WO2026007332A1 - 车端电芯的析锂风险判断方法、装置、设备、存储介质及产品 - Google Patents

车端电芯的析锂风险判断方法、装置、设备、存储介质及产品

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Publication number
WO2026007332A1
WO2026007332A1 PCT/CN2024/137896 CN2024137896W WO2026007332A1 WO 2026007332 A1 WO2026007332 A1 WO 2026007332A1 CN 2024137896 W CN2024137896 W CN 2024137896W WO 2026007332 A1 WO2026007332 A1 WO 2026007332A1
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WIPO (PCT)
Prior art keywords
risk
soc
voltage
lithium plating
vehicle
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PCT/CN2024/137896
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English (en)
French (fr)
Inventor
李梦霞
吴杰余
冯春
刘爽
吴胜杰
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Dongfeng Motor Group Co Ltd
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Dongfeng Motor Group Co Ltd
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Publication of WO2026007332A1 publication Critical patent/WO2026007332A1/zh
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/005—Testing of electric installations on transport means
    • G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
    • G01R31/007—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks using microprocessors or computers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements

Definitions

  • This application relates to the field of battery cell technology, and in particular to a method, apparatus, equipment, storage medium and product for assessing the risk of lithium plating in vehicle-mounted battery cells.
  • Lithium-ion batteries are widely used in electric vehicles, hybrid vehicles, and other energy storage systems. Under extreme conditions such as low temperatures and high-rate charge/discharge, lithium-ion batteries are prone to lithium plating, where lithium ions form metallic lithium on the negative electrode surface. This phenomenon leads to battery capacity decay, increased internal resistance, and even safety hazards such as short circuits and thermal runaway. Therefore, accurately assessing and preventing lithium plating risks is crucial.
  • the main objective of this application is to provide a method, apparatus, device, storage medium, and product for assessing the risk of lithium plating in vehicle-mounted battery cells, aiming to solve the technical problem of how to improve the accuracy of assessing the risk of lithium plating in vehicle-mounted battery cells.
  • this application provides a method for assessing the lithium plating risk of vehicle-side battery cells, the method comprising the following steps:
  • the SOC dataset contains SOC data within the lithium plating risk SOC range, obtain the voltage data corresponding to the SOC data.
  • the vehicle-side battery cell is determined to have a high risk of lithium plating.
  • the method before the step of determining the current pulse charge/discharge condition based on the pulse charge/discharge request, the method further includes:
  • charge and discharge cycle tests are performed on the vehicle-end battery cell to obtain test SOC dataset and test voltage dataset;
  • test SOC dataset and the test voltage dataset are preprocessed, and voltage and SOC change curves are generated based on the preprocessed test SOC dataset and the preprocessed test voltage dataset.
  • the lithium plating risk SOC range and the lithium plating risk boundary voltage are determined.
  • the step of determining the lithium plating risk SOC range and the lithium plating risk boundary voltage based on the voltage-SOC change curve includes:
  • the lithium plating risk SOC range is determined based on the test SOC data corresponding to the boundary of the lithium plating range;
  • the lithium plating risk boundary voltage is determined based on the test voltage data corresponding to the boundary of the lithium plating risk SOC range.
  • the method further includes:
  • the vehicle-mounted battery cell is deemed to have a low risk of lithium plating.
  • the SOC dataset contains SOC data within the lithium plating risk SOC range, determine whether the voltage data corresponding to the SOC data exceeds the lithium plating risk boundary voltage.
  • the vehicle-side battery cell has a risk of lithium plating.
  • the method further includes:
  • the cycle life of the vehicle-end battery cell is predicted.
  • the current health status of the vehicle-side battery cell is obtained based on the cycle life, the SOC dataset, and the voltage dataset.
  • the method further includes:
  • this application also proposes a lithium plating risk assessment device for vehicle-side battery cells, the vehicle-side battery cell lithium plating risk assessment device comprising:
  • the operating condition determination module is used to determine the current pulse charge/discharge operating condition based on the pulse charge/discharge request.
  • the data acquisition module is used to acquire the SOC dataset and voltage dataset of the vehicle-side battery cell under the current pulse charge and discharge conditions.
  • the voltage acquisition module is used to acquire the voltage data corresponding to the SOC data if there is SOC data in the SOC dataset within the SOC range with lithium plating risk.
  • the risk assessment module is used to determine that the vehicle-end battery cell has a high risk of lithium plating if the voltage data exceeds the lithium plating risk threshold voltage.
  • this application also proposes a lithium plating risk assessment device for vehicle-mounted battery cells.
  • the device includes: a memory, a processor, and a lithium plating risk assessment program for vehicle-mounted battery cells stored in the memory and executable on the processor.
  • the lithium plating risk assessment program for vehicle-mounted battery cells is configured to implement the steps of the lithium plating risk assessment method for vehicle-mounted battery cells as described above.
  • this application also proposes a storage medium storing a lithium plating risk assessment program for vehicle-side battery cells.
  • the lithium plating risk assessment program for vehicle-side battery cells is executed by a processor, it implements the steps of the lithium plating risk assessment method for vehicle-side battery cells as described above.
  • this application also proposes a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the lithium plating risk assessment method for vehicle-side battery cells as described above.
  • This application first determines the current pulse charge/discharge condition based on the pulse charge/discharge request; then, it acquires the SOC and voltage datasets of the vehicle-side battery cell under the current pulse charge/discharge condition; if the SOC dataset contains SOC data within the lithium plating risk SOC range, it then acquires the corresponding voltage data; if the voltage data exceeds the lithium plating risk boundary voltage, it determines that the vehicle-side battery cell has a high lithium plating risk.
  • This application provides a comprehensive and accurate data foundation by acquiring SOC and voltage data in real time, improves the targeting of the analysis by identifying data within the lithium plating risk SOC range, and accurately determines high lithium plating risk by comparing actual voltage data with the risk boundary voltage, thus improving the accuracy of lithium plating risk assessment for vehicle-side battery cells.
  • Figure 1 is a flowchart illustrating the first embodiment of the lithium plating risk assessment method for vehicle-side battery cells in this application.
  • Figure 2 is a schematic diagram of a sub-process of the second embodiment of the lithium plating risk assessment method for vehicle-side battery cells in this application;
  • Figure 3 is a schematic diagram of a sub-process in the third embodiment of the lithium plating risk assessment method for vehicle-side battery cells in this application.
  • Figure 4 is a schematic diagram of the module structure of the lithium plating risk assessment device for vehicle-side battery cells according to an embodiment of this application;
  • Figure 5 is a schematic diagram of the hardware operating environment involved in the lithium plating risk assessment method for vehicle-side battery cells in this embodiment of the application.
  • Lithium-ion batteries are widely used in electric vehicles, hybrid vehicles, and other energy storage systems. Under extreme conditions such as low temperatures and high-rate charge/discharge, lithium-ion batteries are prone to lithium plating, where lithium ions form metallic lithium on the negative electrode surface. This phenomenon leads to battery capacity decay, increased internal resistance, and even safety hazards such as short circuits and thermal runaway. Therefore, accurately assessing and preventing lithium plating risks is crucial.
  • the main solution of this application is as follows: First, determine the current pulse charge/discharge condition based on the pulse charge/discharge request; then, obtain the SOC dataset and voltage dataset of the vehicle-side battery cell under the current pulse charge/discharge condition; if there is SOC data in the SOC dataset within the lithium plating risk SOC range, then obtain the voltage data corresponding to the SOC data; if the voltage data exceeds the lithium plating risk boundary voltage, it is determined that the vehicle-side battery cell has a high lithium plating risk.
  • This application provides a comprehensive and accurate data foundation by acquiring SOC and voltage data in real time. By identifying data within the SOC range where lithium plating risk exists, it improves the targeting of the analysis. By comparing actual voltage data with the risk boundary voltage, it accurately determines high lithium plating risk, thereby improving the accuracy of lithium plating risk assessment for vehicle-side battery cells.
  • the execution entity of the method in this embodiment can be a computing service device with data processing, network communication, and program execution functions, or it can be the lithium plating risk assessment device for vehicle-side battery cells described above, which has the same or similar functions.
  • This embodiment and the following embodiments will be described using a lithium plating risk assessment device for vehicle-side battery cells as an example.
  • FIG. 1 is a flowchart illustrating the first embodiment of the lithium plating risk assessment method for vehicle-side battery cells of this application.
  • the method for assessing the lithium plating risk of the vehicle-side battery cell includes the following steps:
  • S1 Determine the current pulse charge/discharge condition based on the pulse charge/discharge request.
  • a pulse charge/discharge request is a control signal sent by the Battery Management System (BMS) to the charging or discharging equipment, designed to optimize battery performance through intermittent charging or discharging operations.
  • BMS Battery Management System
  • This request typically includes specific charging and discharging parameters, such as current amplitude, pulse duration, and interval time.
  • the current pulse charge/discharge condition refers to the actual operating conditions of the battery after receiving the pulse charge/discharge request. These conditions include parameters such as the pulse current amplitude, duration, and interval time, reflecting the battery's charge/discharge state within a specific time period.
  • the lithium plating risk assessment equipment for vehicle-mounted battery cells includes a Battery Management System.
  • the system analyzes the parameters in the request, such as the amplitude, duration, and interval of the pulse current. Subsequently, the BMS dynamically adjusts and determines the specific pulse charge/discharge conditions based on the real-time state of the battery (including parameters such as SOC, voltage, and temperature). For example, if the battery temperature is currently low, the BMS may appropriately adjust the amplitude and duration of the pulse charging current to avoid overcharging and discharging, which could damage the battery.
  • the parameters in the request such as the amplitude, duration, and interval of the pulse current.
  • the BMS dynamically adjusts and determines the specific pulse charge/discharge conditions based on the real-time state of the battery (including parameters such as SOC, voltage, and temperature). For example, if the battery temperature is currently low, the BMS may appropriately adjust the amplitude and duration of the pulse charging current to avoid overcharging and discharging, which could damage the battery.
  • the BMS activates the data acquisition system to monitor the battery's SOC and voltage changes in real time under that condition.
  • This real-time data is not only used to verify and adjust the charge/discharge condition but also provides a basis for subsequent battery status assessment and risk determination.
