Impedance spectrum and safety threshold-based online lithium precipitation detection framework and method for energy storage lithium battery

Abstract

The invention provides an energy storage lithium battery online lithium precipitation detection framework and method based on an impedance spectrum and a safety threshold value, relates to the field of energy storage battery safety state diagnosis and energy management, and solves potential safety hazards caused by lithium precipitation in the operation process of an energy storage power station. According to the method, the relaxation time distribution characteristic value of the battery impedance spectrum is obtained by identifying the shelving state of the energy storage battery after charging and applying alternating current excitation with specific frequency combination and combining high-speed sampling and impedance spectrum analysis technologies. According to the method, no additional reference electrode is needed, in-situ and online detection is achieved, and the influence of the charging process on impedance change is effectively avoided. Through multiple times of detection and comparison of characteristic value changes, normalization and differentiation are carried out, then comparison with a lithium precipitation safety threshold value is carried out, and if the threshold value is exceeded, lithium precipitation occurs. According to the method, a reliable lithium precipitation detection means is provided for safety diagnosis and state estimation of the energy storage power station, the charging strategy can be optimized, abnormal attenuation of the battery capacity is prevented, and the probability of occurrence of safety accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy storage battery safety status diagnosis and energy management, and in particular to an energy storage lithium battery online lithium plating detection architecture and method based on impedance spectrum and safety threshold. Background Art

[0002] Lithium-ion batteries used in energy storage power stations have the characteristics of high energy density and large capacity. As the scale of lithium-ion energy storage systems expands, their safety issues are becoming more and more prominent. During the operation of lithium-ion batteries, low temperature and high-rate charging can easily cause lithium deposition at the negative electrode of the battery, which in turn induces lithium dendrites, pierces the diaphragm, causes internal short circuits, and leads to thermal runaway. Due to the large capacity of lithium-ion batteries used in energy storage power stations, the severity of accidents caused by internal short circuits will be more serious than those caused by small-capacity batteries used in vehicles. Therefore, realizing lithium deposition detection of lithium-ion batteries used in energy storage power stations is of great significance to enhancing the safety of battery energy storage systems.

[0003] At present, the in-situ lithium deposition detection for lithium-ion batteries is mainly carried out by installing three electrodes to monitor the negative electrode potential or directly measuring the impedance, thereby detecting lithium deposition. Invention patent CN114019385B discloses a lithium deposition detection method based on single-frequency impedance testing, but it is necessary to install a reference electrode to form a three-electrode battery. In addition, the single-frequency impedance test needs to be implemented in conjunction with an electrochemical workstation and cannot be used for actual online lithium deposition detection. Invention CN115298561A discloses a lithium deposition detection method for an electrochemical device, which obtains the internal resistance change curve corresponding to different SOCs by performing intermittent charging operations on the battery, but the interruption time is 8 to 10 seconds, which reduces the charging efficiency. Secondly, the zero-crossing point of the second-order differential curve of the internal resistance is used as the basis for judging lithium deposition, which lacks model and theoretical support. Invention patent CN112394289B discloses a method for detecting lithium deposition during charging of lithium-ion batteries. A disturbance signal of a series of frequencies is superimposed on the constant current charging current, and the impedance under the characteristic frequency set is obtained through time-frequency analysis. The resistance parameter Rct is fitted by an equivalent circuit model, and multiple measurements are made to obtain the change trend of Rct with SOC, and then determine whether lithium deposition occurs. The detection process involves the superposition of multi-frequency disturbance signals and the process of parameter fitting, which is costly and complex, and it is difficult to achieve online lithium deposition detection. The above patents perform lithium deposition detection during the charging process, which cannot avoid the influence of charging current and SOC on impedance changes, and are difficult to operate in practice. In addition, there is no specific lithium deposition detection method for large-capacity batteries such as lithium-ion batteries for energy storage power stations. Compared with small batteries, large-capacity batteries have a more significant averaging effect, and the impedance or internal resistance reduction caused by lithium deposition is difficult to be intuitively reflected in the change of impedance spectrum, resulting in failure of lithium deposition detection of energy storage batteries. Summary of the invention

