A lithium-ion battery fault diagnosis method based on lithium plating detection

Abstract

The invention discloses a lithium ion battery fault diagnosis method based on lithium plating detection, comprising the following steps: obtaining data such as battery thickness increment, battery temperature, ambient temperature, charge and the like through experiments; establishing a basic model for battery lithium plating layer detection, a thermal expansion compensation model and a lithium embedding expansion compensation model; adding pressure measuring points to repeat the experiment and establishing a gasification expansion compensation model; establishing a lithium ion battery lithium plating layer detection model; inputting the real-time measured battery temperature and ambient temperature, charge and battery thickness increment into each compensation model to obtain a corresponding thickness compensation value, subtracting the thickness compensation values ​​of thermal expansion, lithium embedding expansion and gasification expansion from the real-time measured battery thickness increment value to obtain a basic lithium plating layer thickness value approximately measured in real time; establishing a lithium ion battery lithium plating layer thickness detection estimator, detecting the lithium plating layer thickness online according to the real-time measured battery thickness, ambient temperature, battery temperature and charge and the like data, and performing lithium plating fault diagnosis.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium ion batteries, and in particular relates to a lithium ion battery fault diagnosis method based on lithium precipitation detection. Background Art

[0002] Lithium-ion batteries in automotive applications must meet high standards due to a wide range of environmental impacts. In addition to mechanical stability and safety in crash situations, lithium-ion batteries also face challenges in large ambient temperature variations, and obtaining acceptable charging rates at low temperatures remains a major challenge.

[0003] At temperatures below 10°C, lithium-ion batteries with standard graphite-based anodes (the most commonly used anode material today) can only be charged at a fraction of the nominal charge rate. The limiting factor is the rate at which lithium ions are inserted into the anode, which is significantly reduced at lower temperatures. When a specific charging current is exceeded, this leads to the lithium plating reaction, where metallic lithium is deposited on the anode.

[0004] This reaction can negatively affect the performance, reliability and safety of the battery. The deposited lithium is no longer used for ion charge transfer, resulting in capacity loss. In addition, the deposited metallic lithium may react with the electrolyte to form a new SEI film, resulting in an increase in internal resistance.

[0005] In addition to electrical parameters, safety aspects of the lithium deposition reaction must also be considered. Lithium is not deposited uniformly on the anode, but grows through the separator in the form of dendrites. This can, at least in theory, lead to higher self-discharge due to soft shorts or thermal runaway due to short circuits. In order to ensure the safety, performance and long life of lithium-ion batteries, every effort must be made to avoid extreme operating conditions to prevent the occurrence of lithium deposition reactions, and to incorporate lithium deposition detection into battery fault diagnosis to improve the reliability of fault diagnosis.

[0006] Under normal conditions, lithium-ion batteries do not contain metallic lithium at all. Lithium ions move only in the electrolyte, inserting into the anode when charging and into the cathode when discharging. Under certain operating conditions, especially when charging at low temperatures and / or high currents, lithium ions are deposited on the anode surface instead of being embedded in the graphite lattice. Only when the anode potential is below the Li / Li at 0V can the lithium ions be deposited on the anode surface. + Depending on the temperature and charging current, this can happen at any time after the charging process has started or if the battery is overcharged.

[0007] During the lithium deposition process, there is a coexistence between lithium ions being inserted into the crystal lattice and being deposited on the anode surface. This state remains unchanged until the charging process stops or the current in the constant voltage mode is small enough that the anode potential can be restored to the Li / Li + above potential.

[0008] Most of the lithium deposition is reversible, and the reduction of the lithium deposition layer can be achieved either by subsequent chemical intercalation into the graphite lattice (which requires sufficient relaxation time) or by dissolution during the subsequent discharge process. The quantitative methods currently used for non-destructive detection of lithium deposition layers are mainly divided into two categories:

[0009] (1) Detection of negative anode potential and Li / Li + , for example, using a reference electrode or a half-cell.

[0010] (2) Electrical measurement of battery degradation through lithium plating, such as capacity loss and internal resistance increase.

[0011] Method (1) is to measure the anode potential in the half-cell or via a built-in reference electrode, which requires extensive modifications to a given cell. In addition, the reference electrode may interfere with the electrode process and negatively affect the measurement (CN113422115A).

