Rapid detection method and system for power storage battery of electric vehicle

Abstract

The invention relates to the technical field of electric vehicle power storage battery detection, and discloses a rapid detection method and system for an electric vehicle power storage battery, a detection device is connected with a vehicle OBD interface and a charging pile, and the detection device automatically identifies the type of the power storage battery and a vehicle communication protocol; a safety detection process is started, and insulation resistance detection, voltage withstanding test and over-charge and over-discharge test are completed in sequence; executing a health state detection process, including performing a 0.5-10C multiplying power charge-discharge test and internal resistance analysis in a room temperature environment, so that the internal resistance detection error is less than or equal to 5%; the BMS function of the power storage battery is verified, and the SOC estimation error of the BMS is calibrated; and the detection data is uploaded to a cloud platform through a wireless network, and the cloud platform generates a comprehensive detection report based on a preset SOH calculation model and recommends an echelon utilization scene. Reliable data support is provided for battery echelon utilization, and meanwhile the detection cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle power battery testing technology, specifically to a rapid testing method and system for electric vehicle power batteries. Background Technology

[0002] With the rapid development of the global new energy vehicle industry and the continuous increase in the number of electric vehicles, the state of health (SOH) of power batteries, as core energy storage components, directly affects vehicle range, safety performance, and service life. Meanwhile, a large number of power batteries are gradually entering their retirement cycle. According to industry reports, the scale of retired power batteries in my country will exceed 200 GWh by 2025. The need for the cascade utilization (such as energy storage systems and low-speed electric vehicles) and safe disposal of retired batteries is becoming increasingly urgent, and accurate testing is a prerequisite for the efficient recycling of retired batteries.

[0003] Current electric vehicle power battery testing technology has significant shortcomings: First, the equipment is bulky and limited in application scenarios. Traditional testing equipment (such as fixed testing systems) is large and heavy, relying on fixed charging stations or laboratory environments, and cannot meet the rapid testing needs of outdoor / mobile scenarios such as repair stations and retired battery recycling points. Some portable devices also suffer from insufficient testing accuracy due to simplified functions. Second, the functions are limited and the evaluation is incomplete. Existing equipment mainly focuses on basic electrical performance testing such as capacity and voltage, lacking testing of safety performance such as insulation resistance and withstand voltage, and does not integrate functional verification of the battery management system (BMS) (such as SOC estimation error calibration). First, the accuracy of current / voltage measurement makes it difficult to comprehensively assess battery safety and usability. Second, data management is inefficient and lacks support. Test data relies heavily on manual recording and local storage, lacking the ability to transmit to the cloud platform in real time. This makes it impossible to form a full life cycle health record for batteries, resulting in low accuracy in the tiered utilization of retired batteries and high risks of secondary use. Third, the compatibility and adaptability are poor. Different brands of vehicles (such as Tesla, BYD, and NIO) have different battery interface protocols (such as GB / T27930 and ISO15118). Existing equipment is difficult to adapt to the testing needs of multiple vehicle models, and protocol recognition is time-consuming and has a low success rate.

[0004] The aforementioned deficiencies mean that existing testing technologies cannot meet the testing needs of power batteries in all scenarios, including use and maintenance, and retirement screening. This not only reduces battery testing efficiency but also increases the safety risks and costs of reusing retired batteries, thus hindering the green and circular development of the new energy vehicle industry. Summary of the Invention

[0005] The present invention aims to provide a rapid testing method and system for electric vehicle power batteries to solve the technical problems of existing testing equipment being bulky, having limited functions, inefficient data management, poor compatibility, and unable to simultaneously meet the requirements of safety performance testing, health status assessment and BMS verification, resulting in low efficiency in screening retired batteries and high risks of secondary use.

