CN116125284A - Internal short circuit diagnosis method and system for lithium ion battery - Google Patents
Internal short circuit diagnosis method and system for lithium ion battery Download PDFInfo
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- CN116125284A CN116125284A CN202310027039.1A CN202310027039A CN116125284A CN 116125284 A CN116125284 A CN 116125284A CN 202310027039 A CN202310027039 A CN 202310027039A CN 116125284 A CN116125284 A CN 116125284A
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- 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
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- 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]
- G01R31/378—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
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- 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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/52—Testing for short-circuits, leakage current or ground faults
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- 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
Abstract
The invention relates to a method and a system for diagnosing internal short circuit of a lithium ion battery, belonging to the field of battery fault diagnosis, wherein the method comprises the following steps: establishing an equivalent circuit model of the lithium ion thermal coupling battery; determining equivalent circuit model parameters at different temperatures; building a thermal coupling battery equivalent circuit model and verifying whether the parameter identification result of the thermal coupling battery equivalent circuit model is accurate; the internal short circuit equivalent resistance of the battery is introduced to correct the normal battery, so as to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit model; adding an internal short-circuit equivalent resistor into simulation software to form a lithium ion battery thermal-electric-internal short-circuit equivalent circuit simulation model; determining an optimization target, and continuously carrying out updating iteration by taking the short-circuit resistance of the circuit model as an optimization quantity until the optimization target is minimum; and diagnosing the battery based on the short-circuit resistance when the optimization target is minimum. The method can realize quantitative diagnosis of the severity of the internal short circuit of the lithium ion battery.
Description
Technical Field
The invention relates to the field of battery fault diagnosis, in particular to a method and a system for diagnosing internal short circuit of a lithium ion battery.
Background
The technical development of electric automobiles is mature, and meanwhile, the requirements of consumers on the performances of the electric automobiles are higher and higher, and particularly the endurance mileage is higher and higher. However, while the energy density of lithium ion batteries is increasing, the risk of thermal runaway of lithium ion batteries in abnormal situations is also increasing significantly. Lithium ion power lithium ion batteries are one of the core components of electric automobiles, and although the research and development of lithium ion batteries with high energy density are focused at present, the safety of the lithium ion batteries is also paid attention to. The safety problem of the lithium ion battery is the premise of improving the energy density of the lithium ion battery, and the safety research of the lithium ion battery is inexhaustible power for long-term sustainable development of the electric automobile.
Thermal runaway of lithium ion batteries is mainly initiated by two aspects: on the one hand, the materials and the production process of the lithium ion battery are problematic, and on the other hand, the lithium ion battery is problematic in the use process. There are many reasons for thermal runaway of the lithium ion battery during use, such as internal and external short circuits, excessive charge and discharge, high-rate charge and discharge, high and low temperature environments, cyclic aging, extrusion deformation, and the like of the lithium ion battery. Among them, internal short-circuiting of lithium ion batteries is the most common cause of thermal runaway.
In order to exert the performance of the lithium ion battery to the greatest extent, improve the safety and prolong the service life of the lithium ion battery, the condition of the lithium ion battery must be monitored on line, and meanwhile, the internal short circuit diagnosis of the lithium ion battery must be carried out. The internal short circuit diagnosis of the lithium ion battery is realized by monitoring state information such as temperature, voltage, current and the like of the lithium ion battery in real time and realizing early warning of the internal short circuit of the lithium ion battery through a certain model algorithm.
Several methods for detecting and diagnosing internal short circuits of lithium ion batteries have been proposed by researchers from different angles, for example: (1) a model-based diagnostic method; (2) utilizing a statistical method or the like based on battery pack consistency; (3) a method of diagnosis based on a thermoelectric property threshold; (4) based on external auxiliary measurement circuitry. The equivalent circuit model is simple, has higher precision and is widely applied to engineering, and most engineering diagnosis modes considering economy and efficiency are based on the circuit model for diagnosis.
Most of the existing methods have the following problems: (1) Methods generally used for detecting the state of charge (SOC) of a battery generally require a long time to find the existence of an internal short circuit; (2) The method based on the consistency of the battery pack requires higher consistency of the battery pack, but the condition is difficult to maintain in the use process of the battery, and other interferences such as aging of the battery can influence the detection; (3) Methods that utilize external aids or instruments are often off-line or require relatively high precision and expensive equipment, such as infrared detectors, X-ray diffraction, etc., which are not of great engineering value.