[0012] For the characterization of unmodified cells in large-scale production, measuring the anode potential is not a suitable option. In method (2), the standard procedure for determining the onset of lithium plating is to plot the maximum charge current under different operating conditions while regularly checking the performance degradation. The most important parameter here is the reduction in cell capacity, as it is directly related to the loss of active lithium.

[0013] When the above method (2) is used to detect the lithium deposition layer (CN114487855A, CN 114200322A), due to the indirectness of the measurement parameter types and the uncertainty of the measurement accuracy, the obtained detection results cannot accurately represent the actual thickness of the lithium deposition layer, and it is difficult to provide effective assistance for battery fault diagnosis. Summary of the invention

[0014] In order to overcome the technical problem that it is difficult to further improve the accuracy of lithium deposition layer detection in the existing soft-pack lithium-ion battery lithium deposition fault diagnosis, the present invention provides a lithium-ion battery fault diagnosis method based on lithium deposition detection. The method has small calculation amount, high sensitivity, and fast output speed. It can accurately detect the lithium deposition layer of the battery without destroying the battery cell, thereby providing reliable and effective information for battery fault diagnosis.

[0015] The present invention is achieved by at least one of the following technical solutions.

[0016] A lithium ion battery fault diagnosis method based on lithium plating detection, the method comprising the following steps:

[0017] S1. Place the battery in an experimental measuring device for measuring the thickness of a lithium-ion battery soft pack and fix it. Place the experimental device and the lithium-ion battery in a temperature box to keep the ambient temperature constant. Before starting the formal experiment, charge and discharge the lithium-ion battery several times at a low current to eliminate the residual inside the battery.

[0018] S2. Perform interval charge and discharge experiments on the battery under pre-set standard cycle conditions. After relaxation operation at each point, the measured thickness value remains stable, and the battery thickness increment, battery temperature, ambient temperature, battery current, charge and SOC data during the experiment are obtained;

[0019] S3, according to the experimental data obtained in step S2, using mathematical methods to establish a basic model for battery lithium deposition layer detection related to battery thickness increment, a battery thermal expansion compensation model related to battery temperature and ambient temperature, and a battery lithium embedding expansion compensation model related to charge;

[0020] S4. Keep other experimental conditions in step S2 unchanged, add a measuring point with a spring pressure surface, repeat the standard cycle condition experiment, and obtain the battery thickness increment H at the measuring point with spring pressure during the experiment. bat-p Data, compare the battery thickness data obtained in step S2, and establish a relevant battery gas expansion compensation model;

[0021] S5, combining the battery lithium deposition layer detection basic model in step S3, the battery thermal expansion compensation model, the battery lithium embedding expansion compensation model and the battery gas evolution expansion compensation model in step S4, thereby obtaining a lithium ion battery lithium deposition layer detection model;

[0022] S6, inputting the real-time measured battery temperature and ambient temperature into the battery thermal expansion compensation model established in step S3 to obtain a thermal expansion thickness compensation value H t The real-time measured charge C is input into the battery lithium embedding expansion compensation model established in step S3 to obtain the lithium embedding expansion thickness compensation value H Li-e ; The real-time measured battery thickness increment and the battery thickness increment H at the measuring point with spring pressure bat-p Input the battery gasification expansion compensation model established in step S5 to obtain the gasification expansion thickness compensation value; subtract the thermal expansion thickness compensation value, the lithium embedding expansion thickness compensation value and the gasification expansion thickness compensation value from the real-time measured battery thickness value to obtain the basic thickness value of the battery lithium deposition layer, which is approximately the real-time measured basic thickness value of the battery lithium deposition layer;

[0023] S7. Establish a lithium-ion battery lithium deposition layer thickness detection estimator, detect the lithium deposition layer thickness of the lithium-ion battery in real time online according to the real-time measured battery thickness, ambient temperature, battery temperature and charge data of the lithium-ion battery, judge the occurrence of lithium deposition based on the detection results, and perform battery lithium deposition fault diagnosis.

[0024] Furthermore, in step S1, the experimental measurement device uses a micrometer or a displacement sensor to measure the thickness of the battery, and the resolution of the micrometer or the displacement sensor is at least 1 μm.

[0025] Furthermore, the measuring points are arranged at least 5 locations, including the center and four corners on the plane formed by the maximum area of ​​the lithium-ion battery, and the measured data are averaged as the battery thickness value to eliminate the influence of battery non-uniformity on the battery thickness measurement.