  • the BMS ensures that operations within each pulse cycle are performed within safe limits, thereby optimizing battery performance and extending its lifespan.
  • the BMS can flexibly adjust the battery's charge/discharge operations to adapt to actual operating conditions. This dynamic adjustment not only improves the accuracy and relevance of data acquisition but also ensures the safe and stable operation of the battery under different conditions.
  • the real-time monitoring and adjustment mechanism enables the BMS to more effectively manage the battery's charge/discharge process, optimize battery performance, reduce the risk of lithium plating, and improve the overall safety and lifespan of the battery system.
  • S2 Obtain the SOC dataset and voltage dataset of the vehicle-side battery cell under the current pulse charge/discharge condition.
  • a voltage dataset refers to the collection of cell voltage data at different times under the current pulse charge/discharge condition. Voltage is one of the important parameters of battery health, reflecting the battery's operating status under different charge/discharge states.
  • a State of Charge (SOC) dataset refers to the collection of cell state-of-charge data at different times under the current pulse charge/discharge condition. SOC is an important indicator of the battery's remaining capacity, usually expressed as a percentage.
  • the BMS activates the real-time data acquisition system to comprehensively monitor the status of the vehicle-side battery cells.
  • the BMS continuously collects the State of Charge (SOC) and voltage data for each pulse cycle.
  • SOC State of Charge
  • the SOC dataset reflects the cell's state of charge at different time points, recording the energy changes throughout the entire pulse charge/discharge process.
  • the voltage dataset records the voltage changes at these time points, providing the voltage characteristics of the cell under different SOCs.
  • the BMS processes and stores the collected SOC and voltage data in real time.
  • the BMS can quickly filter and calibrate the data, removing noise and outliers to ensure data reliability.
  • the stored data includes the timestamp, SOC value, and voltage value for each sampling moment, forming a complete SOC and voltage dataset. This data is not only used for real-time monitoring of the cell status but also provides crucial information for subsequent lithium plating risk analysis and health status assessment of the cells.
  • the BMS can comprehensively understand the cell's operating status under actual working conditions.
  • Real-time data acquisition and processing improves the accuracy and reliability of battery status monitoring, providing a solid data foundation for further risk assessment and optimized management.
  • This precise data acquisition and analysis capability helps to promptly detect abnormal cell states, prevent potential safety hazards, extend battery life, and improve the performance and safety of the entire battery system.
  • S3 If the SOC dataset contains SOC data within the lithium plating risk SOC range, obtain the voltage data corresponding to the SOC data.
  • the lithium plating risk range refers to the region within a battery's specific state of charge (SOC) range where lithium metal plating is more likely to occur. Within these regions, the battery's electrochemical reactions may cause lithium metal to deposit on the negative electrode surface, potentially leading to safety hazards.
  • the voltage data corresponding to the SOC data refers to the battery's voltage value at a specific SOC value. This data is used to analyze the battery's voltage behavior within the lithium plating risk range to help determine whether a lithium plating risk exists.
  • the system analyzes the SOC data to identify any data points falling within the lithium plating risk SOC range.
  • the BMS first compares the real-time acquired SOC data with preset lithium plating risk SOC ranges, filtering out SOC data points located within these risk ranges. These data points represent the battery's current state of charge with a higher risk of lithium plating and require further analysis.
  • the BMS extracts the corresponding voltage data. Specifically, the BMS matches these SOC data points with a stored voltage dataset using timestamps to find the voltage value corresponding to each SOC data point at a specific time point. In this way, the BMS can obtain a detailed set of voltage data associated with the identified high-risk SOC data points. This step ensures that the BMS can accurately analyze the battery's voltage performance within the lithium plating risk range, thereby determining whether the battery faces actual lithium plating risk.
  • the Battery Management System By identifying data points within the lithium plating risk SOC range in the SOC dataset and obtaining the corresponding voltage data, the Battery Management System (BMS) can conduct in-depth analysis of the battery's voltage behavior under high-risk conditions. This process improves the accuracy of lithium plating risk assessment because it combines the battery's state of charge and voltage performance for multi-dimensional analysis. Precise data extraction and analysis help to promptly identify potential lithium plating phenomena, enabling preventative measures to ensure battery safety and reliability. Furthermore, this method provides crucial data support for subsequent battery state optimization and management, further enhancing battery system performance and lifespan.
  • the lithium plating risk boundary voltage is a preset critical voltage range. When the battery voltage exceeds this range, it indicates a high risk of lithium metal plating. These boundary voltage values are determined through experiments and data analysis and are used for risk assessment in the battery management system. High lithium plating risk refers to a high-risk state caused by lithium metal deposition on the negative electrode surface during charging and discharging. This state may lead to battery performance degradation, increased internal resistance, and even safety hazards such as short circuits and thermal runaway.
  • the BMS will further analyze this voltage data to determine whether the battery is in a high-risk state for lithium plating. Specifically, the BMS will compare the extracted voltage data with preset lithium plating risk boundary voltages. If some voltage data is found to exceed these boundary voltage ranges, it indicates that the battery may have a high risk of lithium metal plating.
  • the Battery Management System After confirming that the voltage data exceeds the lithium plating risk threshold voltage, the Battery Management System (BMS) will determine that the vehicle-side battery cell has a high risk of lithium plating. At this point, the BMS will not only record these data points exceeding the threshold but also perform a comprehensive analysis to confirm the reliability of the data and consider whether it is affected by other factors (such as temperature, current fluctuations, etc.). Through this comprehensive analysis, the BMS can accurately identify high-risk states and avoid misjudgments. Upon final confirmation of a high lithium plating risk, the BMS will immediately take corresponding 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 battery safety.
  • 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 battery safety.
  • the BMS significantly improves the accuracy of lithium plating risk assessment. This process ensures battery safety under different operating conditions, enabling timely detection and prevention of potential lithium metal plating risks. High-precision risk assessment and timely protective measures not only extend battery life but also enhance the overall system's safety and reliability. Using this approach, the battery management system can dynamically adapt to actual operating conditions, providing a safer and more efficient battery management solution.
  • This embodiment first determines the current pulse charge/discharge condition based on the pulse charge/discharge request; then, it acquires the SOC and voltage datasets of the vehicle-side battery cell under the current pulse charge/discharge condition; if the SOC dataset contains SOC data within the lithium plating risk SOC range, it then acquires the corresponding voltage data; if the voltage data exceeds the lithium plating risk boundary voltage, it determines that the vehicle-side battery cell has a high lithium plating risk.
  • This embodiment provides a comprehensive and accurate data foundation by acquiring SOC and voltage data in real time, improves the targeting of the analysis by identifying data within the lithium plating risk SOC range, and accurately determines high lithium plating risk by comparing actual voltage data with the risk boundary voltage, thus improving the accuracy of lithium plating risk assessment for vehicle-side battery cells.
  • FIG. 2 is a schematic diagram of a sub-process of the second embodiment of the lithium plating risk assessment method for vehicle-side battery cells of this application.
  • the method before step S1, the method further includes:
  • S1a Based on preset pulse charge and discharge parameters, perform charge and discharge cycle tests on the vehicle-end battery cell to obtain test SOC dataset and test voltage dataset;
  • S1b Preprocess the test SOC dataset and the test voltage dataset, and generate voltage and SOC change curves based on the preprocessed test SOC dataset and the preprocessed test voltage dataset;
  • S1c Determine the lithium plating risk SOC range and the lithium plating risk boundary voltage based on the voltage and SOC change curve.
  • Preset pulse charge/discharge parameters refer to the pulse charge/discharge conditions preset in the Battery Management System (BMS), including parameters such as pulse current amplitude, duration, and interval, used to standardize the charge/discharge testing process.
  • Charge/discharge cycle testing refers to the operation of repeatedly charging and discharging the battery cell under controlled conditions according to the preset pulse charge/discharge parameters to simulate actual usage conditions and collect relevant data.
  • the test SOC dataset is the set of battery cell state-of-charge data collected during the charge/discharge cycle test.
  • the test voltage dataset is the set of battery cell voltage data collected during the charge/discharge cycle test. The voltage vs.
  • the lithium plating risk SOC range is the specific SOC range within the voltage vs. SOC change curve where lithium metal plating is likely to occur in the battery cell.
  • the lithium plating risk boundary voltage is the critical voltage value within the lithium plating risk SOC range; exceeding this voltage range indicates a risk of lithium plating.
  • a systematic charge and discharge cycle test is conducted on the vehicle-side battery cells. This process simulates various operating conditions of the battery cells in actual use, and comprehensive battery cell performance data is collected through multiple charge and discharge operations.
  • SOC state of charge
  • voltage changes of the battery cells are monitored and recorded in real time, forming detailed test SOC datasets and test voltage datasets.
  • the collected data needs to be preprocessed to ensure its accuracy and reliability. Preprocessing steps include noise removal, outlier correction, and data smoothing.
  • the preprocessed test SOC dataset and test voltage dataset are used to generate voltage versus SOC curves. These curves reflect the voltage characteristics of the battery cell at different SOCs and are crucial for determining the risk of lithium plating. Based on the generated voltage versus SOC curves, the SOC ranges and corresponding boundary voltages for lithium plating risk are analyzed and determined. Within these ranges, abrupt changes or abnormal fluctuations in the voltage curves indicate that lithium metal plating may occur in the battery cell. The boundary voltage is a critical criterion; voltages exceeding this range indicate that the battery cell is in a high-risk state for lithium plating.
  • this application can accurately determine the SOC range and boundary voltage for lithium plating risk.
  • This method not only improves the accuracy of risk assessment by the battery management system under actual operating conditions but also provides detailed cell performance data, offering a scientific basis for further optimizing battery management strategies. Through these steps, lithium plating in the cells can be effectively prevented, improving battery safety and lifespan.
  • step S1c includes:
  • S1c1 Extract the intervals in the voltage-SOC change curves where voltage abrupt changes or abnormal voltage fluctuations occur, and use them as lithium plating intervals;
  • S1c3 Determine the lithium plating risk boundary voltage based on the test voltage data corresponding to the boundary of the lithium plating risk SOC range.
  • Voltage abrupt changes or abnormal voltage fluctuations refer to specific intervals in the voltage-SOC curve where the voltage changes drastically or fluctuates unstablely. These phenomena usually indicate abnormal reactions within the battery cell, such as lithium metal deposition.
