Lithium-plating-risk determination method and apparatus for vehicle-side battery cells, and device, storage medium and product

Abstract

The present application relates to a lithium-plating-risk determination method and apparatus for vehicle-side battery cells, and a device, a storage medium and a product. The method comprises: firstly determining the current pulse charge / discharge operating condition on the basis of a pulse charge / discharge request; then, acquiring an SOC dataset and a voltage dataset of a vehicle-side battery cell under the current pulse charge / discharge operating condition; if SOC data within a lithium-plating-risk SOC interval is present in the SOC dataset, further acquiring voltage data corresponding to the SOC data; and if the voltage data exceeds a critical lithium-plating-risk voltage, determining that the vehicle-side battery cell has a high lithium plating risk.

Description

Lithium precipitation risk judgment method, device and equipment of vehicle end battery cell, storage medium and product

[0001] Related applications

[0002] The present application claims priority to Chinese Patent Application No. 202410887397.4, filed on July 3, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] The present application relates to the technical field of battery cells, in particular to a lithium precipitation risk judgment method, device and equipment of vehicle end battery cell, storage medium and product. BACKGROUND

[0004] Lithium ion batteries have been widely used in electric vehicles, hybrid electric vehicles and other energy storage systems. Under extreme conditions such as low temperature and high rate charging and discharging, lithium ion batteries are prone to lithium precipitation, i.e. lithium ions form metallic lithium on the surface of the negative electrode. This phenomenon can cause battery capacity to decay, internal resistance to increase, and even cause safety hazards such as short circuit and thermal runaway. Therefore, it is very important to accurately determine and prevent battery lithium precipitation risk. Existing lithium precipitation risk determination either has a large difference between laboratory environment test results and actual application scenarios, which cannot accurately reflect the state of the battery under actual working conditions, or lacks real-time monitoring and dynamic analysis mechanism, which cannot comprehensively capture real-time data of the battery under different working conditions, or the determination method is simple and cannot combine multi-parameter comprehensive analysis, resulting in insufficient accuracy of the determination result.

[0005] Therefore, how to improve the accuracy of the lithium precipitation risk determination of the vehicle end battery cell becomes a technical problem to be solved.

[0006] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0007] The main purpose of the present application is to provide a lithium precipitation risk judgment method, device and equipment of vehicle end battery cell, storage medium and product, which aims to solve the technical problem of how to improve the accuracy of the lithium precipitation risk determination of the vehicle end battery cell.

[0008] To achieve the above purpose, the present application provides a lithium precipitation risk judgment method of vehicle end battery cell, which comprises the following steps:

[0009] According to the pulse charging and discharging request, determine the current pulse charging and discharging working condition;

[0010] Obtain the SOC data set and voltage data set of the vehicle end battery cell under the current pulse charging and discharging working condition;

[0011] If there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, obtain the voltage data corresponding to the SOC data;

[0012] If the voltage data exceeds the lithium precipitation risk boundary voltage, it is determined that the vehicle-end battery cell has high lithium precipitation risk.

[0013] In an embodiment, before the step of determining the current pulse charge and discharge working condition according to the pulse charge and discharge request, the method further comprises:

[0014] Based on the preset pulse charge and discharge parameters, the vehicle-end battery cell is subjected to charge and discharge cycle test to obtain a test SOC data set and a test voltage data set;

[0015] The test SOC data set and the test voltage data set are preprocessed, and based on the preprocessed test SOC data set and the preprocessed test voltage data set, a voltage and SOC change curve is generated;

[0016] According to the voltage and SOC change curve, the lithium precipitation risk SOC interval and the lithium precipitation risk boundary voltage are determined.

[0017] In an embodiment, the step of determining the lithium precipitation risk SOC interval and the lithium precipitation risk boundary voltage according to the voltage and SOC change curve comprises:

[0018] Extracting the interval in which voltage mutation or abnormal voltage fluctuation occurs in the voltage and SOC change curve as a lithium precipitation interval;

[0019] According to the test SOC data corresponding to the lithium precipitation interval boundary, the lithium precipitation risk SOC interval is determined;

[0020] According to the test voltage data corresponding to the boundary of the lithium precipitation risk SOC interval, the lithium precipitation risk boundary voltage is determined.

[0021] In an embodiment, after the step of obtaining the voltage data corresponding to the SOC data if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, the method further comprises:

[0022] If there is no SOC data in the SOC data set within the lithium precipitation risk SOC interval, it is determined whether there is voltage data in the voltage data set that exceeds the lithium precipitation risk boundary voltage;

[0023] If there is, it is determined that the vehicle-end battery cell has low lithium precipitation risk;

[0024] if the SOC data in the SOC data set is in the lithium precipitation risk SOC interval, determining whether the voltage data corresponding to the SOC data exceeds the lithium precipitation risk boundary voltage;

[0025] if not, determining that the vehicle-end battery cell has a medium lithium precipitation risk.

[0026] In an embodiment, after the step of obtaining the SOC data set and the voltage data set of the vehicle-end battery cell under the current pulse charge-discharge working condition, the method further comprises:

[0027] obtaining expansion force test data of the vehicle-end battery cell, and preprocessing the expansion force test data;

[0028] obtaining an expansion force change curve according to the preprocessed expansion force test data;

[0029] predicting the cycle life of the vehicle-end battery cell based on the expansion force change curve and a preset life prediction model;

[0030] obtaining a current health state of the vehicle-end battery cell according to the cycle life, the SOC data set and the voltage data set;

[0031] adjusting the pulse charge-discharge request based on the current health state.

[0032] In an embodiment, after the step of determining that the vehicle-end battery cell has a high lithium precipitation risk if the voltage data exceeds the lithium precipitation risk boundary voltage, the method further comprises:

[0033] determining whether temperature information and environmental information of the vehicle-end battery cell meet a preset safety standard;

[0034] if so, obtaining a frequency and a duration corresponding to the voltage data;

[0035] based on the frequency and the duration, issuing a high lithium precipitation risk warning and adjusting the pulse charge-discharge request.

