CN115629313A - Pulse current prediction method and device of lithium ion battery and computer equipment - Google Patents

Pulse current prediction method and device of lithium ion battery and computer equipment Download PDF

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CN115629313A
CN115629313A CN202211288420.5A CN202211288420A CN115629313A CN 115629313 A CN115629313 A CN 115629313A CN 202211288420 A CN202211288420 A CN 202211288420A CN 115629313 A CN115629313 A CN 115629313A
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model
parameter
target
prediction model
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刘瑶俊
于文军
翟秀梅
何永武
徐中领
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Sunwoda Electric Vehicle Battery Co Ltd
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    • G—PHYSICS
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    • 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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • 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
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    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • 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
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Abstract

The application provides a pulse current prediction method and device of a lithium ion battery and computer equipment, wherein the method comprises the following steps: acquiring pulse curve data generated by the discharge of the lithium ion battery under at least three preset pulse current limits to obtain the variation trend information of each pulse curve data; constructing an initial pulse curve prediction model based on each change trend information; the initial pulse curve prediction model comprises target model parameters with unknown values; analyzing the data of each pulse curve through an initial pulse curve prediction model to determine parameter values of parameters of a target model, and obtaining a target pulse curve prediction model with known values; and predicting the discharge pulse of the lithium ion battery through a target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery is discharged under the limitation of preset pulse time. By adopting the method, the pulse current testing efficiency of the lithium ion battery can be improved, and the current of the lithium ion battery can be accurately predicted at different pulse times.

Description

Pulse current prediction method and device of lithium ion battery and computer equipment
Technical Field
The application relates to the technical field of lithium ion batteries, in particular to a pulse current prediction method and device of a lithium ion battery and computer equipment.
Background
With the popularization of new energy automobiles, power lithium ion batteries have entered a rapid development stage. The performance of the whole vehicle under different working conditions is directly determined by the pulse charging and discharging capacity of the battery, so that the pulse charging and discharging capacity of the lithium battery and the prediction of the pulse discharging current are necessary.
At present, for the prediction technology of the pulse charge and discharge capacity and the pulse discharge current of the lithium battery, a mode of testing the power performance in various states point by point or a mode of establishing an electrochemical and solid heat transfer coupling model of a battery cell to calibrate a pulse model is generally adopted, although a required result can be obtained, a large amount of time and test resources are still consumed in practice due to the complex flow and the need of a large number of basic parameters, and the prediction requirement of the performance of the lithium battery cannot be met.
Therefore, the existing lithium battery pulse current prediction technology has the technical problem of low test efficiency.
Disclosure of Invention
Therefore, it is necessary to provide a method, an apparatus, and a computer device for predicting a pulse current of a lithium ion battery, so as to implement accurate prediction of a maximum current of the lithium ion battery at different pulse times by analyzing a relationship between a pulse voltage, a pulse current, and a pulse time of the lithium ion battery, and effectively improve efficiency of testing the pulse current of the lithium ion battery without point-by-point testing or a large number of basic parameters.
In a first aspect, the present application provides a pulse current prediction method for a lithium ion battery, including:
acquiring pulse curve data generated by the discharge of the lithium ion battery under at least three preset pulse current limits to obtain the change trend information of each pulse curve data;
constructing an initial pulse curve prediction model based on each change trend information; the initial pulse curve prediction model comprises target model parameters with unknown values;
analyzing the data of each pulse curve through an initial pulse curve prediction model to determine parameter values of parameters of a target model, and obtaining a target pulse curve prediction model with known values;
and predicting the discharge pulse of the lithium ion battery through a target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery is discharged under the limitation of preset pulse time.
In some embodiments of the present application, obtaining pulse curve data generated by discharging a lithium ion battery under at least three preset pulse current limits to obtain variation trend information of each pulse curve data includes: performing discharge pulse test on the lithium ion battery based on preset test temperature, charge state and cut-off voltage to obtain test data of voltage change along with time after the lithium ion battery is discharged from initial voltage to cut-off voltage under the limit of each preset pulse current, so as to obtain pulse curve data; acquiring a voltage drop rate according to each pulse curve data; analyzing each voltage reduction rate to obtain variation trend information; the variation trend information comprises a first variation trend of short-time rapid reduction, a second variation trend of slow reduction and a third variation trend of near-end rapid reduction.
In some embodiments of the present application, the trend information includes a first trend, a second trend, and a third trend, and the constructing the initial pulse curve prediction model based on the trend information includes: constructing a first prediction model containing target model parameters and a first power function based on the first variation trend and the second variation trend; wherein the base of the first power function is less than or equal to the first pulse time at the moment of minimum voltage change rate; the time when the voltage change rate is minimum is determined according to the second derivative of the pulse curve data; constructing a second prediction model comprising target model parameters, a second power function and an exponential function based on the third variation trend; wherein the base of the second power function is a second pulse time greater than the minimum time of the voltage change rate, and the exponent of the exponential function is determined according to the difference between the second pulse time and the minimum time of the voltage change rate; and taking the first prediction model and the second prediction model as initial pulse curve prediction models.
In some embodiments of the present application, the initial pulse curve prediction model includes a first prediction model and a second prediction model, and the analyzing of each pulse curve data by the initial pulse curve prediction model to determine parameter values of target model parameters obtains a target pulse curve prediction model with known values, including: performing second-order derivation processing on each pulse curve data to obtain a corresponding voltage value when a derivation result is zero, and using the voltage value as a target voltage value; obtaining the average value of all target voltage values to obtain a curve segmented voltage value; fitting and analyzing each pulse curve data which is greater than or equal to the curve segmented voltage value through a first prediction model to determine parameter values of target model parameters contained in the first prediction model; fitting and analyzing each pulse curve data smaller than the curve segmented voltage value through a second prediction model to determine parameter values of target model parameters contained in the second prediction model; and taking an initial pulse curve prediction model with a known value as a target pulse curve prediction model.
