Portable power battery capacity detection equipment and detection method
The portable power battery capacity testing equipment enables synchronous data acquisition and high-precision multi-dimensional testing during active charging, solving the problems of limited functionality, insufficient data, and poor scenario adaptability of existing equipment. This improves testing efficiency and reliability and is suitable for multi-user scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-19
- Publication Date
- 2026-04-03
AI Technical Summary
Existing power battery testing equipment has limited functionality, insufficient data acquisition accuracy and comprehensiveness, poor adaptability to various scenarios, low data reliability, and low testing efficiency, failing to meet the diverse needs of multiple users.
Design a portable power battery capacity testing device that integrates a charging/discharging module, a testing module, a communication module, a storage module, an interaction module, and a safety protection module. It features active charging synchronous data acquisition, dual data sources, high-precision data processing, adaptability to multiple scenarios, local caching and breakpoint resume functionality, and support for multi-algorithm analysis.
It enables simultaneous charging and testing, improves data accuracy and comprehensiveness, adapts to multiple scenarios, ensures the reliability and accuracy of test results, and meets the diverse needs of testing stations, insurance companies, and ordinary consumers.
Smart Images
Figure CN121784586A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power battery testing technology for new energy vehicles, specifically to a portable power battery capacity testing device and testing method. Background Technology
[0002] With the rapid development of the new energy vehicle industry, the capacity decay and health status changes of power batteries, as core components, directly affect vehicle range and safety, making the market demand for power battery testing increasingly urgent. However, current technologies in the field of power battery testing have many significant shortcomings, making it difficult to meet the diverse needs of multiple users, including testing stations, insurance companies, and ordinary consumers. Specific problems are as follows: Insufficient functional integration and low testing efficiency: Existing testing equipment and charging equipment are functionally disconnected. The testing equipment only focuses on the single indicator of battery capacity and has no active charging function. It is necessary to rely on the vehicle's own charging process or additional charging equipment to obtain test data, which not only increases the cost of use, but also makes it impossible to carry out charging and testing simultaneously, resulting in a cumbersome and inefficient testing process.
[0003] The data acquisition method is passive, lacking in accuracy and comprehensiveness: Most testing equipment only obtains data by passively parsing vehicle communication network (vehicle CAN / BMS CAN) messages. The acquisition scope is limited to basic parameters such as battery voltage, current, temperature, and SOC, and cannot directly obtain key physical parameters such as charging interface temperature and internal device temperature. Moreover, the data accuracy is limited by the transmission accuracy of vehicle BMS messages, making it difficult to meet the needs of high-precision testing and unable to provide comprehensive and reliable data support for battery status assessment.
[0004] Poor adaptability to different scenarios and obvious limitations in use: Most existing testing equipment is designed for vehicle-mounted use, requiring fixed access to the vehicle's communication network and relying on the vehicle's power supply. It cannot be used independently without the vehicle, making it difficult to adapt to mobile scenarios such as on-site testing by insurance companies, on-site testing of used cars, and self-testing by consumers at home.
[0005] Weak data processing and storage capabilities and insufficient reliability: Existing equipment generally uses the "ampere-hour integration method + simple capacity identification model" to process data. The algorithm is simple and lacks data optimization mechanism; there is no local large-capacity storage and breakpoint resume function, which can easily lead to data loss when the network is interrupted; and the detection process has no traceability capability. When an anomaly occurs, it is difficult to locate the root cause of the problem, which seriously affects the reliability and usability of the detection results.
[0006] Therefore, developing a power battery testing device and method that integrates active charging, high-precision multi-dimensional detection, portable design, and reliable data processing has become an urgent need for the current industry development. Summary of the Invention
[0007] The present invention aims to provide a portable power battery capacity testing device and testing method to solve the problems of existing power battery testing devices, such as limited functionality, insufficient data acquisition accuracy and comprehensiveness, limited testing dimensions, poor scenario adaptability, and low data reliability.