  • Lithium deposition intervals refer to the SOC intervals defined by voltage abrupt changes or abnormal fluctuations in the voltage-SOC curve; within these intervals, lithium metal deposition is more likely to occur in the battery cell.
  • the BMS system analyzes the test voltage data corresponding to the boundaries of these SOC ranges. These voltage data points provide the voltage characteristics of the cell within the lithium plating risk range. Based on this data, the system determines the lithium plating risk boundary voltage; that is, within the lithium plating risk SOC range, a voltage exceeding this boundary value indicates a high risk of lithium plating. These boundary voltage values will serve as key parameters for the BMS system to monitor and protect the cell.
  • this application's method significantly improves the accuracy of lithium plating risk assessment by identifying lithium plating intervals and corresponding risk SOC intervals and boundary voltages.
  • Real-time identification and analysis of cell behavior under actual operating conditions enables the battery management system to more effectively prevent lithium metal plating, enhancing battery safety and reliability.
  • Precise determination of risk intervals and boundary voltages helps the BMS system detect potential risks in their early stages, allowing for timely protective measures, thereby extending battery life and improving the overall performance of the battery system.
  • This embodiment first determines the current pulse charge/discharge condition based on the pulse charge/discharge request; then, it acquires the SOC and voltage datasets of the vehicle-side battery cell under the current pulse charge/discharge condition; if the SOC dataset contains SOC data within the lithium plating risk SOC range, it then acquires the corresponding voltage data; if the voltage data exceeds the lithium plating risk boundary voltage, it determines that the vehicle-side battery cell has a high lithium plating risk.
  • This embodiment provides a comprehensive and accurate data foundation by acquiring SOC and voltage data in real time, improves the targeting of the analysis by identifying data within the lithium plating risk SOC range, and accurately determines high lithium plating risk by comparing actual voltage data with the risk boundary voltage, thus improving the accuracy of lithium plating risk assessment for vehicle-side battery cells.
  • FIG. 3 is a schematic diagram of a sub-process of the third embodiment of the lithium plating risk assessment method for vehicle-side battery cells of this application.
  • step S3 the method further includes:
  • S3a If there is no SOC data within the lithium plating risk SOC range in the SOC dataset, determine whether there is voltage data exceeding the lithium plating risk boundary voltage in the voltage dataset;
  • S3c If the SOC dataset contains SOC data within the lithium plating risk SOC range, determine whether the voltage data corresponding to the SOC data exceeds the lithium plating risk boundary voltage.
  • Low lithium plating risk refers to the risk state when the battery voltage exceeds the lithium plating risk threshold voltage, but the state of charge (SOC) does not fall within the lithium plating risk SOC range.
  • Medium lithium plating risk refers to the risk state when the battery SOC data falls within the lithium plating risk SOC range, but the voltage data does not exceed the lithium plating risk threshold voltage.
  • the BMS system will further analyze the voltage dataset to determine if any voltage data exceeds the lithium plating risk boundary voltage. Specifically, the BMS will check all voltage data points one by one, comparing them to preset lithium plating risk boundary voltages. If any voltage data point exceeds these boundary values, it indicates that the cell may be in a low-lithium plating risk state. Although the SOC does not show a high risk, voltage anomalies indicate a potential lithium plating risk.
  • the BMS system further determines whether the voltage data corresponding to these SOC data exceeds the lithium plating risk boundary voltage. Specifically, the system extracts the voltage value corresponding to each high-risk SOC data point and compares these voltage values with the boundary voltage. If these voltage data do not exceed the boundary value, although the SOC shows some risk, the BMS will determine that the cell is in a medium lithium plating risk state because the voltage does not show any abnormality. This determination method comprehensively considers both SOC and voltage parameters to ensure the accuracy of risk assessment.
  • the BMS system will determine that the cell has a low lithium plating risk. This means that although the current state of charge is relatively safe, voltage anomalies indicate a potential lithium plating risk. If data points in the SOC dataset fall within the lithium plating risk SOC range, but the corresponding voltage data for these SOC data points does not exceed the lithium plating risk boundary voltage, the BMS system will determine that the cell has a medium lithium plating risk. This indicates that the cell does not exhibit voltage anomalies under a high-risk state of charge, but potential lithium plating risks still need to be considered.
  • Determining low lithium plating risk ensures that voltage anomalies can be identified promptly, even when SOC data is normal, preventing potential risks.
  • Determining medium lithium plating risk leverages the high sensitivity of SOC data to detect potential risks in a timely manner, even if voltage data does not exceed the limits. By combining these criteria, the BMS can accurately identify the risk status of cells under different operating conditions, providing more effective prevention and protection measures, and improving the safety and reliability of the battery system.
  • step S4 the method further includes:
  • S2e Adjust the pulse charge/discharge request based on the current health status.
  • Expansion force test data refers to the set of data measuring the forces generated by the volume change of the battery cell during charging and discharging. These data reflect the physical changes of the battery cell during charge-discharge cycles and are closely related to the cell's health and lifespan.
  • the expansion force variation curve generated based on the expansion force test data, shows the trend of expansion force variation of the battery cell under different charge-discharge states.
  • the preset lifespan prediction model is a model built based on historical data and battery cell characteristics, used to predict the cycle life of the battery cell. The model can combine data such as expansion force, SOC, and voltage for lifespan prediction.
  • Cycle life refers to the number of charge-discharge cycles a battery cell can undergo while maintaining a certain capacity and performance.
  • Current health status refers to a comprehensive assessment of the current operating state and health level of the battery cell, combining cycle life, SOC data, and voltage data.
  • the BMS system monitors the changes in expansion force of the cells in real time during charge-discharge cycles, collecting expansion force test data.
  • This data records the volume changes of the battery cells under different SOC and voltage conditions.
  • the BMS preprocesses the expansion force test data, including noise removal, outlier correction, and data smoothing. Based on the preprocessed data, the BMS generates an expansion force variation curve.
  • This curve shows the trend of expansion force changes in the battery cells during different charge-discharge cycles, serving as an important basis for assessing the health status of the battery cells and predicting their lifespan. The curve can reveal the physical changes of the battery cells under different states of charge and voltage conditions.
  • the BMS inputs the expansion force variation curve into a pre-defined lifespan prediction model.
  • This model combines the expansion force variation trend, historical data, and cell characteristics to predict the cell's cycle life.
  • the BMS assesses the cell's current health status. This assessment includes key parameters such as the cell's remaining lifespan, current capacity, and internal resistance variation, comprehensively reflecting the cell's operating status and health level.
  • the model can estimate the number of cycles the cell can continue to operate under certain performance conditions.
  • the BMS dynamically adjusts the pulse charge/discharge requests. Specific adjustments may include reducing the charge/discharge current, adjusting the pulse duration and interval, or even changing the charge/discharge strategy to optimize cell usage conditions and extend their lifespan. Through these adjustments, the BMS can ensure that the cells operate in optimal condition, improving the overall performance and safety of the battery system.
  • the cycle life of the cells can be accurately predicted. This allows for a comprehensive assessment of the current health status of the cells and adjustments to pulse charge/discharge requests accordingly.
  • This dynamic adjustment mechanism not only improves the intelligence level of the battery management system but also significantly extends battery life and enhances the safety and reliability of the battery system. Precise health status assessment and charge/discharge strategy optimization enable the battery to maintain optimal performance under different operating conditions, reducing maintenance costs and enhancing the user experience.
  • step S4 the following is further included:
  • S4a Determine whether the temperature information and environmental information of the vehicle-side battery cell meet the preset safety standards
  • S4c Based on the frequency and the duration, issue a high lithium plating risk alarm and adjust the pulse charge/discharge request.
  • Temperature information includes the internal temperature data of the battery cell, while environmental information includes data on the ambient temperature, humidity, and air pressure surrounding the battery cell.
  • Preset safety standards refer to the safe operating ranges set in the Battery Management System (BMS), including the upper and lower limits of the battery cell temperature and the ambient temperature range, ensuring the battery cell operates under safe conditions.
  • the frequency and duration of voltage data refer to the frequency of recorded abnormal voltage data and the duration of each abnormality when the battery cell voltage exceeds the safety boundary.
  • the high lithium plating risk alarm is an alarm signal issued by the BMS when a high lithium plating risk is detected in the battery cell, notifying the user or system to take necessary protective measures.
  • the BMS continuously monitors the temperature of the battery cells and the surrounding environment.
  • This data includes the internal temperature of the battery cells, ambient temperature, humidity, and air pressure.
  • the system compares this real-time monitoring data with preset safety standards. If all monitored data are within the safety standard range, it indicates that the current operating environment of the battery cells is safe, and subsequent testing and analysis can continue. If any data exceeds the safety standard, the system will immediately take protective measures to prevent the battery cells from operating under unsafe conditions.
  • the BMS will further analyze the voltage data. Specifically, the system will check for outliers in the voltage data set and record the frequency of these abnormal voltage data occurrences and the duration of each anomaly. This information on frequency and duration helps the system assess the severity and possible causes of cell voltage anomalies.
  • the frequency and duration data will serve as important reference indicators for determining whether a cell has a high risk of lithium plating.
  • the BMS will determine that the cell has a high lithium plating risk.
  • the system will immediately issue a high lithium plating risk alarm, notifying the user or system administrator to take necessary protective measures.
  • the BMS will dynamically adjust the pulse charge/discharge requests based on the current risk assessment results. For example, the system may reduce the charge/discharge current, shorten the pulse duration, or increase the pulse interval to reduce the cell's lithium plating risk and ensure the battery operates within a safe range.
  • the risk of high lithium plating in the battery cells can be accurately assessed.
  • the real-time risk assessment and alarm system enables the BMS to take timely protective measures to prevent the battery cells from operating under high-risk conditions.
  • dynamically adjusting pulse charge and discharge requests based on detailed frequency and duration data helps optimize the battery's charge and discharge strategy, extend battery life, and improve system safety and reliability.
  • This embodiment first determines the current pulse charge/discharge condition based on the pulse charge/discharge request; then, it acquires the SOC and voltage datasets of the vehicle-side battery cell under the current pulse charge/discharge condition; if the SOC dataset contains SOC data within the lithium plating risk SOC range, it then acquires the corresponding voltage data; if the voltage data exceeds the lithium plating risk boundary voltage, it determines that the vehicle-side battery cell has a high lithium plating risk.