In some embodiments of the present application, the target model parameters include a first model parameter, a second model parameter and a third model parameter, and the fitting analysis of the respective pulse curve data greater than or equal to the curve segment voltage value by the first prediction model to determine the parameter values of the target model parameters included in the first prediction model includes: fitting and analyzing each pulse curve data which is greater than or equal to the curve segmented voltage value through a first prediction model to obtain parameter values of a first model parameter, a second model parameter and a third model parameter which are associated with each preset pulse current; performing exponential function fitting processing on the parameter value of each first model parameter and the pulse current to obtain a first function expression of the first model parameter; carrying out mean value calculation processing on the parameter values of the second model parameters to obtain second parameter values of the second model parameters; performing linear fitting processing on the parameter values of the third model parameters and the pulse current to obtain a third function expression of the third model parameters; and taking the first function expression, the second parameter value and the third function expression as the parameter values of the target model parameters contained in the first prediction model.
In some embodiments of the present application, the target model parameters further include fourth model parameters and fifth model parameters, and the fitting analysis of the respective pulse curve data smaller than the curve segment voltage value by the second prediction model to determine the parameter values of the target model parameters included in the second prediction model includes: through a second prediction model, fitting and analyzing each pulse curve data smaller than the curve segmented voltage value to obtain parameter values of a fourth model parameter and a fifth model parameter associated with each preset pulse current; performing exponential function fitting processing on the parameter value of each fourth model parameter and the preset pulse current to obtain a fourth function expression of the fourth model parameter; performing linear fitting processing on the parameter values of the fifth model parameters and the preset pulse current to obtain a fifth function expression of the fifth model parameters; and taking the first function expression, the second parameter value, the third function expression, the fourth function expression and the fifth function expression as the parameter values of the target model parameters contained in the second prediction model.
In some embodiments of the present application, after determining the parameter values of the target model parameters included in the first prediction model, the method further includes: determining a pulse time corresponding to a target voltage value in each pulse curve data as a time with the minimum voltage change rate; extracting constant parameter values in the first function expression and the third function expression; determining a time function expression at the moment when the voltage change rate is minimum according to the curve segment voltage value, the constant parameter value and the second parameter value; the time function expression is used for predicting discharge pulses of the lithium ion battery by combining with a target pulse curve prediction model.
In a second aspect, the present application provides a pulse current prediction apparatus for a lithium ion battery, comprising:
the data acquisition module is used for acquiring pulse curve data generated by the lithium ion battery under the limitation of at least three preset pulse currents to obtain the change trend information of each pulse curve data;
the model construction module is used for constructing an initial pulse curve prediction model based on each change trend information; the initial pulse curve prediction model comprises target model parameters with unknown values;
the curve analysis module is used for analyzing the data of each pulse curve through the initial pulse curve prediction model to determine the parameter value of the parameter of the target model and obtain a target pulse curve prediction model with known value;
and the current prediction module is used for predicting the discharge pulse of the lithium ion battery through the target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery is discharged under the preset pulse time limit.
In a third aspect, the present application further provides a computer device, comprising:
one or more processors;
a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the pulse current prediction method of the lithium ion battery.
In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is loaded by a processor to execute the steps in the pulse current prediction method for a lithium ion battery.
In a fifth aspect, embodiments of the present application provide a computer program product or a computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided by the first aspect.
According to the pulse current prediction method, the pulse current prediction device and the computer equipment of the lithium ion battery, the server obtains the change trend information of each pulse curve data by obtaining the pulse curve data generated by the discharge of the lithium ion battery under at least three preset pulse current limits, then an initial pulse curve prediction model is constructed based on each change trend information, each pulse curve data can be analyzed to obtain a target pulse curve prediction model with known values, and finally the discharge pulse prediction is carried out on the lithium ion battery by utilizing the target pulse curve prediction model, so that the maximum pulse current which is about to be generated after the discharge of the lithium ion battery under the preset pulse time limit can be obtained. Therefore, by analyzing the relation among the pulse voltage, the pulse current and the pulse time of the lithium ion battery, the target pulse curve prediction model capable of accurately describing the relation is established, namely the target pulse curve prediction model can be utilized to realize accurate prediction of the pulse current of the lithium ion battery at different pulse times, namely the pulse current is measured without adopting complex modes such as point-by-point test and the like, and the pulse current test efficiency of the lithium ion battery is effectively improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is an application scenario diagram of a pulse current prediction method for a lithium ion battery provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a pulse current prediction method for a lithium ion battery provided in an embodiment of the present application;
FIG. 3 is a pulse plot of pulse voltage versus pulse time provided by an embodiment of the present application;
FIG. 4 is a graph of a first model parameter versus pulse current fit provided by an embodiment of the present application;
FIG. 5 is a plot of a third model parameter versus pulse current as provided in an embodiment of the present application;
FIG. 6 is a graph of a fourth model parameter versus pulse current fit provided by an embodiment of the present application;
FIG. 7 is a graph of a fifth model parameter fitted to a pulse current according to an embodiment of the present disclosure;
FIG. 8 is a schematic diagram illustrating a verification effect of a target pulse curve prediction model provided in an embodiment of the present application;
fig. 9 is a schematic structural diagram of a pulse current prediction apparatus of a lithium ion battery provided in an embodiment of the present application;
fig. 10 is a schematic structural diagram of a computer device in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In the description of the present application, the term "for example" is used to mean "serving as an example, instance, or illustration". Any embodiment described herein as "for example" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes are not shown in detail to avoid obscuring the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
In this embodiment, the pulse current prediction method for the lithium ion battery provided in this embodiment may be applied to a pulse current prediction system for the lithium ion battery shown in fig. 1. The pulse current prediction system of the lithium ion battery comprises a terminal 102 and a server 104. The terminal 102 may be a device that includes both receiving and transmitting hardware, i.e., a device having receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device having a single line display or a multi-line display or a cellular or other communication device without a multi-line display. The terminal 102 may be a desktop terminal or a mobile terminal, the terminal 102 may be one of a mobile phone, a tablet computer and a notebook computer, and the terminal 102 may even be a discharge device including, but not limited to, various electronic devices depending on battery driving. The server 104 may be an independent server, or may be a server network or a server cluster composed of servers, which includes, but is not limited to, a computer, a network host, a single network server, an edge server, a plurality of network server sets, or a cloud server composed of a plurality of servers. Among them, the Cloud server is constituted by a large number of computers or web servers based on Cloud Computing (Cloud Computing). In addition, a communication connection is established between the terminal 102 and the server 104 through a network, which may be any one of a wide area network, a local area network, and a metropolitan area network.
