fig. 9 is a schematic structural diagram of an apparatus for estimating a state of health of a battery according to an embodiment of the present application, and as shown in fig. 9, an apparatus 900 for estimating a state of health of a battery includes:
a first obtaining module 901, configured to obtain a first current and a first terminal voltage of a battery at a first time within a standby time of the battery, where the first current and the first terminal voltage are generated based on a reference dc pulse signal;
a first determining module 902 for determining a test SOH of the battery based on the first current and the first terminal voltage;
a second obtaining module 903, configured to obtain a second current and a second end voltage of the battery at a second time in the battery operation time, where the first time and the second time are in a preset time interval, and the second current and the second end voltage are generated when the battery operates;
a second determination module 904 for determining an estimated SOH of the battery based on the second current, the second terminal voltage, and a prediction algorithm;
an adjusting module 905, configured to, when it is determined that a condition for adjusting a prediction algorithm parameter is satisfied based on the test SOH and the estimated SOH, adjust the prediction algorithm parameter to obtain a target prediction algorithm, so as to estimate the SOH of the battery based on the target prediction algorithm.
In some embodiments, the pool health estimation device 900 further comprises:
the generating module is used for generating a control signal when the battery is detected to be in a standby state, wherein the control signal is used for controlling the battery to generate a reference direct current pulse signal;
and the sending module is used for sending the control to the battery so as to obtain a first current and a first terminal voltage of the battery.
In some embodiments, the first determining module 902 includes:
a first determination unit configured to determine a first internal resistance of the battery based on the first current and the first voltage;
a second determining unit for determining a test SOH of the battery based on the first internal resistance.
In some embodiments, the second determining unit includes:
the acquisition subunit is used for acquiring the initial internal resistance and the service life ending internal resistance of the battery;
a first determining subunit configured to determine a first difference value based on the initial internal resistance of the battery and the first internal resistance;
a second determining subunit, configured to determine a second difference value based on the initial internal resistance and the end-of-life internal resistance of the battery;
a third determining subunit configured to determine a test SOH of the battery based on the first difference and the second difference.
In some embodiments, the pool health estimation device 900 further comprises:
and the judging module is used for determining whether the condition for adjusting the parameters of the prediction algorithm is met or not based on the test SOH and the estimated SOH.
In some embodiments, the determining module comprises:
a third determination unit for determining a difference between the test SOH and the estimated SOH;
the fourth determining unit is used for determining the size relation between the difference value and a preset threshold value;
and a fifth determining unit, configured to determine whether a condition for adjusting the budget algorithm parameter is satisfied based on the magnitude relationship, wherein in a case where it is determined that the condition for adjusting the prediction algorithm parameter is not satisfied, the prediction algorithm is determined as the target prediction algorithm.
In some embodiments, the battery state of health estimation device 900 further comprises:
a fourth determining module for determining a first internal resistance based on the first current and the first terminal voltage;
a fifth determining module, configured to determine a target equivalent circuit model based on the first internal resistance;
and the updating module is used for updating the internal resistance parameter of the battery to be the internal resistance parameter corresponding to the target equivalent circuit model.
It should be noted that, in the embodiment of the present application, if the above estimation method of the state of health of the battery is implemented in the form of a software functional module and is sold or used as a standalone product, it may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to implement the steps in the estimation method of the state of health of the battery provided in the above embodiment.
The embodiment of the application provides an electronic device; fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 10, the electronic device 1000 includes: a processor 1001, at least one communication bus 1002, a user interface 1003, at least one external communication interface 1004, and a memory 1005. Wherein the communication bus 1002 is configured to enable connected communication between these components. The user interface 1003 may include a display screen, and the external communication interface 1004 may include a standard wired interface and a wireless interface, among others. The processor 1001 is configured to execute a program of the estimation method of the state of health of the battery stored in the memory to realize the steps in the estimation method of the state of health of the battery provided in the above-described embodiment.
The above description of the display device and storage medium embodiments is similar to the description of the method embodiments above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the computer device and the storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It is to be noted here that: the above description of the storage medium and device embodiments, similar to the description of the method embodiments above, has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element identified by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps of implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer-readable storage medium, and when executed, executes the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application, which are essentially or partly contributing to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of estimating state of health of a battery, comprising:
acquiring a first current and a first terminal voltage of a battery at a first moment in the standby time of the battery, wherein the first current and the first terminal voltage are generated based on a reference direct current pulse signal;
determining a test state of health, SOH, of the battery based on the first current and the first terminal voltage;
acquiring a second current and a second terminal voltage of the battery at a second moment in the battery running time, wherein the first moment and the second moment are in a preset time interval, and the second current and the second terminal voltage are generated when the battery runs;
determining an estimated SOH of the battery based on the second current, the second terminal voltage, and a prediction algorithm;
and under the condition that the condition of adjusting the prediction algorithm parameters is determined to be met based on the test SOH and the estimated SOH, adjusting the prediction algorithm parameters to obtain a target prediction algorithm so as to estimate the SOH of the battery based on the target prediction algorithm.
2. The method of claim 1, further comprising:
when the battery is detected to be in a standby state, generating a control signal, wherein the control signal is used for controlling the battery to generate a reference direct current pulse signal;
and sending the control signal to the battery to obtain a first current and a first terminal voltage of the battery.
3. The method of claim 1, the determining a test state of health (SOH) of the battery based on the first current and the first terminal voltage, comprising:
determining a first internal resistance of the battery based on the first current and the first voltage;
determining a test SOH for the battery based on the first internal resistance.