The invention provides the optimal diagnosis method based on the model, only voltage and current signals are needed for a period of time, and the short circuit condition is diagnosed through an optimal algorithm, so that the health state of the battery can be simply and rapidly evaluated, and the method has a certain application value in engineering.
Disclosure of Invention
The invention aims to provide a method and a system for diagnosing internal short circuit of a lithium ion battery, which realize rapid and accurate diagnosis of battery health based on a multi-coupling equivalent circuit.
In order to achieve the above object, the present invention provides the following solutions:
in a first aspect, the present invention provides a method for diagnosing an internal short circuit of a lithium ion battery, the method comprising:
establishing an equivalent circuit model of the lithium ion thermal coupling battery;
carrying out parameter identification on the equivalent circuit model of the lithium ion thermal coupling battery at different temperatures through pulse charge and discharge tests to obtain equivalent circuit model parameters at different temperatures;
building a thermal coupling battery equivalent circuit model in simulation software according to the equivalent circuit model parameters at different temperatures, and verifying whether the identification result of the thermal coupling battery equivalent circuit model parameters is accurate;
if the step is accurate, executing the next step, and if the step is not accurate, returning to the first step;
on the basis of the lithium ion thermal coupling battery equivalent circuit model, the internal short circuit equivalent resistance of the battery is introduced to correct a normal battery, so that a lithium ion battery thermal-electric-internal short circuit equivalent circuit model is formed;
adding an internal short circuit equivalent resistor on the basis of a lithium ion thermal coupling equivalent circuit model in simulation software to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model;
taking the root mean square and average error minimum value of the lithium ion battery thermal-electric-internal short circuit equivalent circuit model output voltage and the actual battery output voltage as an optimization target, and taking the short circuit resistance of the circuit model as an optimization quantity, and continuously updating and iterating until the optimization target is minimum;
and diagnosing the battery based on the short-circuit resistance when the optimization target is minimum.
Optionally, the simulation software is Simulink.
Optionally, through pulse charge and discharge test, parameter identification is performed on the equivalent circuit model of the lithium ion thermal coupling battery at different temperatures to obtain equivalent circuit model parameters at different temperatures, which specifically includes:
and identifying open-circuit voltage, ohmic internal resistance and RC links of the equivalent circuit model of the lithium ion thermal coupling battery through pulse charge and discharge tests, and simultaneously testing four groups of equivalent circuit models with temperature correction under different temperatures.
Optionally, a particle swarm optimization algorithm is adopted to continuously update and iterate until the optimization target is minimum.
Optionally, diagnosing the battery based on the short-circuit resistance when the optimization target is minimum specifically includes:
judging whether the short circuit resistance exceeds a first threshold value, if so, judging that the battery has no short circuit fault;
and judging whether the short circuit resistance is lower than a second threshold value, and if so, judging that the battery has short circuit fault.
In a second aspect, based on the above method in the present invention, the present invention additionally provides a lithium ion battery internal short circuit diagnosis system, the diagnosis system comprising:
the lithium ion thermal coupling battery equivalent circuit model building module is used for building a lithium ion thermal coupling battery equivalent circuit model;
the equivalent circuit model parameter determining module is used for carrying out parameter identification on the lithium ion thermal coupling battery equivalent circuit model at different temperatures through pulse charge and discharge tests to obtain equivalent circuit model parameters at different temperatures;
the verification module is used for building a thermal coupling battery equivalent circuit model in simulation software according to the equivalent circuit model parameters at different temperatures and verifying whether the identification result of the thermal coupling battery equivalent circuit model parameters is accurate or not; if the module is accurate, executing the lower module, and if the module is not accurate, returning to the first module;
the lithium ion battery thermal-electric-internal short circuit equivalent circuit model building module is used for introducing battery internal short circuit equivalent resistance to correct a normal battery on the basis of the lithium ion thermal coupling battery equivalent circuit model to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit model;
the lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model building module is used for adding an internal short circuit equivalent resistor on the basis of a lithium ion thermal coupling equivalent circuit model in simulation software to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model;
the optimization module is used for taking the root mean square and average error minimum value of the output voltage of the lithium ion battery thermal-electric-internal short circuit equivalent circuit model and the actual battery output voltage as an optimization target, and taking the short circuit resistance of the circuit model as an optimization quantity, and continuously carrying out updating iteration until the optimization target is minimum;
and the battery diagnosis module is used for diagnosing the battery based on the short-circuit resistance when the optimization target is minimum.
