Dynamic diagnosis method, device and equipment of power battery and medium

Abstract

The invention discloses a dynamic diagnosis method, device and equipment of a power battery and a medium, and relates to the technical field of new energy automobile maintenance and detection.The dynamic diagnosis method comprises the steps that first state data, second state data and third state data of the power battery are obtained; calculating the monomer voltage range, the highest monomer voltage, the temperature distribution range, the highest absolute temperature, the direct-current internal resistance, the polarization characteristic and the SOH value of the power battery based on the acquired data; and determining a safety risk index of the power battery based on the calculated parameter value. The first state data of the power battery in the first preset time period before full charging, the second state data of the power battery in the second preset time period after self-load starting and the third state data of the power battery in the full charging and discharging process are obtained from the BMS message, and then based on the first state data, the second state data and the third state data, the power battery in the full charging and discharging process is obtained. According to the invention, fault diagnosis of the power battery can be carried out through the fast charging port without disassembling the battery pack.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle repair and testing technology, and in particular to a dynamic diagnostic method, device, equipment and medium for power batteries. Background Technology

[0002] With the rapid increase in the number of new energy vehicles, the maintenance and state of health (SOH) assessment of power batteries has become a pain point in the industry. Existing technologies suffer from the following main problems: (1) The testing is complicated and risky: Traditional deep testing (such as capacity calibration) often requires disassembling the battery pack (packing), which is complicated and carries the risk of high voltage electric shock, damage to battery sealing and coolant leakage.

[0003] (2) Limitations of static testing: Ordinary testing equipment mostly reads data from the battery management system (BMS) under static conditions, which cannot reflect the true chemical characteristics and capacity decay of the battery under charge and discharge load.

[0004] (3) Data shallowness: Ordinary charging piles or general diagnostic instruments can only obtain data from the on-board diagnostic system (OBD) and cannot read the deep data of individual cells, making it difficult to discover minor "consistency" hidden dangers. Summary of the Invention

[0005] The purpose of this application is to provide a dynamic diagnostic method, device, equipment, and medium for power batteries, so as to achieve accurate diagnosis of power batteries without disassembling the battery pack.

[0006] To achieve the above objectives, this application provides the following solution.

[0007] Firstly, this application provides a dynamic diagnostic method for a power battery, comprising: Acquire first state data, second state data, and third state data of the power battery; the first state data is the state data of the power battery within a first preset time period before full charging, the second state data is the state data of the power battery within a second preset time period after self-load is turned on, and the third state data is the state data of the power battery during the full charging and discharging process. The individual cell voltage range, highest individual cell voltage, temperature distribution range, and highest absolute temperature of the power battery are determined based on the first state data. The DC internal resistance and polarization characteristics of the power battery are determined based on the total voltage in the second state data. The SOH value of the power battery is determined based on the third state data. The safety risk index of the power battery is determined based on the individual cell voltage range, maximum individual cell voltage, temperature distribution range, maximum absolute temperature, DC internal resistance, polarization characteristics, and SOH value, as well as the fault codes and insulation resistance values ​​in the second state data.

[0008] Secondly, this application provides a dynamic diagnostic device for a power battery, including: a main control MCU unit, a power management unit, a protocol control unit, a network communication unit, a measurement and acquisition unit, and a cloud server; The main control MCU unit is connected to the BMS system of the power battery through the protocol control unit; the main control MCU unit is used to obtain the first state data and the second state data of the power battery from the BMS system; The main control MCU unit is also connected to the control terminal of the power management unit, and the power management unit is connected to the power battery. The power management unit is used to charge or discharge the power battery according to the control of the main control MCU unit. The main control MCU unit is also connected to the measurement and acquisition unit. The measurement and acquisition circuit is set in the charging and discharging circuit formed by the power management unit and the power battery. The measurement and acquisition unit is used to collect the third state data of the power battery and send the third state data to the main control MCU unit. The main control MCU unit is also connected to the cloud server through the network communication unit; the main control MCU unit is also used to send the first status data, the second status data and the third status data to the cloud server. The cloud server is used to determine the safety risk index of the power battery using the aforementioned dynamic diagnostic method for power batteries.

[0009] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described dynamic diagnostic method for a power battery.

[0010] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described dynamic diagnostic method for a power battery.

[0011] According to the specific embodiments provided in this application, this application has the following technical effects.