[0004] In response to the problem that the internal state of lithium-ion batteries used in energy storage power stations is opaque and lithium deposition cannot be accurately detected, the present invention proposes an online lithium deposition detection architecture and method for energy storage lithium batteries based on impedance spectrum and safety threshold, which will help adjust the charging strategy under different working conditions and avoid the accelerated battery capacity decay and safety problems caused by lithium deposition. The present invention obtains the change in the characteristic value of the charge transfer process by analyzing the relaxation time distribution of the impedance spectrum during the shelving process, and then realizes lithium deposition detection by setting a lithium deposition safety threshold. The present invention does not rely solely on the change in impedance value, thereby greatly reducing the impact of the average effect of large-capacity batteries. The proposed method can effectively detect lithium deposition batteries under different capacities, temperatures, and rates, so as to timely diagnose the lithium deposition status of energy storage power stations and improve the safety of battery energy storage systems.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] An energy storage lithium battery online lithium plating detection architecture based on impedance spectrum and safety threshold, the architecture comprises a battery cluster composed of a battery PACK, a primary BMS, a secondary BMS, a tertiary BMS master control, and an energy storage converter PCS; the energy storage converter PCS has an active excitation mode and is connected in series with the battery cluster composed of the battery PACK; the primary BMS is connected to the battery cells in the battery PACK to collect the voltage and temperature of all the battery cells, and the primary BMS is connected to the secondary BMS via a CAN bus; the secondary BMS collects the battery cluster current, and the secondary BMS is interconnected with the tertiary BMS master control via a CAN bus, and the uploaded data includes the single cell voltage, cluster voltage, cluster current, temperature, and battery abnormality alarm, and then the electrochemical impedance spectrum is calculated and lithium plating is judged; the tertiary BMS master control is interconnected with the PCS via Modbus-TCP, and sends commands such as impedance spectrum detection, operation strategy, and protection action to the PCS; one side of the PCS is connected to the power grid side to realize energy transmission.

[0007] A method for detecting online lithium deposition of energy storage lithium batteries based on impedance spectrum and safety threshold comprises the following steps:

[0008] Step 1) Determine the working status of the battery, which is divided into the following five states: charging, set aside after charging, discharging, set aside after discharging, and long-term set aside;

[0009] Step 2) identifying whether the storage state of the charged battery meets the detection conditions;

[0010] Step 3) After the detection conditions are met, a mode switching instruction is issued to the primary BMS of the battery cluster to be tested to increase the sampling frequency from a low-speed sampling rate to a high-speed sampling rate;

[0011] Step 4) Issue an excitation instruction to the battery cluster PCS with impedance spectrum detection function, switch the PCS function to the AC excitation function, and start applying AC current excitation of 1kHz-0.01Hz and different frequency combinations to the battery cluster to be detected;

[0012] Step 5) After one excitation is completed, the impedance is calculated on the edge computing terminal or EMS; the voltage of each battery cell and the amplitude and phase angle of the excitation current at different frequencies are calculated by segmented window Fourier transform; the impedance at different frequencies is calculated and the impedance spectrum is drawn;

[0013] Step 6) Perform relaxation time distribution analysis on the impedance spectrum to obtain characteristic values ​​of different electrochemical processes;

[0014] Step 7) After the analysis is completed, save the characteristic value, stop BMS high-speed sampling and PCS excitation, wait for the next test, and after a certain interval, jump to step 3) and repeat steps 3)-7) until three tests are completed;

[0015] Step 8) After the three tests are completed, the impedance spectrum is calculated to perform the change of the relaxation time distribution DRT characteristic value, and compared with the set lithium plating safety threshold, the test result is given, and the test is completed.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. The detection method proposed in the present invention obtains the characteristic value of the battery charge transfer process by analyzing the relaxation time distribution of the electrochemical impedance spectrum, and compares the normalized characteristic value with the safety threshold to judge lithium precipitation. It can effectively avoid the problem of difficulty in extracting lithium precipitation features due to the averaging effect. Compared with the existing technology, it has distinct innovation and originality.

[0018] 2. The detection method proposed in the present invention makes full use of the existing architecture of PCS and BMS in structure, can realize in-situ detection, and greatly reduces the cost of transformation and detection.

[0019] 3. The detection method proposed in the present invention is carried out during the battery storage process, which will not affect the normal operation of the battery energy storage. The battery object can be a lithium-ion battery with different materials for energy storage, including one of lithium iron phosphate batteries, nickel cobalt manganese oxide batteries or lithium cobalt oxide batteries, adapted to different temperature and rate conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a diagram of the overall detection platform architecture of the present invention;

[0021] Figure 2 It is the overall detection flow chart of the present invention;

[0022] Figure 3 This is a typical electrochemical impedance spectrum of a lithium-ion battery;

[0023] Figure 4 This is the relaxation time distribution diagram of the battery without lithium plating;

[0024] Figure 5 This is the relaxation time distribution diagram of the lithium-ion battery;

[0025] Figure 6 It is the lithium precipitation criterion of the characteristic value of the relaxation time distribution of the impedance spectrum. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme 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 embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above-mentioned purpose, the present invention adopts the following technical scheme.