Optionally, the simulation software is Simulink.
Optionally, the battery diagnosis module includes:
the first judging unit is used for judging whether the short circuit resistance exceeds a first threshold value, and judging that the battery has no short circuit fault if the short circuit resistance exceeds the first threshold value;
and the second judging unit is used for judging whether the short circuit resistance is lower than a second threshold value, and judging that the battery has short circuit fault if the short circuit resistance is lower than the second threshold value.
In a third aspect, the present invention provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the above-described lithium ion battery internal short circuit diagnosis method.
In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described lithium ion battery internal short circuit diagnosis method.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the method of the invention has the advantages that the internal short circuit of the lithium ion battery is diagnosed based on the thermal-electric-internal short circuit coupling circuit model, the traditional problem of battery state observation and identification is converted into the problem of optimization, and the diagnosis can be rapidly and accurately performed based on the particle swarm optimization algorithm;
in addition, a thermal coupling correction term is added on the traditional circuit model, so that an algorithm based on the model can be more accurately attached to an actual battery.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for diagnosing internal short circuit of a lithium ion battery according to the present invention;
fig. 2 is an equivalent circuit of a lithium ion battery used in the present invention;
FIG. 3 is a schematic diagram of a pulse charge and discharge test for identifying parameters of an equivalent circuit model according to the present invention;
FIG. 4 is a schematic diagram of an improved equivalent circuit model of a lithium ion battery based on an equivalent circuit model;
FIG. 5 is a schematic diagram of a second-order equivalent circuit of the internal short circuit of the lithium ion battery according to the present invention;
fig. 6 is a flowchart of the steps of the internal short circuit recognition algorithm of the lithium ion battery based on the particle swarm optimization algorithm PSO in the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for diagnosing internal short circuit of a lithium ion battery, which are based on a multi-coupling equivalent circuit to realize quantitative diagnosis of severity of the internal short circuit of the lithium ion battery.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flow chart of a method for diagnosing internal short circuit of a lithium ion battery according to the present invention, and as shown in fig. 1, the method of the present invention comprises the steps of:
step 1: and establishing an equivalent circuit model of the lithium ion battery.
Step 2: and respectively identifying parameters of the equivalent circuit model of the lithium ion battery through pulse charge and discharge tests at different temperatures.
The invention adopts a first-order equivalent circuit model to simulate the output characteristics of a battery, and the model is shown in figure 2.
Parameters to be identified by the first-order equivalent circuit model of the lithium ion battery comprise E ocv 、R 0 、C 1 、R 1 . And carrying out parameter identification by adopting a pulse charge and discharge test as shown in fig. 3, so as to obtain a battery equivalent circuit model.
Wherein E is ocv Represents the open circuit voltage of the battery, which is the equilibrium potential of the battery in the absence of an externally excited equilibrium state, which is related to the positive and negative electrode material properties of the battery, E ocv As a function of battery state of charge, SOC. R is R 0 Represents ohmic resistance, C 1 、R 1 Representing the polarization capacitance and resistance of the polarization link. The invention selects the first-order RC circuit, so that the polarization link of the battery has only one RC inertia link characterization.
The output response of the battery can be expressed as formula (1):
in which I L Representing the input current τ 1 Is the time constant of RC link, V out For the battery output voltage, t represents time.
As shown in FIG. 3, the sections B-C and D-E in FIG. 3 correspond to R in the second-order equivalent circuit model of the lithium ion battery 0 Voltage drop generated, thus R 0 Identified by the means of formulas (2) and (3), the final R 0 The identification result of (2) is the formula (4).