[0012] This application provides a dynamic diagnostic method, apparatus, device, and medium for power batteries. First, it acquires first, second, and third state data of the power battery. Based on the first state data, it determines the individual cell voltage range, maximum individual cell voltage, temperature distribution range, and maximum absolute temperature of the power battery. Based on the total voltage in the second state data, it determines the DC internal resistance and polarization characteristics of the power battery. Based on the third state data, it determines the state of equilibrium (SOH) value of the power battery. Based on the individual cell voltage range, maximum individual cell voltage, temperature distribution range, maximum absolute temperature, DC internal resistance, polarization characteristics, and SOH value of the power battery, as well as the fault codes and insulation resistance values ​​in the second state data, it determines the safety risk index of the power battery. This application obtains first state data of the power battery within a first preset time period before full charging, second state data within a second preset time period after self-load activation, and third state data during the full charge and discharge process from BMS messages. Based on the first, second, and third state data, this application can perform fault diagnosis of the power battery through the fast charging port without disassembling the battery pack. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating a dynamic diagnostic method for a power battery according to an embodiment of this application.

[0015] Figure 2 This is a hardware structure diagram of a dynamic diagnostic device for a power battery provided in an embodiment of this application.

[0016] Figure 3 This is a software configuration diagram of a dynamic diagnostic device for a power battery provided in one embodiment of this application.

[0017] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] This application aims to provide a dynamic diagnostic method, device, equipment, and medium for power batteries, which utilizes a special "charge-discharge side diagnostic" mode to achieve fast charging output using a 220V power supply. The system supports both rapid "8-minute" characteristic assessment and deep non-destructive capacity testing during "full charge and discharge," achieving non-destructive, accurate, and efficient battery status evaluation.

[0021] In one exemplary embodiment, a dynamic diagnostic method for a power battery is provided, such as... Figure 1 As shown, it includes the following steps 101-105.

[0022] Step 101: Obtain the first state data, the second state data, and the third state data of the power battery; the first state data is the state data of the power battery within a first preset time period before full charging, the second state data is the state data of the power battery within a second preset time period after the self-load is turned on, and the third state data is the state data of the power battery during the full charging and discharging process. Step 102: Determine the individual cell voltage range, highest individual cell voltage, temperature distribution range, and highest absolute temperature of the power battery based on the first state data. Step 103: Determine the DC internal resistance and polarization characteristics of the power battery based on the total voltage in the second state data; Step 104: Determine the SOH value of the power battery based on the third state data; Step 105: Determine the safety risk index of the power battery based on the individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, and SOH value, as well as the fault codes and insulation resistance values ​​in the second state data.

[0023] In another exemplary embodiment, the aforementioned first state data includes the voltage and temperature of each individual cell in the power battery at different sampling times. The aforementioned individual cell voltage range is the difference between the highest and lowest voltages among the individual cells at the same sampling time; the aforementioned highest individual cell voltage is the highest voltage among the individual cells at the same sampling time; the aforementioned temperature distribution range is the difference between the highest and lowest temperatures among the individual cells at the same sampling time; and the aforementioned highest absolute temperature is the highest temperature among the individual cells at the same sampling time.

[0024] In another exemplary embodiment, the first preset time period is 5 minutes long, and the second preset time period is 3 minutes long. The acquisition of the first and second state data is performed in a rapid detection mode of "5 minutes of charging + 3 minutes of discharging." Its core lies in utilizing the electrochemical "broom effect" to capture the extreme characteristics of the battery at the full charge critical point and under dynamic load. The data collected at the input end via the GB / T 27930 protocol for the first 5 minutes of full charge forms the basis for static consistency evaluation. By reading the individual cell voltage list in the BMS message in real time, the individual cell voltage range (the difference between the highest and lowest voltages) is calculated. Based on the built-in scoring strategy, if the range exceeds 0.3V, 0.4V, or 0.5V, a level 3, level 2, or level 1 alarm will be triggered respectively, deducting points with different weights. This directly generates the rating for the "excessive individual cell voltage difference" indicator in the report. Simultaneously, the system monitors the highest single-cell voltage and compares it with the theoretical upper limit of the battery chemistry system (e.g., 3.65V for lithium iron phosphate or 4.2V for ternary lithium). If the measured value exceeds the upper limit by 1.02, 1.03, or 1.05 times, it is judged as having different degrees of overvoltage risk, resulting in deductions in the "Single Cell Overvoltage" and "Total Voltage Overvoltage" sections of the report. Regarding temperature probe data, the system not only monitors whether the absolute temperature exceeds the high-temperature threshold of 50℃-60℃, but also calculates the temperature distribution range. If the temperature difference exceeds the range of 8℃-30℃, it is judged as an abnormality in the heat dissipation system or a risk of thermal runaway of the cell. This constitutes the evaluation basis for "High Battery Temperature" and "Excessive Temperature Range" in the report.