[0026] Furthermore, the lithium-ion battery in step S1 is a high-energy soft-pack secondary lithium-ion battery cell that can be charged and discharged cyclically.

[0027] Furthermore, the battery thermal expansion compensation model established in step S3 is used to study the effect of battery heat on battery thickness and eliminate measurement errors caused by battery thermal expansion; the thickness compensation value of the battery thermal expansion is obtained by heating the battery through short charge and discharge pulses.

[0028] Furthermore, the battery lithium embedding expansion compensation model established in step S3 is used to eliminate the measurement error caused by the additional expansion of the anode due to lithium embedding, and the thickness compensation value of the battery lithium embedding expansion is calculated by inputting the battery transfer charge C, the expansion coefficient of the carbon-based anode material and the molar volume parameter into the battery lithium embedding expansion compensation model.

[0029] Furthermore, the battery gasification expansion compensation model established in step S4 is used to eliminate the measurement error caused by the battery expansion caused by the side reaction of electrolyte decomposition or the generation of a new solid electrolyte interface (Solid Electrolyte Interphase, SEI) layer, and the thickness compensation value of the battery gasification expansion is obtained by setting a control measuring point under pressure conditions.

[0030] Furthermore, the lithium ion battery lithium deposition layer detection model expression is:

[0031]

[0032] Where: T bat is the battery temperature; T amb is the ambient temperature; C is the charge; ΔH is the battery thickness increment H measured in step S2 bat The battery thickness increment H measured in step S4 bat-p The difference, ΔH = Hbat -H bat-p ;

[0033] H Li-p is the basic thickness value of the lithium deposition layer of the lithium-ion battery;

[0034] H t is the battery thermal expansion thickness compensation value, which is only related to the battery temperature T bat With ambient temperature T amb Related, using the functional relationship f(T bat ,T amb )express;

[0035] H Li-e is the thickness compensation value of lithium insertion expansion caused by lithium insertion into the anode, which is calculated based on the charge transfer amount C and is expressed by the function relationship g(C);

[0036] H g It is the compensation value of the gas expansion thickness inside the battery, which is only related to ΔH and is expressed by the function relationship h(ΔH).

[0037] Furthermore, in step S7, a lithium-ion battery lithium deposition layer thickness detection estimator is established based on a lithium-ion battery lithium deposition layer detection model, and inputs the detection values ​​of battery thickness increment, ambient temperature, battery temperature and charge, and outputs the lithium-ion battery lithium deposition layer thickness value calculated by the model.

[0038] Furthermore, in step S7, if the thickness of the lithium deposition layer detected online is greater than the preset reference value H for lithium deposition, ref , it is judged that lithium deposition occurs in the battery. The thicker the lithium deposition layer, the more serious the lithium deposition failure of the battery.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention provides a lithium-ion battery fault diagnosis method based on lithium deposition detection, which can quickly and accurately detect the lithium deposition layer of the battery without destroying the battery cell by incorporating the battery thickness increment that is easy to measure in real time into the lithium deposition layer detection of the lithium-ion battery. The method has small calculation amount, high sensitivity, fast output speed, and high detection accuracy, which can greatly improve the accuracy and credibility of lithium deposition layer detection of lithium-ion batteries, and can provide more reliable and effective lithium deposition detection information for battery fault diagnosis, further optimize the control and maintenance of the battery by the battery management system, thereby improving the health of the battery and extending the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a lithium-ion battery fault diagnosis method based on lithium plating detection according to an embodiment of the present invention;

[0042] Figure 2 Schematic diagram of the structure of an experimental device for measuring the thickness of a lithium-ion soft-pack battery according to an embodiment of the present invention;

[0043] Figure 3 A curve diagram showing the change of battery thickness with increasing charge in a single charging process of an embodiment of the present invention, with and without lithium plating;

[0044] Figure 4 It is a structural schematic diagram of a battery thickness measuring device after adding a spring pressure action measuring point according to an embodiment of the present invention;

[0045] Among them: 1—aluminum plate; 2—gap; 3—battery; 4—caulking agent; 5—micrometer; 6—spring; 7—plate. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation examples.

