Method, device and system for detecting lithium precipitation of battery cell of energy storage battery system

Abstract

The invention discloses a method, a device and a system for detecting lithium precipitation of a battery cell of an energy storage battery system. The method comprises the following steps: acquiring battery cell parameter data through a local analysis unit; transmitting the cell parameter data to a cloud analysis unit; the local analysis unit performs preliminary analysis on the cell parameter data based on a preset local model; the cloud analysis unit performs deep analysis on the cell parameter data based on a preset cloud model; and according to a predetermined decision logic, integrating analysis results of the local analysis unit and the cloud analysis unit, and judging whether lithium precipitation occurs in the battery cell. According to the lithium precipitation detection method for the battery cell of the energy storage battery system, comprehensive and accurate detection of the lithium precipitation state of the battery cell is realized by combining the local analysis unit and the cloud analysis unit. The local analysis unit can quickly perform preliminary analysis, and the cloud analysis unit can perform deep analysis by using a more complex model, so that the detection accuracy and efficiency are greatly improved by combining the local analysis unit and the cloud analysis unit.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a lithium plating detection method and system for a battery management system. Background Art

[0002] With the rapid development of new energy technologies, lithium-ion batteries are widely used in electric vehicles, energy storage systems and other fields. However, lithium-ion batteries may undergo lithium deposition during use, which seriously affects the performance and safety of the battery. Therefore, accurate detection of the lithium deposition state of the battery is very important for the battery management system.

[0003] Existing battery lithium plating detection methods usually rely on a single data source or analysis unit for judgment. This method is easily affected by factors such as data collection errors and environmental interference, resulting in insufficient reliability of the detection results. For example, relying solely on locally collected voltage data for analysis is difficult to fully reflect the actual state of the battery, especially when the battery is in different working states. A single detection method is difficult to adapt to complex usage scenarios.

[0004] In addition, the lithium plating detection methods in the prior art often use fixed data acquisition schemes, which cannot be flexibly adjusted according to the actual working state of the battery. This rigid acquisition method not only affects the accuracy of the data, but also may increase the energy consumption and computing burden of the system. Especially when performing impedance data acquisition, different battery states require different frequency scanning strategies, and the existing methods are difficult to meet this demand.

[0005] Existing battery management systems lack mechanisms for dealing with inconsistencies between local and remote analysis results. Such inconsistencies may be caused by a variety of factors, such as data transmission delays and differences in analysis algorithms. If there is no reasonable way to deal with them, it will directly affect the accuracy of the system's decision-making and may even lead to incorrect lithium precipitation judgments.

[0006] These problems make it difficult for existing battery lithium plating detection methods to meet the growing demand for battery management. Especially in application scenarios requiring high precision and high reliability, the shortcomings of existing technologies are more prominent. Summary of the invention

[0007] The present invention provides a method for detecting lithium deposition in a battery cell of an energy storage battery system, a device for detecting lithium deposition in a battery cell and an energy storage battery system, which are used to solve the problems of low accuracy and low efficiency in detecting lithium deposition in a battery cell in the prior art.

[0008] The method for detecting lithium plating in battery cells of an energy storage battery system comprises the following steps: collecting battery cell parameter data through a local analysis unit; transmitting the battery cell parameter data to a cloud analysis unit; the local analysis unit performs a preliminary analysis on the battery cell parameter data based on a preset local model; the cloud analysis unit performs an in-depth analysis on the battery cell parameter data based on a preset cloud model; and judging whether lithium plating has occurred in the battery cell by combining the analysis results of the local analysis unit and the cloud analysis unit according to a predetermined decision logic.

[0009] Furthermore, the battery cell parameter data includes single cell voltage, battery cell temperature, battery cell AC impedance value and system charge and discharge current.

[0010] Specifically, the step of collecting battery cell parameter data by the local analysis unit includes: performing an AC impedance frequency sweep on the battery cell when the battery cell is in a static state; and performing impedance sampling on the battery cell at a specific frequency point when the battery cell is in a charging or discharging state.