[0025] In another exemplary embodiment, the second state data described above includes the total voltage, fault code, and insulation resistance of the power battery at different sampling times; The DC internal resistance and polarization characteristics of the power battery are determined based on the total voltage in the second state data, specifically including:

[0026] In another exemplary embodiment, during the discharge / dynamic response phase after full charge, real-time data at the moment of load activation is acquired via the GB / T 32960 protocol. The input voltage drop amplitude (ΔV) and voltage drop slope (dV / dt) are processed by a cloud algorithm to calculate the DC internal resistance (DCR) and polarization characteristics of the battery cells. If the voltage of a certain string of cells drops too quickly under load, the model will identify it as a phantom voltage or excessive internal resistance, and this fault can be identified and detected by calculating the DC internal resistance and polarization characteristics. Combining the SOC value jump reported by the BMS (such as a jump amplitude exceeding 10%-30%), this embodiment of the application will deduct points from the reported "SOC jump" and "measured SOH" indicators, because this usually indicates battery aging or inaccurate algorithm estimation. In addition, it will read the BMS fault codes and insulation resistance data. If the insulation resistance is lower than 100Ω / V or 500Ω / V, or if there are serious fault codes such as thermal runaway or main relay sticking, it will directly trigger a level one or level two alarm and deduct points. The specific abnormal cause will be displayed in the "Insulation Alarm" and "Fault Code" sections of the report.

[0027] In another exemplary embodiment, the aforementioned DC internal resistance (DCR) and polarization characteristics can also be used to support the scoring logic of "measured SOH" and "SOC jump": when the algorithm calculates that the internal resistance is too high or the polarization is severe based on the voltage drop amplitude (ΔV), it will determine that the battery is aging or has "phantom charge", thereby triggering a deduction of one to three levels in the SOH index. In addition, these parameters are also used to generate the anomaly tracing inference at the end of the report. The cloud-based big data model will call these data to translate the abstract deductions into specific repair suggestions, such as interpreting voltage anomalies as "specific cell internal resistance is too high" or "contact impedance is abnormal".

[0028] For example, the DC internal resistance and polarization parameters of the battery cell are first calculated using the voltage drop amplitude (ΔV). These physical parameters are then input into the SOH estimation model. If the internal resistance is too high, the model will determine that the battery capacity retention rate has decreased, thus outputting a lower SOH value (e.g., SOH = 65%). This value is then compared with the scoring criteria, triggering the secondary deduction rule of "SOH < 70%", ultimately resulting in a deduction for the "Measured SOH" item. Similarly, if the virtual voltage caused by the internal resistance leads to a sudden voltage drop under load, the BMS will correct the SOC value, resulting in a jump greater than 20%, thus triggering a deduction for the "SOC jump" item.

[0029] In this process, a simulated real load is used for discharge. Although the system performs a simulated circuit spectrum of fluctuating current discharge during the deep capacity measurement phase, the internal resistance calculation is locked at the initial 3 minutes of discharge after full charge. During this phase, a high-frequency measurement unit independent of the BMS can be used to accurately capture the step current and instantaneous voltage drop generated when the load (such as an air conditioner or heater) is turned on. Due to the sufficiently high sampling frequency (millisecond level), the system can complete the internal resistance calibration using the ΔV / ΔI at the initial moment before the current fluctuates with the operating conditions. The subsequent converter discharge is used for subsequent capacity integration and dynamic response testing. The two are performed step by step in the time domain without interference.

[0030] In another exemplary embodiment, the aforementioned third state data includes the total current of the power battery at different sampling times.

[0031] In another exemplary embodiment, step 104 described above can be replaced by steps 201-205.

[0032] Step 201: Construct the charge-discharge curve of the power battery based on the total current of the power battery at different sampling times.

[0033] Step 202: Determine the start and end points of the target process in the charge-discharge curve using the open-circuit voltage method; the target process is either a charging process or a discharging process.

[0034] Step 203: Based on the start and end points of the target process and the charge / discharge curve, the absolute capacity value of the target process is determined using the ampere-hour integration method.

[0035] Step 204: Calculate the ratio of the absolute capacity value to the vehicle's rated capacity, which is taken as the SOH value.