[0006] To solve the above problems, the present invention adopts the following technical solution: Option 1: A rapid testing method for electric vehicle power batteries, comprising the following steps: Step 1: Connect the testing equipment to the vehicle's OBD interface and the charging station respectively. The testing equipment will automatically identify the power battery type and vehicle communication protocol. Step 2: Initiate the safety testing process, and sequentially complete the insulation resistance test, withstand voltage test, and overcharge and over-discharge tests; among them, the insulation resistance test value is ≥10MΩ, and there is no breakdown phenomenon during the withstand voltage test; Step 3: Perform the health status detection process, including conducting 0.5C-10C rate charge and discharge tests and internal resistance analysis at room temperature to ensure that the internal resistance detection error is ≤5%, and collecting data on the remaining capacity, internal resistance growth rate, and voltage consistency of the power battery. Step 4: Verify the BMS function of the power battery, calibrate the SOC estimation error of the BMS to ensure that the error after calibration is ≤3%, and ensure that the accuracy of current and voltage measurement after calibration is ≤2%. Step 5: Upload the detection data from Steps 2 to 4 to the cloud platform via wireless network. The cloud platform generates a comprehensive detection report based on the preset SOH calculation model and recommends tiered utilization scenarios.

[0007] Beneficial effects: Through the design of the whole process of "safety testing - health assessment - BMS verification - cloud analysis", the safety performance, health status and BMS function of power batteries can be comprehensively tested. The accuracy requirements of key test parameters are clearly defined (such as internal resistance error ≤5% and SOC error ≤3%), ensuring that the test results are accurate and reliable, and providing comprehensive data support for the secondary use of batteries.

[0008] Furthermore, in step 1, the vehicle communication protocol includes the GB / T27930 protocol and the ISO15118 protocol. The detection equipment automatically identifies the protocol through the programmable OBD interface module, and the identification time is ≤3 seconds.

[0009] Beneficial effects: By adapting to mainstream communication protocols through the programmable OBD interface module and limiting the protocol recognition time to ≤3 seconds, the compatibility and detection efficiency of multiple vehicle brands are greatly improved, solving the problems of poor compatibility and slow recognition of existing equipment.

[0010] Furthermore, in step 3, the health status detection also includes high and low temperature charge and discharge tests in an environment of -20℃ to 50℃. The detection equipment corrects the influence of ambient temperature on the detection data through a built-in temperature compensation algorithm.

[0011] Beneficial effects: It supports wide temperature range (-20℃-50℃) detection and introduces a temperature compensation algorithm to ensure the detection accuracy of the equipment under different outdoor environmental conditions, expand the applicable scenarios of the equipment, and meet the detection needs of cold and high temperature regions.

[0012] Furthermore, in step 5, the SOH calculation model is constructed based on the GB / T34015.3 standard, integrating parameters from three dimensions: remaining capacity, internal resistance growth rate, and voltage consistency. The accuracy of the SOH calculation results is ≥95%.

[0013] Beneficial effects: Based on national standards, a multi-dimensional SOH calculation model is constructed with an accuracy rate of ≥95%, ensuring the authority and accuracy of battery health status assessment, avoiding misjudgments caused by single-parameter assessment, and providing a reliable basis for battery life prediction.

[0014] Furthermore, the total detection time for steps 2 to 5 is ≤30 minutes.

[0015] Beneficial effects: The single test time is limited to ≤30 minutes and the operation is automated, which greatly reduces labor costs and testing time. Compared with traditional equipment (single test time is 1-2 hours), the efficiency is improved by more than 60%, meeting the needs of rapid testing.

[0016] Option 2: A rapid testing system for electric vehicle power batteries, comprising hardware modules and software modules; The hardware module includes a data acquisition unit, a control unit, and a communication unit; The data acquisition unit includes voltage and current sensors, a temperature probe, and an insulation detection circuit. The voltage and current sensors support DC voltage detection of 200-750V and current detection of 0-200A, with an accuracy of ≤±0.5%. The control unit has a built-in ARM processor for charging and discharging control, real-time data processing, and fault diagnosis. The communication unit integrates CAN bus, Bluetooth, and Wi-Fi modules for data interaction with the vehicle's OBD interface, charging pile, and cloud platform. The software module includes a dynamic algorithm library and a human-computer interaction interface; The dynamic algorithm library stores the SOH calculation model and temperature compensation algorithm; The human-machine interface is used to display test results, historical records and device status, and supports one-click generation of test reports.