  • This embodiment provides a comprehensive and accurate data foundation by acquiring SOC and voltage data in real time, improves the targeting of the analysis by identifying data within the lithium plating risk SOC range, and accurately determines high lithium plating risk by comparing actual voltage data with the risk boundary voltage, thus improving the accuracy of lithium plating risk assessment for vehicle-side battery cells.
  • This application embodiment also provides a lithium plating risk assessment device for vehicle-side battery cells.
  • Figure 4 is a schematic diagram of the module structure of the lithium plating risk assessment device for vehicle-side battery cells according to this application embodiment.
  • the lithium plating risk assessment device for vehicle-side battery cells includes:
  • the operating condition determination module 401 is used to determine the current pulse charge/discharge operating condition based on the pulse charge/discharge request.
  • Data acquisition module 402 is used to acquire the SOC dataset and voltage dataset of the vehicle-side battery cell under the current pulse charge and discharge condition;
  • Voltage acquisition module 403 is used to acquire voltage data corresponding to the SOC data if there is SOC data in the SOC dataset within the SOC range with lithium plating risk.
  • the risk determination module 404 is used to determine that the vehicle-end battery cell has a high risk of lithium plating if the voltage data exceeds the lithium plating risk boundary voltage.
  • the lithium plating risk assessment device for vehicle-side battery cells provided in this application adopts the lithium plating risk assessment method for vehicle-side battery cells in the above embodiments, and can solve the technical problem of how to improve the accuracy of lithium plating risk assessment for vehicle-side battery cells.
  • the beneficial effects of the lithium plating risk assessment device for vehicle-side battery cells provided in this application are the same as the beneficial effects of the lithium plating risk assessment method for vehicle-side battery cells provided in the above embodiments, and other technical features in the lithium plating risk assessment device for vehicle-side battery cells are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.
  • This application provides a lithium plating risk assessment device for vehicle-mounted battery cells.
  • the device includes: at least one processor; and a memory communicatively connected to the at least one processor.
  • the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the lithium plating risk assessment method for vehicle-mounted battery cells described in the above embodiments.
  • the lithium plating risk assessment device for vehicle-mounted battery cells in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers.
  • the lithium plating risk assessment device for vehicle-mounted battery cells shown in Figure 5 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
  • the lithium plating risk assessment device for vehicle-side battery cells may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which 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 into a random access memory (RAM) 1004.
  • the RAM 1004 also stores various programs and data required for the operation of the lithium plating risk assessment device for vehicle-side battery cells.
  • the processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005.
  • An input/output (I/O) interface 1006 is also connected to the bus.
  • I/O interface 1006 input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009.
  • Communication device 1009 allows the lithium plating risk assessment device for vehicle-side battery cells to communicate wirelessly or wiredly with other devices to exchange data. Although a lithium plating risk assessment device for vehicle-side battery cells with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.
  • embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts.
  • the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002.
  • processing device 1001 it performs the functions defined in the methods of the embodiments disclosed in this application.
  • the lithium plating risk assessment device for vehicle-mounted battery cells provided in this application employing the lithium plating risk assessment method for vehicle-mounted battery cells described in the above embodiments, can solve the technical problem of how to improve the accuracy of lithium plating risk assessment for vehicle-mounted battery cells.
  • the beneficial effects of the lithium plating risk assessment device for vehicle-mounted battery cells provided in this application are the same as those of the lithium plating risk assessment method for vehicle-mounted battery cells provided in the above embodiments, and other technical features in this lithium plating risk assessment device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.
  • This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the lithium plating risk assessment method for vehicle-side battery cells in the above embodiments.
  • computer-readable program instructions i.e., a computer program
  • the computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
  • the computer-readable storage medium may 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 may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
  • the aforementioned computer-readable storage medium may be included in the lithium plating risk assessment device for the vehicle-side battery cell; or it may exist independently and not be installed in the lithium plating risk assessment device for the vehicle-side battery cell.