[0036] In the above embodiments, the third state data is acquired in a full charge / discharge mode. In this mode, the power battery performs a complete cycle, and the input continuously collects the total current and time data for the entire cycle. Using the ampere-hour integral method, the system calculates the total charge or discharge capacity by integrating the current over time, and calibrates the start and end points of the charge / discharge curve (0% SOC and 100% SOC) using the open-circuit voltage method to eliminate instrument errors. The final calculated absolute capacity value is compared with the input vehicle rated capacity, and the resulting percentage is the accurate SOH value. This SOH value is not only output as an independent capacity report, but is also fed back into the safety status evaluation report—if the measured SOH is lower than 80%, 70%, or 60%, a deduction of level three to level one will be triggered in the "measured SOH" indicator according to the scoring strategy, thereby ensuring that the safety evaluation report also reflects the battery's capacity health.

[0037] In another exemplary embodiment, in step 105 above, the deduction results of all indicators are weighted and accumulated, and then deducted from the full score (usually 100 points). The final safety risk index (e.g., 85 points) will directly determine the safety level in the report (e.g., "very safe" or "unsafe"). This is based on these abnormal characteristics (e.g., "corrosion of a module sampling harness" or "high internal resistance of a specific battery cell").

[0038] In another exemplary embodiment, step 105 described above can be replaced by steps 301 and 302.

[0039] Determine the fault level of each individual cell, including individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, SOH value, fault code, and insulation resistance value. The safety risk index of a power battery is calculated using the following formula based on the fault level of the individual cell voltage range, the highest individual cell voltage, the temperature distribution range, the highest absolute temperature, the DC internal resistance, the polarization characteristics, the SOH value, the fault code, and the insulation resistance value. ; in, , , , , , , , , These are the scores for the highest level of fault corresponding to the individual unit voltage range, highest individual unit voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, SOH value, fault code, and insulation resistance value. , , , , , , , , The weights corresponding to the fault levels of individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, SOH value, fault code, and insulation resistance value are respectively assigned.

[0040] In another exemplary embodiment, after step 105 above, the method further includes: generating a maintenance report by matching the maintenance case library of the power battery with the fault level of the individual cell voltage range, the highest individual cell voltage, the temperature distribution range, the highest absolute temperature, the DC internal resistance, the polarization characteristics, the SOH value, the fault code, the insulation resistance value, and the safety risk index using a cloud-based large model; the maintenance report includes: a diagnostic report and maintenance recommendations.

[0041] In this embodiment, based on these abnormal characteristics (such as "corrosion of a module sampling harness" or "high internal resistance of a specific cell"), the cloud-based big model will automatically match the maintenance case library, generate an anomaly result analysis and maintenance suggestions at the end of the report, and call the parts database to calculate the reference price for replacing the module or the whole package.

[0042] The cloud-based large model used in this application is a specialized model that is a general-purpose large language model based on the Transformer architecture (such as Llama 3 or GPT-4 architecture) after vertical domain fine-tuning. The inventors' improvement to this model lies in the introduction of a "battery mechanism vector database" and "RAG (retrieval enhancement generation) technology".

[0043] Based on the same inventive concept, this application also provides a dynamic diagnostic device for a power battery to implement the dynamic diagnostic method for power batteries described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the dynamic diagnostic device for power batteries provided below can be found in the limitations of the dynamic diagnostic method for power batteries described above, and will not be repeated here.

[0044] In an exemplary embodiment, a dynamic diagnostic device for a power battery is provided, comprising: a main control MCU unit, a power management unit, a protocol control unit, a network communication unit, a measurement and acquisition unit, and a cloud server; the main control MCU unit is connected to the power battery's BMS system via the protocol control unit; the main control MCU unit is used to acquire first state data and second state data of the power battery from the BMS system; the main control MCU unit is also connected to the control terminal of the power management unit, which is connected to the power battery, and is used to charge or discharge the power battery according to the control of the main control MCU unit; the main control MCU unit is also connected to the measurement and acquisition unit, the measurement and acquisition circuit being arranged in the charging and discharging circuit formed by the power management unit and the power battery; the measurement and acquisition unit is used to acquire third state data of the power battery and send the third state data to the main control MCU unit; the main control MCU unit is also connected to the cloud server via the network communication unit; the main control MCU unit is also used to send the first state data, the second state data, and the third state data to the cloud server; the cloud server is used to determine the safety risk index of the power battery using the dynamic diagnostic method for the power battery in the above embodiments.

[0045] In another exemplary embodiment, the above-mentioned device further includes an optional maintenance power supply; the control terminal of the optional maintenance power supply is connected to the main control MCU unit; the optional maintenance power supply is used to supply power to the vehicle's electrical equipment when the power battery fails or is under maintenance.