[0027] The present invention proposes an online lithium deposition detection architecture for energy storage lithium batteries based on impedance spectrum and safety threshold. The overall detection platform architecture integrating PCS and BMS hardware and software is shown in Figure 1 . Figure 1 It includes a battery cluster composed of battery PACK, a first-level BMS, a second-level BMS, a third-level BMS master control, and an energy storage converter PCS. The energy storage converter PCS has an active excitation mode and is connected in series with the battery cluster composed of the battery PACK; the first-level BMS is connected to the battery cells in the battery PACK, collects the voltage and temperature of all the battery cells, and is connected to the second-level BMS through the CAN bus; the second-level BMS collects the battery cluster current, and is interconnected with the third-level BMS master control through the CAN bus. The uploaded data includes the single cell voltage, cluster voltage, cluster current, temperature, battery abnormality alarm, etc., and then calculates the electrochemical impedance spectrum and judges lithium precipitation; the third-level BMS master control is interconnected with the PCS through Modbus-TCP, and sends impedance spectrum detection, operation strategy, protection action and other instructions to the PCS; one side of the PCS is connected to the grid side to realize energy transmission. The battery uses energy storage lithium batteries.

[0028] Furthermore, the present invention proposes an online lithium deposition detection method for energy storage lithium batteries based on impedance spectroscopy and safety threshold. The specific detection process is shown in Figure 2 , used to detect the lithium deposition of batteries in situ in energy storage power stations and make judgments, including the following steps:

[0029] Step 1) Determine the working status of the energy storage battery, which is divided into the following five states: charging, set aside after charging, discharging, set aside after discharging, and long-term set aside (more than 6 hours after charging and discharging);

[0030] Step 2) Identify whether the standby state after charging meets the detection conditions, the condition is that no charging or discharging is performed within 2 hours, and the time since the last detection exceeds 1 month;

[0031] Step 3) After the detection conditions are met, a mode switching instruction is issued to the primary BMS of the battery cluster to be tested, and the sampling frequency is increased from the conventional low-speed sampling rate to the high-speed sampling rate;

[0032] Step 4) Issue an excitation instruction to the battery cluster PCS with impedance spectrum detection function, switch the PCS function to the AC excitation function, and start applying AC current excitation of 1kHz-0.01Hz and different frequency combinations to the battery cluster to be detected;

[0033] Step 5) After one excitation is completed, calculate the impedance on the edge computing terminal or EMS. Calculate the amplitude and phase angle of each battery cell voltage and excitation current at different frequencies through segmented window Fourier transform. Calculate the impedance at different frequencies and draw the impedance spectrum;

[0034] Step 6) Performing a distribution relaxation time (DRT) analysis on the impedance spectrum to obtain characteristic values ​​of different electrochemical processes;

[0035] Step 7) After the analysis is completed, save the characteristic value. Stop the BMS high-speed sampling and PCS excitation, and wait for the next test. The time interval is set to 1h. After the 1h interval time is up, jump to step 3) and repeat steps 3)-7) until three tests are completed;

[0036] Step 8) After the three tests are completed, the impedance spectrum is calculated to perform the change of the relaxation time distribution DRT characteristic value, and compared with the set lithium plating safety threshold, and the test result is given. The test is completed.

[0037] Furthermore, in the step 1), the timing of lithium deposition detection of the energy storage lithium-ion battery is related to the current state. The operating battery is always in the following five states: charging, shelving after charging, discharging, shelving after discharging, and long-term shelving (more than 6 hours after charging and discharging). The real-time selection of the lithium deposition detection method should be in the shelving state after charging.

[0038] Furthermore, the step 2) is specifically as follows: since the lithium deposition detection principle requires measuring the electrochemical impedance spectrum of the two-hour standby process, after identifying that the current energy storage battery cluster is in the standby state after charging, it is determined whether the two-hour standby detection condition is met. At the same time, in order to avoid frequent detection, a one-month detection cycle is set. The shortest time interval between two lithium deposition detections is one month.