  • the aforementioned computer-readable storage medium carries one or more programs.
  • the device causes the vehicle-side battery cell to: determine the current pulse charge/discharge condition based on the pulse charge/discharge request; acquire the SOC dataset and voltage dataset of the vehicle-side battery cell under the current pulse charge/discharge condition; if the SOC dataset contains SOC data within the lithium plating risk SOC range, acquire the voltage data corresponding to the SOC data; if the voltage data exceeds the lithium plating risk boundary voltage, determine that the vehicle-side battery cell has a high lithium plating risk.
  • Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof.
  • These programming languages include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as C or similar programming languages.
  • the program code can be executed entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server.
  • 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 it can be connected to an external computer (e.g., via the Internet using an Internet service provider).
  • LAN local area network
  • WAN wide area network
  • each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function.
  • the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.
  • each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
  • the modules described in the embodiments of this application can be implemented in software or hardware.
  • the names of the modules do not necessarily limit the functionality of the unit itself.
  • the readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for determining the lithium plating risk of vehicle-side battery cells. This solves the technical problem of how to improve the accuracy of determining the lithium plating risk of vehicle-side battery cells. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the lithium plating risk determination method for vehicle-side battery cells provided in the above embodiments, and will not be elaborated upon here.
  • This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the lithium plating risk assessment method for vehicle-side battery cells as described above.
  • the computer program product provided in this application can solve the technical problem of how to improve the accuracy of lithium plating risk assessment for vehicle-side battery cells.
  • the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the lithium plating risk assessment method for vehicle-side battery cells provided in the above embodiments, and will not be repeated here.

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Abstract

本申请涉及一种车端电芯的析锂风险判断方法、装置、设备、存储介质及产品。该方法先根据脉冲充放电请求，确定当前脉冲充放电工况；然后获取车端电芯在当前脉冲充放电工况下的SOC数据集与电压数据集；若SOC数据集中存在析锂风险SOC区间内的SOC数据，再获取SOC数据对应的电压数据；若电压数据超出析锂风险边界电压，判定车端电芯存在高析锂风险。

Description

车端电芯的析锂风险判断方法、装置、设备、存储介质及产品
相关申请
本申请要求于2024年7月3日申请的、申请号为202410887397.4的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及电芯技术领域，尤其涉及一种车端电芯的析锂风险判断方法、装置、设备、存储介质及产品。
 背景技术
锂离子电池在电动汽车、混合动力汽车及其他储能系统中得到了广泛应用。在低温、高倍率充放电等极端工况下，锂离子电池容易发生析锂现象，即锂离子在负极表面形成金属锂。这种现象会导致电池容量衰减、内阻增大，甚至引发安全隐患如短路、热失控等。因此，准确判定和防范电池析锂风险非常重要。现有的析锂风险判定要么由于实验室环境测试结果与实际应用场景存在较大差异，无法准确反映电池在实际工况下的状态，要么缺乏实时监控和动态分析机制，无法全面捕捉电池在不同工况下的实时数据，或者判定方法简单，未能结合多参数综合分析，导致判定结果精准度不足。
因此，如何提高车端电芯析锂风险判定的精准度成为一个亟待解决的技术问题。
上述内容仅用于辅助理解本申请的技术方案，并不代表承认上述内容是现有技术。
 发明内容
本申请的主要目的在于提供了一种车端电芯的析锂风险判断方法、装置、设备、存储介质及产品，旨在解决如何提高车端电芯析锂风险判定的精准度的技术问题。
为实现上述目的，本申请提供了一种车端电芯的析锂风险判断方法，所述方法包括以下步骤：
根据脉冲充放电请求，确定当前脉冲充放电工况；
获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集；
若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据；
若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险。
在一实施例中，在所述根据脉冲充放电请求，确定当前脉冲充放电工况的步骤之前，所述方法还包括：
基于预设脉冲充放电参数，对所述车端电芯进行充放电循环测试，得到测试SOC数据集与测试电压数据集；
对所述测试SOC数据集与所述测试电压数据集进行预处理，并基于预处理后的所述测试SOC数据集与预处理后的所述测试电压数据集，生成电压与SOC变化曲线；
根据所述电压与SOC变化曲线，确定所述析锂风险SOC区间与所述析锂风险边界电压。
在一实施例中，所述根据所述电压与SOC变化曲线，确定所述析锂风险SOC区间与所述析锂风险边界电压的步骤，包括：
提取所述电压与SOC变化曲线中出现电压突变或电压异常波动的区间，作为析锂区间；
根据所述析锂区间边界对应的测试SOC数据，确定所述析锂风险SOC区间；
根据所述析锂风险SOC区间的边界对应的测试电压数据，确定所述析锂风险边界电压。
在一实施例中，在所述若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据的步骤之后，所述方法还包括：
若所述SOC数据集中不存在析锂风险SOC区间内的SOC数据，判断所述电压数据集中是否存在超出所述析锂风险边界电压的电压数据；
若存在，判定所述车端电芯存在低析锂风险；
若所述SOC数据集中存在所述析锂风险SOC区间内的SOC数据，判断所述SOC数据对应的电压数据是否超出所述析锂风险边界电压；
若不超出，判定所述车端电芯存在中析锂风险。
在一实施例中，在所述获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集的步骤之后，所述方法还包括：
获取所述车端电芯的膨胀力测试数据，并对所述膨胀力测试数据进行所述预处理；
根据预处理后的膨胀力测试数据，得到膨胀力变化曲线；
基于所述膨胀力变化曲线与预设寿命预测模型，预测所述车端电芯的循环寿命；
根据所述循环寿命、所述SOC数据集以及所述电压数据集，得到所述车端电芯的当前健康状态；
基于所述当前健康状态，调整所述脉冲充放电请求。
在一实施例中，在所述若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险的步骤之后，所述方法还包括：
判断所述车端电芯的温度信息与环境信息是否满足预设安全标准；
若是，获取所述电压数据对应的频率与持续时间；
基于所述频率与所述持续时间，发出高析锂风险警报，并调整所述脉冲充放电请求。
此外，为实现上述目的，本申请还提出一种车端电芯的析锂风险判断装置，所述车端电芯的析锂风险判断装置包括：
工况确定模块，用于根据脉冲充放电请求，确定当前脉冲充放电工况；
数据获取模块，用于获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集；
电压获取模块，用于若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据；
风险判定模块，用于若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险。
此外，为实现上述目的，本申请还提出一种车端电芯的析锂风险判断设备，所述设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的车端电芯的析锂风险判断程序，所述车端电芯的析锂风险判断程序配置为实现如上文所述的车端电芯的析锂风险判断方法的步骤。
此外，为实现上述目的，本申请还提出一种存储介质，所述存储介质上存储有车端电芯的析锂风险判断程序，所述车端电芯的析锂风险判断程序被处理器执行时实现如上文所述的车端电芯的析锂风险判断方法的步骤。
此外，为实现上述目的，本申请还提出一种计算机程序产品，所述计算机程序产品包括计算机程序，所述计算机程序被处理器执行时实现如上文所述的车端电芯的析锂风险判断方法的步骤。
本申请先根据脉冲充放电请求，确定当前脉冲充放电工况；然后获取车端电芯在当前脉冲充放电工况下的SOC数据集与电压数据集；若SOC数据集中存在析锂风险SOC区间内的SOC数据，再获取SOC数据对应的电压数据；若电压数据超出析锂风险边界电压，判定车端电芯存在高析锂风险。本申请通过实时获取SOC和电压数据，提供全面、准确的数据基础，通过识别析锂风险SOC区间内的数据，提高分析的针对性，通过对比实际电压数据和风险边界电压，精准判定高析锂风险，提高了车端电芯析锂风险判定的精准度。
 附图说明
图1为本申请车端电芯的析锂风险判断方法第一实施例的流程示意图；
图2为本申请车端电芯的析锂风险判断方法第二实施例中一子流程示意图；
图3为本申请车端电芯的析锂风险判断方法第三实施例中一子流程示意图；
图4为本申请实施例车端电芯的析锂风险判断装置的模块结构示意图；
图5为本申请实施例中车端电芯的析锂风险判断方法涉及的硬件运行环境的设备结构示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
 本发明的实施方式
应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