[0017] Beneficial effects: By integrating the high-precision sensor (accuracy ≤ ±0.5%) of the hardware module with the algorithm library and visualization interface of the software module, the system can accurately collect, process and display the detection data in real time, and make the hardware parameters clear (such as voltage 200-750V, current 0-200A), ensuring that the system is compatible with mainstream power battery specifications.

[0018] Furthermore, the overall weight of the hardware module is ≤5kg, and the device casing adopts an IP55 protection rating design, and is equipped with an emergency stop button and overcurrent protection circuit.

[0019] Beneficial effects: Weighing ≤5kg, it meets the portability requirements and supports one-handed operation and outdoor mobile use; IP55 protection rating, emergency stop button and overcurrent protection circuit improve the equipment's resistance to environmental interference and operational safety, avoiding equipment damage or safety accidents caused by dust, water stains or overload during the testing process.

[0020] Furthermore, the programmable OBD interface module of the communication unit supports automatic recognition of the GB / T27930 protocol and the ISO15118 protocol.

[0021] Beneficial effects: Clearly defines the scope of protocol adaptation and the number of vehicle models to be adapted (20 or more), as well as the success rate (100%), solves the problem of poor compatibility of existing equipment, ensures the universality of the system in multiple brand vehicle models, and reduces the user's equipment procurement costs.

[0022] Furthermore, the software module also includes a cloud data interaction submodule, which is used to upload the detection data to the cloud platform in real time. The cloud platform stores the detection data and generates battery health records, supporting historical data queries and recommendations for tiered utilization scenarios.

[0023] Beneficial effects: Through cloud-based data interaction and health record management, the long-term storage and traceability of test data can be achieved, providing data support for the full life cycle management of batteries. At the same time, it can accurately recommend tiered utilization scenarios (such as energy storage and low-speed power) and improve the recycling rate of retired batteries.

[0024] Furthermore, the control unit also has a built-in fault diagnosis submodule. When the detection data exceeds the preset threshold, that is, when the insulation resistance is <10MΩ or the voltage exceeds the limit, the audible and visual alarm is automatically triggered and the detection process is stopped.

[0025] Beneficial effects: Through the fault diagnosis submodule and automatic alarm mechanism, abnormal situations in the testing process can be monitored in real time, avoiding testing risks caused by battery failure (such as fire, equipment damage) and improving the safety of the testing process.

[0026] The advantages of this invention are: 1. High integration and portability: It integrates safety detection, health assessment and BMS verification functions into one device. The device weighs ≤5kg and supports one-handed operation, solving the problems of bulky and limited scenarios of traditional devices and adapting to outdoor / mobile detection needs. 2. Comprehensiveness and accuracy of testing: Covers safety performance (insulation, withstand voltage), health status (SOH, internal resistance, capacitance), BMS function (SOC calibration, accuracy verification), key parameters with clear accuracy (e.g., internal resistance error ≤5%, SOC error ≤3%), and the evaluation results are comprehensive and reliable; 3. High efficiency, automation and low cost: The time for a single test is ≤30 minutes. The automated operation requires no manual intervention, reducing maintenance costs by 40% and significantly improving efficiency compared to traditional equipment. 4. Strong compatibility and wide adaptability: Supports protocols such as GB / T27930 and ISO15118, adapts to 20 or more multi-brand car models, and the protocol recognition time is ≤3 seconds, solving the problem of poor compatibility of existing equipment; 5. Intelligent data management: Test data is uploaded to the cloud in real time to generate battery health records, providing data-driven decision-making for secondary use and improving the recycling rate of retired batteries; 6. Wide environmental adaptability and safety: Supports wide temperature range detection from -20℃ to 50℃, IP55 protection rating and multiple protection circuits (emergency stop, overcurrent) to ensure safe and stable operation of the equipment in complex environments. Attached Figure Description

[0027] Figure 1 This is a logic block diagram of the system in an embodiment of the present invention.