[0036] In addition, to achieve the above-mentioned purpose, the application further provides a vehicle-end battery cell lithium precipitation risk determination device, which comprises:

[0037] a working condition determination module configured to determine a current pulse charge-discharge working condition according to a pulse charge-discharge request;

[0038] a data acquisition module configured to obtain an SOC data set and a voltage data set of the vehicle-end battery cell under the current pulse charge-discharge working condition;

[0039] acquire voltage data corresponding to the SOC data if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval;

[0040] determine that the high lithium precipitation risk exists in the vehicle terminal battery cell if the voltage data exceeds the lithium precipitation risk boundary voltage.

[0041] In addition, to achieve the above-mentioned purpose, the present application also provides a lithium precipitation risk judgment device for a vehicle terminal battery cell, which comprises a memory, a processor, and a lithium precipitation risk judgment program for a vehicle terminal battery cell stored in the memory and executable on the processor, and the lithium precipitation risk judgment program for a vehicle terminal battery cell is configured to implement the steps of the lithium precipitation risk judgment method for a vehicle terminal battery cell as described above.

[0042] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium having a lithium precipitation risk judgment program for a vehicle terminal battery cell stored thereon, and the lithium precipitation risk judgment program for a vehicle terminal battery cell implements the steps of the lithium precipitation risk judgment method for a vehicle terminal battery cell as described above when executed by a processor.

[0043] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product comprising a computer program, and the computer program implements the steps of the lithium precipitation risk judgment method for a vehicle terminal battery cell as described above when executed by a processor.

[0044] The present application first determines the current pulse charging and discharging working condition according to the pulse charging and discharging request, then acquires the SOC data set and the voltage data set of the vehicle terminal battery cell under the current pulse charging and discharging working condition, and if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, the voltage data corresponding to the SOC data is acquired, and if the voltage data exceeds the lithium precipitation risk boundary voltage, it is determined that the high lithium precipitation risk exists in the vehicle terminal battery cell. The present application provides a comprehensive and accurate data basis by acquiring the SOC and voltage data in real time, improves the pertinence of analysis by identifying the data within the lithium precipitation risk SOC interval, accurately determines the high lithium precipitation risk by comparing the actual voltage data and the risk boundary voltage, and improves the accuracy of the lithium precipitation risk judgment of the vehicle terminal battery cell. BRIEF DESCRIPTION OF DRAWINGS

[0045] FIG. 1 is a flowchart of a first embodiment of the lithium precipitation risk judgment method for a vehicle terminal battery cell of the present application;

[0046] FIG. 2 is a sub-flowchart of a second embodiment of the lithium precipitation risk judgment method for a vehicle terminal battery cell of the present application;

[0047] FIG. 3 is a sub-flowchart of a third embodiment of the lithium precipitation risk judgment method for a vehicle terminal battery cell of the present application;

[0048] Fig. 4 is a schematic diagram of a module structure of a lithium precipitation risk judgment device for a vehicle terminal battery cell according to an embodiment of the present application;

[0049] Fig. 5 is a schematic diagram of a device structure of a hardware operating environment involved in a lithium precipitation risk judgment method for a vehicle terminal battery cell according to an embodiment of the present application.

[0050] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Embodiments of the present application

[0051] It should be understood that the specific embodiments described herein are merely intended to explain the present application and not to limit the present application.

[0052] In order to better understand the technical solutions of the present application, the specific embodiments will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Lithium ion batteries are widely used in electric vehicles, hybrid electric vehicles and other energy storage systems. Under extreme conditions such as low temperature and high rate charging and discharging, lithium ion batteries are prone to lithium precipitation, i.e. lithium ions form metal lithium on the surface of the negative electrode. This phenomenon will cause battery capacity attenuation, internal resistance increase, and even cause safety hazards such as short circuit, thermal runaway, etc. Therefore, it is very important to accurately determine and prevent battery lithium precipitation risk. The existing lithium precipitation risk determination either has a large difference between the laboratory environment test results and the actual application scene, which cannot accurately reflect the state of the battery under actual working conditions, or lacks real-time monitoring and dynamic analysis mechanism, which cannot comprehensively capture real-time data of the battery under different working conditions, or the determination method is simple and cannot combine multi-parameter comprehensive analysis, resulting in insufficient accuracy of the determination result. Therefore, how to improve the accuracy of the lithium precipitation risk determination of the vehicle terminal battery cell has become a technical problem to be solved.

[0054] The main solution of the present application is: first, determine the current pulse charging and discharging condition according to the pulse charging and discharging request; then, obtain the SOC data set and the voltage data set of the vehicle terminal battery cell under the current pulse charging and discharging condition; if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, then obtain the voltage data corresponding to the SOC data; if the voltage data exceeds the lithium precipitation risk boundary voltage, it is determined that the vehicle terminal battery cell has high lithium precipitation risk.

[0055] The present application provides a comprehensive and accurate data basis by real-time acquisition of SOC and voltage data, improves the pertinence of analysis by identifying data within the lithium precipitation risk SOC interval, and accurately determines high lithium precipitation risk by comparing the actual voltage data with the risk boundary voltage, thereby improving the accuracy of the lithium precipitation risk determination of the vehicle terminal battery cell.

[0056] The execution subject of the method of the embodiment can be a computing service device with data processing, network communication, and program running functions, or can be the above-mentioned vehicle-side lithium precipitation risk judgment device with the same or similar functions. The embodiment and each of the following embodiments will be described taking the vehicle-side lithium precipitation risk judgment device as an example.

[0057] Based on this, the first embodiment of the vehicle-side lithium precipitation risk judgment method of the application is proposed. Please refer to FIG. 1, which is a flowchart of the first embodiment of the vehicle-side lithium precipitation risk judgment method of the application.

[0058] In the embodiment, the vehicle-side lithium precipitation risk judgment method includes the following steps:

[0059] S1: According to the pulse charging and discharging request, determine the current pulse charging and discharging working condition.

[0060] The pulse charging and discharging request is a control signal sent by the battery management system (BMS) to the charging or discharging device, aiming to optimize the battery performance through intermittent charging or discharging operation. The request usually contains specific charging and discharging parameters, including current amplitude, pulse duration and interval time, etc. The current pulse charging and discharging working condition refers to the actual operation condition of the battery after receiving the pulse charging and discharging request. These conditions include pulse current amplitude, duration, interval time and other parameters, reflecting the charging and discharging state of the battery in a specific time period. The battery management system is included in the vehicle-side lithium precipitation risk judgment device.