为了更好的理解本申请的技术方案，下面将结合说明书附图以及具体的实施方式进行详细的说明。
锂离子电池在电动汽车、混合动力汽车及其他储能系统中得到了广泛应用。在低温、高倍率充放电等极端工况下，锂离子电池容易发生析锂现象，即锂离子在负极表面形成金属锂。这种现象会导致电池容量衰减、内阻增大，甚至引发安全隐患如短路、热失控等。因此，准确判定和防范电池析锂风险非常重要。现有的析锂风险判定要么由于实验室环境测试结果与实际应用场景存在较大差异，无法准确反映电池在实际工况下的状态，要么缺乏实时监控和动态分析机制，无法全面捕捉电池在不同工况下的实时数据，或者判定方法简单，未能结合多参数综合分析，导致判定结果精准度不足。因此，如何提高车端电芯析锂风险判定的精准度成为一个亟待解决的技术问题。
本申请的主要解决方案是：先根据脉冲充放电请求，确定当前脉冲充放电工况；然后获取车端电芯在当前脉冲充放电工况下的SOC数据集与电压数据集；若SOC数据集中存在析锂风险SOC区间内的SOC数据，再获取SOC数据对应的电压数据；若电压数据超出析锂风险边界电压，判定车端电芯存在高析锂风险。
本申请通过实时获取SOC和电压数据，提供全面、准确的数据基础，通过识别析锂风险SOC区间内的数据，提高分析的针对性，通过对比实际电压数据和风险边界电压，精准判定高析锂风险，提高了车端电芯析锂风险判定的精准度。
本实施例方法的执行主体可以是具有数据处理、网络通信以及程序运行功能的计算服务设备，也可以是具有相同或相似功能的上述车端电芯的析锂风险判断设备。本实施例及下述各实施例将以车端电芯的析锂风险判断设备为例进行说明。
基于此，提出本申请车端电芯的析锂风险判断方法的第一实施例，请参照图1，图1为本申请车端电芯的析锂风险判断方法第一实施例的流程示意图。
在本实施例中，该车端电芯的析锂风险判断方法包括以下步骤：
S1：根据脉冲充放电请求，确定当前脉冲充放电工况。
脉冲充放电请求是电池管理系统（BMS）向充电或放电设备发出的控制信号，旨在通过间歇性的充电或放电操作优化电池性能。该请求通常包含具体的充电和放电参数，包括电流幅值、脉冲持续时间和间隔时间等。当前脉冲充放电工况指电池在接收到脉冲充放电请求后的实际操作条件。这些条件包括脉冲电流的幅值、持续时间、间隔时间等参数，反映电池在具体时间段内的充放电状态。车端电芯的析锂风险判断设备中包括电池管理系统。
具体的，当BMS接收到脉冲充放电请求时，系统会解析该请求中的各项参数，例如脉冲电流的幅值、持续时间和间隔时间。随后，BMS会结合当前电池的实时状态（包括SOC、电压、温度等参数），动态调整和确定具体的脉冲充放电工况。例如，如果电池当前温度较低，BMS可能会适当调整脉冲充电电流的幅值和持续时间，以避免过度充放电对电池造成损害。
进一步的，在确定当前脉冲充放电工况的过程中，BMS会启动数据采集系统，实时监测电池在该工况下的SOC和电压变化。这些实时数据不仅用于验证和调整充放电工况，还为后续的电池状态评估和风险判定提供了基础。BMS通过持续监控和记录电池在不同脉冲条件下的表现，确保每个脉冲周期内的操作都在安全范围内进行，从而优化电池性能并延长其使用寿命。
通过根据脉冲充放电请求确定当前脉冲充放电工况，BMS能够灵活调整电池的充放电操作，以适应实际运行条件。这种动态调整不仅提高了数据采集的准确性和相关性，还确保了电池在不同工况下的安全和稳定运行。实时监测和调整的机制，使得BMS能够更有效地管理电池的充放电过程，优化电池性能，减少析锂风险，提高电池系统的整体安全性和使用寿命。
S2：获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集。
电压数据集指的是在当前脉冲充放电工况下，不同时刻电芯的电压数据集合。电压是电池健康状态的重要参数之一，反映了电池在不同充放电状态下的工作情况。SOC（State of Charge）数据集指在当前脉冲充放电工况下，不同时刻电芯的荷电状态数据集合。SOC是衡量电池剩余电量的重要指标，通常以百分比表示。
具体的，在确定当前脉冲充放电工况后，BMS启动实时数据采集系统，对车端电芯进行全面的状态监测。首先，通过安装在电芯上的高精度传感器，BMS持续采集电芯在每个脉冲周期内的SOC和电压数据。SOC数据集反映了电芯在不同时间点的荷电状态，记录了电芯在整个脉冲充放电过程中的能量变化情况。电压数据集则记录了电芯在这些时间点的电压变化，提供了电芯在不同SOC下的电压特征。
进一步的，为了确保数据的准确性和完整性，BMS会将采集到的SOC和电压数据进行实时处理和存储。通过高速数据处理模块，BMS能够快速过滤和校准数据，去除噪声和异常值，确保数据的可靠性。存储的数据包括每个采样时刻的时间戳、SOC值和电压值，形成完整的SOC和电压数据集。这些数据不仅用于实时监控电芯状态，还为后续的电芯析锂风险分析和健康状态评估提供了重要依据。
通过获取车端电芯在当前脉冲充放电工况下的SOC数据集和电压数据集，BMS能够全面了解电芯在实际工作条件下的运行状态。实时采集和处理数据，提高了电池状态监测的精度和可靠性，为进一步的风险判定和优化管理提供了坚实的数据基础。这种精准的数据采集和分析能力，有助于及时发现电芯的异常状态，预防潜在的安全隐患，延长电池的使用寿命，提升整个电池系统的性能和安全性。
S3：若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据。
析锂风险SOC区间指电池在特定荷电状态（SOC）范围内更容易发生锂金属析出的区域。在这些区域内，电池的电化学反应可能导致锂金属在负极表面沉积，进而引发安全隐患。SOC数据对应的电压数据指在特定SOC值下，电池对应的电压值。这些数据用于分析电池在析锂风险区间内的电压行为，帮助判定是否存在析锂风险。
具体的，在BMS实时监测并获取SOC和电压数据集后，系统会对SOC数据进行分析，以识别其中是否存在落入析锂风险SOC区间的数据点。BMS首先会将实时获取的SOC数据与预设的析锂风险SOC区间进行对比，筛选出位于这些风险区间内的SOC数据点。这些数据点代表电池当前处于较高析锂风险的荷电状态，需要进行进一步的分析。
进一步的，一旦识别出位于析锂风险SOC区间内的SOC数据点，BMS会提取这些SOC数据点对应的电压数据。具体而言，BMS通过时间戳将这些SOC数据点与存储的电压数据集进行匹配，找到每个SOC数据点在特定时间点对应的电压值。通过这种方式，BMS能够获取一组详细的电压数据，这些数据与识别出的高风险SOC数据点相关联。此步骤确保BMS可以准确分析电池在析锂风险区间内的电压表现，进而判断电池是否存在实际的析锂风险。
通过在SOC数据集中识别析锂风险SOC区间内的数据点，并获取这些SOC数据对应的电压数据，BMS能够深入分析电池在高风险状态下的电压行为。这一过程提高了析锂风险判定的精准度，因为它结合了电池的荷电状态和电压表现，从多维度进行分析。精准的数据提取和分析有助于及时发现潜在的析锂现象，采取预防措施，确保电池的安全性和可靠性。此外，这种方法还能为后续的电池状态优化和管理提供关键数据支持，进一步提升电池系统的性能和使用寿命。
S4：若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险。
析锂风险边界电压是预设的电压临界值范围，电池电压超出该范围时，表明电池存在较高的锂金属析出风险。这些边界电压值是通过实验和数据分析确定的，用于在电池管理系统中进行风险判定。高析锂风险指电池在充放电过程中由于锂金属在负极表面沉积而导致的高风险状态，这种状态可能引发电池性能衰减、内阻增大，甚至引起安全隐患如短路和热失控。
具体的，在获取了SOC数据对应的电压数据后，BMS会对这些电压数据进行进一步分析，以判定电池是否处于高析锂风险状态。具体而言，BMS会将提取到的电压数据与预设的析锂风险边界电压进行比较。若发现某些电压数据超出了这些边界电压范围，则表示电池可能存在较高的锂金属析出风险。
在确认电压数据超出析锂风险边界电压后，BMS将判定车端电芯存在高析锂风险。此时，BMS不仅会记录这些超出边界的数据点，还会进行综合分析，确认这些数据的可靠性，并考虑是否受到其他因素（如温度、电流波动等）的影响。通过这种全面的分析，BMS能够准确识别高风险状态，避免误判。在最终确认高析锂风险后，BMS会立即采取相应的保护措施，如降低充放电电流、调整充放电策略，甚至停止充放电操作，以确保电池的安全性。
通过将电压数据与析锂风险边界电压进行比较，并判定高析锂风险，BMS显著提高了析锂风险判定的精准度。这个过程确保了电池在不同工作条件下的安全性，能够及时发现和防范潜在的锂金属析出风险。高精度的风险判定和及时的保护措施，不仅延长了电池的使用寿命，还提升了整体系统的安全性能和可靠性。借助这种方法，电池管理系统能够动态适应实际工况，提供更为安全和高效的电池管理解决方案。
本实施例先根据脉冲充放电请求，确定当前脉冲充放电工况；然后获取车端电芯在当前脉冲充放电工况下的SOC数据集与电压数据集；若SOC数据集中存在析锂风险SOC区间内的SOC数据，再获取SOC数据对应的电压数据；若电压数据超出析锂风险边界电压，判定车端电芯存在高析锂风险。本实施例通过实时获取SOC和电压数据，提供全面、准确的数据基础，通过识别析锂风险SOC区间内的数据，提高分析的针对性，通过对比实际电压数据和风险边界电压，精准判定高析锂风险，提高了车端电芯析锂风险判定的精准度。
基于上述第一实施例，提出本申请车端电芯的析锂风险判断方法的第二实施例。请参阅图2，图2为本申请车端电芯的析锂风险判断方法第二实施例中一子流程示意图。
如图2所示，在本实施例中，在步骤S1之前，所述方法还包括：
S1a：基于预设脉冲充放电参数，对所述车端电芯进行充放电循环测试，得到测试SOC数据集与测试电压数据集；
S1b：对所述测试SOC数据集与所述测试电压数据集进行预处理，并基于预处理后的所述测试SOC数据集与预处理后的所述测试电压数据集，生成电压与SOC变化曲线；
S1c：根据所述电压与SOC变化曲线，确定所述析锂风险SOC区间与所述析锂风险边界电压。
预设脉冲充放电参数指在电池管理系统（BMS）中预先设定的脉冲充放电条件，包括脉冲电流幅值、持续时间、间隔时间等参数，用于标准化充放电测试过程。充放电循环测试指在控制环境下，依据预设的脉冲充放电参数，对电芯进行多次充电和放电的操作，以模拟实际使用条件并采集相关数据。测试SOC数据集是在充放电循环测试过程中采集的电芯荷电状态数据集合。测试电压数据集是在充放电循环测试过程中采集的电芯电压数据集合。电压与SOC变化曲线是通过对预处理后的测试SOC数据集和测试电压数据集进行拟合，得到的描述电芯电压随荷电状态变化的曲线。析锂风险SOC区间为在电压与SOC变化曲线中，特定的SOC范围内电芯易于发生锂金属析出的区间。析锂风险边界电压为在析锂风险SOC区间内，对应的电压临界值，超出该电压范围表示存在析锂风险。
具体的，根据预设的脉冲充放电参数，对车端电芯进行系统的充放电循环测试。此过程模拟电芯在实际使用中的各种工况，通过多次充放电操作，采集全面的电芯性能数据。测试过程中，实时监测和记录电芯的SOC和电压变化，形成详细的测试SOC数据集和测试电压数据集。
进一步的，采集到的数据需要进行预处理，以确保其准确性和可靠性。预处理步骤包括去除噪声、异常值校正和数据平滑处理。经过预处理后的测试SOC数据集和测试电压数据集被用来生成电压与SOC变化曲线。此变化曲线反映了电芯在不同SOC下的电压特征，是判定析锂风险的重要依据。根据生成的电压与SOC变化曲线，分析并确定析锂风险SOC区间和对应的析锂风险边界电压。在这些区间内，电压曲线的突变或异常波动提示电芯可能发生锂金属析出。边界电压是关键的判定标准，超出此范围的电压意味着电芯处于高析锂风险状态。
通过基于预设脉冲充放电参数进行充放电循环测试，并生成电压与SOC变化曲线，本申请能够准确地确定析锂风险SOC区间和边界电压。此方法不仅提高了电池管理系统在实际工作条件下的风险判定精准度，还提供了详细的电芯性能数据，为进一步优化电池管理策略提供了科学依据。通过这些步骤，能够有效预防电芯的析锂现象，提升电池的安全性和使用寿命。
基于上述第一实施例，在本实施例中，步骤S1c包括：
S1c1：提取所述电压与SOC变化曲线中出现电压突变或电压异常波动的区间，作为析锂区间；
S1c2：根据所述析锂区间边界对应的测试SOC数据，确定所述析锂风险SOC区间；
S1c3：根据所述析锂风险SOC区间的边界对应的测试电压数据，确定所述析锂风险边界电压。
电压突变或电压异常波动的区间指在电压与SOC变化曲线中，电压出现急剧变化或不稳定波动的特定区间，这些现象通常预示着电芯内部可能发生了异常反应，如锂金属析出。析锂区间指的是在电压与SOC变化曲线中，由电压突变或异常波动确定的SOC区间，这些区间内电芯更容易发生锂金属析出。
具体的，在生成的电压与SOC变化曲线中，BMS系统通过算法识别电压的突变点和异常波动区域。这些突变或波动通常反映了电芯在特定SOC条件下的异常电化学反应，可能预示着锂金属析出的发生。通过精确识别这些区域，系统将这些区域标记为析锂区间，并记录下相应的SOC范围。根据识别出的析锂区间，BMS系统提取这些区间边界对应的测试SOC数据。这些SOC数据点代表电芯在实际充放电测试过程中，处于高析锂风险的荷电状态。通过分析这些SOC数据点，系统确定析锂风险SOC区间，这些区间是电芯最容易发生锂金属析出的荷电状态范围。