[0028] Figure 2 This is a flowchart of a method according to an embodiment of the present invention.

[0029] Figure 3 This is a schematic diagram of device testing according to an embodiment of the present invention.

[0030] Figure 4 This is an external view of the portable testing device according to an embodiment of the present invention. Detailed Implementation

[0031] The following detailed description illustrates the specific implementation method: As attached Figure 1 As shown: The electric vehicle power battery rapid testing system of the present invention includes a hardware module and a software module; The hardware module includes a data acquisition unit, a control unit, and a communication unit; The data acquisition unit includes voltage and current sensors, a temperature probe, and an insulation detection circuit. The voltage and current sensors support DC voltage detection of 200-750V and current detection of 0-200A, with an accuracy of ≤±0.5%. The control unit incorporates an ARM processor for charge / discharge control, real-time data processing, and fault diagnosis. It also includes a fault diagnosis submodule that automatically triggers an audible and visual alarm and halts the testing process when detection data exceeds preset thresholds (i.e., insulation resistance < 10MΩ or voltage exceeds limits). This fault diagnosis submodule and automatic alarm mechanism monitor abnormal situations during the testing process in real time, preventing risks caused by battery malfunctions (such as fire or equipment damage) and improving the safety of the testing process.

[0032] The communication unit integrates CAN bus, Bluetooth, and Wi-Fi modules for data interaction with the vehicle's OBD interface, charging pile, and cloud platform. The programmable OBD interface module of the communication unit supports automatic recognition of GB / T27930 and ISO15118 protocols and is compatible with 20 or more different vehicle brands (including Tesla, BYD, and NIO), with a 100% compatibility success rate. By clearly defining the protocol compatibility scope, the number of compatible vehicle models (20 or more), and the success rate (100%), the system addresses the poor compatibility issues of existing equipment, ensuring system universality across multiple vehicle brands and reducing user equipment procurement costs.

[0033] Among them, such as Figure 4 As shown, the hardware module of this invention is designed as a carrying case, with the entire hardware module weighing ≤5kg. The device casing features an IP55 protection rating and is equipped with an emergency stop button and overcurrent protection circuit. This invention commercializes the entire testing system, with a hardware weight of ≤5kg meeting portability requirements, supporting one-handed operation and outdoor mobile use. The IP55 protection rating, emergency stop button, and overcurrent protection circuit enhance the device's resistance to environmental interference and operational safety, preventing equipment damage or safety accidents caused by dust, water stains, or overload during testing.

[0034] The software module includes a dynamic algorithm library, a human-computer interaction interface, and a cloud data interaction sub-module. The dynamic algorithm library stores the SOH calculation model and temperature compensation algorithm; The human-machine interface is a 7-inch touchscreen, used to display test results, historical records and device status, and supports one-click generation of test reports.

[0035] The cloud-based data interaction submodule uploads test data to the cloud platform in real time. The cloud platform stores the test data and generates battery health records, supporting historical data queries and recommendations for secondary use scenarios. Through cloud-based data interaction and health record management, long-term storage and traceability of test data are achieved, providing data support for the full life cycle management of batteries. At the same time, it accurately recommends secondary use scenarios (such as energy storage and low-speed power), improving the recycling rate of retired batteries.

[0036] The system of this invention integrates high-precision sensors (accuracy ≤ ±0.5%) in the hardware module with algorithm library and visualization interface in the software module to achieve accurate acquisition, real-time processing and intuitive display of detection data. The hardware parameters are clearly defined (such as voltage 200-750V, current 0-200A), ensuring that the system is compatible with mainstream power battery specifications.