Section C-D in FIG. 3, from t 1 The battery first-order equivalent circuit enters zero state response at the moment, the initial voltage of the RC link is zero, and the battery output voltage in the period of time can be obtained by the formula (1) as follows:
the functional relation of the battery output voltage and time in the section C-D is abstracted by coefficient substitution and can be expressed as a formula (6):
therefore, the battery output voltage of the C.fwdarw.D segment can be fitted to obtain 3 coefficients a in the formula (6) 1 ，a 2 ，a 3 And the other parameters of the second-order equivalent circuit model of the lithium ion battery can be obtained according to the formula (6) as shown in the formula (7):
in the section e→f in fig. 3, the pulse current of the battery is removed, the RC link in the equivalent circuit of this period is zero input response, and the output voltage of the battery in this period is:
the functional relation of the battery output voltage and time in the E-F section is abstracted by coefficient substitution and can be expressed as a formula (9):
similarly, the battery output voltage of the E-F section can be fitted to obtain the coefficient of the formula (9)
The parameters of the equivalent circuit model are obtained, and the same test is carried out at four groups of different temperatures at the same time, so that the circuit parameters under all temperature groups are finally obtained. Since the temperature influence is taken into consideration, the temperature term in the battery is increased as in formula (11):
t in b ，T 0 Respectively the temperature of the battery and the ambient temperature, q is the heat dissipation coefficient, C m For the specific heat capacity of the battery, A s And m is the mass of the battery, and is the heat dissipation area.
Step 3: building a thermal coupling battery equivalent circuit model in simulation software according to the equivalent circuit model parameters at different temperatures, and verifying whether the identification result of the thermal coupling battery equivalent circuit model parameters is accurate; if the result is accurate, the next step is executed, and if the result is not accurate, the first step is returned.
Specifically, according to the parameters of the thermal-electrical coupling equivalent circuit model identified in the step 2, the equivalent circuit model is built in the Simulink through a discrete state equation of the battery equivalent circuit model. The input data of the Simulink model are time and current data of battery working conditions and measured voltage data of an actual battery, and the output data are output voltages of an electric-thermal equivalent circuit model, so that the fitting degree of the built healthy battery model and the actual battery without internal short circuit is verified.
Step 4: and on the basis of the lithium ion thermal coupling battery equivalent circuit model, the battery internal short circuit equivalent resistance is introduced to correct the normal battery, so that the lithium ion battery thermal-electric-internal short circuit equivalent circuit model is formed.
Specifically, by introducing the battery internal short-circuit equivalent resistance, the lithium ion thermal-electric-internal short-circuit coupling equivalent circuit model is obtained by improvement on the basis of the thermal-electric equivalent circuit model. Based on a normal lithium ion battery equivalent circuit model, introducing a battery internal short circuit equivalent resistor R isc And forming a thermal-electric-internal short circuit coupling equivalent circuit model of the lithium ion battery, as shown in fig. 4. At this time, define the state variable and input output as
Wherein, soc is the charge state of the battery, V 1 Representing the polarization voltage of the battery, T b Is the temperature of the battery, and the input quantity is the external current I of the battery L And battery voltage V out Output is also V out。 The discrete state space expression of the thermal-electric-internal short circuit coupling battery is as follows (13):
in f (x) o (k)，u 0 (k) Can be represented as (14)
G (x) 0 (k)，u 0 (k))＝(E ocv (soc)+I L (k)R 0 +V 1 (k))·ξ
Wherein ζ=r isc (k-1)/(R 0 +R isc (k-1)) corrects an abnormal drop in output voltage caused by an internal short-circuit resistance. The whole circuit model takes the battery temperature T into consideration b The influence on the battery parameters makes the model more accurate.
Step 5: and adding an internal short circuit equivalent resistor on the basis of a lithium ion thermal coupling equivalent circuit model in simulation software to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model.
Specifically, an internal short circuit equivalent resistance module is added on the basis of an equivalent circuit model in the Simulink to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit Simulink model, and the model is specifically shown in fig. 5.
Step 6: and taking the root mean square and average error minimum value of the output voltage of the lithium ion battery thermal-electric-internal short circuit equivalent circuit model and the actual battery output voltage as an optimization target, and taking the short circuit resistance of the circuit model as an optimization quantity, and continuously updating and iterating until the optimization target is minimum.
Specifically, taking the minimum value of the linear combination of the root mean square error and the average error of the output voltage of the internal thermal-electric-internal short circuit equivalent circuit model of the lithium ion battery and the actual battery output voltage as an optimization target, taking the short circuit resistance Risc in the model as an optimization quantity, optimizing through a particle swarm optimization PSO algorithm, and continuously updating and iterating until the optimization target is minimum. The error is a linear combination of root mean square error and average voltage error as shown in equations (15) (16) (17).
f(S rmse ，S diff )＝a·S rmse +b·S diff (17)
Wherein k represents the number of sampled voltage data in a selected time period, V real For measuring voltage of actual battery, V sim Is the output voltage of the thermo-electric-internal short circuit equivalent circuit model.