[0061] Specifically, when the BMS receives the pulse charging and discharging request, the system will analyze the parameters in the request, such as the amplitude, duration and interval time of the pulse current. Then, the BMS will combine the real-time state of the battery (including SOC, voltage, temperature and other parameters) to dynamically adjust and determine the specific pulse charging and discharging working condition. For example, if the current temperature of the battery is low, the BMS may appropriately adjust the amplitude and duration of the pulse charging current to avoid excessive charging and discharging damage to the battery.

[0062] Further, in the process of determining the current pulse charging and discharging working condition, the BMS will start the data acquisition system to monitor the SOC and voltage changes of the battery in real time under this working condition. These real-time data are not only used to verify and adjust the charging and discharging working condition, but also provide a basis for subsequent battery state evaluation and risk judgment. The BMS continuously monitors and records the performance of the battery under different pulse conditions to ensure that the operation in each pulse cycle is within the safe range, thereby optimizing the battery performance and prolonging its service life.

[0063] By determining the current pulse charging and discharging working condition according to the pulse charging and discharging request, the BMS can flexibly adjust the charging and discharging operation of the battery to adapt to the actual operating conditions. This dynamic adjustment not only improves the accuracy and relevance of data collection, but also ensures the safety and stability of the battery under different working conditions. The mechanism of real-time monitoring and adjustment enables the BMS to more effectively manage the charging and discharging process of the battery, optimize battery performance, reduce the risk of lithium precipitation, and improve the overall safety and service life of the battery system.

[0064] S2: Obtain the SOC data set and voltage data set of the vehicle-side battery cell under the current pulse charging and discharging working condition.

[0065] The voltage data set refers to the collection of voltage data of the battery cell at different time points under the current pulse charging and discharging working condition. Voltage is one of the important parameters of battery health status, reflecting the working condition of the battery under different charging and discharging states. The SOC (State of Charge) data set refers to the collection of state of charge data of the battery cell at different time points under the current pulse charging and discharging working condition. SOC is an important indicator to measure the remaining capacity of the battery, usually expressed in percentage.

[0066] Specifically, after determining the current pulse charging and discharging working condition, the BMS starts the real-time data collection system to conduct comprehensive state monitoring of the vehicle-side battery cell. First, through the high-precision sensors installed on the battery cell, the BMS continuously collects the SOC and voltage data of the battery cell within each pulse cycle. The SOC data set reflects the state of charge of the battery cell at different time points, recording the energy change of the battery cell during the entire pulse charging and discharging process. The voltage data set records the voltage change of the battery cell at these time points, providing the voltage characteristics of the battery cell under different SOC.

[0067] Further, in order to ensure the accuracy and integrity of the data, the BMS will process and store the collected SOC and voltage data in real time. Through the high-speed data processing module, the BMS can quickly filter and calibrate the data, remove noise and outliers, and ensure the reliability of the data. The stored data includes the time stamp, SOC value and voltage value of each sampling time, forming a complete SOC and voltage data set. These data not only serve for real-time monitoring of the battery cell state, but also provide an important basis for subsequent analysis of the risk of lithium precipitation of the battery cell and evaluation of the health status of the battery cell.

[0068] By obtaining the SOC dataset and voltage dataset of the battery at the current pulse charging and discharging condition, the BMS can comprehensively understand the running state of the battery under actual working conditions. Real-time data collection and processing improve the accuracy and reliability of battery state monitoring, providing a solid data foundation for further risk judgment and optimized management. This precise data collection and analysis capability helps to timely detect abnormal states of the battery, prevent potential safety hazards, prolong the service life of the battery, and improve the performance and safety of the entire battery system.

[0069] S3: If there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, obtain the voltage data corresponding to the SOC data.

[0070] The lithium precipitation risk SOC interval refers to the area where the battery is more likely to precipitate lithium metal within a certain state of charge (SOC) range. Within these areas, the electrochemical reaction of the battery may cause lithium metal to deposit on the negative electrode surface, thereby triggering safety hazards. The voltage data corresponding to the SOC data refers to the voltage value of the battery at a specific SOC value. These data are used to analyze the voltage behavior of the battery within the lithium precipitation risk interval, helping to determine whether there is a risk of lithium precipitation.

[0071] Specifically, after the BMS real-time monitors and obtains the SOC and voltage dataset, the system will analyze the SOC data to identify whether there are data points falling into the lithium precipitation risk SOC interval. The BMS will first compare the real-time obtained SOC data with the pre-set lithium precipitation risk SOC interval, and filter out the SOC data points located within these risk intervals. These data points represent the current state of charge of the battery at a high risk of lithium precipitation, which needs to be further analyzed.

[0072] Further, once the SOC data points within the lithium precipitation risk SOC interval are identified, the BMS will extract the voltage data corresponding to these SOC data points. Specifically, the BMS matches these SOC data points with the stored voltage dataset through the time stamp, and finds the voltage value corresponding to each SOC data point at a specific time point. In this way, the BMS can obtain a set of detailed voltage data associated with the identified high-risk SOC data points. This step ensures that the BMS can accurately analyze the voltage performance of the battery within the lithium precipitation risk interval, and further determine whether the battery has an actual risk of lithium precipitation.

[0073] By identifying data points within the lithium precipitation risk SOC interval in the SOC dataset and obtaining the voltage data corresponding to these SOC data, the BMS can deeply analyze the voltage behavior of the battery in a high-risk state. This process improves the accuracy of lithium precipitation risk determination, as it combines the state of charge and voltage performance of the battery for multi-dimensional analysis. Precise data extraction and analysis help to discover potential lithium precipitation phenomena in a timely manner, take preventive measures, and ensure the safety and reliability of the battery. In addition, this method can also provide key data support for subsequent battery state optimization and management, further improving the performance and service life of the battery system.

[0074] S4: If the voltage data exceeds the lithium precipitation risk boundary voltage, it is determined that the vehicle-end cell has a high lithium precipitation risk.

[0075] The lithium precipitation risk boundary voltage is a preset voltage critical value range. When the battery voltage exceeds this range, it indicates that the battery has a high risk of lithium metal precipitation. These boundary voltage values are determined through experiments and data analysis and are used for risk determination in the battery management system. High lithium precipitation risk refers to a high-risk state of the battery caused by the deposition of lithium metal on the negative electrode surface during charging and discharging. This state may cause battery performance degradation, increased internal resistance, and even safety hazards such as short circuits and thermal runaway.