[0037] Using the above systems according to Figure 3 As shown, the system is productized into a portable testing terminal. It connects to an external DC charging station via a cable, to the electric vehicle via a double-ended cable, to an external ODB module via Bluetooth, and to the cloud via a 4G or 5G network. The method for rapid testing of electric vehicle power batteries is as follows: Figure 2 As shown: Step 1: Connect the testing equipment to the vehicle's OBD interface and the charging station respectively. The testing equipment will automatically identify the power battery type and vehicle communication protocol. In step 1, the vehicle communication protocols include GB / T27930 and ISO15118. The testing equipment automatically identifies the protocols through a programmable OBD interface module, with an identification time of ≤3 seconds. By adapting to mainstream communication protocols through the programmable OBD interface module and limiting the protocol identification time to ≤3 seconds, the compatibility and testing efficiency of multiple vehicle brands are significantly improved, solving the problems of poor compatibility and slow identification of existing equipment.

[0038] Step 2: Initiate the safety testing process, and sequentially complete the insulation resistance test, withstand voltage test, and overcharge and over-discharge tests; among them, the insulation resistance test value is ≥10MΩ, and there is no breakdown phenomenon during the withstand voltage test; Step 3: Perform the health status detection process, including conducting 0.5C-10C rate charge and discharge tests and internal resistance analysis at room temperature to ensure that the internal resistance detection error is ≤5%, and collecting data on the remaining capacity, internal resistance growth rate, and voltage consistency of the power battery. Step 3, the health status detection also includes high and low temperature charge and discharge tests in an environment of -20℃ to 50℃. The detection equipment corrects the impact of ambient temperature on the detection data through a built-in temperature compensation algorithm. It supports wide temperature range (-20℃ to 50℃) detection and introduces a temperature compensation algorithm to ensure the detection accuracy of the detection equipment under different outdoor environmental conditions, expand the applicable scenarios of the equipment, and meet the detection needs of cold and high temperature regions.

[0039] Step 4: Verify the BMS function of the power battery, calibrate the SOC estimation error of the BMS to ensure that the error after calibration is ≤3%, and ensure that the accuracy of current and voltage measurement after calibration is ≤2%. Recommended scenarios for secondary utilization include energy storage and low-speed power applications.

[0040] In step 5, the SOH calculation model is constructed based on the GB / T34015.3 standard, integrating parameters from three dimensions: remaining capacity, internal resistance growth rate, and voltage consistency. The accuracy of the SOH calculation results is ≥95%. Constructing a multi-dimensional SOH calculation model based on national standards, with an accuracy limit of ≥95%, ensures the authority and accuracy of battery health status assessment, avoids misjudgments caused by single-parameter assessments, and provides a reliable basis for battery life prediction.

[0041] The total testing time for steps 2 to 5 is ≤30 minutes, and no manual intervention is required during the testing process, achieving automated testing. Limiting the time for a single test to ≤30 minutes and operating automatically significantly reduces labor costs and testing time. Compared to traditional equipment (which takes 1-2 hours per test), efficiency is improved by more than 60%, meeting the needs for rapid testing.

[0042] The method of this invention, through the design of a whole process of "safety testing - health assessment - BMS verification - cloud analysis", realizes the comprehensive testing of the safety performance, health status and BMS function of power batteries, and clearly defines the accuracy requirements of key testing parameters (such as internal resistance error ≤5%, SOC error ≤3%), to ensure that the test results are accurate and reliable, and provide comprehensive data support for the cascade utilization of batteries.

[0043] The specific implementation process is as follows: Example 1 This embodiment is for testing energy storage batteries.

[0044] 1. Testing conditions: room temperature 25℃, vehicle SOC=30%, connected to a 60kW DC charging pile.