[0047] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0048] Example 1

[0049] like Figure 1 A lithium-ion battery fault diagnosis method based on lithium plating detection is shown, comprising the following steps:

[0050] S1. Place the battery in an experimental device specially used to measure the thickness of lithium-ion battery soft pack batteries and fix it. The experimental device and the lithium-ion battery are placed in a temperature box to maintain a constant ambient temperature (about 25°C). Before starting the formal experiment, the lithium-ion battery is charged and discharged several times at a low current (2-3A) to eliminate the internal residue of the battery;

[0051] S2. Under the preset standard cycle conditions, the battery is charged and discharged at a rate of C / 4 every 1Ah. After 1 hour of relaxation at each point, the measured thickness value remains stable, and the battery thickness increment H during the experiment is obtained. bat , battery temperature T bat 、Ambient temperature T amb , battery current I, charge C and SOC data;

[0052] The relaxation operation in the experiment is to allow the battery to reach equilibrium after a sufficient rest time to obtain relatively stable thickness, temperature and other data. It should be noted that due to factors such as battery inhomogeneity, dendritic deposition, thermal expansion, anode lithium insertion and gas evolution, the actual thickness measurement value will be larger than the theoretical measurement value.

[0053] Figure 3 The figure shows the curve of the battery thickness changing with the increase of charge during the single charge process of the lithium-ion battery. In the low current charging condition, the battery does not undergo lithium deposition reaction, but the battery undergoes lithium deposition reaction under the high current charging condition. The rate of increase of battery thickness is much faster than that of low current charging, and the peak value of battery thickness is reached in the constant voltage stage. When the charging rate is reduced or the charging process is stopped in the constant voltage mode, the subsequent embedding of lithium deposition becomes the dominant process, which leads to a reduction in metallic lithium at the anode, thereby causing a reduction in battery thickness. It should be noted that not all lithium deposition is reversible. Active lithium is lost through SEI construction reaction with the electrolyte or due to loss of contact with the active surface area, forming isolated lithium metal particles or layers. This irreversible part of lithium deposition and the release of gas will lead to a permanent increase in thickness.

[0054] S3, based on the experimental data obtained in step S2, using mathematical methods to establish the battery thickness increment H bat The basic model for battery lithium deposition layer detection is established with the battery temperature T bat 、Ambient temperature T amb The battery thermal expansion compensation model related to the battery is established, and the battery lithium insertion expansion compensation model related to the charge C is established;

[0055] The basic model for battery lithium deposition layer detection is established based on the battery thickness increment value measured at each measuring point in the experimental measurement device. The obtained battery thickness increment H bat is the average value of the thickness increment measured at each measuring point, that is,

[0056]

[0057] Where: n is the number of measuring points (dimension is 1); H bat-i is the thickness increment value (μm) measured at the i-th measuring point.

[0058] The battery thermal expansion compensation model is used to study the effect of battery heat on battery thickness and eliminate the measurement error caused by battery thermal expansion. The thickness compensation value of the battery thermal expansion is obtained by heating the battery with short charge and discharge pulses. The purpose of the short charge and discharge pulses is to keep the SOC constant so that the battery thermal expansion compensation model is only related to the battery temperature T bat 、Ambient temperature T amb Related, use H t =f(T bat,T amb For a battery cell with a nominal capacity of 20Ah, a current of 5A is generally used for 5min charging and 5min discharging to obtain a pulse heating battery.

[0059] The battery lithium embedding expansion compensation model is used to eliminate the measurement error caused by the additional expansion of the anode due to lithium embedding. The thickness compensation value of the battery lithium embedding expansion is calculated by inputting parameters such as the battery transfer charge C, the expansion coefficient and molar volume of the carbon-based anode material into the battery lithium embedding expansion compensation model.

[0060] Taking carbon-based anode lithium-ion batteries as an example, the estimated calculation process of the change in battery thickness caused by lithium embedding into the anode corresponding to the transfer of unit charge is as follows.

[0061] 1Ah of charge is transferred from the cathode to the anode, and the amount of lithium substance is:

[0062]

[0063] Where: n Li is the amount of lithium substance (mol); N A is Avogadro's constant (1 / mol); e is the elementary charge (As); C is the transferred charge (As).