[0011] Furthermore, the preset local model is a SOC-EIS curve model.

[0012] Specifically, the preset cloud model includes: an empirical formula model of the lithium plating starting point based on the charging rate, load density and temperature; and a static lithium plating judgment model based on impedance curve comparison.

[0013] Furthermore, the predetermined decision logic includes: when the analysis results of the local analysis unit and the cloud analysis unit are consistent, directly determining the lithium deposition state of the battery cell; when the analysis results of the local analysis unit and the cloud analysis unit are inconsistent, recording their respective judgment results, and determining the lithium deposition state of the battery cell when one of them continuously determines that the number of times lithium deposition occurs in the battery cell reaches a preset threshold.

[0014] The battery cell lithium plating detection device includes: a local analysis unit, which is used to collect battery cell parameter data and perform preliminary analysis; a cloud analysis unit, which is communicatively connected to the local analysis unit and is used to receive the battery cell parameter data and perform in-depth analysis; a decision module, which is respectively connected to the local analysis unit and the cloud analysis unit, and is used to combine the analysis results of the local analysis unit and the cloud analysis unit according to a predetermined decision logic to determine whether lithium plating occurs in the battery cell.

[0015] Specifically, the local analysis unit includes: an analog front-end module, used to collect single cell voltage, battery cell temperature and battery cell AC impedance value; a current sensor, used to collect system charging and discharging current; a processor, electrically connected to the analog front-end module and the current sensor, used to receive the collected data and execute a preset local model for preliminary analysis; a communication module, electrically connected to the processor, used to transmit the battery cell parameter data and preliminary analysis results to the cloud analysis unit.

[0016] Furthermore, the cloud analysis unit includes: a data receiving module, used to receive the battery cell parameter data from the local analysis unit; a deep analysis module, connected to the data receiving module, used to perform deep analysis on the battery cell parameter data based on a preset cloud model; and a result output module, connected to the deep analysis module, used to output the deep analysis result to the decision module.

[0017] Specifically, the local analysis unit further includes a storage module, and the storage module is used to store a preset local model, and the preset local model is a SOC-EIS curve model.

[0018] Furthermore, the preset cloud model includes: an empirical formula model of the lithium plating starting point based on the charging rate, load density and temperature; and a static lithium plating judgment model based on impedance curve comparison.

[0019] Specifically, the decision module includes: a result comparison unit, used to compare the analysis results of the local analysis unit and the cloud analysis unit; a counter, used to record the number of times one of the two analysis units continuously judges that lithium deposition occurs in the battery cell when the analysis results are inconsistent; and a judgment unit, used to judge the lithium deposition status of the battery cell when the number of times recorded by the counter reaches a preset threshold.

[0020] The energy storage battery system includes: a plurality of battery cells, each battery cell including one or more battery cells; the battery cell lithium deposition detection device; and a power management unit electrically connected to the battery cell and the battery cell lithium deposition detection device, and used to adjust the working state of the battery cell according to the detection result of the battery cell lithium deposition detection device.

[0021] The energy storage battery system cell lithium deposition detection method provided by the present invention realizes comprehensive and accurate detection of the cell lithium deposition state by combining a local analysis unit and a cloud analysis unit. The local analysis unit can quickly perform preliminary analysis, while the cloud analysis unit can use a more complex model for in-depth analysis. The combination of the two greatly improves the accuracy and efficiency of the detection. At the same time, the results of the two analysis units are comprehensively judged through a predetermined decision logic, further improving the reliability of the detection results.

[0022] In addition, the present invention also provides a corresponding battery cell lithium plating detection device and an energy storage battery system. By setting a local analysis unit, a cloud analysis unit and a decision module in the device, the hardware implementation of the detection method is realized. When the detection device is integrated into the energy storage battery system, the working state of the battery cell can be adjusted in time according to the detection results, and the battery performance degradation and safety problems caused by lithium plating can be prevented, thereby extending the service life of the energy storage battery system and improving the overall performance and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for detecting lithium deposition in a battery cell of an energy storage battery system in one embodiment of the present invention;

[0024] Figure 2 It is a structural block diagram of a battery cell lithium deposition detection device in one embodiment of the present invention;

[0025] Figure 3 is a schematic structural diagram of an energy storage battery system in one embodiment of the present invention;

[0026] Figure 4 is a schematic diagram of the structure of a local analysis unit in an embodiment of the present invention;

[0027] Figure 5 A flow chart for constructing a local BMS lithium analysis model in one embodiment of the present invention.