[0045] 2. Process: (1) Safety test: insulation resistance test (150MΩ), withstand voltage test (1000V / 60s without breakdown).

[0046] (2) Health assessment: Remaining capacity ≥ 80%, internal resistance growth rate ≤ 15%, voltage consistency difference ≤ 0.05V.

[0047] (3) Results: The qualified batteries are marked as "energy storage grade" and are recommended for use in photovoltaic energy storage systems.

[0048] Example 2 This embodiment is for power battery testing.

[0049] 1. Testing conditions: Low temperature -10℃, vehicle SOC=50%, suitable for electric bus battery packs.

[0050] 2. Process: (1) Rate test: Discharge at 2C rate, capacity decay ≤10%, peak power ≥100kW.

[0051] (2) BMS verification: SOC estimation error ≤2%, current measurement error ≤1.5%.

[0052] (3) Results: The qualified batteries were marked as "power level" and used in low-speed logistics vehicles.

[0053] Example 3 This embodiment verifies compatibility across multiple vehicle brands.

[0054] (1) Detection scenario: Model A (Tesla Model 3, 3 years old): Battery interface protocol is ISO15118, BMS encrypted communication.

[0055] Model B (XPeng P7, 4-year service life): Battery interface protocol is GB / T27930, BMS data format is non-standard.

[0056] (2) Problems and Solutions: Problem: Data reading fails due to the BMS encryption protocol of vehicle model A; non-standard data format of vehicle model B cannot be parsed.

[0057] Solution: Call the protocol adaptive module of the programmable OBD interface to automatically switch to ISO15118 encrypted communication mode; enable the data format conversion engine to map the non-standard data of vehicle model B to the GB / T34015.3 standard format in real time.

[0058] The test results are shown in Table 1: Table 1

[0059] Example 4 This embodiment demonstrates a rapid screening method for older batteries.

[0060] 1. Testing scenario: A retired battery from a shared car (7 years of service life, more than 1200 cycles) needs to be evaluated to determine its suitability for low-speed electric vehicles.

[0061] 2. Key issue: Battery internal resistance surges (>40% of initial value), capacity decays to 65% at room temperature, but BMS shows voltage remains normal at SOC=50%.

[0062] 3. Solution Highlights: The dynamic algorithm library identifies minor distortions in the voltage curve and, combined with the characteristics of sudden increases in internal resistance, determines that the battery has a local micro-short circuit; the AI ​​model suggests terminating the detection and marking it as "scrap grade" to avoid thermal runaway caused by subsequent charging and discharging.

[0063] User value: Traditional equipment fails to detect micro-short circuit risks, but this invention avoids safety hazards in advance. User feedback: "The equipment identifies potentially dangerous batteries within 15 minutes, avoiding losses from secondary use."

[0064] For Example 6, the detection of the same scenario was compared using the system of the present invention and an existing detection instrument.

[0065] Comparative experiment: A mainstream portable testing instrument (Brand A) and a fixed testing system (Brand B) were selected and compared under the same conditions (room temperature 25℃, SOC=50%, same retired battery pack). The comparison results are shown in Table 2. Table 2

[0066] As can be clearly seen from Table 2, the present invention has significantly smaller detection errors than existing detectors, shorter single detection time, more detection and verification items, faster data transmission speed, and longer service life.

[0067] Compared with existing testing instruments and methods, this invention features an integrated portable design (≤5kg) suitable for mobile scenarios; comprehensive testing (safety + health + BMS) for more thorough evaluation; automated cloud data management to support tiered utilization; and multi-protocol compatibility for strong versatility.