Those skilled in the art will appreciate that the application environment shown in fig. 1 is only one application scenario applicable to the present application, and does not constitute a limitation to the application scenario of the present application, and that other application environments may include more or fewer devices than those shown in fig. 1. For example, only 1 server is shown in fig. 1. It is to be understood that the pulse current prediction system of the lithium ion battery may further include one or more other devices, which are not limited herein. In addition, the pulse current prediction system of the lithium ion battery can further comprise a memory for storing data, such as pulse curve data.
It should be noted that the scene schematic diagram of the pulse current prediction system of the lithium ion battery shown in fig. 1 is merely an example, and the pulse current prediction system of the lithium ion battery and the scene described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not form a limitation on the technical solution provided in the embodiment of the present invention.
Referring to fig. 2, an embodiment of the present application provides a method for predicting a pulse current of a lithium ion battery, which is mainly exemplified by applying the method to the server 104 in fig. 1, and the method includes steps S201 to S204, which are specifically as follows:
s201, pulse curve data generated by discharging of the lithium ion battery under at least three preset pulse current limits are obtained, and the change trend information of each pulse curve data is obtained.
Among them, a lithium ion battery is a type of secondary battery (rechargeable battery) that mainly operates by movement of lithium ions between a positive electrode and a negative electrode.
The current or voltage pulse that appears repeatedly in cycles is referred to as a pulse current, and the preset pulse current in the embodiment of the present application may be a current value preset to measure any value of the performance of the lithium ion battery, for example, the preset pulse current may be "215A"),"229.5A", "243A", "256.5A", "270A", etc. It should be noted that the five preset pulse currents are values provided as measurement bases in the embodiments of the present application, and it is not excluded that pulse currents with other values are selected for performance measurement in other embodiments, and it is only required to be clear that the number of the preset pulse currents selected as the measurement bases is not less than 3. That is, the preset pulse current may be represented as "I n ", n is a positive integer no less than 3.
In specific implementation, in order to improve the lithium ion battery pulse current testing efficiency, the server 104 may analyze the relationship between the pulse voltage (U) of the lithium ion battery and the pulse current (I) and the pulse time (T) to realize accurate prediction of the maximum current of the lithium ion battery at different pulse times, and before that, it is required to obtain pulse curve data generated by discharging of the lithium ion battery under each preset pulse current limit to determine the relationship between the pulse voltage (v) and the pulse time (T) at different pulse currents, so as to obtain the pulse curve data reflecting the pulse voltage (v) and the pulse time (T) as shown in fig. 3.
Specifically, the pulse curve data may be obtained by placing a lithium ion battery in a certain discharge device, as shown in fig. 1, the terminal 102 may be the discharge device at this time, then debugging the ambient temperature and the charge state in the discharge device as fixed parameters in the discharge process of the lithium ion battery, and finally controlling the lithium ion battery to complete one discharge pulse operation one by one according to the preset pulse current, so as to obtain pulse curve data generated by the discharge of the lithium ion battery under each preset pulse current limit.
Of course, the pulse profile data may also be obtained without depending on the discharge device, that is, the terminal 102 shown in fig. 1 is not a discharge device, and then the server 104 may obtain the currently required pulse profile data by one of the following manners: 1. in a common network configuration, the server 104 receives pulse curve data from the terminal 102 or other cloud devices with network connections established; 2. in a preset block chain network, the server 104 may synchronously acquire pulse curve data from other terminal nodes or server nodes, where the block chain network may be a public chain, a private chain, or the like; 3. in the preset tree structure, the server 104 may request the pulse profile data from a higher level server or poll the pulse profile data from a lower level server.
Further, after the server 104 acquires the pulse curve data shown in fig. 3, the trend of all curves in the pulse curve data changing with time may be analyzed, so as to obtain the change trend information of each pulse curve data. Among them, the trend analyzing step involved in the present embodiment will be described in detail below.
In one embodiment, this step includes: performing discharge pulse test on the lithium ion battery based on preset test temperature, charge state and cut-off voltage to obtain test data of voltage change along with time after the lithium ion battery is discharged from initial voltage to cut-off voltage under the limit of each preset pulse current, so as to obtain pulse curve data; acquiring voltage drop rate according to each pulse curve data; analyzing each voltage reduction rate to obtain variation trend information; the variation trend information comprises a first variation trend of short-time rapid reduction, a second variation trend of slow reduction and a third variation trend of near-end rapid reduction.
Here, the test temperature refers to an ambient temperature set in predicting the pulse current of the lithium ion battery, and for example, the test temperature may be preset to "25 ℃", but is not limited to this value.
The State of Charge refers to a State of Charge (SOC) set in the pulse current prediction process of the lithium ion battery, and represents a ratio between a remaining capacity and a total available capacity of the battery after the battery is used or left for a long time, and is usually expressed by a percentage, for example, the State of Charge may be preset to "50%", but is not limited to this value.
The cut-off voltage is a voltage that drops to a minimum operating voltage value at which the battery is not suitable for further discharging when the battery is discharged, and for example, the cut-off voltage may be preset to "2.7V", but is not limited thereto.
In a specific implementation, in order to obtain the variation trend information of the pulse curve data, the server 104 first needs to obtain the pulse curve data. With reference to the foregoing embodiments, it can be known that a primary condition for acquiring the pulse curve data provided in the embodiments of the present application is that the test temperature and the charge state of the lithium ion battery are fixed and unchanged, then the battery is controlled to discharge from the initial voltage to the preset cut-off voltage under the limitation of each preset pulse current, and the recorded data is the pulse curve data.
For example, taking a Ternary Lithium ion Battery (Ternary Lithium Battery) as an example, the preset test temperature is "25 ℃", the charge state is "50%", and the cut-off voltage is "2.7V", and the Ternary Lithium ion Battery is controlled to perform a discharge pulse test in sequence according to the preset pulse current "215A", "229.5A", "243A", "256.5A", "270A", and "297A", so as to obtain the pulse curve data shown in fig. 3.