[0028] Markings in the figure: 10, battery cell, 20, battery cell lithium analysis detection device, 30, power management unit, 100, local analysis unit, 200, cloud analysis unit, 300, decision module, 110, analog front-end module, 120, current sensor, 130, processor, 140, communication module, 150, storage module. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the described embodiments are only used to explain the technical principles of the present invention, rather than to limit the scope of protection thereof. Those skilled in the art should understand that, without departing from the spirit and scope of the present invention, various changes, modifications or equivalent substitutions may be made to these embodiments. These changes, modifications or equivalent substitutions shall all be deemed to fall within the scope of protection defined by the claims of the present invention.

[0030] See also Figure 1 , which shows a flow chart of a method for detecting lithium deposition in a battery cell of an energy storage battery system in an embodiment of the present invention. The method comprises the following steps:

[0031] S10. Collecting cell parameter data through a local analysis unit.

[0032] S20, transmitting the cell parameter data to a cloud analysis unit.

[0033] S30: The local analysis unit performs a preliminary analysis on the battery cell parameter data based on a preset local model.

[0034] S40: The cloud analysis unit performs an in-depth analysis on the battery cell parameter data based on a preset cloud model.

[0035] S50. According to a predetermined decision logic, the analysis results of the local analysis unit and the cloud analysis unit are combined to determine whether lithium deposition occurs in the battery cell.

[0036] In this embodiment, the battery cell parameter data includes single cell voltage, battery cell temperature, battery cell AC impedance value and system charge and discharge current. These parameters can fully reflect the working state of the battery cell and provide a basis for accurately judging lithium precipitation.

[0037] Specifically, in step S10, the process of the local analysis unit collecting the cell parameter data includes two stages:

[0038] 1) When the battery cell is in a static state, perform an AC impedance sweep on the battery cell. This process is usually performed in the frequency range of 0.01Hz to 1000Hz to obtain the impedance characteristics of the battery cell at different frequencies.

[0039] 2) When the battery cell is in the charge and discharge state, the impedance of the battery cell is sampled at specific frequency points. Two specific frequency points are selected here: Fmax (mid-frequency point) and Ftr (low-frequency point). The impedance data at these two frequency points have a strong recognition ability for lithium plating judgment.

[0040] In step S30, the preset local model is a SOC-EIS curve model. The construction process of the model is as follows: Figure 5 As shown, the following steps are included:

[0041] 1) Pre-treat healthy batteries.

[0042] 2) Build the test tooling.

[0043] 3) Select two frequency points: mid-frequency and low-frequency.

[0044] 4) Perform 5 charge and discharge cycles on the cells of a battery pack.

[0045] 5) Construct the SOC-EIS curve.

[0046] 6) Import the constructed SOC-EIS curve into the local analysis unit.

[0047] Based on this preset SOC-EIS curve model, the local analysis unit makes a preliminary judgment on lithium plating by comparing the difference between the impedance data collected in real time and the model predicted value. Specifically, the local analysis unit samples the impedance values ​​at medium and low frequencies in real time, from 0% to 100% SOC value, and samples every 10% SOC. Whether lithium plating has occurred is determined by comparing the difference between each sampled impedance value and the impedance value without lithium plating. If the difference is greater than 50%, it is determined that lithium plating has occurred.

[0048] In step S40, the preset cloud model includes two sub-models:

[0049] 1) An empirical formula model of the lithium plating starting point based on charging rate, load density and temperature. The model can be expressed as:

[0050]

[0051] Among them, y is the initial state of charge value of lithium precipitation reaction, c is the charging rate, x is the load density, T is the temperature, and α, β, γ, and ε are unknown coefficients. Through a large amount of experimental data and model simulation, the values ​​of these coefficients can be determined, thereby achieving an accurate estimation of the starting point of lithium precipitation under different working conditions.