[0068] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A rapid testing method for power batteries in electric vehicles, characterized in that, Includes the following steps: Step 1: Connect the testing equipment to the vehicle's OBD interface and the charging station respectively. The testing equipment will automatically identify the power battery type and vehicle communication protocol. Step 2: Initiate the safety testing process, and sequentially complete the insulation resistance test, withstand voltage test, and overcharge and over-discharge tests; among them, the insulation resistance test value is ≥10MΩ, and there is no breakdown phenomenon during the withstand voltage test; Step 3: Perform the health status detection process, including conducting 0.5C-10C rate charge and discharge tests and internal resistance analysis at room temperature to ensure that the internal resistance detection error is ≤5%, and collecting data on the remaining capacity, internal resistance growth rate, and voltage consistency of the power battery. Step 4: Verify the BMS function of the power battery, calibrate the SOC estimation error of the BMS to ensure that the error after calibration is ≤3%, and ensure that the accuracy of current and voltage measurement after calibration is ≤2%. Step 5: Upload the detection data from Steps 2 to 4 to the cloud platform via wireless network. The cloud platform generates a comprehensive detection report based on the preset SOH calculation model and recommends tiered utilization scenarios.

2. The rapid testing method for electric vehicle power batteries according to claim 1, characterized in that, In step 1, the vehicle communication protocols include the GB / T27930 protocol and the ISO15118 protocol. The testing equipment automatically identifies the protocols through the programmable OBD interface module, and the identification time is ≤3 seconds.

3. The rapid testing method for electric vehicle power batteries according to claim 1, characterized in that, In step 3, the health status detection also includes high and low temperature charge and discharge tests in an environment of -20℃ to 50℃. The detection equipment corrects the influence of ambient temperature on the detection data through a built-in temperature compensation algorithm.

4. The rapid testing method for electric vehicle power batteries according to claim 1, characterized in that, In step 5, the SOH calculation model is constructed based on the GB / T34015.3 standard, integrating parameters from three dimensions: remaining capacity, internal resistance growth rate, and voltage consistency. The accuracy of the SOH calculation results is ≥95%.

5. The rapid testing method for electric vehicle power batteries according to claim 1, characterized in that, The total detection time from step 2 to step 5 is ≤30 minutes.

6. A rapid testing system for electric vehicle power batteries, characterized in that, Includes hardware modules and software modules; The hardware module includes a data acquisition unit, a control unit, and a communication unit; The data acquisition unit includes voltage and current sensors, a temperature probe, and an insulation detection circuit. The voltage and current sensors support DC voltage detection of 200-750V and current detection of 0-200A, with an accuracy of ≤±0.5%. The control unit has a built-in ARM processor for charging and discharging control, real-time data processing, and fault diagnosis. The communication unit integrates CAN bus, Bluetooth, and Wi-Fi modules for data interaction with the vehicle's OBD interface, charging pile, and cloud platform. The software module includes a dynamic algorithm library and a human-computer interaction interface; The dynamic algorithm library stores the SOH calculation model and temperature compensation algorithm; The human-machine interface is used to display test results, historical records and device status, and supports one-click generation of test reports.

7. The rapid testing system for electric vehicle power batteries according to claim 6, characterized in that, The overall weight of the hardware module is ≤5kg, and the device casing adopts an IP55 protection rating design, and is equipped with an emergency stop button and overcurrent protection circuit.

8. The rapid testing system for electric vehicle power batteries according to claim 6, characterized in that, The programmable OBD interface module of the communication unit supports automatic recognition of GB / T27930 and ISO15118 protocols.

9. The rapid testing system for electric vehicle power batteries according to claim 6, characterized in that, The software module also includes a cloud data interaction submodule, which is used to upload the detection data to the cloud platform in real time. The cloud platform stores the detection data and generates battery health records, supporting historical data queries and recommendations for tiered utilization scenarios.

10. The rapid testing system for electric vehicle power batteries according to claim 6, characterized in that, The control unit also has a built-in fault diagnosis submodule. When the detection data exceeds the preset threshold, that is, when the insulation resistance is <10MΩ or the voltage exceeds the limit, it will automatically trigger an audible and visual alarm and stop the detection process.

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