Further, as can be seen from analyzing the pulse curve data shown in fig. 3, the voltage variation curve of the ternary lithium ion battery with time can be described as three stages: (1) The terminal voltage of the battery is rapidly reduced in the initial stage, the larger the discharge multiplying power is, the faster the voltage is reduced, the reduction response is in the millisecond level, and the first change trend of short-time rapid reduction is presented due to the ohmic internal resistance of the battery; (2) The battery voltage enters a slowly changing stage, namely a platform area of the battery, the smaller the discharge multiplying power is, the longer the platform area duration is, the higher the platform voltage is, the slower the voltage drops, and a second changing trend of slow drop is presented; (3) When the battery capacity is close to the end of discharge, the battery voltage starts to drop sharply until the discharge cut-off voltage is reached, and the larger the current is, the faster the drop is, and a third change trend of sudden and sudden drop appears.
Of course, the division of the variation trend only stays at the physical phenomenon, the actual division can be analyzed by calculating the voltage reduction speed, curve fitting can be performed on each pulse curve data, then second-order derivation processing is performed on the fitting result to obtain the voltage reduction rate, the voltage reduction rate of each curve is further comprehensively analyzed, the voltage variation rate threshold value used for dividing the variation trend is determined, and finally the turning point of the voltage reduction rate is analyzed by combining the threshold value, so that the variation trend information of all the pulse curve data can be determined.
S202, constructing an initial pulse curve prediction model based on each change trend information; the initial pulse curve prediction model comprises target model parameters with unknown values.
The target model parameter is a model parameter whose value is unknown, but whose value or expression needs to be determined by analyzing the pulse curve data, for example, the target model parameter includes but is not limited to at least one of the following: "a, b, c, d, e".
In specific implementation, in order to improve the lithium ion battery pulse current testing efficiency, the server 104 may analyze a small amount of pulse curve data, first construct a pulse curve prediction model for explaining the relationship among pulse voltage (U), pulse current (I), and pulse time (T), and then debug the model, so that the model parameters may be highly matched with the current testing scene, and accurate prediction of the battery pulse current may be achieved. Therefore, investment on basic test data can be saved, a test link is simplified, pulse current test efficiency is improved, a reliable result can be output based on a specific parameter relation, and finally pulse current prediction accuracy is improved. The model building steps according to the present embodiment will be described in detail below.
In one embodiment, the trend information includes a first trend, a second trend, and a third trend, and the step includes: constructing a first prediction model containing target model parameters and a first power function based on the first variation trend and the second variation trend; wherein the base of the first power function is less than or equal to the first pulse time at the moment of minimum voltage change rate; the time when the voltage change rate is minimum is determined according to the second derivative of the pulse curve data; constructing a second prediction model comprising target model parameters, a second power function and an exponential function based on the third variation trend; the base number of the second power function is a second pulse time which is larger than the minimum moment of the voltage change rate, and the exponent of the exponential function is determined according to the difference between the second pulse time and the minimum moment of the voltage change rate; and taking the first prediction model and the second prediction model as initial pulse curve prediction models.
Wherein, the power function is a function "y = x" with base as independent variable, power as dependent variable and exponent as constant α "(. Alpha. -a rational number). The time at which the voltage change rate is minimal may be denoted as "t 0 ", which can be obtained by calculating the second derivative of each pulse profile data.
In a specific implementation, in combination with the analysis of the above embodiment, a voltage variation curve of the ternary lithium ion battery with time may be described as three stages: (1) The terminal voltage of the battery is rapidly reduced in the initial stage, the larger the discharge multiplying power is, the faster the voltage is reduced, the reduction response is in the millisecond level and is caused by the ohmic internal resistance of the battery, so a parameter 'c' can be adopted to represent the voltage reduction result in the stage, and the 'c' and the current are in a linear relation; (2) The battery voltage enters a stage of slow change, namely a platform area of the battery, the smaller the discharge multiplying power is, the longer the platform area duration is, the higher the platform voltage is, the slower the voltage drop is, so that the empirical formula 'a x t' can be adopted in the stage b "means," b "is typically a constant value," a "is related to current; (3) When the battery capacity is close to the end of discharge, the battery voltage begins to drop sharply until the discharge cut-off voltage is reached, the more the current is, the faster the drop is, and the formula'd x exp (e x (t-t) in the form of an exponential function is added at the final stage according to the curve shape 0 ) ") and then explores the relationship of the parameters" d "," e "to the current.
Specifically, the server 104 may construct a first prediction model "V = c + a × t" including the target model parameters and the first power function based on the first trend of change and the second trend of change b ；t≤t 0 ". Meanwhile, the server 104 may further construct a second prediction model "V = c + a × t" including the target model parameter, the second power function, and the exponential function based on the third variation trend b +d*exp(e*(t-t 0 ))；t>t 0 ”。
Thus, the initial pulse profile prediction model is represented as:
V＝c+a*t b ；t≤t 0
V＝c+a*t b +d*exp(e*(t-t 0 ))；t>t 0
s203, analyzing the data of each pulse curve through the initial pulse curve prediction model to determine the parameter value of the target model parameter, and obtaining a target pulse curve prediction model with known value.
In a specific implementation, after the server 104 constructs the initial pulse curve prediction model by analyzing the variation trend information of the pulse curve data, the model may be used to analyze the pulse curve data obtained in the preamble step, and specifically, which numerical values or expressions can be used for expressing each pulse curve data for the target model parameters in the analysis model, so as to determine the parameter numerical values of the target model parameters, and further obtain the target pulse curve prediction model.
In one embodiment, the initial pulse profile prediction model comprises a first prediction model and a second prediction model, and the step comprises: performing second-order derivation processing on each pulse curve data to obtain a corresponding voltage value when a derivation result is zero, and taking the voltage value as a target voltage value; obtaining the average value of all target voltage values to obtain a curve segmented voltage value; fitting and analyzing each pulse curve data which is greater than or equal to the curve segmented voltage value through a first prediction model to determine parameter values of target model parameters contained in the first prediction model; fitting and analyzing each pulse curve data smaller than the curve segmented voltage value through a second prediction model to determine parameter values of target model parameters contained in the second prediction model; and taking an initial pulse curve prediction model with a known value as a target pulse curve prediction model.