[0046] In the above-described device embodiments, such as Figure 2 As shown, the aforementioned main control MCU unit, power management unit, protocol control unit, network communication unit, and measurement and acquisition unit are located at the field end, forming an intelligent hardware terminal. This intelligent hardware terminal integrates charging and discharging functions and multi-protocol diagnostic functions. To enable interaction between the user and the cloud server, the aforementioned device also includes a user terminal.

[0047] In the above-mentioned device, the intelligent hardware terminal serves as the perception and execution layer of the system. Its function is to connect to new energy vehicles or offline battery packs, perform charging and discharging control, protocol handshake and multi-dimensional data acquisition. Its output data (including voltage, current, temperature and BMS messages) is transmitted unidirectionally to the cloud server through the wireless network, and it also receives control commands (such as start / stop, emergency stop) issued by the cloud server. The cloud server, serving as the computing and decision-making layer, receives real-time data uploaded by the terminal, calculates battery health and safety scores using its built-in "self-developed safety scoring algorithm" (based on improvements to national standards such as GB / T 38661) and "SOH estimation model," and generates maintenance reports using a large cloud model. Its output data (diagnostic reports, scores, and maintenance recommendations) is then transmitted to the user terminal.

[0048] The user terminal serves as the presentation layer, used to display battery status and repair solutions to users, and to relay user commands to the smart hardware terminal.

[0049] In another exemplary embodiment, the aforementioned smart hardware terminal serves as the core for data acquisition and execution, uploading deep battery data to a cloud server via an encrypted channel; the cloud server then performs SOH calculations and fault analysis.

[0050] In another embodiment, the smart hardware terminal of this application adopts a modular design, with each module connected to the main control MCU via an internal bus, including: (1) Main control MCU unit: As the core brain of the hardware terminal, it is responsible for coordinating the work of various modules, data aggregation, time synchronization and edge computing. It controls the power output of the power management unit, processes the BMS data read by the protocol control unit, and interacts with the cloud through the network communication module.

[0051] (2) Power Management Unit: Internally integrates Power Factor Correction (PFC), LLC resonant converter and bidirectional inverter circuit. The input terminal is connected to single-phase 220V AC power, and the output terminal can be adjusted to output up to 750V DC power (charging mode) or feed back / consume battery power (discharging mode).

[0052] (3) Measurement and acquisition unit: connected in series in the DC output circuit, independently acquires total voltage and total current (ms-level sampling). The total current is used to calculate the ampere-hour integral, and the total voltage is used to compare with the total voltage obtained from the BMS message to realize the verification of BMS data.

[0053] (4) Protocol control unit: It is equipped with multiple communication protocol stacks, including GB / T 27930 and GB / T 32960, as well as a private protocol cracking library for specific car companies, to ensure that it can read deep data such as the highest voltage, lowest voltage, unit temperature, and insulation resistance inside the BMS.

[0054] (5) Network communication unit: integrates 4G / 5G / Wi-Fi module, responsible for uploading the encrypted data stream packaged by the main control MCU to the cloud server in real time.

[0055] (6) Optional maintenance power supply: As an independent or CAN bus controlled module, it provides DC 50-750V adjustable output, which can be used to supply power to the air conditioning compressor, PTC heater, etc. without disassembling the vehicle's high-voltage system, to determine whether the fault is a component or a circuit fault.

[0056] The data transmission and connection relationships among the various modules in the intelligent hardware terminal are as follows: The main control MCU unit is the data hub, connecting to the protocol control unit, high-precision metering unit, and power management unit via internal buses such as SPI / UART. Specifically, the power management unit feeds back its operating status (temperature, voltage, current) to the main control MCU unit and receives PWM control signals from the main control MCU unit to adjust the power; the protocol control unit transmits the read vehicle VIN code and BMS deep data (individual cell voltage, fault codes) to the main control MCU unit; the measurement and acquisition unit sends independently collected physical voltage and current data to the main control MCU unit. The main control MCU unit timestamps and encrypts all the above data before sending it to the cloud server outside the system via the network communication unit.

[0057] In another exemplary embodiment, software settings are implemented in both the main control MCU unit and the cloud server. These software settings are used to achieve dynamic diagnosis of the power battery based on the steps in the various method embodiments described above, in conjunction with the hardware results.

[0058] In another exemplary embodiment, such as Figure 3 As shown, the specific software settings in the main control MCU unit are as follows: This application provides two core capacity testing modes: a rapid assessment mode and a deep, precise capacity testing mode. In different scenarios, the rapid assessment mode and the deep, precise capacity testing mode can be used separately or simultaneously to complete rapid or in-depth testing of the power battery. When used separately, only some indicators are tested; items not tested are not penalized.