[0064] In a fully charged state, every six carbon atoms provide space for one lithium atom. Therefore, every 1Ah of charge transfer consumes 6·n Li = 0.2239 mol of carbon. The molar volume of carbon in the hexagonal structure is V m,C =5.31×10 - 6 m 3 mol -1 Therefore, for every 1Ah of charge transferred, the carbon material will occupy 1.189×10 -6 m 3 The carbon-based anode material expands by about 10% due to the insertion of lithium. Therefore, charging 1Ah results in the following volume change:

[0065]

[0066] Where: ΔV C is the volume change of carbon due to lithium insertion (m 3 ); is the expansion coefficient of carbon to LiC6 (about 10%); V m,C is the molar volume of carbon (m 3 mol -1 ).

[0067] The test cell used in the experiment in step S1 contains an electrode with an effective area of ​​188 mm×122 mm. Therefore, lithium insertion into the anode will lead to an increase in thickness of about 5.184 μm per 1 Ah charge of the battery, which is independent of the number of layers inside the battery.

[0068] S4. Keep other experimental conditions in step S2 unchanged, add a measuring point with a spring pressure surface, repeat the standard cycle condition experiment, and obtain the battery thickness increment H at the measuring point with spring pressure during the experiment. bat-p Data, compare the battery thickness data H obtained in step S2 bat , establish relevant battery gasification expansion compensation model;

[0069] In order to eliminate the measurement errors caused by the side reactions such as electrolyte decomposition or new SEI film formation, which lead to gas evolution inside the battery and battery expansion, such as Figure 4 As shown, a micrometer, a spring 6 and a small partition 7 are added to the measuring device, so that a new measuring point with a spring pressure action surface is added 3 cm adjacent to the original measuring point, and a measuring point with a spring pressure action surface is added 1.5 cm around the measuring point. 2 The area of ​​the spring pressure generates a pressure of about 2 bar, making the pressure between the electrode layers much higher than the value in the surrounding area. If gas escapes, the measuring point without spring pressure will show a significantly larger battery thickness increase than the measuring point with spring pressure.

[0070] S5, combining the battery lithium deposition layer detection basic model in step S3, the battery thermal expansion compensation model, the battery lithium embedding expansion compensation model and the battery gas evolution expansion compensation model in step S4, thereby obtaining a lithium ion battery lithium deposition layer detection model;

[0071] The lithium ion battery lithium deposition layer detection model is composed of a battery lithium deposition layer detection basic model, a battery thermal expansion compensation model, a battery lithium embedding expansion compensation model and a battery gas evolution expansion compensation model. The lithium ion battery lithium deposition layer detection model is expressed as:

[0072]

[0073] Where: T bat is the battery temperature; T amb is the ambient temperature; C is the charge; ΔH is the battery thickness increment H measured in step S2 bat The battery thickness increment H measured in step S4 bat-p The difference, ΔH = H bat -H bat-p ;

[0074] H Li-p is the basic thickness value of the lithium deposition layer of the lithium-ion battery;

[0075] H tis the thickness compensation value of the battery thermal expansion, which is only related to the battery temperature T bat With ambient temperature T amb Related, using the functional relationship f(T bat ,T amb )express;

[0076] H Li-e It is the thickness compensation value of the battery expansion caused by lithium embedding in the anode, which is calculated according to the charge transfer amount C and expressed by the function relationship g(C);

[0077] H g It is the thickness compensation value for the gas expansion inside the battery, which is only related to ΔH and is expressed by the function relationship h(ΔH).

[0078] In this embodiment, the lithium deposition layer detection model of the lithium ion battery is established by polynomial fitting and formula calculation, as follows:

[0079] First, according to the experimental data, select the appropriate order (depending on the data distribution of the battery thermal expansion thickness increment as the battery temperature and ambient temperature change), and fit the battery thermal expansion thickness compensation value H t With variable T bat , T amb A polynomial regression model is established based on the relationship between the thermal expansion of the battery and the thermal expansion of the battery:

[0080] H t =f(T bat ,T amb )

[0081] Thickness compensation value H based on the expansion of the battery caused by lithium insertion into the anode Li-e The relationship with variable C is used to establish the battery lithium embedding expansion compensation model:

[0082]

[0083] in: is the expansion coefficient of carbon to LiC6 (about 10%); V m,C is the molar volume of carbon (m 3 mol -1 ); e is the elementary charge (As); N A is Avogadro's constant (1 / mol); S ele is the effective area of ​​the electrode (m 2 ); C is the transferred charge (As).