[0052] 2) Static lithium deposition judgment model based on impedance curve comparison. This model determines whether lithium deposition has occurred by comparing the impedance curve collected in real time with the standard impedance curve of a healthy battery cell. Specifically, if the diameter of the arc of the intermediate frequency part is significantly reduced (by more than 50%), or the overall curve is significantly displaced (by more than 30%), it is judged that lithium deposition has occurred in the battery cell.

[0053] In step S50, the working state of the battery (stationary, charging / discharging, or fault) is first determined, and then corresponding detection strategies are adopted according to different states:

[0054] 1) In a static state, the local analysis unit performs an AC impedance sweep in the range of 0.01 to 1000 Hz.

[0055] 2) In the charge and discharge state, the local analysis unit performs an AC impedance sweep at two frequency points, Fmax and Ftr.

[0056] 3) In the fault state, if there is no fault in the communication between the BMS and the AFE, an AC impedance sweep is performed in the range of 0.01 to 1000 Hz.

[0057] Regardless of the state, both local and cloud will perform lithium deposition diagnosis simultaneously. If the judgment results of the two are consistent, the battery cell lithium deposition event and battery cell number will be directly reported. If the judgment results are inconsistent, the respective judgment results will be recorded. When one party continuously judges that the number of times lithium deposition occurs in the battery cell reaches a preset threshold (3 times in this embodiment), it is determined that lithium deposition occurs in the battery cell and relevant information is reported.

[0058] Through the collaborative work of the local analysis unit and the cloud analysis unit, the real-time nature of the local analysis and the deep computing power of the cloud analysis are fully utilized to improve the accuracy and reliability of the battery cell lithium analysis detection. At the same time, through the multiple judgment mechanisms, the possibility of false alarms is reduced, providing a strong guarantee for the safe operation of the energy storage battery system.

[0059] See also Figure 2 , which shows a structural block diagram of a battery cell lithium deposition detection device in an embodiment of the present invention. The device comprises:

[0060] The local analysis unit 100 is used to collect cell parameter data and perform preliminary analysis;

[0061] A cloud analysis unit 200 is connected to the local analysis unit 100 for receiving the cell parameter data and performing in-depth analysis;

[0062] The decision module 300 is connected to the local analysis unit 100 and the cloud analysis unit 200 respectively, and is used to integrate the analysis results of the local analysis unit 100 and the cloud analysis unit 200 according to a predetermined decision logic to determine whether lithium deposition occurs in the battery cell.

[0063] Please read further Figure 4 , which shows the detailed structure of the local analysis unit 100. The local analysis unit 100 includes:

[0064] The analog front-end module 110 is used to collect the cell voltage, cell temperature and cell AC impedance value. In this embodiment, the analog front-end module 110 is implemented by an AFE (Analog Front End) chip, which is placed on the cell aluminum bar through an FPC (Flexible Printed Circuit) flexible circuit board to achieve accurate parameter collection.

[0065] The current sensor 120 is used to collect the system charge and discharge current. The current sensor 120 uses a high-precision Hall current sensor, which can accurately measure the system's charge and discharge current and provide important reference data for lithium precipitation judgment.

[0066] The processor 130 is electrically connected to the analog front-end module 110 and the current sensor 120, and is used to receive the collected data and execute a preset local model for preliminary analysis. The processor 130 uses a high-performance MCU (Microcontroller Unit) with sufficient computing power to execute complex analysis algorithms.

[0067] The communication module 140 is electrically connected to the processor 130 and is used to transmit the cell parameter data and the preliminary analysis results to the cloud analysis unit 200. The communication module 140 supports multiple communication protocols, such as CAN, RS485, etc., to ensure the reliability and real-time performance of data transmission.