[0059] (1) Quick assessment mode (8-minute method): suitable for daily quick physical examination: This mode will capture the voltage, temperature, etc. of each individual cell at the end of charging (SOC 95%-100%), as well as the voltage drop slope in the first 3 minutes of discharge after full charge (SOC 100%-98%).

[0060] In the "rapid evaluation mode" implemented by software and algorithms, the component that captures data at the end of charging (SOC 95%-100%) and the beginning of discharging (SOC 100%-98%) is the "main control MCU unit" in the smart hardware terminal.

[0061] The logic for determining the last five minutes of charging is as follows: the main control MCU unit monitors the SOC value reported by the BMS in real time. When the SOC reaches 95%, the "charging end sampling trigger" is triggered to collect the first state data.

[0062] The logic for determining the three-minute full discharge cycle is as follows: when the charging current returns to zero (SOC 100%), the main control MCU unit controls the power management unit to switch to discharge mode, and triggers the "discharge initial sampling trigger" the instant the discharge current is detected. High-frequency data within these critical time windows (including individual cell voltage range and voltage rise / fall slope) are tagged by the main control MCU unit and then prioritized for transmission to the "feature extraction module" on the cloud server for slope calculation and analysis.

[0063] (2) Deep Precision Capacity Measurement Mode (Full Fill and Discharge Method, as shown in the attached document) Figure 3 (As shown): Applicable to battery recycling assessment, used car transactions, or troubleshooting.

[0064] In the deep and accurate capacity measurement mode, the relationships between each step and the input and output are as follows: The first step, "automatic preprocessing," aims to clear residual charge to establish the integration starting point. The intelligent hardware terminal controls the discharge to the cutoff voltage, outputting a State of Charge (SOC) of 0%. This state serves as the input for the second step, "full charge process," which measures the charging capacity and calibrates the full charge state. The intelligent hardware terminal executes constant current and constant voltage charging, outputting the charged amount of electricity (in W). h_inThe fully charged state serves as the input for the third step, "operating condition discharge," which aims to simulate the capacity performance under real-world usage scenarios. The hardware terminal controls the discharge current based on the operating condition spectrum sent from the cloud and outputs real-time VIT data streams during the discharge process. These data streams serve as the input for the fourth step, "capacity calculation," which aims to quantify the actual battery capacity and output an absolute capacity value (Ah). Finally, all process data are summarized as the input for the fifth step, "cloud analysis," which aims to perform health rating and fault location and output a final test report.

[0065] ① Automatic preprocessing: The main control MCU unit, in coordination with the power management unit, controls the vehicle to discharge to the BMS low-voltage cutoff point and waits for the voltage to stabilize, establishing the zero point of capacity integration. The specific process is as follows: The main control MCU unit reads the minimum voltage limit allowed by the BMS through the protocol. When it detects that the total voltage of the battery pack or the minimum voltage of a single cell is close to the limit, the main control MCU unit sends a command to the power management unit to gradually reduce the discharge current until it is turned off.

[0066] ② Full charge process: When the power battery is charged with constant current and constant voltage according to the standard charging curve, the main control MCU unit, together with the measurement and acquisition unit, records the amount of charge as a reference.

[0067] The recorded charge amount is used to calculate coulombic efficiency (discharge capacity / charge capacity) and as a basis for assisting in determining whether the battery has an internal micro-short circuit (such as the charge amount being much greater than the rated capacity but unable to discharge).

[0068] ③ Operating Condition Discharge: After fully charged, the main control MCU unit, in conjunction with the power management unit, controls the internal load to discharge. In this embodiment, a bidirectional inverter circuit is provided in the power management unit. This bidirectional inverter circuit is used to achieve non-constant current discharge, which can simulate real driving current fluctuations based on the "road spectrum file" sent by the cloud server. The working principle of this process is as follows: the main control MCU unit modulates the duty cycle of the bidirectional inverter circuit according to the preset current command, converting the high-voltage DC power of the power battery into AC power to feed back to the grid or consume it through internal energy-consuming components, thereby simulating load discharge.

[0069] In another exemplary embodiment, the software settings of the cloud server are as follows: ① Capacity Calculation: Combining Ah Counting and Open Circuit Voltage (OCV) methods, the absolute capacity of the current battery pack is calculated, down to the capacity contribution of each cell in the string, thereby generating an authoritative test report with legal validity.