In a specific implementation, to determine the parameter values of the target model parameters, the server 104 may first perform smoothing processing on each obtained pulse curve data, then perform linear fitting on the smooth curve to obtain a corresponding curve expression, then perform second-order derivation processing, calculate the voltage change rate of the pulse curve at each time point, and further use the pulse time (corresponding to the abscissa) with the voltage change rate of zero as the aforementioned minimum voltage change rate time "t 0 ", and a pulse voltage (corresponding to the ordinate) whose voltage change rate is zero as a target voltage value" V t0 ”。
Further, based on the above pointsAnalyzing the strategy, namely analyzing the pulse curve data corresponding to each preset pulse current one by one to obtain the target voltage values V corresponding to different pulse currents t0 ", and then calculates all target voltage values" V t0 "the curve segment voltage value for segment analysis of the pulse curve can be obtained.
For example, the preset pulse currents "215A", "229.5A", "243A", "256.5A", "270A" and "297A" are taken as examples, and the target voltage value "V" is t0 "and the calculation results of the curve segment voltage values are shown in table 1 below:
Figure BDA0003900322550000121
furthermore, the pulse curve data of V ≧ 3.1744V is taken, and a first prediction model of V = c + a t is adopted b Performing fitting analysis to obtain values of target model parameters c, a and b under different pulse currents; taking pulse curve data of V < 3.1744V, and using a second prediction model of V = c + a t b +d*exp(e*(t-t 0 ) Carrying out fitting analysis to obtain the values of the target model parameters d and e under different pulse currents.
For example, following the parameter settings in the previous embodiment, taking the preset pulse currents "215A", "229.5A", "243A", "256.5A" and "270A" as examples, the parameter numerical analysis of the target model parameters "a, b, c, d, e" at different preset pulse currents is shown in the following table 2:
current (A) c a b d e
270 3.36567 -0.0745279 0.647812 -0.0026414 0.8391168
256.5 3.37723 -0.0702998 0.648767 -0.003356 0.721939
243 3.38943 -0.0664814 0.64643 -0.0041683 0.6055752
229.5 3.40159 -0.063444 0.638086 -0.0050732 0.4941909
215 3.41589 -0.0610234 0.621834 -0.0062905 0.3800334
Therefore, the parameter values of the target model parameters 'a, b, c, d and e' are all in a known state, and a target pulse curve prediction model can be obtained. However, as can be seen from analyzing the values in table 2, the parameter data of some target model parameters have a certain difference when the pulse current is different, so to make the prediction accuracy of the target pulse curve prediction model higher, the parameter values of the target model parameters "a, b, c, d, e" need to be further analyzed to ensure that the target model parameters are fixed when the pulse current values are different, i.e., the fluctuation influence on the prediction result is not generated. Among them, the target model parameter analysis step involved in the present embodiment will be described in detail below.
In one embodiment, the target model parameters include a first model parameter, a second model parameter and a third model parameter, and the fitting analysis of the respective pulse curve data greater than or equal to the curve segment voltage value by the first prediction model to determine the parameter values of the target model parameters included in the first prediction model includes: fitting and analyzing each pulse curve data which is greater than or equal to the curve segmented voltage value through a first prediction model to obtain parameter values of a first model parameter, a second model parameter and a third model parameter which are associated with each preset pulse current; performing exponential function fitting processing on the parameter value of each first model parameter and the preset pulse current to obtain a first function expression of the first model parameter; carrying out mean value calculation processing on the parameter values of the second model parameters to obtain second parameter values of the second model parameters; performing linear fitting processing on the parameter value of each third model parameter and a preset pulse current to obtain a third function expression of the third model parameter; and taking the first function expression, the second parameter value and the third function expression as the parameter values of the target model parameters contained in the first prediction model.
Wherein the first model parameter may be "a", the second model parameter may be "b", and the third model parameter may be "c".
In specific implementation, based on the analysis of the results in table 2 in the previous embodiment, it can be known that, except for the target model parameter "b", the parameter values of other target model parameters are greatly changed, so that the average value "0.64" of "b" can be taken as the corresponding parameter value, and the relationships between the other target model parameters "a and c" and the pulse current "I" are as follows:
a＝-exp(a 1 *I+a 2 )
c＝c 1 *I+c 2
the relation between "a, c" and the pulse current "I" is determined according to the data analysis results shown in fig. 4 and 5, that is, the preset pulse currents "215A", "229.5A", "243A", "256.5A" and "270A" are taken as examples, and the relation between "a, c" and the current "I" is shown in fig. 4 and 5. Wherein, because "a" is a number less than zero, so can adopt the exponential function to carry on the analysis fitting; the small variation of the 'b' is not greatly related to the 'I', so the average value of all the 'b' values can be taken as the value of the second parameter; the relation between the 'c' and the current 'I' is linear, so that the analysis fitting can be performed by adopting linearity, and specific expressions of the first functional expression and the third functional expression are as follows:
a＝-exp(0.00366*I-3.59263)
c＝-0.00091*I+3.61122
thus, the first prediction model "V = c + a × t b "the target model parameters" a, b, c "can be determined. The target pulse curve prediction model comprises: v = -0.00091I +3.61122-exp (0.00366I-3.59263) × t 0.64 ；t≤t 0 And the reality can be expressed as:
V＝(c 1 *I+c 2 )-exp(a 1 *I+a 2 )*t b ；t≤t 0
in one embodiment, the target model parameters further include fourth model parameters and fifth model parameters, and the fitting analysis of the respective pulse curve data smaller than the curve segment voltage value by the second prediction model to determine the parameter values of the target model parameters included in the second prediction model includes: fitting and analyzing each pulse curve data smaller than the curve segmented voltage value through a second prediction model to obtain parameter values of a fourth model parameter and a fifth model parameter associated with each preset pulse current; performing exponential function fitting processing on the parameter value of each fourth model parameter and the preset pulse current to obtain a fourth function expression of the fourth model parameter; performing linear fitting processing on the parameter values of the fifth model parameters and the preset pulse current to obtain a fifth function expression of the fifth model parameters; and taking the first function expression, the second parameter value, the third function expression, the fourth function expression and the fifth function expression as the parameter values of the target model parameters contained in the second prediction model.