进一步的，在确定了析锂风险SOC区间后，BMS系统进一步分析这些SOC区间边界对应的测试电压数据。这些电压数据点提供了电芯在析锂风险区间的电压特征。系统根据这些数据确定析锂风险边界电压，即在析锂风险SOC区间内，电压超出此边界值即表示存在高析锂风险。这些边界电压值将作为BMS系统监控和保护电芯的关键参数。
通过提取电压与SOC变化曲线中出现电压突变或异常波动的区间，确定析锂区间和相应的风险SOC区间及边界电压，本申请方法显著提高了析锂风险判定的精准度。实时识别和分析电芯在实际工况下的行为，使得电池管理系统能够更有效地预防锂金属析出的发生，增强电池的安全性和可靠性。精确的风险区间和边界电压的确定，帮助BMS系统在早期阶段发现潜在风险，及时采取保护措施，从而延长电池的使用寿命，提升电池系统的整体性能。
本实施例先根据脉冲充放电请求，确定当前脉冲充放电工况；然后获取车端电芯在当前脉冲充放电工况下的SOC数据集与电压数据集；若SOC数据集中存在析锂风险SOC区间内的SOC数据，再获取SOC数据对应的电压数据；若电压数据超出析锂风险边界电压，判定车端电芯存在高析锂风险。本实施例通过实时获取SOC和电压数据，提供全面、准确的数据基础，通过识别析锂风险SOC区间内的数据，提高分析的针对性，通过对比实际电压数据和风险边界电压，精准判定高析锂风险，提高了车端电芯析锂风险判定的精准度。
基于上述第二实施例，提出本申请车端电芯的析锂风险判断方法的第三实施例。请参阅图3，图3为本申请车端电芯的析锂风险判断方法第三实施例中一子流程示意图。
在本实施例中，在步骤S3之后，所述方法还包括：
S3a：若所述SOC数据集中不存在析锂风险SOC区间内的SOC数据，判断所述电压数据集中是否存在超出所述析锂风险边界电压的电压数据；
S3b：若存在，判定所述车端电芯存在低析锂风险；
S3c：若所述SOC数据集中存在所述析锂风险SOC区间内的SOC数据，判断所述SOC数据对应的电压数据是否超出所述析锂风险边界电压；
S3d：若不超出，判定所述车端电芯存在中析锂风险。
低析锂风险指的是电池电压超出析锂风险边界电压，但SOC未落入析锂风险SOC区间时的风险状态。中析锂风险指的是电池SOC数据落入析锂风险SOC区间，但电压数据未超出析锂风险边界电压时的风险状态。
具体的，在获取的SOC数据集中，如果未发现任何数据点落入析锂风险SOC区间，则BMS系统会进一步分析电压数据集中是否存在超出析锂风险边界电压的电压数据。具体来说，BMS会逐一检查所有电压数据点，将其与预设的析锂风险边界电压进行对比。如果存在任何电压数据点超出这些边界值，则表明电芯可能处于低析锂风险状态。尽管SOC未显示出高风险，但电压异常表明存在潜在的析锂风险。
进一步的，在SOC数据集中发现落入析锂风险SOC区间的数据后，BMS系统会进一步判断这些SOC数据对应的电压数据是否超出析锂风险边界电压。具体而言，系统将提取每个高风险SOC数据点对应的电压值，并将这些电压值与边界电压进行对比。如果这些电压数据未超出边界值，尽管SOC显示出一定的风险，但由于电压未表现出异常，BMS将判定电芯处于中析锂风险状态。这种判定方法综合考虑了SOC和电压两个参数，确保风险评估的准确性。
如果SOC数据集中没有任何数据点落入析锂风险SOC区间，但电压数据集中存在超出析锂风险边界电压的电压数据，BMS系统将判定电芯存在低析锂风险。这意味着尽管当前荷电状态相对安全，但电压异常提示潜在的析锂风险。如果SOC数据集中有数据点落入析锂风险SOC区间，但这些SOC数据点对应的电压数据未超出析锂风险边界电压，BMS系统将判定电芯存在中析锂风险。这表明电芯在高风险荷电状态下未表现出电压异常，但仍需警惕潜在的析锂风险。
通过在SOC数据集和电压数据集的双重基础上进行析锂风险判定，能够更全面、准确地评估电芯的析锂风险。低析锂风险的判定确保即使在SOC数据无异常的情况下，电压异常也能被及时识别，防范潜在风险。而中析锂风险的判定则通过SOC数据的高敏感性，及时发现潜在风险，尽管电压数据未超出边界。综合这些判定标准，BMS能够在不同工况下精确识别电芯的风险状态，提供更有效的预防和保护措施，提升电池系统的安全性和可靠性。
基于上述第二实施例，在本实施例中，在步骤S4之后，所述方法还包括：
S2a：获取所述车端电芯的膨胀力测试数据，并对所述膨胀力测试数据进行所述预处理；
S2b：根据预处理后的膨胀力测试数据，得到膨胀力变化曲线；
S2c：基于所述膨胀力变化曲线与预设寿命预测模型，预测所述车端电芯的循环寿命；
S2d：根据所述循环寿命、所述SOC数据集以及所述电压数据集，得到所述车端电芯的当前健康状态；
S2e：基于所述当前健康状态，调整所述脉冲充放电请求。
膨胀力测试数据指在充放电过程中测量的电芯体积变化产生的力的数据集合。这些数据反映了电芯在充放电循环中的物理变化，与电芯的健康状态和寿命密切相关。膨胀力变化曲线基于膨胀力测试数据生成的曲线，显示了电芯在不同充放电状态下的膨胀力变化趋势。预设寿命预测模型是一种基于历史数据和电芯特性建立的模型，用于预测电芯的循环寿命。模型可以结合膨胀力、SOC和电压等数据进行寿命预测。循环寿命指的是电芯在保持一定容量和性能条件下，能够经历的充放电循环次数。当前健康状态指的是结合循环寿命、SOC数据和电压数据，综合评估电芯的当前工作状态和健康水平。
具体的，通过在电芯上安装的膨胀力传感器，BMS系统在充放电循环过程中实时监测电芯的膨胀力变化，采集膨胀力测试数据。这些数据记录了电芯在不同SOC和电压条件下的体积变化。为了确保数据的准确性和一致性，BMS会对膨胀力测试数据进行预处理，包括去除噪声、校正异常值和数据平滑处理。在预处理数据的基础上，BMS生成膨胀力变化曲线。该曲线显示了电芯在不同充放电循环中的膨胀力变化趋势，是评估电芯健康状态和预测寿命的重要依据。曲线可以揭示电芯在不同荷电状态和电压条件下的物理变化规律。
进一步的，BMS将膨胀力变化曲线输入预设的寿命预测模型。该模型结合膨胀力变化趋势、历史数据和电芯特性，预测电芯的循环寿命。通过结合循环寿命预测结果、SOC数据集和电压数据集，BMS评估电芯的当前健康状态。这一评估包括电芯的剩余寿命、当前容量、内阻变化等关键参数，全面反映电芯的工作状态和健康水平过分析电芯在多次充放电循环中的膨胀力变化，模型能够估算电芯在保持一定性能条件下可以继续使用的循环次数。
进一步的，基于当前健康状态评估结果，BMS对脉冲充放电请求进行动态调整。具体调整措施可能包括降低充放电电流、调整脉冲持续时间和间隔，甚至改变充放电策略，以优化电芯的使用条件，延长其寿命。通过这些调整，BMS能够确保电芯在最佳状态下工作，提升电池系统的整体性能和安全性。
通过获取和预处理电芯的膨胀力测试数据，并结合膨胀力变化曲线和预设寿命预测模型，准确预测电芯的循环寿命。能够全面评估电芯的当前健康状态，并基于此调整脉冲充放电请求。这种动态调整机制不仅提高了电池管理系统的智能化水平，还显著延长了电池的使用寿命，提升了电池系统的安全性和可靠性。精准的健康状态评估和充放电策略优化，使电池在不同工况下都能保持最佳性能，减少了维护成本，增强了用户体验。
基于上述第二实施例，在本实施例中，在步骤S4之后，还包括：
S4a：判断所述车端电芯的温度信息与环境信息是否满足预设安全标准；
S4b：若是，获取所述电压数据对应的频率与持续时间；
S4c：基于所述频率与所述持续时间，发出高析锂风险警报，并调整所述脉冲充放电请求。
温度信息包括电芯的内部温度数据，环境信息包括电芯周围的环境温度、湿度、气压等数据。预设安全标准指的是电池管理系统（BMS）中设定的安全操作范围，包括电芯的温度上限和下限、环境温度范围等，确保电芯在安全条件下运行。电压数据对应的频率与持续时间指的是在检测到电芯电压超出安全边界时，记录的电压异常数据出现的频率和每次异常持续的时间。高析锂风险警报指的是当检测到电芯存在高析锂风险时，BMS发出的警报信号，通知用户或系统采取必要的防护措施。
具体的，BMS会持续监测车端电芯的温度信息和周围的环境信息。这些数据包括电芯的内部温度、环境温度、湿度和气压等。系统将这些实时监测的数据与预设的安全标准进行比较。如果所有监测数据均在安全标准范围内，则说明电芯当前的工作环境是安全的，可以继续进行后续的检测和分析。如果有任何数据超出安全标准，则系统将立即采取保护措施，防止电芯在不安全条件下工作。
进一步的，在确认温度信息和环境信息满足安全标准后，BMS将进一步分析电压数据。具体而言，系统会检查电压数据集中是否存在异常值，并记录这些异常电压数据出现的频率和每次异常持续的时间。这些频率和持续时间的信息可以帮助系统评估电芯电压异常的严重程度和可能的原因。频率和持续时间的数据将作为判断电芯是否存在高析锂风险的重要参考指标。
进一步的，根据记录的电压数据对应的频率与持续时间，如果发现电芯存在持续的电压异常，且这些异常电压数据超出预设的析锂风险边界电压，BMS将判定电芯存在高析锂风险。系统会立即发出高析锂风险警报，通知用户或系统管理员采取必要的防护措施。同时，BMS会根据当前的风险评估结果，动态调整脉冲充放电请求。例如，系统可能会降低充放电电流、缩短脉冲持续时间或增加脉冲间隔，以降低电芯的析锂风险，确保电池在安全范围内工作。
通过判断车端电芯的温度信息与环境信息是否满足预设安全标准，并在满足安全标准的前提下，获取电压数据对应的频率与持续时间，能够精准地评估电芯的高析锂风险。实时的风险评估和警报系统，使得BMS能够及时采取防护措施，避免电芯在高风险条件下工作。此外，基于详细的频率和持续时间数据进行动态调整脉冲充放电请求，有助于优化电池的充放电策略，延长电池使用寿命，提升系统的安全性和可靠性。
本实施例先根据脉冲充放电请求，确定当前脉冲充放电工况；然后获取车端电芯在当前脉冲充放电工况下的SOC数据集与电压数据集；若SOC数据集中存在析锂风险SOC区间内的SOC数据，再获取SOC数据对应的电压数据；若电压数据超出析锂风险边界电压，判定车端电芯存在高析锂风险。本实施例通过实时获取SOC和电压数据，提供全面、准确的数据基础，通过识别析锂风险SOC区间内的数据，提高分析的针对性，通过对比实际电压数据和风险边界电压，精准判定高析锂风险，提高了车端电芯析锂风险判定的精准度。
本申请实施例还提供一种车端电芯的析锂风险判断装置，请参照图4，图4为本申请实施例车端电芯的析锂风险判断装置的模块结构示意图，所述车端电芯的析锂风险判断装置包括：
工况确定模块401，用于根据脉冲充放电请求，确定当前脉冲充放电工况；
数据获取模块402，用于获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集；
电压获取模块403，用于若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据；
风险判定模块404，用于若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险。
本申请实施例提供的车端电芯的析锂风险判断装置，采用上述实施例中的车端电芯的析锂风险判断方法，能够解决如何提高车端电芯析锂风险判定的精准度的技术问题。与现有技术相比，本申请实施例提供的车端电芯的析锂风险判断装置的有益效果与上述实施例提供的车端电芯的析锂风险判断方法的有益效果相同，且所述车端电芯的析锂风险判断装置中的其他技术特征与上述实施例方法公开的特征相同，在此不做赘述。
本申请提供一种车端电芯的析锂风险判断设备，车端电芯的析锂风险判断设备包括：至少一个处理器；以及，与至少一个处理器通信连接的存储器；其中，存储器存储有可被至少一个处理器执行的指令，指令被至少一个处理器执行，以使至少一个处理器能够执行上述实施例中的车端电芯的析锂风险判断方法。
下面参考图5，其示出了适于用来实现本申请实施例的车端电芯的析锂风险判断设备的结构示意图。本申请实施例中的车端电芯的析锂风险判断设备可以包括但不限于诸如移动电话、笔记本电脑、数字广播接收器、PDA（Personal Digital Assistant：个人数字助理）、PAD（Portable Application Description：平板电脑）、PMP（Portable Media Player：便携式多媒体播放器）、车载终端（例如车载导航终端）等等的移动终端以及诸如数字TV、台式计算机等等的固定终端。图5示出的车端电芯的析锂风险判断设备仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。
如图5所示，车端电芯的析锂风险判断设备可以包括处理装置1001（例如中央处理器、图形处理器等），其可以根据存储在只读存储器（ROM：Read Only Memory）1002中的程序或者从存储装置1003加载到随机访问存储器（RAM：Random Access Memory）1004中的程序而执行各种适当的动作和处理。在RAM1004中，还存储有车端电芯的析锂风险判断设备操作所需的各种程序和数据。处理装置1001、ROM1002以及RAM1004通过总线1005彼此相连。输入/输出（I/O）接口1006也连接至总线。通常，以下系统可以连接至I/O接口1006：包括例如触摸屏、触摸板、键盘、鼠标、图像传感器、麦克风、加速度计、陀螺仪等的输入装置1007；包括例如液晶显示器（LCD：Liquid Crystal Display）、扬声器、振动器等的输出装置1008；包括例如磁带、硬盘等的存储装置1003；以及通信装置1009。通信装置1009可以允许车端电芯的析锂风险判断设备与其他设备进行无线或有线通信以交换数据。虽然图中示出了具有各种系统的车端电芯的析锂风险判断设备，但是应理解的是，并不要求实施或具备所有示出的系统。可以替代地实施或具备更多或更少的系统。