[0070] The absolute capacity calculation, combining the ampere-hour integration method and the open-circuit voltage method, is performed by the computing engine of the cloud server. The calculation is based on data including: real-time current I(t), time t, and terminal voltage V(t) uploaded by the smart hardware terminal, as well as the corresponding OCV-SOC curve for this battery model stored in the cloud database. The calculation formula is: ; in, The actual amount of electricity discharged is obtained by the ampere-hour integration method. and These are the precise SOC values ​​at the start and end of the discharge, determined by looking up a table using the open-circuit voltage method (OCV). Combining these two values ​​eliminates the error caused by inaccurate SOC values ​​on the instrument panel.

[0071] The method for accurately determining the capacity contribution of each cell string is as follows: During deep discharge, a cloud server monitors the voltage drop curve of each cell string. When the entire battery pack stops discharging because a particular "short-line" cell reaches its cutoff voltage first, the system records the total discharged capacity at this time as the actual capacity of that "short-line" cell. For other cells that have not yet reached their cutoff voltage, this embodiment uses their remaining SOC corresponding to their current voltage to inversely calculate their theoretical total capacity. The calculation formula is as follows: ; in, This represents the actual capacity of a single battery cell i. This represents the discharge amount of a single battery cell i. The remaining power is estimated using the OCV curve.

[0072] In this embodiment of the application, the information contained in the test report is not limited to absolute capacity and cell capacity contribution, but also includes in detail: current battery SOH value (%), based on safety score (0-100 points), individual cell voltage consistency range, internal resistance consistency distribution map, abnormal cell number location, suggested repair plan (such as "replace module 3" or "perform equalization maintenance"), and estimated price of parts required for repair.

[0073] ② Cloud-based analysis: Data packets are uploaded via 4G / Wi-Fi. The cloud-based large model is compared with standard battery data models of the same vehicle model and mileage, and the repair case library is called. If a battery cell has an excessively high voltage at the end of charging and a rapid voltage drop at the beginning of discharging, the system determines that the cell has insufficient capacity or excessive internal resistance.

[0074] The data received by the cloud server is a complete charging and discharging process data packet sent by the smart hardware terminal, specifically including: timestamps of the entire process, total voltage, total current, voltage of all individual cells, all temperature probe data, fault codes reported by the BMS, and insulation resistance values. The specific process of cloud analysis is as follows: First, the data cleaning module removes lost or noisy data; second, the cleaned data is used to calculate the safety risk index; simultaneously, the feature extraction module extracts the "8-minute" slope feature and the full charge / discharge capacity feature; finally, these features and fault codes are input into the cloud-based large model to generate the final text-based maintenance report, which includes a diagnostic report and maintenance recommendations.

[0075] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a dynamic diagnostic method for a power battery.

[0076] Those skilled in the art will understand that Figure 4 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0077] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0078] Compared with the prior art, the dynamic diagnostic method, apparatus, equipment and medium for power batteries provided in this application have the following significant advantages: (1) Dual-mode capacity measurement is flexible and efficient: The dual-mode capacity measurement method enables laboratory-level accurate measurement without disassembly, meeting the needs of all scenarios from quick repair shops to battery recycling plants.

[0079] (2) Strong site adaptability: For the rapid evaluation mode, only 220V power supply is needed to achieve fast charging power output, so that ordinary roadside shops and parking lots can also have the ability of professional battery repair bases.

[0080] (3) Deep diagnostic depth: Combining proprietary protocols with dynamic operating conditions, it can detect minor hidden dangers that cannot be identified by static detection (such as false current, poor contact under high magnification).

[0081] (4) Intelligent one-stop service: It realizes a closed loop from fault discovery, cause analysis to repair pricing, and reduces the technical threshold for repair personnel.

[0082] (5) High safety: The battery pack does not need to be disassembled throughout the process, which maximizes the safety of operators and equipment.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0084] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0085] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0086] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A dynamic diagnostic method for a power battery, characterized in that, include: Acquire the first state data, second state data, and third state data of the power battery; The first state data is the state data of the power battery within a first preset time period before full charging; the second state data is the state data of the power battery within a second preset time period after self-load is turned on; and the third state data is the state data of the power battery during the full charging and discharging process. The individual cell voltage range, highest individual cell voltage, temperature distribution range, and highest absolute temperature of the power battery are determined based on the first state data. The DC internal resistance and polarization characteristics of the power battery are determined based on the total voltage in the second state data. The SOH value of the power battery is determined based on the third state data. The safety risk index of the power battery is determined based on the individual cell voltage range, maximum individual cell voltage, temperature distribution range, maximum absolute temperature, DC internal resistance, polarization characteristics, and SOH value, as well as the fault codes and insulation resistance values ​​in the second state data.