The lithium ion battery internal short circuit equivalent resistance identification optimization method is shown in fig. 6, wherein the input working condition current of the lithium ion battery and the output voltage of the real battery under the working condition are firstly input, and then particle swarm optimization initialization parameters including iteration times, population quantity, population range and the like are defined. The optimization parameters are short circuit resistances of the equivalent circuit model, the actual short circuit resistances in early stage of internal short circuit are not too small in the population range, 2 omega-200 omega can be generally selected, and the population quantity can be 10.
Step 7: and diagnosing the battery based on the short-circuit resistance when the optimization target is minimum.
Specifically, after the optimization algorithm is used, the short-circuit resistance Risc in the model is continuously adjusted until the error is minimum in order to reach the optimization target minimum. At this time, the optimized quantity Risc can be regarded as a short-circuit resistance existing in the real battery, if Risc is large, the battery can be regarded as not having a short circuit, namely, whether the short-circuit resistance exceeds a first threshold value is judged, and if the short-circuit resistance exceeds the first threshold value, the battery is judged not to have a short-circuit fault; if the Risc is below a certain threshold, it is considered that a short circuit has occurred in the battery, and the severity of the short circuit may be represented by an optimized Risc, i.e. determining whether the short circuit resistance is below a second threshold, and if it is below the second threshold, determining that a short circuit fault has occurred in the battery.
Based on the above method in the present invention, the present invention additionally provides a lithium ion battery internal short circuit diagnosis system, comprising:
the lithium ion thermal coupling battery equivalent circuit model building module is used for building a lithium ion thermal coupling battery equivalent circuit model;
the equivalent circuit model parameter determining module is used for carrying out parameter identification on the lithium ion thermal coupling battery equivalent circuit model at different temperatures through pulse charge and discharge tests to obtain equivalent circuit model parameters at different temperatures;
the verification module is used for building a thermal coupling battery equivalent circuit model in simulation software according to the equivalent circuit model parameters at different temperatures and verifying whether the identification result of the thermal coupling battery equivalent circuit model parameters is accurate or not; if the module is accurate, executing the lower module, and if the module is not accurate, returning to the first module;
the lithium ion battery thermal-electric-internal short circuit equivalent circuit model building module is used for introducing battery internal short circuit equivalent resistance to correct a normal battery on the basis of the lithium ion thermal coupling battery equivalent circuit model to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit model;
the lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model building module is used for adding an internal short circuit equivalent resistor on the basis of a lithium ion thermal coupling equivalent circuit model in simulation software to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model;
the optimization module is used for taking the root mean square and average error minimum value of the output voltage of the lithium ion battery thermal-electric-internal short circuit equivalent circuit model and the actual battery output voltage as an optimization target, and taking the short circuit resistance of the circuit model as an optimization quantity, and continuously carrying out updating iteration until the optimization target is minimum;
and the battery diagnosis module is used for diagnosing the battery based on the short-circuit resistance when the optimization target is minimum.
The specific battery diagnosis module includes:
the first judging unit is used for judging whether the short circuit resistance exceeds a first threshold value, and judging that the battery has no short circuit fault if the short circuit resistance exceeds the first threshold value;
and the second judging unit is used for judging whether the short circuit resistance is lower than a second threshold value, and judging that the battery has short circuit fault if the short circuit resistance is lower than the second threshold value.
The invention further provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the lithium ion battery internal short circuit diagnosis method.
The present invention further provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described lithium ion battery internal short circuit diagnosis method.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. A method for diagnosing an internal short circuit of a lithium ion battery, the method comprising:
establishing an equivalent circuit model of the lithium ion thermal coupling battery;
carrying out parameter identification on the equivalent circuit model of the lithium ion thermal coupling battery at different temperatures through pulse charge and discharge tests to obtain equivalent circuit model parameters at different temperatures;
building a thermal coupling battery equivalent circuit model in simulation software according to the equivalent circuit model parameters at different temperatures, and verifying whether the identification result of the thermal coupling battery equivalent circuit model parameters is accurate;
if the step is accurate, executing the next step, and if the step is not accurate, returning to the first step;
on the basis of the lithium ion thermal coupling battery equivalent circuit model, the internal short circuit equivalent resistance of the battery is introduced to correct a normal battery, so that a lithium ion battery thermal-electric-internal short circuit equivalent circuit model is formed;
adding an internal short circuit equivalent resistor on the basis of a lithium ion thermal coupling equivalent circuit model in simulation software to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model;
taking the root mean square and average error minimum value of the lithium ion battery thermal-electric-internal short circuit equivalent circuit model output voltage and the actual battery output voltage as an optimization target, and taking the short circuit resistance of the circuit model as an optimization quantity, and continuously updating and iterating until the optimization target is minimum;
and diagnosing the battery based on the short-circuit resistance when the optimization target is minimum.