[0068] The storage module 150 is used to store a preset local model, which is a SOC-EIS curve model. The storage module 150 uses a large-capacity flash memory and can store multiple sets of SOC-EIS curve data to meet the analysis requirements of different types of batteries.

[0069] In this embodiment, the working process of the local analysis unit 100 is as follows:

[0070] 1. The analog front-end module 110 collects the cell voltage, temperature and AC impedance value of the battery in real time. Different impedance sampling strategies are used according to the different working states of the battery:

[0071] - Under static condition, perform AC impedance sweep in the range of 0.01 to 1000 Hz.

[0072] - Under the charge and discharge state, two specific frequency points, Fmax (mid-frequency point) and Ftr (low-frequency point), are selected for impedance sampling.

[0073] 2. The current sensor 120 synchronously collects the charging and discharging current of the system.

[0074] 3. The processor 130 receives data from the analog front-end module 110 and the current sensor 120 , and calls a preset SOC-EIS curve model from the storage module 150 .

[0075] 4. The processor 130 executes a preliminary analysis algorithm to compare the impedance data collected in real time with the predicted value of the SOC-EIS curve model. If the difference exceeds a preset threshold (50% in this embodiment), it is preliminarily determined that lithium deposition may occur.

[0076] 5. The communication module 140 transmits the original cell parameter data and preliminary analysis results to the cloud analysis unit 200.

[0077] The cloud analysis unit 200 includes:

[0078] A data receiving module, used for receiving the cell parameter data from the local analysis unit 100;

[0079] A deep analysis module is connected to the data receiving module and is used to perform deep analysis on the cell parameter data based on a preset cloud model. The preset cloud model includes:

[0080] 1) Empirical formula model of lithium plating starting point based on charging rate, load density and temperature:

[0081]

[0082] Among them, y is the initial state of charge value of the lithium precipitation reaction, c is the charging rate, x is the load density, and T is the temperature.

[0083] 2) Static lithium precipitation judgment model based on impedance curve comparison.

[0084] The result output module is connected to the depth analysis module and is used to output the depth analysis result to the decision module 300 .

[0085] The decision module 300 includes:

[0086] A result comparison unit, used to compare the analysis results of the local analysis unit 100 and the cloud analysis unit 200;

[0087] A counter, used to record the number of times one of the local analysis unit 100 and the cloud analysis unit 200 continuously determines that lithium deposition occurs in the battery cell when the analysis results are inconsistent;

[0088] The judging unit is used to judge the lithium deposition state of the battery cell when the number of times recorded by the counter reaches a preset threshold (3 times in this embodiment).

[0089] The workflow of the decision module 300 is as follows:

[0090] 1. Receive and compare the analysis results of the local analysis unit 100 and the cloud analysis unit 200.

[0091] 2. If the two results are consistent, directly determine the lithium deposition status of the battery cell.

[0092] 3. If the results are inconsistent, start the counter to record the number of consecutive lithium precipitation judgments.

[0093] 4. When the number of consecutive lithium deposition events reaches a preset threshold, it is determined that lithium deposition has occurred in the battery cell.

[0094] 5. Output the final lithium plating judgment result.

[0095] Through this multi-level and multi-dimensional analysis and decision-making mechanism, the battery cell lithium deposition detection device of the present invention can improve the accuracy and reliability of lithium deposition detection, and provide a strong guarantee for the safe operation of the energy storage battery system. At the same time, the collaborative working mode of the local analysis unit and the cloud analysis unit not only ensures the real-time detection, but also makes full use of the powerful computing power of the cloud, realizing efficient and accurate lithium deposition detection.

[0096] See also Figure 3 , which shows a schematic diagram of the structure of an energy storage battery system in an embodiment of the present invention. The system includes:

[0097] 1. Multiple battery units 10: Each battery unit contains one or more cells. These battery units are connected in series or in parallel to form the core of the entire energy storage system.

[0098] 2. Battery cell lithium deposition detection device 20: the detection device described in detail above, including a local analysis unit, a cloud analysis unit and a decision module. The device is responsible for real-time monitoring of the status of each battery cell and timely detection of possible lithium deposition.