[0076] Specifically, after obtaining the voltage data corresponding to the SOC data, the BMS will further analyze these voltage data to determine whether the battery is in a high lithium precipitation risk state. Specifically, the BMS will compare the extracted voltage data with the preset lithium precipitation risk boundary voltage. If some voltage data exceeds these boundary voltage ranges, it indicates that the battery may have a high risk of lithium metal precipitation.

[0077] After confirming that the voltage data exceeds the lithium precipitation risk boundary voltage, the BMS will determine that the vehicle-end cell has a high lithium precipitation risk. At this time, the BMS will not only record these data points that exceed the boundary, but also perform comprehensive analysis to confirm the reliability of these data and consider whether they are affected by other factors (such as temperature, current fluctuations, etc.). Through this comprehensive analysis, the BMS can accurately identify high-risk states and avoid false positives. After finally confirming the high lithium precipitation risk, the BMS will immediately take appropriate protective measures, such as reducing the charging and discharging current, adjusting the charging and discharging strategy, or even stopping the charging and discharging operation, to ensure the safety of the battery.

[0078] By comparing the voltage data with the lithium precipitation risk boundary voltage and determining high lithium precipitation risk, the BMS significantly improves the accuracy of lithium precipitation risk determination. This process ensures the safety of the battery under different working conditions and can timely detect and prevent potential lithium metal precipitation risk. High-precision risk determination and timely protection measures not only prolong the service life of the battery, but also improve the safety performance and reliability of the overall system. With this method, the battery management system can dynamically adapt to actual working conditions and provide safer and more efficient battery management solutions.

[0079] The embodiment first determines the current pulse charging and discharging working condition according to the pulse charging and discharging request, then obtains the SOC data set and voltage data set of the vehicle-side battery cell under the current pulse charging and discharging working condition, and if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, the voltage data corresponding to the SOC data is obtained, and if the voltage data exceeds the lithium precipitation risk boundary voltage, it is determined that the vehicle-side battery cell has high lithium precipitation risk. This embodiment provides comprehensive and accurate data basis by obtaining real-time SOC and voltage data, improves the pertinence of analysis by identifying data within the lithium precipitation risk SOC interval, accurately determines high lithium precipitation risk by comparing actual voltage data with risk boundary voltage, and improves the accuracy of lithium precipitation risk determination of the vehicle-side battery cell.

[0080] Based on the above first embodiment, a second embodiment of the lithium precipitation risk determination method for the vehicle-side battery cell is proposed. Please refer to FIG. 2, which is a sub-process flowchart of the second embodiment of the lithium precipitation risk determination method for the vehicle-side battery cell.

[0081] As shown in FIG. 2, in this embodiment, before step S1, the method further comprises:

[0082] S1a: based on the preset pulse charging and discharging parameters, performing charging and discharging cycle test on the vehicle-side battery cell to obtain test SOC data set and test voltage data set;

[0083] S1b: pre-processing the test SOC data set and the test voltage data set, and generating a voltage-SOC change curve based on the pre-processed test SOC data set and the pre-processed test voltage data set;

[0084] S1c: determining the lithium precipitation risk SOC interval and the lithium precipitation risk boundary voltage according to the voltage-SOC change curve.

[0085] The preset pulse charging and discharging parameters refer to the pulse charging and discharging conditions preset in the battery management system (BMS), including parameters such as pulse current amplitude, duration, interval time, etc., which are used for standardizing the charging and discharging test process. The charging and discharging cycle test refers to the operation of charging and discharging the battery cell multiple times under controlled conditions according to the preset pulse charging and discharging parameters, in order to simulate actual use conditions and collect relevant data. The test SOC data set is a collection of battery state of charge data collected during the charging and discharging cycle test. The test voltage data set is a collection of battery voltage data collected during the charging and discharging cycle test. The voltage and SOC change curve is a curve describing the change of battery voltage with state of charge, obtained by fitting the preprocessed test SOC data set and test voltage data set. The lithium precipitation risk SOC interval is the interval in which the battery is prone to lithium metal precipitation in a specific SOC range in the voltage and SOC change curve. The lithium precipitation risk boundary voltage is the voltage threshold value corresponding to the lithium precipitation risk SOC interval. Exceeding this voltage range indicates the risk of lithium precipitation.

[0086] Specifically, according to the preset pulse charging and discharging parameters, the vehicle-side battery cell is subjected to systematic charging and discharging cycle test. This process simulates various working conditions of the battery cell in actual use, and collects comprehensive battery performance data through multiple charging and discharging operations. During the test process, the SOC and voltage changes of the battery cell are monitored and recorded in real time to form detailed test SOC data set and test voltage data set.

[0087] Further, the collected data needs to be preprocessed to ensure its accuracy and reliability. The preprocessing steps include noise removal, outlier correction and data smoothing. The preprocessed test SOC data set and test voltage data set are used to generate the voltage and SOC change curve. This change curve reflects the voltage characteristics of the battery cell at different SOC, and is an important basis for determining the lithium precipitation risk. According to the generated voltage and SOC change curve, the lithium precipitation risk SOC interval and the corresponding lithium precipitation risk boundary voltage are analyzed and determined. In these intervals, the mutation or abnormal fluctuation of the voltage curve indicates that the battery cell may precipitate lithium metal. The boundary voltage is a key criterion, and the voltage exceeding this range means that the battery cell is in a high lithium precipitation risk state.

[0088] By performing charging and discharging cycle test based on preset pulse charging and discharging parameters and generating voltage and SOC change curve, the present application can accurately determine the lithium precipitation risk SOC interval and the boundary voltage. This method not only improves the accuracy of risk determination of the battery management system under actual working conditions, but also provides detailed battery performance data, which provides a scientific basis for further optimizing the battery management strategy. Through these steps, the lithium precipitation phenomenon of the battery cell can be effectively prevented, and the safety and service life of the battery can be improved.

[0089] Based on the first embodiment described above, in this embodiment, step S1c comprises:

[0090] S1c1: Extract the intervals of voltage mutation or abnormal voltage fluctuation in the voltage and SOC change curve as lithium precipitation intervals;

[0091] S1c2: Determine the lithium precipitation risk SOC interval according to the test SOC data corresponding to the lithium precipitation interval boundary;

[0092] S1c3: Determine the lithium precipitation risk boundary voltage according to the test voltage data corresponding to the boundary of the lithium precipitation risk SOC interval.