Wherein the fourth model parameter may be "d" and the fifth model parameter may be "e".
In specific implementation, based on the analysis of the results in table 2 in the previous embodiment, it can be known that, except for the target model parameter "b", the parameter values of other target model parameters are greatly changed, and the relationship between the target model parameters "d and e" and the pulse current "I" is as follows:
d＝-exp(d 1 *I+d 2 )
e＝e 1 *I+e 2
the relation between "d, e" and the pulse current "I" is determined according to the data analysis results of fig. 6 and fig. 7, that is, taking the preset pulse currents "215A", "229.5A", "243A", "256.5A", "270A" as examples, the relation between "d, e" and the current "I" is shown in fig. 6 and fig. 7. Wherein, because "d" is a number less than zero, so can adopt exponential function to carry on the analysis fitting; the relation between the 'e' and the current 'I' is linear, so that the analysis fitting can be performed by adopting linearity, and specific expressions of the fourth functional expression and the fifth functional expression are as follows:
d＝-exp(-0.01568*I-1.68672)
e＝0.00836*I-1.42212
thus, the second prediction model "V = c + a × t b +d*exp(e*(t-t 0 ) "a, b, c, d, e" can be determined. The target pulse curve prediction model comprises: v = -0.00091I +3.61122-exp (0.00366I-3.59263) × t 0.64 -exp(-0.01568*I-1.68672)*exp((0.00836*I-1.42212)*(t-t 0 ))；t>t 0 And the reality can be expressed as:
V＝(c 1 *I+c 2 )-exp(a 1 *I+a 2 )*t b -exp(d 1 *I+d 2 )*exp((e 1 *I+e 2 )*(t-t 0 ))；t>t 0
in one embodiment, after determining the parameter values of the target model parameters included in the first prediction model, the method further includes: determining a pulse time corresponding to a target voltage value in each pulse curve data as a time with the minimum voltage change rate; extracting constant parameter values in the first function expression and the third function expression; determining a time function expression at the moment when the voltage change rate is minimum according to the curve segment voltage value, the constant parameter value and the second parameter value; the time function expression is used for predicting discharge pulses of the lithium ion battery by combining with a target pulse curve prediction model.
In a specific implementation, the above embodiment has analyzed and determined parameter values of all target model parameters "a, b, c, d, and e", but a time function expression at a time when the voltage change rate is minimum needs to be further determined, and the time when the voltage change rate corresponding to each preset pulse current is minimum is analyzed and data at all times is fitted, so that the following expression is obtained:
Figure BDA0003900322550000151
as can be seen from analyzing the above expressions, the time function expression is actually determined according to the curve segment voltage value, the second parameter value, and the constant parameter values in the first function expression and the third function expression, and actually can be expressed as:
Figure BDA0003900322550000152
and t is 0 ≥0。
And S204, performing discharge pulse prediction on the lithium ion battery through a target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery is discharged under the preset pulse time limit.
In specific implementation, based on the analysis, a target pulse curve prediction model for predicting discharge pulses of the lithium ion battery can be constructed, that is, the target pulse curve prediction model is composed of the following three parts:
V＝(c 1 *I+c 2 )-exp(a 1 *I+a 2 )*t b ；t≤t 0
V＝(c 1 *I+c 2 )-exp(a 1 *I+a 2 )*t b -exp(d 1 *I+d 2 )*exp((e 1 *I+e 2 )*(t-t 0 ))；t>t 0
Figure BDA0003900322550000161
and t is 0 ≥0
Wherein the model parameter "a" is 1 、a 2 、b、c 1 、c 2 、d 1 、d 2 、e 1 、e 2 "all can carry out actual adjustment based on different test temperatures, charge states, and cut-off voltages, the pulse current prediction method provided in the embodiment of the present application actually has described a debugging process for preparation for prediction, and aims to obtain a target pulse curve prediction model for predicting discharge pulses of a lithium ion battery. In this regard, to check the prediction accuracy of the target pulse curve prediction model, taking the pulse current "I =297A" as an example, firstly, the target pulse curve prediction model is analyzed to obtain a predicted value as shown by a dotted line in fig. 8; secondly, the actual discharge of the lithium battery is recorded, so that the measured value shown by a solid line (a connecting line of diamond-shaped recorded values at different moments) in fig. 8 can be obtained. As can be seen, the two are highly superposed,therefore, the target pulse curve prediction model can be used for predicting the maximum current corresponding to different pulse times under the conditions of specified temperature and SOC.
For example, under the "25 ℃ C" temperature and "50%" SOC conditions, the maximum pulse current at different pulse times is predicted as shown in Table 3 below:
pulse time(s) 2.85 5.00 8.00 10.00 15.00
Current (A) 479.3 358.2 296.5 271.8 233.2
The above results further illustrate that the pulse current prediction method for the lithium ion battery provided by the embodiment of the present application can improve the pulse current test efficiency of the lithium ion battery, and can further improve the maximum pulse current prediction accuracy of the lithium ion battery at different pulse times.
In the method for predicting the pulse current of the lithium ion battery in the embodiment, the server obtains the change trend information of each pulse curve data by obtaining the pulse curve data generated by the lithium ion battery discharging under at least three preset pulse current limits, then constructs an initial pulse curve prediction model based on each change trend information, namely analyzes each pulse curve data to obtain a target pulse curve prediction model with a known value, and finally performs discharge pulse prediction on the lithium ion battery by using the target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery discharging under the preset pulse time limit. Therefore, the target pulse curve prediction model capable of accurately describing the relation is established by analyzing the relation among the pulse voltage, the pulse current and the pulse time of the lithium ion battery, namely the target pulse curve prediction model can be utilized to realize accurate prediction of the pulse current of the lithium ion battery under different pulse times, namely the pulse current is measured without adopting complicated modes such as point-by-point testing and the like, and the pulse current testing efficiency of the lithium ion battery is effectively improved.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In order to better implement the pulse current prediction method for the lithium ion battery provided in the embodiment of the present application, on the basis of the pulse current prediction method for the lithium ion battery provided in the embodiment of the present application, a pulse current prediction apparatus for the lithium ion battery is further provided in the embodiment of the present application, as shown in fig. 9, the pulse current prediction apparatus 900 for the lithium ion battery includes:
the data acquisition module 910 is configured to acquire pulse curve data generated by discharging of the lithium ion battery under at least three preset pulse current limits, and obtain variation trend information of each pulse curve data;
a model construction module 920, configured to construct an initial pulse curve prediction model based on each change trend information; the initial pulse curve prediction model comprises target model parameters with unknown values;
a curve analysis module 930, configured to analyze data of each pulse curve through the initial pulse curve prediction model to determine a parameter value of a target model parameter, so as to obtain a target pulse curve prediction model with a known value;
and the current prediction module 940 is used for predicting the discharge pulse of the lithium ion battery through the target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery is discharged under the preset pulse time limit.