[0099] 3. Power management unit 30: electrically connected to the battery cell 10 and the battery cell lithium deposition detection device 20. The main function of the power management unit 30 is to dynamically adjust the working state of the battery cell according to the detection result of the battery cell lithium deposition detection device 20.

[0100] The specific working process of the power management unit 30 is as follows:

[0101] 1. Receive the detection results from the battery cell lithium plating detection device 20 in real time.

[0102] 2. When it is detected that a certain battery cell may have lithium deposition, the power management unit 30 will take the following measures:

[0103] a. Reduce the charging current of the battery cell where the battery is located, or stop charging completely.

[0104] b. If lithium plating is detected during discharge, the discharge load of the battery cell is reduced.

[0105] c. In severe cases, the affected battery cell may be completely isolated to prevent safety hazards.

[0106] 3. At the same time, the power management unit 30 will adjust the working status of other normally working battery units to ensure the normal operation of the entire energy storage system.

[0107] 4. The power management unit 30 is also responsible for reporting abnormal conditions to the superior control system to facilitate timely maintenance.

[0108] Through this real-time monitoring and dynamic adjustment, the energy storage battery system of the present invention can maximize the battery life and improve the safety and reliability of the system while ensuring the normal operation of the system. In addition, this intelligent management method also provides a basis for predictive maintenance of the energy storage system, which helps to reduce operation and maintenance costs and improve the overall economic benefits of the system.

[0109] The present invention provides a method for detecting lithium deposition in a battery cell of an energy storage battery system, a device for detecting lithium deposition in a battery cell, and an energy storage battery system. By combining local analysis with cloud analysis, comprehensive and accurate detection of the lithium deposition status of the battery cell is achieved, thereby improving the safety and reliability of the energy storage battery system.

[0110] The energy storage battery system cell lithium plating detection method of the present invention adopts a mode of collaborative work between a local analysis unit and a cloud analysis unit. The local analysis unit can quickly identify potential lithium plating risks by collecting cell parameter data in real time and performing preliminary analysis based on a preset SOC-EIS curve model. The cloud analysis unit uses more complex models, such as an empirical formula model for the lithium plating starting point based on charging rate, load density and temperature, and a static lithium plating judgment model based on impedance curve comparison, for in-depth analysis. This dual analysis mechanism improves the accuracy and reliability of detection.

[0111] The battery cell lithium analysis detection device of the present invention comprises a local analysis unit, a cloud analysis unit and a decision module. The local analysis unit integrates an analog front-end module, a current sensor, a processor, a communication module and a storage module, and can efficiently collect and process battery cell data. The decision module reduces the possibility of misjudgment by integrating the analysis results of the local and cloud sides and adopting a predetermined decision logic.

[0112] In addition, the present invention also provides an energy storage battery system integrating the above detection device. The system dynamically adjusts the working state of the battery unit according to the detection results through the power management unit, thereby preventing battery performance degradation and safety problems caused by lithium plating.

[0113] The present invention has been described in detail through the above specific embodiments, but it should be understood that the above contents are only illustrative and not intended to limit the scope of the present invention. A person of ordinary skill in the art may make various modifications and variations to the present invention according to specific application scenarios and actual needs without departing from the spirit and scope of the present invention, and these modifications and variations are all within the scope of protection of the present invention.

Claims

1. A method for detecting lithium deposition in a battery cell of an energy storage battery system, characterized in that: The following steps are involved: Collect cell parameter data through local analysis unit; Transmitting the battery cell parameter data to a cloud analysis unit; The local analysis unit performs a preliminary analysis on the battery cell parameter data based on a preset local model; The cloud analysis unit performs in-depth analysis on the battery cell parameter data based on a preset cloud model; According to a predetermined decision logic, the analysis results of the local analysis unit and the cloud analysis unit are combined to determine whether lithium plating occurs in the battery cell.

2. The method for detecting lithium deposition in a battery cell of an energy storage battery system according to claim 1, characterized in that: The battery cell parameter data includes single cell voltage, battery cell temperature, battery cell AC impedance value and system charge and discharge current.