[0093] The interval of voltage mutation or abnormal voltage fluctuation refers to a specific interval in the voltage and SOC change curve where the voltage changes sharply or fluctuates abnormally, which usually indicates that abnormal reactions may occur inside the battery cell, such as lithium metal precipitation. The lithium precipitation interval refers to the SOC interval determined by voltage mutation or abnormal fluctuation in the voltage and SOC change curve, and the battery cell is more likely to precipitate lithium metal in these intervals.

[0094] Specifically, in the generated voltage and SOC change curve, the BMS system identifies the mutation points and abnormal fluctuation areas of the voltage through algorithms. These mutations or fluctuations usually reflect abnormal electrochemical reactions of the battery cell under certain SOC conditions, which may indicate the occurrence of lithium metal precipitation. By accurately identifying these areas, the system labels these areas as lithium precipitation intervals and records the corresponding SOC range. According to the identified lithium precipitation intervals, the BMS system extracts the test SOC data corresponding to the boundaries of these intervals. These SOC data points represent the state of charge of the battery cell in the actual charge and discharge test process, which is at high risk of lithium precipitation. By analyzing these SOC data points, the system determines the lithium precipitation risk SOC interval, which is the state of charge range where the battery cell is most likely to precipitate lithium metal.

[0095] Further, after determining the lithium precipitation risk SOC interval, the BMS system further analyzes the test voltage data corresponding to the boundaries of these SOC intervals. These voltage data points provide the voltage characteristics of the battery cell in the lithium precipitation risk interval. The system determines the lithium precipitation risk boundary voltage according to these data, i.e. in the lithium precipitation risk SOC interval, if the voltage exceeds this boundary value, it indicates that there is a high risk of lithium precipitation. These boundary voltage values will be used as key parameters for the BMS system to monitor and protect the battery cell.

[0096] By extracting the interval where the voltage mutation or abnormal fluctuation occurs in the voltage and SOC change curve, the lithium precipitation interval and the corresponding risk SOC interval and boundary voltage are determined. The method of the application significantly improves the accuracy of lithium precipitation risk determination. Real-time identification and analysis of the behavior of the battery under actual working conditions enable the battery management system to more effectively prevent the occurrence of lithium metal precipitation, enhancing the safety and reliability of the battery. The determination of the accurate risk interval and boundary voltage helps the BMS system to discover potential risks in the early stage and take protective measures in time, thereby prolonging the service life of the battery and improving the overall performance of the battery system.

[0097] The embodiment first determines the current pulse charging and discharging working condition according to the pulse charging and discharging request; then acquires the SOC data set and voltage data set of the vehicle-side battery under the current pulse charging and discharging working condition; if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, the voltage data corresponding to the SOC data is acquired; if the voltage data exceeds the lithium precipitation risk boundary voltage, it is determined that the vehicle-side battery has a high lithium precipitation risk. The embodiment provides a comprehensive and accurate data basis by acquiring the SOC and voltage data in real time, improves the pertinence of analysis by identifying data within the lithium precipitation risk SOC interval, accurately determines the high lithium precipitation risk by comparing the actual voltage data with the risk boundary voltage, and improves the accuracy of lithium precipitation risk determination of the vehicle-side battery.

[0098] Based on the above-mentioned second embodiment, a third embodiment of the lithium precipitation risk determination method of the vehicle-side battery of the application is proposed. Please refer to FIG. 3, which is a sub-flowchart of the third embodiment of the lithium precipitation risk determination method of the vehicle-side battery of the application.

[0099] In the embodiment, after step S3, the method further comprises:

[0100] S3a: if there is no SOC data in the SOC data set within the lithium precipitation risk SOC interval, it is determined whether there is voltage data in the voltage data set that exceeds the lithium precipitation risk boundary voltage;

[0101] S3b: if there is, it is determined that the vehicle-side battery has a low lithium precipitation risk;

[0102] S3c: if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, it is determined whether the voltage data corresponding to the SOC data exceeds the lithium precipitation risk boundary voltage;

[0103] S3d: if not, it is determined that the vehicle-side battery has a medium lithium precipitation risk.

[0104] The low lithium precipitation risk refers to a risk state when the battery voltage exceeds the lithium precipitation risk boundary voltage, but the SOC does not fall into the lithium precipitation risk SOC interval. The medium lithium precipitation risk refers to a risk state when the battery SOC data falls into the lithium precipitation risk SOC interval, but the voltage data does not exceed the lithium precipitation risk boundary voltage.

[0105] Specifically, in the obtained SOC data set, if no data point is found to fall into the lithium precipitation risk SOC interval, the BMS system will further analyze whether there is voltage data exceeding the lithium precipitation risk boundary voltage in the voltage data set. Specifically, the BMS will check all voltage data points one by one and compare them with the preset lithium precipitation risk boundary voltage. If there is any voltage data point exceeding these boundary values, it indicates that the battery cell may be in a low lithium precipitation risk state. Although the SOC does not show high risk, the voltage anomaly indicates potential lithium precipitation risk.

[0106] Further, after finding data falling into the lithium precipitation risk SOC interval in the SOC data set, the BMS system will further determine whether the voltage data corresponding to these SOC data exceeds the lithium precipitation risk boundary voltage. Specifically, the system will extract the voltage value corresponding to each high-risk SOC data point and compare these voltage values with the boundary voltage. If these voltage data do not exceed the boundary values, although the SOC shows a certain risk, since the voltage does not show an anomaly, the BMS will determine that the battery cell is in a medium lithium precipitation risk state. This determination method considers both SOC and voltage parameters to ensure the accuracy of risk assessment.

[0107] If there is no data point in the SOC data set falling into the lithium precipitation risk SOC interval, but there is voltage data exceeding the lithium precipitation risk boundary voltage in the voltage data set, the BMS system will determine that the battery cell has a low lithium precipitation risk. This means that although the current state of charge is relatively safe, the voltage anomaly indicates potential lithium precipitation risk. If there are data points in the SOC data set falling into the lithium precipitation risk SOC interval, but the voltage data corresponding to these SOC data points does not exceed the lithium precipitation risk boundary voltage, the BMS system will determine that the battery cell has a medium lithium precipitation risk. This indicates that the battery cell does not show voltage anomalies in a high-risk state of charge, but potential lithium precipitation risk still needs to be vigilant.