[0084] Thickness compensation value H based on gas expansion inside the battery g With variable H bat , H bat-p The relationship between the battery gasification expansion compensation model is established:

[0085]

[0086] Where: n is the number of measuring points (dimension is 1); H bat-i is the thickness increment value measured at the i-th measuring point (μm); H bat-p-i It is the thickness increment value (μm) under the action of spring pressure measured at the i-th measuring point.

[0087] Secondly, based on the real-time battery thickness increment value H bat , combining the basic model for battery lithium deposition layer detection, the battery thermal expansion compensation model, the battery lithium embedding expansion compensation model and the battery gas evolution expansion compensation model to obtain a lithium-ion battery lithium deposition layer detection model;

[0088] Finally, under the premise of ensuring safety, the experimental battery was dissected on site to further measure the metallic lithium and obtain the actual thickness of the battery lithium deposition layer to verify the effectiveness and accuracy of the model.

[0089] S6. The real-time measured battery temperature T bat 、Ambient temperature T amb Input the battery thermal expansion compensation model established in step S3 to obtain the thermal expansion thickness compensation value H t The real-time measured charge C is input into the battery lithium embedding expansion compensation model established in step S3 to obtain the lithium embedding expansion thickness compensation value H Li-e , the real-time measured battery thickness increment H bat and H bat-p Input the battery gas expansion compensation model established in step S5 to obtain the gas expansion thickness compensation value H g , using the real-time measured battery thickness value H bat Subtract the thermal expansion thickness compensation value H t , Lithium embedding expansion thickness compensation value H Li-e and gas expansion thickness compensation value H g , get the basic thickness value H of the battery lithium deposition layer Li-p , which is approximated as the basic thickness value of the battery lithium deposition layer measured in real time;

[0090] The lithium-ion battery fault diagnosis method and model based on lithium plating detection mainly considers the three factors of battery thermal expansion, anode lithium embedding expansion and gas escape that significantly interfere with the measurement of battery lithium plating layer thickness, while ignoring the factors with minimal interference such as the battery cathode effect and lithium dendritic deposition.

[0091] S7. Establish a lithium-ion battery lithium deposition layer thickness detection estimator to detect the lithium deposition layer thickness of the lithium-ion battery in real time online based on real-time measured data such as the battery thickness, ambient temperature, battery temperature and charge of the lithium-ion battery, determine the occurrence of lithium deposition based on the detection results, and perform battery lithium deposition fault diagnosis.

[0092] The lithium ion battery lithium deposition layer thickness detection estimator is mainly based on the lithium ion battery lithium deposition layer detection model, and the battery thickness increment H is input. bat and H bat-p 、Ambient temperature T amb , battery temperature T bat The detection value of the charge C and the output model calculate the thickness of the lithium layer of the lithium-ion battery H Li-p If the lithium deposition layer thickness value H obtained by online detection Li-p Greater than the preset reference value H for lithium plating ref (generally set to 1-2μm), it is judged that battery electrolysis has occurred.

[0093] According to the thickness of the detected lithium deposition layer, the severity of the lithium deposition failure of the battery can be estimated and diagnosed. The thicker the lithium deposition layer, the more serious the lithium deposition failure of the battery, and the worse the performance, reliability and safety of the battery.

[0094] The battery thickness increment that can be easily measured online in real time is included in the estimation of the lithium plating layer detection of lithium-ion batteries because the battery thickness increment is coupled with the lithium plating reaction that occurs during the charging process of the battery. The lithium plating layer of the battery can be detected quickly and accurately without destroying the battery pack, which can greatly improve the accuracy and reliability of lithium plating layer detection of lithium-ion batteries. It can provide more reliable and effective lithium plating detection information for battery fault diagnosis, and further optimize the battery management system's control and maintenance of the battery, thereby improving the health of the battery and extending the battery life.

[0095] Example 2

[0096] The experimental measurement device of this embodiment is as follows Figure 2 As shown, the experimental soft-pack battery 3 to be measured is placed between the upper and lower aluminum plates 1 screwed together at a fixed distance; a gap 2 is left between the upper aluminum plate and the battery to provide space for expansion (the gap height depends on the installation method of the micrometer, generally 5 to 10 mm); the lower aluminum plate is connected to the experimental soft-pack battery 3 through a caulking agent 4, which eliminates the measurement error caused by battery sliding and provides a good thermal connection. The temperature of the bottom plate remains unchanged. In addition, in order to keep the ambient temperature constant, the entire device is placed in a temperature box.