[0008] To achieve the above objectives, the present invention adopts the following technical solution: A portable power battery capacity testing device includes: a charging / discharging module, a testing module, a communication module, a storage module, an interaction module, and a safety protection module; The charging and discharging module is used to convert AC power into DC power to actively charge the vehicle. It includes two power specifications: 3kW and 7kW. The input end is compatible with AC 220V power grid. The 3kW specification uses a household three-prong plug for connection, while the 7kW specification uses a fixed installation of an electricity meter for connection. The output end uses a national standard 9-prong DC charging gun. The detection module integrates independent voltage and current sampling units and BMS communication units, which are used to actively collect charging voltage, charging current, battery cell temperature, charging interface temperature, and internal equipment temperature. At the same time, it parses vehicle BMS messages to obtain SOC and equalization status, forming a dual data source. The communication module is used for bidirectional interaction between data and the cloud platform; The storage module is used for data caching; The interactive module includes a display screen and physical buttons for parameter configuration and detection result display; The security protection module is used to configure security protection functions; It also includes a cloud-based algorithm platform that works in conjunction with each module. The cloud-based algorithm platform has preset data processing algorithms for receiving data uploaded by the device, performing analysis and calculations, and generating detection results.
[0009] The principle and advantages of this solution are as follows: In practical applications, after the device is connected to the power grid, it actively charges the vehicle through the charging and discharging module. The detection module simultaneously collects active sampling data and BMS parsing data. The dual-source data is uploaded to the cloud algorithm platform via the communication module. Through multi-algorithm analysis, comprehensive detection results are generated and displayed. This solution not only solves the problem of existing equipment having limited functionality and requiring additional charging equipment, enabling simultaneous charging and detection; it also overcomes the limitations of passive data collection, improving data accuracy and comprehensiveness; it fills the gap in single capacity detection, enabling multi-dimensional assessment of battery health and safety; at the same time, its portable design and high protection level make it suitable for use in multiple scenarios; and through local caching and breakpoint resume, it ensures data reliability, fully meeting the diverse needs of testing stations, insurance companies, and ordinary consumers.
[0010] Preferably, as an improvement, when the network is interrupted, the collected data is temporarily stored in the storage module, and the data is automatically resumed after the network is restored.
[0011] Technical benefits: Prevents data loss due to network failures and ensures the integrity of test data.
[0012] Preferably, as an improvement, during the data acquisition process, a network clock protocol is used to synchronize the timestamps of the BMS current and the current acquired by the device.
[0013] Technical benefits: Ensures that the starting point and sampling interval of the two types of data collection are consistent, and the timestamp error is controlled within a reasonable range. This avoids deviations in subsequent data processing such as differential calculation and ratio analysis caused by clock asynchrony, thereby improving the accuracy of the detection results.
[0014] Preferably, as an improvement, the safety protection functions include DC short circuit, reverse connection, current and voltage limiting, flame retardancy, over-temperature and under-voltage protection functions, as well as insulation self-test and vehicle insulation detection functions.
[0015] Technical benefits: It constructs a multi-layered safety protection system to effectively avoid safety risks such as short circuits, overheating, and insulation failure during charging and testing. It is adaptable to complex outdoor environments, ensures the safety of equipment and vehicle batteries, and improves product reliability and user trust.
[0016] Preferably, as an improvement, the detection results include battery health status, abnormal detection of single cell voltage, abnormal detection of voltage rise rate, abnormal detection of SOC, abnormal detection of battery temperature, detection of system insulation status, detection of equivalent DC internal resistance, diagnosis of battery lithium plating, abnormal detection of charging interface temperature, abnormal detection of battery temperature rise rate, detection of system charging current exceeding limit, and detection of system total voltage exceeding limit.
[0017] Technical benefits: Breaking through the limitations of existing equipment that only detects battery capacity, this system forms a comprehensive assessment system that includes health status, safety risks, and process anomalies. It can directly support the judgment of battery aging, early warning of safety risks, and fault location, meeting the needs of multiple users for comprehensive battery health status assessment.
[0018] Preferably, as an improvement, the parameter configuration includes inputting driving mileage, battery rated voltage, rated capacity, battery type, license plate number, and VIN code parameters.
[0019] Technical benefits: It enables precise binding of vehicle and battery information, providing a foundation for personalized analysis on the cloud-based algorithm platform, making the test results more consistent with the specific characteristics of the vehicle battery; at the same time, it facilitates the traceability and management of test data, and is suitable for scenarios that require data traceability, such as used car transactions and insurance claims.