[0108] By determining the lithium precipitation risk on the basis of both the SOC data set and the voltage data set, the lithium precipitation risk of the battery cell can be more comprehensively and accurately assessed. The determination of low lithium precipitation risk ensures that voltage anomalies can be identified in time even if there is no abnormality in the SOC data, preventing potential risks. The determination of medium lithium precipitation risk timely discovers potential risks through the high sensitivity of the SOC data, although the voltage data does not exceed the boundary. By integrating these determination criteria, the BMS can accurately identify the risk state of the battery cell under different working conditions, provide more effective prevention and protection measures, and improve the safety and reliability of the battery system.

[0109] Based on the second embodiment described above, in this embodiment, after step S4, the method further comprises:

[0110] S2a: Obtain the swelling force test data of the vehicle-side battery cell, and preprocess the swelling force test data;

[0111] S2b: Obtain a swelling force change curve based on the preprocessed swelling force test data;

[0112] S2c: Predict the cycle life of the vehicle-side battery cell based on the swelling force change curve and a preset life prediction model;

[0113] S2d: Obtain the current health state of the vehicle-side battery cell based on the cycle life, the SOC data set, and the voltage data set;

[0114] S2e: Adjust the pulse charge-discharge request based on the current health state.

[0115] Swelling force test data refers to a set of data measured during the charge-discharge process, which reflects the physical changes of the battery cell during the charge-discharge cycle and is closely related to the health state and life of the battery cell. The swelling force change curve is a curve generated based on the swelling force test data, which shows the trend of the swelling force change of the battery cell under different charge-discharge states. The preset life prediction model is a model established based on historical data and battery cell characteristics, which is used to predict the cycle life of the battery cell. The model can combine data such as swelling force, SOC, and voltage to predict the life. Cycle life refers to the number of charge-discharge cycles that the battery cell can undergo while maintaining certain capacity and performance conditions. The current health state refers to the comprehensive evaluation of the current working state and health level of the battery cell based on the cycle life, SOC data, and voltage data.

[0116] Specifically, through the swelling force sensor installed on the battery cell, the BMS system monitors the swelling force change of the battery cell in real time during the charge-discharge cycle and collects swelling force test data. These data record the volume change of the battery cell under different SOC and voltage conditions. In order to ensure the accuracy and consistency of the data, the BMS will preprocess the swelling force test data, including removing noise, correcting outliers, and data smoothing. Based on the preprocessed data, the BMS generates a swelling force change curve. This curve shows the trend of the swelling force change of the battery cell in different charge-discharge cycles, and is an important basis for evaluating the health state and predicting the life of the battery cell. The curve can reveal the physical change law of the battery cell under different state of charge and voltage conditions.

[0117] Further, the BMS inputs the swelling force change curve into a pre-set life prediction model. This model combines the swelling force change trend, historical data, and cell characteristics to predict the cycle life of the cell. By combining the cycle life prediction results, SOC data set, and voltage data set, the BMS evaluates the current health status of the cell. This evaluation includes key parameters such as the remaining life, current capacity, and internal resistance change of the cell, comprehensively reflecting the working state and health level of the cell. By analyzing the swelling force change of the cell in multiple charge and discharge cycles, the model can estimate the number of cycles the cell can continue to use under certain performance conditions.

[0118] Further, based on the current health status evaluation results, the BMS dynamically adjusts the pulse charge and discharge request. Specific adjustment measures may include reducing the charge and discharge current, adjusting the pulse duration and interval, or even changing the charge and discharge strategy to optimize the use conditions of the cell and extend its life. Through these adjustments, the BMS can ensure that the cell works in the best state, improving the overall performance and safety of the battery system.

[0119] By obtaining and preprocessing the swelling force test data of the cell, combining the swelling force change curve and the pre-set life prediction model, the cycle life of the cell can be accurately predicted. The current health status of the cell can be comprehensively evaluated, and the pulse charge and discharge request can be adjusted based on this. This dynamic adjustment mechanism not only improves the intelligent level of the battery management system, but also significantly extends the service life of the battery, improving the safety and reliability of the battery system. Precise health status evaluation and charge and discharge strategy optimization enable the battery to maintain optimal performance under different working conditions, reducing maintenance costs and enhancing user experience.

[0120] Based on the above second embodiment, in this embodiment, after step S4, it further includes:

[0121] S4a: Determine whether the temperature information of the cell at the vehicle end and the environmental information meet the pre-set safety standards;

[0122] S4b: If yes, obtain the frequency and duration corresponding to the voltage data;

[0123] S4c: Based on the frequency and the duration, issue a high lithium analysis risk warning and adjust the pulse charge and discharge request.

[0124] The temperature information includes internal temperature data of the battery cell, and the environmental information includes environmental temperature, humidity, air pressure, and other data around the battery cell. The preset safety standard refers to the safety operating range set in the battery management system (BMS), including the upper and lower limits of the temperature of the battery cell, the range of environmental temperature, etc., to ensure that the battery cell operates under safe conditions. The frequency and duration corresponding to the voltage data refer to the frequency of the recorded abnormal voltage data and the duration of each abnormality when the voltage of the battery cell is detected to exceed the safety boundary. The high lithium precipitation risk warning refers to the warning signal issued by the BMS when the battery cell is detected to have a high lithium precipitation risk, notifying the user or the system to take necessary protective measures.

[0125] Specifically, the BMS continuously monitors the temperature information and the surrounding environmental information of the vehicle-side battery cell. These data include the internal temperature of the battery cell, the environmental temperature, humidity, and air pressure, etc. The system compares these real-time monitored data with the preset safety standard. If all the monitored data are within the safety standard range, it means that the current working environment of the battery cell is safe, and subsequent detection and analysis can continue. If any data exceeds the safety standard, the system will immediately take protective measures to prevent the battery cell from working under unsafe conditions.

[0126] Further, after confirming that the temperature information and the environmental information meet the safety standard, the BMS will further analyze the voltage data. Specifically, the system will check whether there are abnormal values in the voltage data set and record the frequency of these abnormal voltage data and the duration of each abnormality. These frequency and duration information can help the system evaluate the severity and possible causes of the voltage abnormality of the battery cell. The frequency and duration data will be important reference indicators for judging whether the battery cell has a high lithium precipitation risk.