In one embodiment, the data obtaining module 910 is further configured to perform a discharge pulse test on the lithium ion battery based on preset test temperature, a charge state, and a cut-off voltage, so as to obtain test data that the voltage changes with time after the lithium ion battery is discharged from an initial voltage to the cut-off voltage under each preset pulse current limit, and obtain pulse curve data; acquiring a voltage drop rate according to each pulse curve data; analyzing each voltage reduction rate to obtain variation trend information; the variation trend information comprises a first variation trend of short-time rapid reduction, a second variation trend of slow reduction and a third variation trend of sudden and sudden final reduction.
In one embodiment, the trend information includes a first trend, a second trend, and a third trend, and the model construction module 920 is further configured to construct a first prediction model including target model parameters and a first power function based on the first trend and the second trend; wherein the base of the first power function is less than or equal to the first pulse time at the moment of minimum voltage change rate; the time when the voltage change rate is minimum is determined according to the second derivative of the pulse curve data; constructing a second prediction model comprising target model parameters, a second power function and an exponential function based on the third variation trend; wherein the base of the second power function is a second pulse time greater than the minimum time of the voltage change rate, and the exponent of the exponential function is determined according to the difference between the second pulse time and the minimum time of the voltage change rate; and taking the first prediction model and the second prediction model as initial pulse curve prediction models.
In one embodiment, the initial pulse curve prediction model includes a first prediction model and a second prediction model, and the curve analysis module 930 is further configured to perform second-order derivation processing on each pulse curve data to obtain a voltage value corresponding to a derivation result being zero as a target voltage value; obtaining the average value of all target voltage values to obtain a curve segmented voltage value; fitting and analyzing each pulse curve data which is greater than or equal to the curve segmented voltage value through a first prediction model to determine parameter values of target model parameters contained in the first prediction model; fitting and analyzing each pulse curve data smaller than the curve segmented voltage value through a second prediction model to determine parameter values of target model parameters contained in the second prediction model; and taking an initial pulse curve prediction model with a known value as a target pulse curve prediction model.
In one embodiment, the target model parameters include a first model parameter, a second model parameter and a third model parameter, and the curve analysis module 930 is further configured to fit and analyze each pulse curve data greater than or equal to the curve segment voltage value through the first prediction model to obtain parameter values of the first model parameter, the second model parameter and the third model parameter associated with each preset pulse current; performing exponential function fitting processing on the parameter values of the first model parameters to obtain first function expressions of the first model parameters; carrying out mean value calculation processing on the parameter values of the second model parameters to obtain second parameter values of the second model parameters; performing linear fitting processing on the parameter values of the third model parameters to obtain a third function expression of the third model parameters; and taking the first function expression, the second parameter value and the third function expression as the parameter values of the target model parameters contained in the first prediction model.
In one embodiment, the target model parameters further include a fourth model parameter and a fifth model parameter, and the curve analysis module 930 is further configured to fit and analyze each pulse curve data smaller than the curve segment voltage value through the second prediction model to obtain parameter values of the fourth model parameter and the fifth model parameter associated with each preset pulse current; performing exponential function fitting processing on the parameter value of each fourth model parameter and the preset pulse current to obtain a fourth function expression of the fourth model parameter; performing linear fitting processing on the parameter values of the fifth model parameters and the preset pulse current to obtain a fifth function expression of the fifth model parameters; and taking the first function expression, the second parameter value, the third function expression, the fourth function expression and the fifth function expression as the parameter values of the target model parameters contained in the second prediction model.
In one embodiment, the curve analysis module 930 is further configured to determine a pulse time corresponding to the target voltage value in each pulse curve data as a time when the voltage change rate is minimum; extracting constant parameter values in the first function expression and the third function expression; determining a time function expression of the moment with the minimum voltage change rate according to the curve segment voltage value, the constant parameter value and the second parameter value; the time function expression is used for predicting discharge pulses of the lithium ion battery by combining with a target pulse curve prediction model.
In the embodiment, by analyzing the relationship among the pulse voltage, the pulse current and the pulse time of the lithium ion battery, a target pulse curve prediction model capable of accurately describing the relationship is established, that is, the target pulse curve prediction model can be utilized to realize accurate prediction of the pulse current of the lithium ion battery at different pulse times, that is, the pulse current is measured without adopting a complex mode such as point-to-point test, and the pulse current test efficiency of the lithium ion battery is effectively improved.
It should be noted that, for specific limitations of the pulse current prediction apparatus for a lithium ion battery, reference may be made to the above limitations of the pulse current prediction method for a lithium ion battery, and details are not repeated here. All or part of each module in the pulse current prediction device of the lithium ion battery can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the electronic device, or can be stored in a memory in the electronic device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 10. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The database of the computer device is used to store texture coordinate data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of pulse current prediction for a lithium ion battery.
It will be appreciated by those skilled in the art that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The pulse current prediction method, the pulse current prediction device, and the computer device for the lithium ion battery provided in the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present invention, and the description of the above embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A pulse current prediction method of a lithium ion battery is characterized by comprising the following steps:
acquiring pulse curve data generated by the discharge of a lithium ion battery under at least three preset pulse current limits to obtain the change trend information of each pulse curve data;
constructing an initial pulse curve prediction model based on each change trend information; wherein the initial pulse curve prediction model comprises target model parameters of which the numerical values are unknown;
analyzing the data of each pulse curve through the initial pulse curve prediction model to determine the parameter values of the parameters of the target model, and obtaining a target pulse curve prediction model with known values;
and predicting the discharge pulse of the lithium ion battery through the target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery is discharged under the preset pulse time limit.