[0039] Furthermore, the step 3) is specifically as follows: In the traditional operation mode of BMS, considering the large number of battery cells in a power station cluster or power station, in order to avoid collecting too much data, the voltage sampling period is usually maintained at a low level, generally around 50ms. During the lithium deposition detection process, in order to achieve high-frequency impedance measurement, it is necessary to temporarily increase the sampling frequency of BMS. After the detection is completed, the BMS sampling frequency is switched to the traditional low-speed mode. This can not only achieve impedance measurement, but also ensure that the amount of data exceeds the storage range. See attached Figure 1 .

[0040] Furthermore, in step 4), the PCS not only has the traditional charge and discharge mode, but also adds an active excitation mode. This mode is parallel to the charge and discharge mode and does not interfere with each other. During the lithium deposition detection process, the traditional charge and discharge work is not performed, and only active current excitation is applied to the battery cluster. The frequency range of active current excitation is set to 1kHz-0.01Hz. The specific measurement frequency points are distributed logarithmically, that is, 10 3 , 10 2.9 …10 -1.9 , 10 -2 , a total of 51 measurement points. Two cycles of excitation signal are applied to each frequency measurement point.

[0041] Furthermore, in step 4), the amplitude of active excitation depends on the battery internal resistance and voltage amplitude. In order to satisfy the Kramers-Kronig relationship of impedance data and the three prerequisites of impedance measurement: causality, linearity, and stability, the battery voltage response amplitude is generally 10mV. The internal resistance of the energy storage battery is extremely small, generally 0.5mΩ. In order to obtain a voltage response of 10mV, the active current excitation amplitude is about 20A.

[0042] Furthermore, the step 5) is specifically as follows: performing a segmented adaptive window Fourier transform on the collected current and voltage signals to extract the voltage and current amplitudes and phase angles, thereby obtaining the impedance. Define the voltage of the jth battery in a battery cluster as V cj , the excitation current applied to the battery cluster is equal, defined as I c . Perform Fourier transform on voltage and current.

[0043] (1)

[0044] Where, T k is the period of the kth frequency point; is the kth frequency point, f j k is the kth frequency point of the jth battery; t h is the starting time of each frequency excitation; , are the cosine Fourier coefficient and sine Fourier coefficient of the excitation current respectively; , are the cosine Fourier coefficient and sine Fourier coefficient of the battery response voltage, respectively.

[0045] The impedance is calculated as follows:

[0046] (2)

[0047] The real and imaginary parts of the impedance are:

[0048] (3)

[0049] In the formula, is the impedance amplitude of the jth battery at the kth frequency point; is the impedance phase angle of the jth battery at the kth frequency point. A typical impedance spectrum is as follows Figure 3 shown.

[0050] Furthermore, in step 6), the DRT analysis of the impedance spectrum regards the electrochemical system as an ohmic resistor and an infinite number of polarization processes in series, that is, a Voigt circuit in the equivalent circuit model, which can approximate the impedance model of any electrochemical system, thereby avoiding the problem of prior assumptions of the model. The relationship between the relaxation time distribution and the impedance spectrum is as follows:

[0051] (4)

[0052] In the formula, Z DRT is the impedance expressed by the relaxation time distribution function; f is the EIS scanning point frequency; R0 is the ohmic resistance of the battery; L is the inductance connected in series during the DRT fitting process to indicate the characteristic that the imaginary part of the EIS in the high frequency band is greater than 0; Z pol represents the battery polarization impedance; τ represents the relaxation time; g(τ) is the relaxation time distribution function.

[0053] Furthermore, in step 6), the characteristics of different electrochemical processes correspond to the central relaxation time of the characteristic peaks of the DRT analysis results, the characteristic peaks representing charge transfer are identified in the DRT analysis results, and the corresponding central relaxation time τ is extracted. ct,k,n , represents the characteristic value of the nth battery measured for the kth time.

[0054] Furthermore, in step 7), the lithium plating detection needs to obtain the impedance spectra at different times during the storage process, so the time interval is set to 1h and the test is performed three times in a row. In each waiting test interval, the PCS excitation and BMS high-speed sampling are stopped to avoid long-term sampling occupying too much storage. After the interval time is up, jump to step 3). The impedance spectrum relaxation time distribution corresponding to the battery without lithium plating is shown in Figure 4The relaxation time distribution of the impedance spectrum corresponding to the lithium-ion battery is shown in Figure 5 .

[0055] Furthermore, the step 8) is specifically as follows: obtaining three consecutive impedance spectra, extracting corresponding charge transfer features, normalizing and differentiating them, and comparing them with the lithium deposition threshold.