[0127] Further, according to the recorded frequency and duration corresponding to the voltage data, if it is found that the battery cell has persistent voltage abnormalities, and these abnormal voltage data exceed the preset lithium precipitation risk boundary voltage, the BMS will determine that the battery cell has a high lithium precipitation risk. The system will immediately issue a high lithium precipitation risk warning to notify the user or the system administrator to take necessary protective measures. At the same time, the BMS will dynamically adjust the pulse charging and discharging request according to the current risk assessment result. For example, the system may reduce the charging and discharging current, shorten the pulse duration, or increase the pulse interval to reduce the lithium precipitation risk of the battery cell, ensuring that the battery operates within a safe range.

[0128] By judging whether the temperature information and the environmental information of the vehicle-end battery cell meet the preset safety standard, and under the premise of meeting the safety standard, the frequency and the duration corresponding to the voltage data are obtained, the high lithium precipitation risk of the battery cell can be accurately evaluated. Real-time risk evaluation and alarm system enables the BMS to take protective measures in time to avoid the battery cell working under high-risk conditions. In addition, dynamic adjustment of pulse charging and discharging request based on detailed frequency and duration data helps to optimize the charging and discharging strategy of the battery, prolong the service life of the battery, and improve the safety and reliability of the system.

[0129] The embodiment first determines the current pulse charging and discharging working condition according to the pulse charging and discharging request; then obtains the SOC data set and the voltage data set of the vehicle-end battery cell under the current pulse charging and discharging working condition; if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, the voltage data corresponding to the SOC data is obtained; if the voltage data exceeds the lithium precipitation risk boundary voltage, it is determined that the vehicle-end battery cell has high lithium precipitation risk. The embodiment provides comprehensive and accurate data basis by obtaining the SOC and voltage data in real time, improves the pertinence of analysis by identifying data within the lithium precipitation risk SOC interval, and accurately determines the high lithium precipitation risk by comparing the actual voltage data with the risk boundary voltage, thereby improving the accuracy of the lithium precipitation risk determination of the vehicle-end battery cell.

[0130] The embodiment of the application also provides a lithium precipitation risk judgment device for a vehicle-end battery cell, please refer to Fig. 4, which is a module structure schematic diagram of the lithium precipitation risk judgment device for a vehicle-end battery cell, the lithium precipitation risk judgment device for a vehicle-end battery cell comprises:

[0131] The working condition determination module 401 is configured to determine the current pulse charging and discharging working condition according to the pulse charging and discharging request;

[0132] The data acquisition module 402 is configured to obtain the SOC data set and the voltage data set of the vehicle-end battery cell under the current pulse charging and discharging working condition;

[0133] The voltage acquisition module 403 is configured to obtain the voltage data corresponding to the SOC data if there is SOC data within the lithium precipitation risk SOC interval in the SOC data set;

[0134] The risk determination module 404 is configured to determine that the vehicle-end battery cell has high lithium precipitation risk if the voltage data exceeds the lithium precipitation risk boundary voltage.

[0135] The vehicle-side battery cell lithium precipitation risk judgment device provided by the embodiments of the present application can solve the technical problem of how to improve the accuracy of vehicle-side battery cell lithium precipitation risk judgment. Compared with the prior art, the vehicle-side battery cell lithium precipitation risk judgment device provided by the embodiments of the present application has the same beneficial effects as the vehicle-side battery cell lithium precipitation risk judgment method provided by the above embodiments, and other technical features of the vehicle-side battery cell lithium precipitation risk judgment device are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0136] The present application provides a vehicle-side battery cell lithium precipitation risk judgment device, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the vehicle-side battery cell lithium precipitation risk judgment method in the above embodiments.

[0137] Reference is made to FIG. 5, which shows a structural schematic diagram of a vehicle-side battery cell lithium precipitation risk judgment device suitable for implementing the embodiments of the present application. The vehicle-side battery cell lithium precipitation risk judgment device in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, as well as fixed terminals such as digital TVs, desktop computers, and the like. The vehicle-side battery cell lithium precipitation risk judgment device shown in FIG. 5 is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0138] As shown in FIG. 5, the lithium precipitation risk judgment device of the vehicle terminal battery cell can include a processing device 1001 (for example, a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In the RAM 1004, various programs and data required for the operation of the lithium precipitation risk judgment device of the vehicle terminal battery cell are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the lithium precipitation risk judgment device of the vehicle terminal battery cell to communicate with other devices wirelessly or by wire to exchange data. Although the lithium precipitation risk judgment device of the vehicle terminal battery cell with various systems is shown in the figure, it should be understood that all the systems shown are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0139] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are performed.

[0140] The lithium precipitation risk judgment device of the vehicle terminal battery cell provided in the present application adopts the lithium precipitation risk judgment method of the above-mentioned embodiments, and can solve the technical problem of how to improve the accuracy of the lithium precipitation risk judgment of the vehicle terminal battery cell. Compared with the prior art, the lithium precipitation risk judgment device of the vehicle terminal battery cell provided in the present application has the same beneficial effects as the lithium precipitation risk judgment method of the above-mentioned embodiments, and other technical features in the lithium precipitation risk judgment device of the vehicle terminal battery cell are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0141] Portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the description of the embodiments above, specific features, structures, materials or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0142] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any modifications or equivalents of the application should be construed as falling within the scope of the application. The scope of the application should be determined by the appended claims.

[0143] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e., computer programs) for performing the method for judging the lithium precipitation risk of the vehicle-end battery cell in the above embodiments.

[0144] The computer readable storage medium provided by the present application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), etc., or any suitable combination thereof.

[0145] The above computer readable storage medium can be included in the vehicle-end battery cell lithium precipitation risk judgment device; or can exist separately and not be assembled into the vehicle-end battery cell lithium precipitation risk judgment device.

[0146] The computer readable storage medium described above carries one or more programs, when the one or more programs are executed by the vehicle-side lithium precipitation risk judgment device, the vehicle-side lithium precipitation risk judgment device is caused to: determine a current pulse charging and discharging working condition according to a pulse charging and discharging request; obtain an SOC data set and a voltage data set of the vehicle-side battery under the current pulse charging and discharging working condition; if there is an SOC data in the SOC data set in a lithium precipitation risk SOC interval, obtain voltage data corresponding to the SOC data; and if the voltage data exceeds the lithium precipitation risk boundary voltage, determine that the vehicle-side battery has a high lithium precipitation risk. Computer program code for performing the operations of the present application can be written in one or more programming languages or combinations of languages including object oriented programming languages such as Java, Smalltalk, C++ as well as conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0147] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or combinations of hardware and software.