特别地，根据本申请公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本申请公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置从网络上被下载和安装，或者从存储装置1003被安装，或者从ROM1002被安装。在该计算机程序被处理装置1001执行时，执行本申请公开实施例的方法中限定的上述功能。
本申请提供的车端电芯的析锂风险判断设备，采用上述实施例中的车端电芯的析锂风险判断方法，能解决如何提高车端电芯析锂风险判定的精准度的技术问题。与现有技术相比，本申请提供的车端电芯的析锂风险判断设备的有益效果与上述实施例提供的车端电芯的析锂风险判断方法的有益效果相同，且该车端电芯的析锂风险判断设备中的其他技术特征与上一实施例方法公开的特征相同，在此不做赘述。
本申请公开的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式的描述中，具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。
本申请提供一种计算机可读存储介质，具有存储在其上的计算机可读程序指令（即计算机程序），计算机可读程序指令用于执行上述实施例中的车端电芯的析锂风险判断方法。
本申请提供的计算机可读存储介质例如可以是U盘，但不限于电、磁、光、电磁、红外线、或半导体的系统、系统或器件，或者任意以上的组合。计算机可读存储介质的更具体地例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器（RAM：Random Access Memory）、只读存储器（ROM：Read Only Memory）、可擦式可编程只读存储器（EPROM：Erasable Programmable Read Only Memory或闪存）、光纤、便携式紧凑磁盘只读存储器（CD-ROM：CD-Read Only Memory）、光存储器件、磁存储器件、或者上述的任意合适的组合。在本实施例中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、系统或者器件使用或者与其结合使用。计算机可读存储介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、RF（Radio Frequency：射频）等等，或者上述的任意合适的组合。
上述计算机可读存储介质可以是车端电芯的析锂风险判断设备中所包含的；也可以是单独存在，而未装配入车端电芯的析锂风险判断设备中。
上述计算机可读存储介质承载有一个或者多个程序，当上述一个或者多个程序被车端电芯的析锂风险判断设备执行时，使得车端电芯的析锂风险判断设备：根据脉冲充放电请求，确定当前脉冲充放电工况；获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集；若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据；若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险。可以以一种或多种程序设计语言或其组合来编写用于执行本申请的操作的计算机程序代码，上述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网（LAN：Local Area Network）或广域网（WAN：Wide Area Network）—连接到用户计算机，或者，可以连接到外部计算机（例如利用因特网服务提供商来通过因特网连接）。
附图中的流程图和框图，图示了按照本申请各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本申请实施例中所涉及到的模块可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，模块的名称在某种情况下并不构成对该单元本身的限定。
本申请提供的可读存储介质为计算机可读存储介质，所述计算机可读存储介质存储有用于执行上述车端电芯的析锂风险判断方法的计算机可读程序指令（即计算机程序），能够解决如何提高车端电芯析锂风险判定的精准度的技术问题。与现有技术相比，本申请提供的计算机可读存储介质的有益效果与上述实施例提供的车端电芯的析锂风险判断方法的有益效果相同，在此不做赘述。
本申请实施例提供一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现如上述的车端电芯的析锂风险判断方法的步骤。
本申请提供的计算机程序产品能够解决如何提高车端电芯析锂风险判定的精准度的技术问题。与现有技术相比，本申请实施例提供的计算机程序产品的有益效果与上述实施例提供的车端电芯的析锂风险判断方法的有益效果相同，在此不做赘述。
以上仅为本申请的实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利处理范围内。

Claims (10)

  1. 一种车端电芯的析锂风险判断方法，其中，所述方法包括：
    根据脉冲充放电请求，确定当前脉冲充放电工况；
    获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集；
    若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据；
    若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险。
  2. 如权利要求1所述的方法，其中，在所述根据脉冲充放电请求，确定当前脉冲充放电工况的步骤之前，所述方法还包括：
    基于预设脉冲充放电参数，对所述车端电芯进行充放电循环测试，得到测试SOC数据集与测试电压数据集；
    对所述测试SOC数据集与所述测试电压数据集进行预处理，并基于预处理后的所述测试SOC数据集与预处理后的所述测试电压数据集，生成电压与SOC变化曲线；
    根据所述电压与SOC变化曲线，确定所述析锂风险SOC区间与所述析锂风险边界电压。
  3. 如权利要求2所述的方法，其中，所述根据所述电压与SOC变化曲线，确定所述析锂风险SOC区间与所述析锂风险边界电压的步骤，包括：
    提取所述电压与SOC变化曲线中出现电压突变或电压异常波动的区间，作为析锂区间；
    根据所述析锂区间边界对应的测试SOC数据，确定所述析锂风险SOC区间；
    根据所述析锂风险SOC区间的边界对应的测试电压数据，确定所述析锂风险边界电压。
  4. 如权利要求1所述的方法，其中，在所述若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据的步骤之后，所述方法还包括：
    若所述SOC数据集中不存在析锂风险SOC区间内的SOC数据，判断所述电压数据集中是否存在超出所述析锂风险边界电压的电压数据；
    若存在，判定所述车端电芯存在低析锂风险；
    若所述SOC数据集中存在所述析锂风险SOC区间内的SOC数据，判断所述SOC数据对应的电压数据是否超出所述析锂风险边界电压；
    若不超出，判定所述车端电芯存在中析锂风险。
  5. 如权利要求2所述的方法，其中，在所述获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集的步骤之后，所述方法还包括：
    获取所述车端电芯的膨胀力测试数据，并对所述膨胀力测试数据进行所述预处理；
    根据预处理后的膨胀力测试数据，得到膨胀力变化曲线；
    基于所述膨胀力变化曲线与预设寿命预测模型，预测所述车端电芯的循环寿命；
    根据所述循环寿命、所述SOC数据集以及所述电压数据集，得到所述车端电芯的当前健康状态；
    基于所述当前健康状态，调整所述脉冲充放电请求。
  6. 如权利要求1所述的方法，其中，在所述若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险的步骤之后，所述方法还包括：
    判断所述车端电芯的温度信息与环境信息是否满足预设安全标准；
    若是，获取所述电压数据对应的频率与持续时间；
    基于所述频率与所述持续时间，发出高析锂风险警报，并调整所述脉冲充放电请求。
  7. 一种车端电芯的析锂风险判断装置，其中，所述装置包括：
    工况确定模块，用于根据脉冲充放电请求，确定当前脉冲充放电工况；
    数据获取模块，用于获取所述车端电芯在所述当前脉冲充放电工况下的SOC数据集与电压数据集；
    电压获取模块，用于若所述SOC数据集中存在析锂风险SOC区间内的SOC数据，获取所述SOC数据对应的电压数据；
    风险判定模块，用于若所述电压数据超出所述析锂风险边界电压，判定所述车端电芯存在高析锂风险。
  8. 一种计算机设备，其中，所述设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的车端电芯的析锂风险判断程序，所述车端电芯的析锂风险判断程序配置为实现如权利要求1至6中任一项所述的车端电芯的析锂风险判断方法的步骤。
  9. 一种存储介质，其中，所述存储介质上存储有车端电芯的析锂风险判断程序，所述车端电芯的析锂风险判断程序被处理器执行时实现如权利要求1至6中任一项所述的车端电芯的析锂风险判断方法的步骤。
  10. 一种计算机程序产品，其中，所述计算机程序产品包括计算机程序，所述计算机程序被处理器执行时实现如权利要求1至6中任一项所述的车端电芯的析锂风险判断方法的步骤。
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CN118837757A (zh) * 2024-07-03 2024-10-25 东风汽车集团股份有限公司 车端电芯的析锂风险判断方法、装置、设备、存储介质及产品
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116338478A (zh) * 2023-03-30 2023-06-27 蜂巢能源科技股份有限公司 一种在线检测析锂的方法
CN116455020A (zh) * 2023-03-10 2023-07-18 华为数字能源技术有限公司 电池的管理方法和管理装置
CN116472633A (zh) * 2022-04-12 2023-07-21 东莞新能安科技有限公司 电化学装置管理方法、装置、充电装置、电池系统及介质
CN116754970A (zh) * 2023-05-29 2023-09-15 国联汽车动力电池研究院有限责任公司 锂离子电池低温充电析锂评估方法及相关设备
US20240036116A1 (en) * 2021-05-26 2024-02-01 Lg Energy Solution, Ltd. Battery Monitoring Apparatus and Method
CN117572253A (zh) * 2023-12-05 2024-02-20 瑞浦兰钧能源股份有限公司 锂电池析锂监测方法、装置、存储介质及电子装置
CN118837757A (zh) * 2024-07-03 2024-10-25 东风汽车集团股份有限公司 车端电芯的析锂风险判断方法、装置、设备、存储介质及产品

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112108400B (zh) * 2020-08-07 2022-03-04 合肥国轩高科动力能源有限公司 一种预测软包电池循环性能的测试方法
CN112180260A (zh) * 2020-09-24 2021-01-05 欣旺达电动汽车电池有限公司 电池析锂窗口分析方法、析锂检测方法、设备及存储介质

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20240036116A1 (en) * 2021-05-26 2024-02-01 Lg Energy Solution, Ltd. Battery Monitoring Apparatus and Method
CN116472633A (zh) * 2022-04-12 2023-07-21 东莞新能安科技有限公司 电化学装置管理方法、装置、充电装置、电池系统及介质
CN116455020A (zh) * 2023-03-10 2023-07-18 华为数字能源技术有限公司 电池的管理方法和管理装置
CN116338478A (zh) * 2023-03-30 2023-06-27 蜂巢能源科技股份有限公司 一种在线检测析锂的方法
CN116754970A (zh) * 2023-05-29 2023-09-15 国联汽车动力电池研究院有限责任公司 锂离子电池低温充电析锂评估方法及相关设备
CN117572253A (zh) * 2023-12-05 2024-02-20 瑞浦兰钧能源股份有限公司 锂电池析锂监测方法、装置、存储介质及电子装置
CN118837757A (zh) * 2024-07-03 2024-10-25 东风汽车集团股份有限公司 车端电芯的析锂风险判断方法、装置、设备、存储介质及产品

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