2. The method of claim 1, wherein the obtaining pulse curve data generated by discharging the lithium ion battery under at least three preset pulse current limits to obtain the variation trend information of each pulse curve data comprises:
performing discharge pulse test on the lithium ion battery based on preset test temperature, charge state and cut-off voltage to obtain test data of voltage change along with time after the lithium ion battery is discharged from initial voltage to the cut-off voltage under the limitation of each preset pulse current, so as to obtain pulse curve data;
acquiring a voltage drop rate according to each pulse curve data;
analyzing each voltage reduction rate to obtain the change trend information;
the change trend information comprises a first change trend of a short-time rapid decrease, a second change trend of a slow decrease and a third change trend of an imminent sudden decrease.
3. The method of claim 1, wherein the trend information includes a first trend, a second trend, and a third trend, and wherein constructing an initial pulse profile prediction model based on each of the trend information comprises:
constructing a first prediction model comprising the target model parameters and a first power function based on the first trend and the second trend; wherein the base of the first power function is a first pulse time less than or equal to the moment when the voltage change rate is minimum; the voltage rate of change minimum time is determined from the second derivative of the pulse curve data;
constructing a second prediction model comprising the target model parameter, a second power function and an exponential function based on the third variation trend; wherein a base of the second power function is a second pulse time greater than the voltage rate of change minimum time, an exponent of the exponential function being determined according to a difference between the second pulse time and the voltage rate of change minimum time;
and taking the first prediction model and the second prediction model as the initial pulse curve prediction model.
4. The method of claim 1, wherein the initial pulse profile prediction model comprises a first prediction model and a second prediction model, and wherein analyzing each of the pulse profile data via the initial pulse profile prediction model to determine a parameter value of the target model parameter results in a target pulse profile prediction model with a known value, comprises:
performing second-order derivation processing on each pulse curve data to obtain a corresponding voltage value when a derivation result is zero, and taking the voltage value as a target voltage value;
obtaining the average value of each target voltage value to obtain a curve segmented voltage value;
fitting and analyzing each pulse curve data which is greater than or equal to the curve segment voltage value through the first prediction model to determine parameter values of target model parameters contained in the first prediction model;
fitting and analyzing each pulse curve data smaller than the curve segment voltage value through the second prediction model to determine parameter values of target model parameters contained in the second prediction model;
and taking an initial pulse curve prediction model with a known value as the target pulse curve prediction model.
5. The method of claim 4, wherein said target model parameters comprise a first model parameter, a second model parameter, and a third model parameter, and wherein said fitting, by said first predictive model, each pulse curve data greater than or equal to said curve segment voltage value to determine a parametric value for a target model parameter contained by said first predictive model comprises:
fitting and analyzing each pulse curve data which is greater than or equal to the curve segment voltage value through the first prediction model to obtain parameter values of the first model parameter, the second model parameter and the third model parameter which are associated with each preset pulse current;
performing exponential function fitting processing on the parameter value of each first model parameter and the predicted pulse current to obtain a first function expression of the first model parameter; and
performing mean value calculation processing on the parameter values of the second model parameters to obtain second parameter values of the second model parameters; and
performing linear fitting processing on the parameter value of each third model parameter and a preset pulse current to obtain a third function expression of the third model parameter;
and taking the first function expression, the second parameter value and the third function expression as parameter values of target model parameters contained in the first prediction model.
6. The method of claim 5, wherein said target model parameters further include fourth model parameters and fifth model parameters, and said fitting, by said second predictive model, each pulse curve data less than said curve segment voltage value to determine parameter values for target model parameters contained by said second predictive model comprises:
through the second prediction model, fitting and analyzing each pulse curve data smaller than the curve segmented voltage value to obtain parameter values of the fourth model parameter and the fifth model parameter associated with each preset pulse current;
performing exponential function fitting processing on the parameter value of each fourth model parameter and a preset pulse current to obtain a fourth function expression of the fourth model parameter; and
performing linear fitting processing on the parameter value of each fifth model parameter and a preset pulse current to obtain a fifth function expression of the fifth model parameter;
and taking the first function expression, the second parameter value, the third function expression, the fourth function expression and the fifth function expression as parameter values of target model parameters contained in the second prediction model.
7. The method of claim 5, wherein after said determining the parameter values of the target model parameters contained in the first predictive model, further comprising:
determining a pulse time corresponding to the target voltage value in each pulse curve data as a time with the minimum voltage change rate;
extracting constant parameter values in the first function expression and the third function expression;
determining a time function expression of the moment with the minimum voltage change rate according to the curve segment voltage value, the constant parameter value and the second parameter value; and the time function expression is used for predicting the discharge pulse of the lithium ion battery by combining the target pulse curve prediction model.
8. A pulse current prediction apparatus for a lithium ion battery, comprising:
the data acquisition module is used for acquiring pulse curve data generated by the discharge of the lithium ion battery under at least three preset pulse current limits to obtain the variation trend information of each pulse curve data;
the model construction module is used for constructing an initial pulse curve prediction model based on each change trend information; wherein the initial pulse curve prediction model comprises target model parameters of which the numerical values are unknown;
the curve analysis module is used for analyzing the data of each pulse curve through the initial pulse curve prediction model to determine parameter values of parameters of the target model so as to obtain a target pulse curve prediction model with known values;
and the current prediction module is used for predicting the discharge pulse of the lithium ion battery through the target pulse curve prediction model to obtain the maximum pulse current to be generated after the lithium ion battery is discharged under the preset pulse time limit.
9. A computer device, comprising:
one or more processors;
a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the method of pulse current prediction for a lithium ion battery of any of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program, which is loaded by a processor to perform the steps of the method for predicting pulse current of a lithium ion battery according to any of claims 1 to 7.
CN202211288420.5A 2022-10-20 2022-10-20 Pulse current prediction method and device of lithium ion battery and computer equipment Pending CN115629313A (en)

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