[0056] Define the normalized deviation D of different relaxation times of τ ct,dev,k as follows:

[0057] (5)

[0058] Where τ ct,k,n τ obtained from the kth measurement of the nth battery ct ; τ ct,0,n τ is the measured time at the beginning of the shelf ct .

[0059] If the impedance spectrum during the storage process satisfies the following formula, it is considered that lithium deposition occurs.

[0060] (6)

[0061] Where Time rest is the shelf time; Δ th To detect the safety threshold for lithium plating.

[0062] Lithium precipitation criterion and region of characteristic value of relaxation time distribution of impedance spectrum Figure 6 As shown, when the lithium plating detection safety threshold is set to 0.15, the lithium plating battery and the non-lithium plating battery can be accurately distinguished.

[0063] Furthermore, in the step 8), the characteristic value representing the negative electrode charge transfer in the relaxation time distribution result of the lithium-ion battery will gradually increase with the shelving process until it reaches a new quasi-stable state. The characteristic value representing the negative electrode charge transfer of the battery without lithium deposition remains approximately unchanged with the shelving process. The principle is: after the battery is lithium-ionized, the presence of metallic lithium accelerates the reaction of the charge transfer process, and the influence of metallic lithium gradually disappears with the shelving. This ultimately leads to a trend of increasing the relaxation time characteristic value of the lithium-ion battery.

Claims

1. An online lithium deposition detection architecture for energy storage lithium batteries based on impedance spectrum and safety threshold, characterized in that: The architecture includes a battery cluster composed of a battery PACK, a primary BMS, a secondary BMS, a tertiary BMS master control, and an energy storage converter PCS; the energy storage converter PCS has an active excitation mode and is connected in series with the battery cluster composed of a battery PACK; the primary BMS is connected to the battery cells in the battery PACK to collect the voltage and temperature of all the battery cells, and the primary BMS is connected to the secondary BMS via a CAN bus; the secondary BMS collects the battery cluster current, and the secondary BMS is interconnected with the tertiary BMS master control via a CAN bus, and the uploaded data includes the single cell voltage, cluster voltage, cluster current, temperature, and battery abnormality alarm, and then calculates the electrochemical impedance spectrum and determines lithium precipitation; the tertiary BMS master control is interconnected with the PCS via Modbus-TCP, and sends instructions such as impedance spectrum detection, operation strategy, and protection action to the PCS; one side of the PCS is connected to the power grid side to realize energy transmission.

2. A detection method for online lithium deposition detection architecture of energy storage lithium battery based on impedance spectrum and safety threshold according to claim 1, characterized in that: The following steps are involved: Step 1) Determine the working status of the battery, which is divided into the following five states: charging, set aside after charging, discharging, set aside after discharging, and long-term set aside; Step 2) identifying whether the storage state of the charged battery meets the detection conditions; Step 3) After the detection conditions are met, a mode switching instruction is issued to the primary BMS of the battery cluster to be tested to increase the sampling frequency from a low-speed sampling rate to a high-speed sampling rate; Step 4) Issue an excitation instruction to the battery cluster PCS with impedance spectrum detection function, switch the PCS function to the AC excitation function, and start applying AC current excitation of 1kHz-0.01Hz and different frequency combinations to the battery cluster to be detected; Step 5) After one excitation is completed, the impedance is calculated on the edge computing terminal or EMS; the voltage of each battery cell and the amplitude and phase angle corresponding to different frequencies of the excitation current are calculated by segmented window Fourier transform; Calculate the impedance at different frequencies and draw the impedance spectrum; Step 6) Perform relaxation time distribution analysis on the impedance spectrum to obtain characteristic values ​​of different electrochemical processes; Step 7) After the analysis is completed, save the characteristic value, stop BMS high-speed sampling and PCS excitation, wait for the next test, and after a certain interval, jump to step 3) and repeat step 3)-step 7) until three tests are completed; Step 8) After the three tests are completed, the impedance spectrum is calculated to perform the change of the relaxation time distribution DRT characteristic value, and compared with the set lithium plating safety threshold, the test result is given, and the test is completed.

3. The detection method according to claim 2, characterized in that: In the step 1), the timing of the battery's lithium plating detection is related to the current state. The battery is always in the following five states: charging, set aside after charging, discharging, set aside after discharging, and long-term set aside. Long-term set aside is defined as more than 6 hours after charging and discharging. The timing of the lithium plating detection method is set aside after charging.