[0097] The micrometer 5 is installed at different positions of the upper plate to measure the battery thickness, and the resolution of the micrometer used is at least 1 μm.

[0098] In the process of measuring the battery thickness, the measuring points of the micrometer are arranged at 5 positions, including the center and four corners, on the plane formed by the largest area of ​​the lithium-ion battery. The measured data are averaged as the battery thickness value to eliminate the influence of battery unevenness on the battery thickness measurement.

[0099] The experimental battery is a high-energy soft-pack secondary lithium-ion battery cell with a nominal capacity of 20 Ah and capable of being charged and discharged cyclically.

[0100] Example 3

[0101] The experimental measurement device of this embodiment is as follows Figure 2 As shown, the experimental soft-pack battery 3 to be measured is placed between the upper and lower aluminum plates 1 screwed together at a fixed distance; a gap 2 is left between the upper aluminum plate and the battery to provide space for expansion (the gap height depends on the installation method of the displacement sensor, generally 5 to 10 mm); the lower aluminum plate is connected to the experimental soft-pack battery 3 through a caulking agent 4, which eliminates the measurement error caused by battery sliding and provides a good thermal connection. The temperature of the bottom plate remains unchanged. In addition, in order to keep the ambient temperature constant, the entire device is placed in a thermostat.

[0102] Displacement sensors are installed at different positions on the upper plate to measure the battery thickness, and the resolution of the displacement sensors used is at least 1 μm.

[0103] In the process of measuring the battery thickness, the measuring points of the displacement sensor are arranged at 5 locations, namely the center and four corners, on the plane formed by the largest area of ​​the lithium-ion battery. The average value of the measured data is taken as the battery thickness value to eliminate the influence of the battery's non-uniformity on the battery thickness measurement.

[0104] The experimental battery is a high-energy soft-pack secondary lithium-ion battery cell with a nominal capacity of 20 Ah and capable of being charged and discharged cyclically.

[0105] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail to a certain extent with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A lithium-ion battery fault diagnosis method based on lithium plating detection, characterized in that: The method comprises the following steps: S1. Place the battery in an experimental measuring device for measuring the thickness of a lithium-ion battery soft pack and fix it. Place the experimental device and the lithium-ion battery in a temperature box to keep the ambient temperature constant. Before starting the formal experiment, charge and discharge the lithium-ion battery several times at a low current to eliminate the residual inside the battery. S2. Perform interval charge and discharge experiments on the battery under pre-set standard cycle conditions. After relaxation operation at each point, the measured thickness value remains stable, and the battery thickness increment, battery temperature, ambient temperature, battery current, charge and SOC data during the experiment are obtained; S3, according to the experimental data obtained in step S2, using mathematical methods to establish a basic model for battery lithium deposition layer detection related to battery thickness increment, a battery thermal expansion compensation model related to battery temperature and ambient temperature, and a battery lithium embedding expansion compensation model related to charge; S4. Keep other experimental conditions in step S2 unchanged, add a measuring point with a spring pressure surface, repeat the standard cycle condition experiment, and obtain the battery thickness increment H at the measuring point with spring pressure during the experiment. bat-p Data, compare the battery thickness data obtained in step S2, and establish a relevant battery gas expansion compensation model; S5, combining the battery lithium deposition layer detection basic model in step S3, the battery thermal expansion compensation model, the battery lithium embedding expansion compensation model and the battery gas expansion compensation model in step S4, thereby obtaining a lithium ion battery lithium deposition layer detection model; the lithium ion battery lithium deposition layer detection model expression is: Where: T bat is the battery temperature; T amb is the ambient temperature; C is the charge; ΔH is the battery thickness increment H measured in step S2 bat The battery thickness increment H measured in step S4 bat-p The difference, ΔH = H bat -H bat-p ; H Li-p is the basic thickness value of the lithium deposition layer of the lithium-ion battery; H t is the battery thermal expansion thickness compensation value, which is only related to the battery temperature T bat With ambient temperature T amb Related, using the functional relationship f(T bat ,T amb )express; H Li-e is the thickness compensation value of lithium insertion expansion caused by lithium insertion into the anode, which is calculated based on the charge transfer amount C and is expressed by the function relationship g(C); H g It is the compensation value of the internal gas expansion thickness of the battery, which is only related to ΔH and is expressed by the function relationship h(ΔH); S6, inputting the real-time measured battery temperature and ambient temperature into the battery thermal expansion compensation model established in step S3 to obtain a thermal expansion thickness compensation value H t The real-time measured charge C is input into the battery lithium embedding expansion compensation model established in step S3 to obtain the lithium embedding expansion thickness compensation value H Li-e ; The real-time measured battery thickness increment and the battery thickness increment H at the measuring point with spring pressure bat-p Input the battery gasification expansion compensation model established in step S5 to obtain the gasification expansion thickness compensation value; subtract the thermal expansion thickness compensation value, the lithium embedding expansion thickness compensation value and the gasification expansion thickness compensation value from the real-time measured battery thickness value to obtain the basic thickness value of the battery lithium deposition layer, which is approximately the real-time measured basic thickness value of the battery lithium deposition layer; S7. Establish a lithium-ion battery lithium deposition layer thickness detection estimator, detect the lithium deposition layer thickness of the lithium-ion battery in real time online according to the real-time measured battery thickness, ambient temperature, battery temperature and charge data of the lithium-ion battery, judge the occurrence of lithium deposition based on the detection results, and perform battery lithium deposition fault diagnosis.