2. The dynamic diagnostic method for power batteries according to claim 1, characterized in that, The first state data includes the voltage and temperature of each individual cell in the power battery at different sampling times; The individual cell voltage range is the difference between the highest and lowest voltages among the individual cells at the same sampling time; the highest individual cell voltage is the highest voltage among the individual cells at the same sampling time. The temperature distribution range is the difference between the highest and lowest temperatures of each individual cell at the same sampling time; the highest absolute temperature is the highest temperature among the individual cells at the same sampling time. The second state data includes the total voltage, fault code, and insulation resistance of the power battery at different sampling times; The third state data includes the total current of the power battery at different sampling times.

3. The dynamic diagnostic method for power batteries according to claim 1, characterized in that, The first status data is obtained from the BMS message via the GB / T 27930 protocol; the second status data is obtained from the BMS message via the GB / T 32960 protocol; and the third status data is obtained by detecting the fast charging interface of the power battery.

4. The dynamic diagnostic method for power batteries according to claim 1, characterized in that, The SOH value of the power battery is determined based on the third state data, specifically including: The charge and discharge curves of the power battery are constructed based on the total current of the power battery at different sampling times. The start and end points of the target process in the charge-discharge curve are determined using the open-circuit voltage method; the target process is either a charging process or a discharging process. Based on the start and end points of the target process and the charge-discharge curve, the absolute capacity value of the target process is determined using the ampere-hour integration method. The ratio of the absolute capacity value to the vehicle's rated capacity is calculated and used as the SOH value.

5. The dynamic diagnostic method for a power battery according to claim 1, characterized in that, Based on the individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, and SOH value of the power battery, as well as the fault codes and insulation resistance values ​​in the second state data, the safety risk index of the power battery is determined, specifically including: Determine the fault level of each individual cell, including individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, SOH value, fault code, and insulation resistance value. The safety risk index of a power battery is calculated using the following formula based on the fault level of the individual cell voltage range, the highest individual cell voltage, the temperature distribution range, the highest absolute temperature, the DC internal resistance, the polarization characteristics, the SOH value, the fault code, and the insulation resistance value. ; in, , , , , , , , , These are the scores for the highest level of fault corresponding to the individual unit voltage range, highest individual unit voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, SOH value, fault code, and insulation resistance value. , , , , , , , , The weights corresponding to the fault levels of individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, SOH value, fault code, and insulation resistance value are respectively assigned.

6. The dynamic diagnostic method for a power battery according to claim 5, characterized in that, Based on the individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, and SOH value of the power battery, as well as the fault codes and insulation resistance values ​​in the second state data, the safety risk index of the power battery is determined, which then includes: Based on the individual cell voltage range, highest individual cell voltage, temperature distribution range, highest absolute temperature, DC internal resistance, polarization characteristics, SOH value, fault code, insulation resistance value, and the aforementioned safety risk index, a maintenance report is generated by matching the power battery maintenance case library with a cloud-based large model. The maintenance report includes a diagnostic report and maintenance recommendations.

7. A dynamic diagnostic device for a power battery, characterized in that, include: The system includes a main control MCU unit, a power management unit, a protocol control unit, a network communication unit, a measurement and acquisition unit, and a cloud server. The main control MCU unit is connected to the BMS system of the power battery through the protocol control unit; the main control MCU unit is used to obtain the first state data and the second state data of the power battery from the BMS system; The main control MCU unit is also connected to the control terminal of the power management unit, and the power management unit is connected to the power battery. The power management unit is used to charge or discharge the power battery according to the control of the main control MCU unit. The main control MCU unit is also connected to the measurement and acquisition unit. The measurement and acquisition circuit is set in the charging and discharging circuit formed by the power management unit and the power battery. The measurement and acquisition unit is used to collect the third state data of the power battery and send the third state data to the main control MCU unit. The main control MCU unit is also connected to the cloud server through the network communication unit; the main control MCU unit is also used to send the first status data, the second status data and the third status data to the cloud server. The cloud server is used to determine the safety risk index of the power battery using the dynamic diagnostic method for the power battery as described in any one of claims 1-6.

8. The dynamic diagnostic device for a power battery according to claim 7, characterized in that, The dynamic diagnostic device for the power battery also includes an optional maintenance power supply; The control terminal of the optional maintenance power supply is connected to the main control MCU unit; The optional maintenance power supply is used to supply power to the vehicle's electrical equipment when the power battery fails or is under maintenance.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the dynamic diagnostic method for a power battery according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the dynamic diagnostic method for the power battery as described in any one of claims 1-6.

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