2. The method for diagnosing an internal short circuit of a lithium ion battery according to claim 1, wherein the simulation software is Simulink.
3. The method for diagnosing internal short circuit of lithium ion battery according to claim 1, wherein the parameters of the equivalent circuit model of the lithium ion thermal coupling battery are identified at different temperatures through pulse charge and discharge test, so as to obtain the parameters of the equivalent circuit model at different temperatures, specifically:
and identifying open-circuit voltage, ohmic internal resistance and RC links of the equivalent circuit model of the lithium ion thermal coupling battery through pulse charge and discharge tests, and simultaneously testing four groups of equivalent circuit models with temperature correction under different temperatures.
4. The method of claim 1, wherein the particle swarm optimization algorithm is used to continuously iterate the updating until the optimization objective is minimized.
5. The method according to claim 1, wherein diagnosing the battery based on the short-circuit resistance at which the optimization target is minimum specifically comprises:
judging whether the short circuit resistance exceeds a first threshold value, if so, judging that the battery has no short circuit fault;
and judging whether the short circuit resistance is lower than a second threshold value, and if so, judging that the battery has short circuit fault.
6. A lithium ion battery internal short circuit diagnostic system, the diagnostic system comprising:
the lithium ion thermal coupling battery equivalent circuit model building module is used for building a lithium ion thermal coupling battery equivalent circuit model;
the equivalent circuit model parameter determining module is used for carrying out parameter identification on the lithium ion thermal coupling battery equivalent circuit model at different temperatures through pulse charge and discharge tests to obtain equivalent circuit model parameters at different temperatures;
the verification module is used for building a thermal coupling battery equivalent circuit model in simulation software according to the equivalent circuit model parameters at different temperatures and verifying whether the identification result of the thermal coupling battery equivalent circuit model parameters is accurate or not; if the module is accurate, executing the lower module, and if the module is not accurate, returning to the first module;
the lithium ion battery thermal-electric-internal short circuit equivalent circuit model building module is used for introducing battery internal short circuit equivalent resistance to correct a normal battery on the basis of the lithium ion thermal coupling battery equivalent circuit model to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit model;
the lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model building module is used for adding an internal short circuit equivalent resistor on the basis of a lithium ion thermal coupling equivalent circuit model in simulation software to form a lithium ion battery thermal-electric-internal short circuit equivalent circuit simulation model;
the optimization module is used for taking the root mean square and average error minimum value of the output voltage of the lithium ion battery thermal-electric-internal short circuit equivalent circuit model and the actual battery output voltage as an optimization target, and taking the short circuit resistance of the circuit model as an optimization quantity, and continuously carrying out updating iteration until the optimization target is minimum;
and the battery diagnosis module is used for diagnosing the battery based on the short-circuit resistance when the optimization target is minimum.
7. The lithium ion battery internal short circuit diagnostic system of claim 6, wherein the simulation software is Simulink.
8. The lithium ion battery internal short circuit diagnostic system of claim 6, wherein the battery diagnostic module comprises:
the first judging unit is used for judging whether the short circuit resistance exceeds a first threshold value, and judging that the battery has no short circuit fault if the short circuit resistance exceeds the first threshold value;
and the second judging unit is used for judging whether the short circuit resistance is lower than a second threshold value, and judging that the battery has short circuit fault if the short circuit resistance is lower than the second threshold value.
9. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the lithium-ion battery internal short circuit diagnosis method according to any one of claims 1 to 5.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the lithium-ion battery internal short-circuit diagnosis method according to any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116953556A (en) * | 2023-09-12 | 2023-10-27 | 苏州大学 | Method, system, medium and equipment for online detection of multivariable redundant fault battery |
| CN116953556B (en) * | 2023-09-12 | 2023-12-05 | 苏州大学 | Method, system, medium and equipment for online detection of multivariable redundant fault battery |
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