3. The method for detecting lithium deposition in a battery cell of an energy storage battery system according to claim 1, characterized in that: The step of collecting cell parameter data by the local analysis unit includes: When the battery cell is in a static state, perform an AC impedance sweep on the battery cell; When the battery cell is being charged or discharged, the impedance of the battery cell is sampled at a specific frequency point.

4. The method for detecting lithium deposition in a battery cell of an energy storage battery system according to claim 1, characterized in that: The preset local model is a SOC-EIS curve model.

5. The method for detecting lithium deposition in a battery cell of an energy storage battery system according to claim 1, characterized in that: The preset cloud model includes: An empirical formula model for the starting point of lithium deposition based on charging rate, load density and temperature; Static lithium precipitation judgment model based on impedance curve comparison.

6. The method for detecting lithium deposition in a battery cell of an energy storage battery system according to claim 1, characterized in that: The predetermined decision logic includes: When the analysis results of the local analysis unit and the cloud analysis unit are consistent, directly determining the lithium deposition state of the battery cell; When the analysis results of the local analysis unit and the cloud analysis unit are inconsistent, their respective judgment results are recorded, and when one of them continuously judges that the number of times lithium deposition occurs in the battery cell reaches a preset threshold, the lithium deposition state of the battery cell is determined.

7. A battery cell lithium deposition detection device, characterized in that: include: Local analysis unit, used to collect battery cell parameter data and perform preliminary analysis; A cloud analysis unit, connected to the local analysis unit for receiving the cell parameter data and performing in-depth analysis; The decision module is connected to the local analysis unit and the cloud analysis unit respectively, and is used to combine the analysis results of the local analysis unit and the cloud analysis unit according to a predetermined decision logic to determine whether lithium deposition occurs in the battery cell.

8. The battery cell lithium deposition detection device according to claim 7, characterized in that: The local analysis unit comprises: Analog front-end module, used to collect single cell voltage, cell temperature and cell AC impedance value; Current sensor, used to collect system charging and discharging current; A processor, electrically connected to the analog front-end module and the current sensor, configured to receive the collected data and execute a preset local model for preliminary analysis; A communication module is electrically connected to the processor and is used to transmit the battery cell parameter data and preliminary analysis results to the cloud analysis unit.

9. The battery cell lithium deposition detection device according to claim 7, characterized in that: The cloud analysis unit comprises: A data receiving module, used for receiving the cell parameter data from the local analysis unit; A deep analysis module, connected to the data receiving module, for performing deep analysis on the battery cell parameter data based on a preset cloud model; The result output module is connected to the depth analysis module and is used to output the depth analysis result to the decision module.

10. The battery cell lithium deposition detection device according to claim 9, characterized in that: The preset cloud model includes: An empirical formula model for the starting point of lithium deposition based on charging rate, load density and temperature; Static lithium precipitation judgment model based on impedance curve comparison.

11. The battery cell lithium deposition detection device according to claim 7, characterized in that: The local analysis unit further includes a storage module, wherein the storage module is used to store a preset local model, wherein the preset local model is a SOC-EIS curve model.

12. The battery cell lithium deposition detection device according to claim 7, characterized in that: The decision module comprises: A result comparison unit, used to compare the analysis results of the local analysis unit and the cloud analysis unit; A counter, used for recording the number of times that one of the local analysis unit and the cloud analysis unit continuously determines that lithium deposition occurs in the battery cell when the analysis results of the local analysis unit and the cloud analysis unit are inconsistent; The judging unit is used to judge the lithium deposition state of the battery cell when the number of times recorded by the counter reaches a preset threshold.

13. An energy storage battery system, characterized in that: include: A plurality of battery units, each battery unit comprising one or more battery cells; The battery cell lithium deposition detection device according to any one of claims 7 to 13, connected to the plurality of battery cells; A power management unit is electrically connected to the battery unit and the battery cell lithium deposition detection device, and is used to adjust the working state of the battery unit according to the detection result of the battery cell lithium deposition detection device.

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