4. The detection method according to claim 2, characterized in that: The step 2) is specifically as follows: after identifying that the current battery cluster is in a post-charging standby state, determining whether the two-hour standby detection condition is met, setting a one-month detection cycle, and the shortest time interval between two lithium plating detections is one month.

5. The detection method according to claim 2, characterized in that: The step 3) is specifically as follows: the voltage sampling period of the BMS is maintained at about 50ms, and during the lithium deposition detection process, the voltage sampling frequency of the BMS is temporarily increased. After the detection is completed, the voltage sampling frequency of the BMS is switched to about 50ms.

6. The detection method according to claim 2, characterized in that: In step 4), the PCS has an active excitation mode. During the lithium deposition detection process, active current excitation is applied to the battery cluster. The frequency range of the active current excitation is set to 1kHz-0.01Hz. The specific measurement frequency points are distributed logarithmically, i.e., 10 3 , 10 2.9 …10 -1.9 , 10 -2 , there are 51 measurement points in total, and two cycles of excitation signal are applied to each frequency measurement point.

7. The detection method according to claim 6, characterized in that: In step 4), the amplitude of active excitation depends on the battery internal resistance and voltage amplitude. The battery voltage response amplitude is generally 10mV, and the battery internal resistance is generally 0.5mΩ. To obtain a 10mV voltage response, the active current excitation amplitude is about 20A.

8. The detection method according to claim 6, characterized in that: The step 5) is specifically as follows: performing a segmented adaptive window Fourier transform on the collected current and voltage signals, extracting the amplitude and phase angle of the voltage and current, thereby obtaining the impedance, and defining the voltage of the jth battery in a battery cluster as V cj , the excitation current applied to the battery cluster is equal, defined as I c , perform Fourier transform on voltage and current, the formula is: （1） Where, T k is the period of the kth frequency point; is the kth frequency point, f j k is the kth frequency point of the jth battery; t h is the starting time of each frequency excitation; , are the cosine Fourier coefficient and sine Fourier coefficient of the excitation current respectively; , are the cosine Fourier coefficient and sine Fourier coefficient of the battery response voltage, respectively; The impedance is calculated as follows: （2） In the formula, is the impedance amplitude of the jth battery at the kth frequency point; is the impedance phase angle of the jth battery at the kth frequency point; The real and imaginary parts of the impedance are: （3）。 9. The detection method according to claim 8, characterized in that: In step 6), the relationship between the relaxation time distribution and the impedance spectrum is as follows: （4） In the formula, Z DRT is the impedance expressed by the relaxation time distribution function; f is the EIS scanning point frequency; R0 is the ohmic resistance of the battery; L is the inductance connected in series during the DRT fitting process to indicate the characteristic that the imaginary part of the EIS in the high frequency band is greater than 0; Z pol represents the battery polarization impedance; τ represents the relaxation time; g(τ) is the relaxation time distribution function.

10. The detection method according to claim 9, characterized in that: In step 6), the characteristics of different electrochemical processes correspond to the central relaxation time of the characteristic peaks of the DRT analysis results. The characteristic peaks representing charge transfer are identified in the DRT analysis results, and the corresponding central relaxation time τ is extracted. ct,k,n , represents the characteristic value of the nth battery measured for the kth time.

11. The detection method according to claim 10, characterized in that: In the step 7), the impedance spectra at different times during the standby process are obtained, the time interval is set to 1h, and the test is performed three times in succession. During each waiting test interval, the PCS excitation and BMS high-speed sampling are stopped. After the interval time is up, jump to step 3).

12. The detection method according to claim 11, characterized in that: The step 8) is specifically as follows: obtaining three consecutive impedance spectra, extracting the corresponding charge transfer features, normalizing and differentiating them, and comparing them with the lithium deposition threshold: Define the normalized deviation D of different relaxation times of τ ct,dev,k as follows: （5） In the formula, τ ct,k,n τ obtained from the kth measurement of the nth battery ct ; τ ct,0,n τ is the measured time at the beginning of the shelf ct ; If the impedance spectrum during the shelf process satisfies the following formula, lithium deposition is considered to have occurred; （6） Where Time rest is the shelf time; Δ th To detect the safety threshold for lithium plating.

13. The detection method according to claim 12, characterized in that: In the step 8), the characteristic value representing the negative electrode charge transfer in the relaxation time distribution result of the lithium deposition battery will gradually increase during the storage process until it reaches a new quasi-stable state, while the characteristic value representing the negative electrode charge transfer of the battery without lithium deposition remains unchanged during the storage process.

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