[0148] The modules involved in the embodiments of the present application can be implemented in a software manner or in a hardware manner. In some cases, the name of the module does not constitute a limitation on the module itself.

[0149] The readable storage medium provided by the application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer programs) for executing the above-mentioned lithium precipitation risk judgment method of the vehicle terminal battery cell, and can solve the technical problem of how to improve the accuracy of the lithium precipitation risk judgment of the vehicle terminal battery cell. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the above-mentioned lithium precipitation risk judgment method of the vehicle terminal battery cell, and will not be repeated here.

[0150] The embodiment of the application provides a computer program product, which comprises a computer program. When the computer program is executed by a processor, the steps of the above-mentioned lithium precipitation risk judgment method of the vehicle terminal battery cell are realized.

[0151] The computer program product provided by the application can solve the technical problem of how to improve the accuracy of the lithium precipitation risk judgment of the vehicle terminal battery cell. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the application are the same as those of the above-mentioned lithium precipitation risk judgment method of the vehicle terminal battery cell, and will not be repeated here.

[0152] The above is only an embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent processing scope of the application.

Claims

1. A method for judging a lithium precipitation risk of a car-end cell, wherein, The method comprises: determining a current pulse charging and discharging working condition according to a pulse charging and discharging request; obtaining an SOC data set and a voltage data set of the vehicle-end battery cell under the current pulse charging and discharging working condition; if there is SOC data in the SOC data set within a lithium precipitation risk SOC interval, obtaining voltage data corresponding to the SOC data; if the voltage data exceeds the lithium precipitation risk boundary voltage, determining that the vehicle-end battery cell has high lithium precipitation risk.

2. The method of claim 1, wherein, Before the step of determining the current pulse charging and discharging working condition according to the pulse charging and discharging request, the method further comprises: based on preset pulse charging and discharging parameters, performing charging and discharging cycle tests on the vehicle-end battery cell to obtain a test SOC data set and a test voltage data set; preprocessing the test SOC data set and the test voltage data set, and generating a voltage and SOC change curve based on the preprocessed test SOC data set and the preprocessed test voltage data set; determining the lithium precipitation risk SOC interval and the lithium precipitation risk boundary voltage according to the voltage and SOC change curve.

3. The method of claim 2, wherein, The step of determining the lithium precipitation risk SOC interval and the lithium precipitation risk boundary voltage according to the voltage and SOC change curve comprises: extracting an interval in which voltage mutation or abnormal voltage fluctuation occurs in the voltage and SOC change curve as a lithium precipitation interval; determining the lithium precipitation risk SOC interval according to the test SOC data corresponding to the lithium precipitation interval boundary; determining the lithium precipitation risk boundary voltage according to the test voltage data corresponding to the boundary of the lithium precipitation risk SOC interval.

4. The method of claim 1, wherein, After the step of obtaining voltage data corresponding to the SOC data if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, the method further comprises: if there is no SOC data in the SOC data set within the lithium precipitation risk SOC interval, determining whether there is voltage data exceeding the lithium precipitation risk boundary voltage in the voltage data set; if there is, determining that the vehicle-end battery cell has low lithium precipitation risk; if there is SOC data in the SOC data set within the lithium precipitation risk SOC interval, determining whether the voltage data corresponding to the SOC data exceeds the lithium precipitation risk boundary voltage; if not, determining that the vehicle-end battery cell has medium lithium precipitation risk.

5. The method of claim 2, wherein, After the step of obtaining the SOC data set and the voltage data set of the vehicle-end battery cell under the current pulse charging and discharging working condition, the method further comprises: obtaining expansion force test data of the vehicle-end battery cell and preprocessing the expansion force test data; obtaining an expansion force change curve according to the preprocessed expansion force test data; predicting the cycle life of the vehicle-end battery cell based on the expansion force change curve and a preset life prediction model; obtaining a current health status of the vehicle-end battery cell according to the cycle life, the SOC data set, and the voltage data set; adjusting the pulse charging and discharging request based on the current health status.

6. The method of claim 1, wherein, After the step of determining that the vehicle-end battery cell has high lithium precipitation risk if the voltage data exceeds the lithium precipitation risk boundary voltage, the method further comprises: determine whether the temperature information and the environmental information of the vehicle-end battery cell meet a preset safety standard; if yes, obtain a frequency and a duration corresponding to the voltage data; based on the frequency and the duration, issue a high lithium precipitation risk warning and adjust the pulse charging and discharging request.

7. A device for judging a lithium precipitation risk of a car-end cell, wherein The device comprises: a working condition determination module configured to determine a current pulse charging and discharging working condition according to the pulse charging and discharging request; a data acquisition module configured to acquire a set of SOC data and a set of voltage data of the vehicle-end battery cell under the current pulse charging and discharging working condition; a voltage acquisition module configured to, if there is SOC data in the set of SOC data within a lithium precipitation risk SOC interval, acquire voltage data corresponding to the SOC data; a risk determination module configured to, if the voltage data exceeds the lithium precipitation risk boundary voltage, determine that the vehicle-end battery cell has a high lithium precipitation risk.

8. A computer device, wherein, The device comprises a memory, a processor, and a lithium precipitation risk determination program of a vehicle-end battery cell stored on the memory and executable on the processor, and the lithium precipitation risk determination program of the vehicle-end battery cell is configured to implement the steps of the lithium precipitation risk determination method of the vehicle-end battery cell according to any one of claims 1 to 6.

9. A storage medium, wherein, The storage medium stores a lithium precipitation risk determination program of a vehicle-end battery cell, and the lithium precipitation risk determination program of the vehicle-end battery cell, when executed by a processor, implements the steps of the lithium precipitation risk determination method of the vehicle-end battery cell according to any one of claims 1 to 6.

10. A computer program product, wherein, The computer program product comprises a computer program, and the computer program, when executed by a processor, implements the steps of the lithium precipitation risk determination method of the vehicle-end battery cell according to any one of claims 1 to 6.

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