2. A lithium-ion battery fault diagnosis method based on lithium deposition detection according to claim 1, characterized in that: In step S1, the experimental measuring device uses a micrometer or a displacement sensor to measure the thickness of the battery, and the resolution of the micrometer or the displacement sensor is at least 1 μm.

3. A lithium-ion battery fault diagnosis method based on lithium deposition detection according to claim 1, characterized in that: The measuring points are arranged at least 5 locations, including the center and four corners on the plane formed by the largest area of ​​the lithium-ion battery, and the measured data are averaged as the battery thickness value to eliminate the influence of battery non-uniformity on battery thickness measurement.

4. A lithium-ion battery fault diagnosis method based on lithium plating detection according to claim 1, characterized in that: The lithium-ion battery in step S1 is a high-energy soft-pack secondary lithium-ion battery cell that can be charged and discharged cyclically.

5. A lithium-ion battery fault diagnosis method based on lithium deposition detection according to claim 1, characterized in that: The battery thermal expansion compensation model established in step S3 is used to study the effect of battery heat on battery thickness and eliminate measurement errors caused by battery thermal expansion; the thickness compensation value of the battery thermal expansion is obtained by heating the battery through short charge and discharge pulses.

6. A lithium-ion battery fault diagnosis method based on lithium plating detection according to claim 1, characterized in that: The battery lithium embedding expansion compensation model established in step S3 is used to eliminate the measurement error caused by the additional expansion of the anode due to lithium embedding. The thickness compensation value of the battery lithium embedding expansion is calculated by inputting the battery transfer charge C, the expansion coefficient of the carbon-based anode material and the molar volume parameters into the battery lithium embedding expansion compensation model.

7. A lithium-ion battery fault diagnosis method based on lithium deposition detection according to claim 1, characterized in that: The battery gas evolution expansion compensation model established in step S4 is used to eliminate the measurement error caused by the side reaction of electrolyte decomposition or the generation of a new solid electrolyte interface layer, which leads to gas evolution inside the battery and causes battery expansion. The thickness compensation value of the battery gas evolution expansion is obtained by setting a control measuring point under pressure conditions.

8. A lithium-ion battery fault diagnosis method based on lithium plating detection according to claim 1, characterized in that: In step S7, a lithium-ion battery lithium deposition layer thickness detection estimator is established based on a lithium-ion battery lithium deposition layer detection model, and inputs the detection values ​​of battery thickness increment, ambient temperature, battery temperature and charge, and outputs the lithium-ion battery lithium deposition layer thickness value calculated by the model.

9. A lithium-ion battery fault diagnosis method based on lithium plating detection according to any one of claims 1 to 8, characterized in that: In step S7, if the thickness of the lithium deposition layer detected online is greater than the preset lithium deposition occurrence reference value H ref , it is judged that lithium deposition occurs in the battery. The thicker the lithium deposition layer, the more serious the lithium deposition failure of the battery.

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