[0020] Preferably, as an improvement, the preset data processing algorithm includes: a data preprocessing algorithm, a SOH calculation algorithm, a sampling accuracy evaluation algorithm, a fault level discrimination algorithm, an equivalent DC internal resistance estimation algorithm, a lithium plating risk diagnosis algorithm, and an anomaly determination algorithm.
[0021] Technical effect: By coordinating multiple algorithms to adapt to different detection needs, it provides a scientific basis for battery evaluation.
[0022] Preferably, as an improvement, the sampling accuracy of the detection module is less than or equal to 0.1% FS.
[0023] Technical benefits: Compared to the ≤0.5% FS sampling accuracy of existing equipment, it significantly improves the acquisition accuracy of core parameters such as voltage and current, reduces data errors, provides high-quality data support for subsequent algorithm analysis and detection result generation, and ensures the reliability of evaluation conclusions.
[0024] It also includes a testing method for a portable power battery capacity testing device, comprising: The pre-processing step involves leaving the vehicle stationary for a preset time before powering it off and connecting the device to the vehicle's DC charging port. The parameter configuration steps involve inputting configuration parameters through the interactive module, entering a password, selecting the charging power specification, and then sending the configuration to the device. The charging and testing process is synchronized. The equipment is started, the charging and discharging module charges the vehicle, and the testing module collects dual-source data in real time and uploads it to the cloud algorithm platform. During the charging process, the SOC is controlled in the range of 30% to 90%, ensuring that the SOC change is ≥5%, and the use of vehicle electrical equipment is prohibited, and personnel are not allowed to stay in the vehicle. The data processing and result output steps are as follows: after charging is completed, the device is left to stand still for a preset time. The cloud algorithm platform processes the data through a preset algorithm, generates a comprehensive report of the test results, and sends the test results back to the device interaction module for display.
[0025] Technical benefits: Standardized testing procedures ensure the standardization and repeatability of testing operations; the simultaneous filling and testing design significantly reduces testing time and improves testing efficiency; SOC range control and static step ensure the effectiveness of data collection and reduce interference from external factors; the visual report display lowers the barrier to entry, adapting to users of different professional levels, and achieving convenient operation, standardized processes, and accurate results. Attached Figure Description
[0026] Figure 1 A schematic diagram of a portable power battery capacity testing device; Figure 2 A schematic diagram of the charging parameter configuration for a portable power battery capacity testing device; Figure 3A schematic diagram of the charging process control of a portable power battery capacity testing device; Figure 4 This is a schematic diagram showing the test results of a portable power battery capacity testing device. Detailed Implementation
[0027] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 As shown: A portable power battery capacity testing device includes: a charging / discharging module, a testing module, a communication module, a storage module, an interaction module, and a safety protection module. These modules work together to achieve integrated charging and battery testing functions. The charging and discharging module is used to convert AC power into DC power for active charging of vehicles. It includes two power specifications: 3kW and 7kW. The input end is compatible with AC 220V power grid. The 3kW specification uses a household three-prong plug for connection, while the 7kW specification uses a fixed installation of an electricity meter for connection. The output end uses a national standard 9-pin DC charging head.
[0028] The detection module integrates independent voltage and current sampling units and a BMS communication unit, which are used to actively collect charging voltage, charging current, battery cell temperature, charging interface temperature, and internal equipment temperature. The sampling accuracy is less than or equal to 0.1% FS. At the same time, it parses vehicle BMS messages to obtain SOC and equalization status. The sampled data is independent of BMS data, forming a dual data source.
[0029] During data acquisition, a high-precision network clock protocol is used to ensure that the timestamp error between the BMS current and the current acquired by the device is less than 10 milliseconds, so as to ensure that the starting point and sampling interval of the two types of data acquisition are consistent and to achieve timestamp synchronization.
[0030] The communication module is used for bidirectional data interaction with the cloud platform; it supports switching between multiple network modes such as WiFi and 4G. When adding a WiFi network, the network name and password must be entered to connect. After connecting to the network, the device automatically detects version updates and supports immediate updates or manual updates later through the version information interface. During the update process, the network must be smooth and the device must be powered on continuously.
[0031] The storage module is used for data caching; the storage module has a local storage capacity of ≥1GB. When the network is interrupted, the collected data is temporarily stored in the storage module, and the data will be automatically resumed after the network is restored.
[0032] The interactive module includes a display screen and physical buttons for parameter configuration and display of test results.
[0033] The parameter configuration includes inputting driving mileage, battery rated voltage, rated capacity, battery type, license plate number, and VIN code parameters.
[0034] The detection results include battery health status, detection of abnormal single cell voltage, abnormal voltage rise rate, abnormal SOC, abnormal battery temperature, system insulation status, equivalent DC internal resistance, battery lithium plating diagnosis, abnormal charging interface temperature, abnormal battery temperature rise rate, excessive system charging current, and excessive system total voltage.
[0035] The safety protection module is used to configure safety protection functions; the safety protection functions include DC short circuit, reverse connection, current and voltage limiting, flame retardancy, over-temperature and under-voltage protection functions, as well as insulation self-test and vehicle insulation detection functions.
[0036] It also includes a cloud-based algorithm platform that works in conjunction with each module. The cloud-based algorithm platform has preset data processing algorithms for receiving data uploaded by the device, performing analysis and calculations, and generating detection results.
[0037] The preset data processing algorithms include: data preprocessing algorithm, SOH calculation algorithm, sampling accuracy evaluation algorithm, fault level discrimination algorithm, equivalent DC internal resistance estimation algorithm, lithium plating risk diagnosis algorithm, and anomaly determination algorithm. The data preprocessing algorithm uses a sliding smoothing method to process the collected data to ensure the validity of the collected data; the temporarily stored data is uploaded to the cloud algorithm platform via wireless networks such as WiFi or 4G, and the detection results are fed back after being calculated by the algorithm platform.
[0038] The SOH calculation algorithm is based on battery model simulation using a second-order equivalent circuit. After generating pseudo-labels, these labels are input into a deep learning model and combined with the current sampled values to calculate the SOH.
[0039] The sampling accuracy evaluation algorithm filters data points with stable current / voltage (variance < 0.1) and slow changes (absolute difference < 5), calculates the relative deviation ratio between the BMS current / voltage and the data collected by the device, and takes the average of the 20% to 70% quantiles of the data with a ratio ≤ 1, multiplied by 100%, as the consistency evaluation index.
[0040] The fault level discrimination algorithm is based on historical charging vehicle big data clustering analysis and combines it with the current test data to automatically determine the fault level.
[0041] The equivalent DC internal resistance estimation algorithm uses the voltage and current change method to estimate the equivalent DC internal resistance.
[0042] The lithium plating risk diagnosis algorithm diagnoses lithium plating risk by analyzing the rate of voltage change.
[0043] The anomaly detection algorithm uses a threshold comparison method to determine voltage, current, and temperature anomalies.
[0044] It also includes a testing method for a portable power battery capacity testing device, comprising: In the preprocessing step, connect the device's 220V power cord (or connect it to a 380V power input) to the vehicle's DC charging port, and check that the device is undamaged and the wires are not stretched or knotted. After the vehicle has been left to stand still for a preset time, power it off. In this embodiment, the vehicle has been left to stand still for more than 10 minutes, and the device interface displays "idle" and the cloud status is "online".
[0045] Parameter configuration steps, such as Figure 2 As shown, click on the power-on interface to enter the basic configuration page, and enter the actual driving mileage, battery rated voltage, rated capacity, battery type (lead-acid, lithium iron phosphate, ternary material), license plate number, VIN code and other parameters; after entering the password, set the charging power limit (3kW or 7kW), and after completing the configuration, click "Deploy Configuration".
[0046] The charging and testing synchronization steps, such as Figure 3 As shown, after entering the charging process control interface and clicking "Start", the device will start charging and detection. The charging and discharging module charges the vehicle, while the detection module collects dual-source data in real time and uploads it to the cloud algorithm platform. During the charging process, the SOC is controlled within the range of 30% to 90%. If the SOC change is ≥5%, the charging will end. The device will automatically generate detection end statistics, which include the start / end SOC, voltage, temperature, maximum charging current, and cumulative charging time. The use of vehicle electrical equipment is prohibited, and personnel are not allowed to stay in the vehicle.
[0047] Data processing and result output steps, such as Figure 4 As shown, after charging is completed, the device is left to stand still for a preset time. In this embodiment, it is left to stand still for 5 minutes. The cloud algorithm platform processes the data through a preset algorithm and generates a comprehensive report of the detection results. The detection results are then sent back to the device interaction module for display.
[0048] 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 portable power battery capacity testing device, characterized in that, include: Charge / discharge module, detection module, communication module, storage module, interaction module, and safety protection module; The charging and discharging module is used to convert AC power into DC power to actively charge the vehicle. It includes two power specifications: 3kW and 7kW. The input end is compatible with AC 220V power grid. The 3kW specification uses a household three-prong plug for connection, while the 7kW specification uses a fixed installation of an electricity meter for connection. The output end uses a national standard 9-prong DC charging gun. The detection module integrates independent voltage and current sampling units and BMS communication units, which are used to actively collect charging voltage, charging current, battery cell temperature, charging interface temperature, and internal equipment temperature. At the same time, it parses vehicle BMS messages to obtain SOC and equalization status, forming a dual data source. The communication module is used for bidirectional interaction between data and the cloud platform; The storage module is used for data caching; The interactive module includes a display screen and physical buttons for parameter configuration and detection result display; The security protection module is used to configure security protection functions; It also includes a cloud-based algorithm platform that works in conjunction with each module. The cloud-based algorithm platform has preset data processing algorithms for receiving data uploaded by the device, performing analysis and calculations, and generating detection results.
2. The portable power battery capacity testing device according to claim 1, characterized in that: When the network is interrupted, the collected data will be temporarily stored in the storage module, and the data will be automatically resumed after the network is restored.
3. The portable power battery capacity testing device according to claim 1, characterized in that: During data acquisition, a network clock protocol is used to synchronize the timestamps of the BMS current and the current acquired by the device.
4. The portable power battery capacity testing device according to claim 1, characterized in that: The safety protection functions include DC short circuit, reverse connection, current and voltage limiting, flame retardancy, over-temperature and under-voltage protection functions, as well as insulation self-test and vehicle insulation detection functions.
5. A portable power battery capacity testing device according to claim 1, characterized in that: The detection results include battery health status, detection of abnormal single cell voltage, abnormal voltage rise rate, abnormal SOC, abnormal battery temperature, system insulation status, equivalent DC internal resistance, battery lithium plating diagnosis, abnormal charging interface temperature, abnormal battery temperature rise rate, excessive system charging current, and excessive system total voltage.
6. The portable power battery capacity testing device according to claim 1, characterized in that: The parameter configuration includes inputting driving mileage, battery rated voltage, rated capacity, battery type, license plate number, and VIN code.
7. The portable power battery capacity testing device according to claim 1, characterized in that: The preset data processing algorithms include: data preprocessing algorithm, SOH calculation algorithm, sampling accuracy evaluation algorithm, fault level discrimination algorithm, equivalent DC internal resistance estimation algorithm, lithium plating risk diagnosis algorithm, and anomaly determination algorithm.
8. A portable power battery capacity testing device according to claim 1, characterized in that: The sampling accuracy of the detection module is less than or equal to 0.1% FS.
9. A testing method for a portable power battery capacity testing device, applied to the portable power battery capacity testing device according to any one of claims 1-8, characterized in that, include: The pre-processing step involves leaving the vehicle stationary for a preset time before powering it off and connecting the device to the vehicle's DC charging port. The parameter configuration steps involve inputting configuration parameters through the interactive module, entering a password, selecting the charging power specification, and then sending the configuration to the device. The charging and testing process is synchronized. The equipment is started, the charging and discharging module charges the vehicle, and the testing module collects dual-source data in real time and uploads it to the cloud algorithm platform. During the charging process, the SOC is controlled in the range of 30% to 90%, ensuring that the SOC change is ≥5%, and the use of vehicle electrical equipment is prohibited, and personnel are not allowed to stay in the vehicle. The data processing and result output steps are as follows: after charging is completed, the device is left to stand still for a preset time. The cloud algorithm platform processes the data through a preset algorithm, generates a comprehensive report of the test results, and sends the test results back to the device interaction module for display.