4. The method of claim 3, wherein said determining a test SOH for the battery based on the first internal resistance comprises:
acquiring initial internal resistance and service life ending internal resistance of the battery;
determining a first difference value based on the initial internal resistance of the battery and the first internal resistance;
determining a second difference value based on the initial internal resistance and the end-of-life internal resistance of the battery;
determining a test SOH for the battery based on the first difference and the second difference.
5. The method of claim 4, further comprising:
based on the test SOH and the estimated SOH, it is determined whether a condition for adjusting a parameter of a predictive algorithm is satisfied.
6. The method of claim 5, wherein determining whether a condition for adjusting a parameter of a predictive algorithm is satisfied based on the test SOH and the estimated SOH comprises:
determining a difference between the test SOH and the estimated SOH;
determining the size relation between the difference value and a preset threshold value;
and determining whether the condition for adjusting the budget algorithm parameter is met or not based on the size relationship, wherein the prediction algorithm is determined to be the target prediction algorithm under the condition that the condition for adjusting the prediction algorithm parameter is determined not to be met.
7. The method of claim 1, further comprising:
determining a first internal resistance based on the first current and the first terminal voltage;
determining a target equivalent circuit model based on the first internal resistance;
and updating the internal resistance parameter of the battery to be the internal resistance parameter corresponding to the target equivalent circuit model.
8. An apparatus for estimating a state of health of a battery, comprising:
the device comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a first current and a first terminal voltage of a battery at a first moment in the standby time of the battery, and the first current and the first terminal voltage are generated based on a reference direct current pulse signal;
a first determination module to determine a test SOH for the battery based on the first current and the first terminal voltage;
a second obtaining module, configured to obtain a second current and a second end voltage of the battery at a second time within a battery running time, where the first time and the second time are within a preset time interval, and the second current and the second end voltage are generated when the battery runs;
a second determination module to determine an estimated SOH of the battery based on the second current, the second terminal voltage, and a prediction algorithm;
and the adjusting module is used for adjusting the prediction algorithm parameters to obtain a target prediction algorithm under the condition that the condition of adjusting the prediction algorithm parameters is determined to be met based on the test SOH and the estimated SOH, so that the estimation of the SOH of the battery is carried out based on the target prediction algorithm.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, performs the method of estimating a state of health of a battery according to any one of claims 1 to 7.
10. A storage medium storing a computer program executable by one or more processors and operable to implement a method of estimating a state of health of a battery as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110312826.1A CN115128494A (en) | 2021-03-24 | 2021-03-24 | Battery state of health estimation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110312826.1A CN115128494A (en) | 2021-03-24 | 2021-03-24 | Battery state of health estimation method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115128494A true CN115128494A (en) | 2022-09-30 |
Family
ID=83373727
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110312826.1A Pending CN115128494A (en) | 2021-03-24 | 2021-03-24 | Battery state of health estimation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115128494A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024093269A1 (en) * | 2022-10-31 | 2024-05-10 | 比亚迪股份有限公司 | Battery state of health prediction method, electronic device, and readable storage medium |
-
2021
- 2021-03-24 CN CN202110312826.1A patent/CN115128494A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024093269A1 (en) * | 2022-10-31 | 2024-05-10 | 比亚迪股份有限公司 | Battery state of health prediction method, electronic device, and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6734784B2 (en) | How to estimate battery health | |
| TWI675210B (en) | Method and system for estimating battery life and performance of rechargeable battery | |
| JP6234946B2 (en) | Battery state estimation device | |
| WO2017000912A2 (en) | Battery state of health detection device and method | |
| CN110249233B (en) | State of health estimation for battery | |
| US20160377686A1 (en) | Degradation estimation method, degradation estimation system, and degradation estimation program | |
| EP2851700B1 (en) | Method and terminal for displaying capacity of battery | |
| JP7067566B2 (en) | Battery monitoring device, computer program and battery monitoring method | |
| US20200309857A1 (en) | Methods, systems, and devices for estimating and predicting battery properties | |
| CN107923943B (en) | High-efficiency battery tester | |
| US20110231122A1 (en) | Method and system for determining the kind of a battery | |
| EP2023154A2 (en) | Battery status detecting method, battery status detecting apparatus, and expression deriving method | |
| US10038325B2 (en) | Electric storage device and deterioration determination method | |
| CN108693475B (en) | Method and apparatus for monitoring a DC power supply | |
| JPH03122581A (en) | Method and apparatus for predicting bat- tery discharge holding time | |
| JP2018147680A (en) | Temperature abnormality determination device, temperature abnormality determination method, and computer program | |
| JP6791002B2 (en) | Deterioration estimation device for secondary batteries | |
| KR20160113011A (en) | Battery remaining power predicting device and battery pack | |
| WO2022244378A1 (en) | Battery state determination method, and battery state determination apparatus | |
| TWI826949B (en) | Battery performance evaluation device and battery performance evaluation method | |
| CN115128494A (en) | Battery state of health estimation method and device, electronic equipment and storage medium | |
| JP5851514B2 (en) | Battery control device, secondary battery system | |
| CN114690039A (en) | Method and device for determining discharging internal resistance model and health degree of battery | |
| JP5985328B2 (en) | Storage battery residual value rating device and program | |
| KR20210011235A (en) | Apparatus and method for diagnosing battery cell |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |