A power battery testing method and system for new energy vehicles

Abstract

The present invention discloses a power battery testing method and system for new energy vehicles, which specifically relates to the field of new energy vehicles, including S1: multi-modal high-precision data automatic acquisition, S2: mining of key elements of power battery performance, S3: data preprocessing, S4: construction and in-depth analysis of multi-dimensional key element collaborative coupling model, S5: battery performance evaluation and S6: visual interaction. When mining the key elements of power battery performance, the present invention comprehensively covers multi-dimensional information such as battery capacity decay analysis, internal resistance change analysis, safety assessment and battery pack consistency consideration. Compared with the prior art, the present invention is more comprehensive. It constructs a series of mathematical models such as battery capacity decay, internal resistance change, overcharge risk, short circuit safety, and capacity and internal resistance consistency assessment. These models work together to converge into a battery effect comprehensive evaluation coefficient model, providing accurate quantitative basis and decision support for comprehensive battery quality evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and more specifically, to a power battery testing method and system for new energy vehicles. Background Art

[0002] As the world pays more and more attention to environmental protection and sustainable development, new energy vehicles, as a substitute for traditional fuel vehicles, are gradually occupying an important share of the automobile market. New energy vehicles mainly include pure electric vehicles, hybrid vehicles and fuel cell vehicles. Among them, power batteries are the core components of pure electric vehicles and hybrid vehicles. Their performance directly determines the key indicators of the vehicle, such as power performance, cruising range, charging time and safety.

[0003] In traditional power battery testing, it is necessary to test various battery properties one by one, such as capacity test, internal resistance test, charge and discharge efficiency test, etc., and each test often needs to be repeated multiple times under different working conditions, resulting in a long entire test cycle. Therefore, applying advanced intelligent manufacturing technologies to power battery testing systems, such as automated testing equipment, smart sensors, industrial Internet technologies, etc., can realize the automation, intelligence and informatization of the testing process, improve production efficiency, reduce costs, and enhance the consistency and stability of product quality.

[0004] However, there are still some shortcomings in its actual use. For example, the testing methods of the prior art mostly test a single or a few battery samples. When faced with large-scale battery production quality control or the screening of new battery materials and structures, the existing testing system is difficult to meet the needs of high-throughput testing. In addition, power batteries will face various complex working conditions in actual use, such as different ambient temperatures, different charge and discharge rates, different states of charge, etc. Although the existing testing system can simulate some basic working conditions, it is still insufficient in simulating complex and changeable actual working conditions. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for testing a power battery for a new energy vehicle, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: A method for testing power batteries for new energy vehicles, comprising: S1: automatic multi-modal high-precision data collection, S2: mining of key elements of power battery performance, S3: data preprocessing, S4: construction and in-depth analysis of a multi-dimensional key element collaborative coupling model, S5: battery performance evaluation, and S6: visual interaction;

[0007] The S1 multi-modal high-precision data automatic acquisition: number the test battery groups and record them as 1, 2, 3...i...n in sequence, filter the power battery feature texts according to different battery groups, and obtain the power battery feature text data;

[0008] The S2 power battery performance key factor mining includes a battery capacity attenuation information mining step, a battery internal resistance change information mining step, a battery safety information mining step, and a battery pack consistency information mining step, which is used to select key texts from the power battery feature text data to obtain the first battery key text;

[0009] The S3 data preprocessing is used to perform standardized processing and analysis on the first battery key text to obtain the second battery key text;

[0010] The S4 multi-dimensional key element collaborative coupling model construction and in-depth analysis: used to import the second battery key text data into the mathematical model of battery capacity attenuation coefficient, the mathematical model of battery internal resistance variation coefficient, the mathematical model of overcharge risk assessment coefficient, the mathematical model of short circuit safety assessment coefficient, the mathematical model of capacity consistency assessment coefficient, the mathematical model of internal resistance consistency assessment coefficient and the mathematical model of battery effect comprehensive assessment coefficient, and process and analyze them to obtain a text output value based on the second battery key text;

[0011] S5 battery performance evaluation: matching the battery effect comprehensive evaluation coefficient in the text output value of the second battery key text with the battery level according to the preset battery level range to determine the battery performance;

[0012] The S6 visual interaction: displays the battery performance evaluation results through an intuitive graphical interface and provides human-computer interaction functions. Users can easily view the battery performance status, input control instructions and make personalized settings through the user information terminal.

[0013] Preferably, the first battery key text specifically refers to: ambient temperature parameter, cycle number parameter, average charge and discharge rate parameter, minimum SOC internal resistance parameter, maximum SOC internal resistance parameter, state of charge SOC parameter, overcharge voltage parameter, overcharge current parameter, overcharge time parameter, short-circuit current parameter, short-circuit temperature rise parameter, short-circuit duration parameter, single cell capacity parameter, average capacity parameter, single cell internal resistance parameter, average internal resistance parameter;

[0014] The ambient temperature parameter refers to the ambient temperature T; the cycle number parameter refers to the cycle number N; the average charge and discharge rate parameter refers to the average charge and discharge rate C; the minimum SOC internal resistance parameter refers to the minimum SOC internal resistance R min ; The maximum SOC internal resistance parameter refers to the maximum SOC internal resistance R max; The state of charge SOC parameter refers to the state of charge SOC parameter s; The overcharge voltage parameter refers to the overcharge voltage V OC ; The overcharge current parameter refers to the overcharge current I OC ; The overcharge time parameter refers to the overcharge time t OC ; The short-circuit current parameter refers to the short-circuit current I SC ; The short-circuit temperature rise parameter refers to the short-circuit temperature rise ΔT SC ; The short circuit duration parameter refers to the short circuit duration t SC The single cell capacity parameter refers to the actual capacity C of the ith single cell in the battery pack. i ; The average capacity parameter refers to the average capacity The internal resistance parameter of the single cell refers to the internal resistance R of the i-th single cell in the battery pack. i ; The average internal resistance parameter refers to the average internal resistance .

[0015] Preferably, the ambient temperature parameter is collected in the following manner:

[0016] Use high-precision temperature sensors placed near the battery to monitor and record ambient temperature data in real time;

[0017] The acquisition method of the cycle number parameter is as follows:

[0018] The battery management system BMS records the number of battery charge and discharge cycles;

[0019] The average charge and discharge rate refers to the average value of the ratio of the charge and discharge current to the rated capacity of the battery within a period of time or in a specific charge and discharge process. The charge and discharge current I is measured by a high-precision ammeter. If the rated capacity C of the battery is known, rated ,but = ;

[0020] The minimum SOC internal resistance parameter specifically refers to the internal resistance at the minimum SOC, which is obtained by discharging the battery to the lowest allowable SOC value and then measuring its internal resistance;

[0021] The maximum SOC internal resistance parameter specifically refers to the internal resistance at the maximum SOC, which is obtained by measuring the internal resistance when the battery is fully charged and reaches the maximum SOC;

[0022] The overcharge voltage specifically refers to the value of the battery exceeding its normal rated charge termination voltage when it is charged. The battery charging voltage is monitored in real time by using a high-precision voltmeter. When it exceeds the rated charge termination voltage, the excess value is recorded as the overcharge voltage.

[0023] The overcharge current specifically refers to the current flowing through the battery during the overcharge process;

[0024] The overcharge time specifically refers to the duration of the battery being in an overcharge state, starting from when the battery voltage exceeds the rated charge termination voltage until the overcharge test is stopped or a dangerous situation occurs, and the recorded time is the overcharge time;

[0025] The short-circuit current specifically refers to the large current generated instantly when the positive and negative electrodes of the battery are directly short-circuited. By connecting a high-precision current sensor in series in the short-circuit test loop, the magnitude and change of the short-circuit current are measured and recorded at the moment of short circuit;

[0026] The short-circuit temperature rise specifically refers to the increase in battery temperature relative to the initial temperature after a short circuit occurs. The temperature rise after the short circuit is calculated by attaching thermistors to key positions on the battery surface, recording the initial temperature before the short circuit, monitoring the temperature change in real time during the short circuit, and calculating the temperature rise after the short circuit.

[0027] The short circuit duration specifically refers to the time interval from the occurrence of the short circuit to the circuit being cut off or the test being stopped. The specific collection method is to start timing from the moment the short circuit occurs and stop timing when the short circuit protection device is activated or the test is terminated manually to obtain the short circuit duration.

[0028] Preferably, the mathematical model of the battery capacity attenuation coefficient is as follows:

[0029] ,in It refers to the battery capacity attenuation coefficient, T refers to the ambient temperature, N refers to the number of cycles, C refers to the average charge and discharge rate, exp refers to the exponential function with e as the base, and j and k refer to constants.

[0030] The mathematical model of the battery internal resistance variation coefficient is as follows:

[0031] + ,in Refers to the battery internal resistance variation coefficient, R min Refers to the minimum SOC internal resistance, R max refers to the maximum SOC internal resistance, s refers to the state of charge SOC parameter, and q refers to the constant;

[0032] The mathematical model of the overcharge risk assessment coefficient is as follows:

[0033] ,in Refers to the overcharge risk assessment coefficient, V OC Refers to the overcharge voltage, I OC Refers to the overcharge current, t OC Refers to the overcharge time, r, b, t, u, v, w refer to constants;

[0034] The mathematical model of the short-circuit safety assessment coefficient is as follows:

[0035] = ,in Refers to the short circuit safety assessment factor, I SC Refers to short-circuit current, ΔT SC Refers to short circuit temperature rise, t SC Refers to the short circuit duration, , , , , Refers to a constant;

[0036] The mathematical model of the capacity consistency evaluation coefficient is as follows:

[0037] = ,in Refers to the capacity consistency assessment coefficient, C i Refers to the actual capacity of the ith single cell in the battery pack, Refers to the average capacity;

[0038] The mathematical model of the internal resistance consistency evaluation coefficient is as follows:

[0039] = ,in Refers to the internal resistance consistency evaluation coefficient, R i Refers to the internal resistance of the ith single cell in the battery pack, Refers to the battery pack Internal resistance of a single cell, Refers to the average internal resistance.

[0040] Preferably, the mathematical model of the comprehensive evaluation coefficient of battery effect is as follows:

[0041] + + + + + ,in Refers to the comprehensive evaluation coefficient of battery effect, Refers to the battery capacity attenuation coefficient, Refers to the battery internal resistance variation coefficient, Refers to the overcharge risk assessment coefficient, Refers to the short-circuit safety assessment factor, Refers to the capacity consistency assessment coefficient, Refers to the internal resistance consistency evaluation coefficient.

[0042] Preferably, the preset battery level interval includes:

[0043] Excellent E ≥ 0.8: When E reaches 0.8 or above, it means that the battery performs well in capacity retention, internal resistance control, safety and consistency. In terms of capacity, the capacity decay rate is low, which can ensure a long driving range; the internal resistance is small, the charging and discharging efficiency is high and the heat generation is low; the risk of overcharging and short circuit is low, and the safety is reliable; the single cells in the battery pack have good consistency in both capacity and internal resistance, which is beneficial to the overall performance and life of the battery pack. This type of battery is suitable for high-end new energy vehicles with extremely high performance requirements or special application scenarios with extremely high reliability requirements;

[0044] Good 0.6≤E<0.8: If E is between 0.6 and 0.8, the battery performance is at a good level, the capacity has a certain degree of attenuation but is still within an acceptable range, the internal resistance is slightly larger but will not have a serious impact on the charge and discharge performance, there is a small risk in terms of safety, and the consistency is also good. This type of battery can be used for ordinary household new energy vehicles or electric equipment for general industrial use;

[0045] Medium 0.4≤E<0.6: When E is between 0.4 and 0.6, the battery performance is moderate, the capacity decay is more obvious, and more frequent charging or battery replacement may be required; the internal resistance is large, which will lead to increased energy loss and heat problems during charging and discharging, and a better thermal management system is needed; the safety risk is increased, and it is necessary to strengthen monitoring during charging and use; the battery pack consistency is average, which may affect the overall balance and life of the battery pack. This type of battery can be used in some occasions that are more cost-sensitive and do not have particularly high performance requirements, such as low-speed electric vehicles or some small power tools;

[0046] Poor 0.2≤E<0.4: If E is between 0.2 and 0.4, the battery performance is poor, the capacity decay is serious, and the endurance is greatly reduced; the internal resistance is too large, which may cause low charging and discharging efficiency, serious heat generation and even affect the battery life; there are major safety risks, and failures are prone to occur during overcharging or short circuit; the battery pack has poor consistency, which will lead to increased imbalance between the single cells in the battery pack, further reducing the overall performance. This type of battery is generally not suitable for new equipment manufacturing, but can be used in some scenarios with extremely low performance requirements and strict safety protection measures, such as some temporary emergency power supplies or backup power systems, and requires close monitoring and regular maintenance;

[0047] Poor E<0.2: When E is less than 0.2, the overall performance of the battery is very poor and can hardly meet the requirements of normal use. The capacity is seriously insufficient, the internal resistance is extremely high, the safety is extremely low, and the consistency is also very poor. This type of battery should be eliminated or deeply repaired and modified before it can be considered for use again.

[0048] Preferably, the user information terminal is used to receive battery performance evaluation results to help users screen out battery packs whose performance does not meet the requirements.

[0049] Preferably, a power battery test system for new energy vehicles comprises: a system central processing unit module, a system operation database and a user information terminal, a multi-modal high-precision data automatic acquisition module, a power battery performance key factor mining module, a data preprocessing module, a multi-dimensional key factor collaborative coupling model construction and in-depth analysis module, a battery performance evaluation module, and a visualization interaction module;

[0050] The system operation database includes all data texts of a power battery test system for new energy vehicles, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module. The user information terminal is an information output device for receiving a power battery test system for new energy vehicles;

[0051] The multimodal high-precision data automatic acquisition module: numbers the test battery groups and records them as 1, 2, 3...i...n in sequence, screens the power battery feature texts according to different battery groups, obtains the power battery feature text data, and outputs the power battery feature text data to the power battery performance key factor mining module;

[0052] The power battery performance key factor mining module includes a battery capacity attenuation information mining unit, a battery internal resistance change information mining unit, a battery safety information mining unit, and a battery pack consistency information mining unit, which is used to select key texts from the power battery feature text data to obtain a first battery key text, and output the first battery key text to the data preprocessing module;

[0053] The data preprocessing module is used to perform standardized processing and analysis on the first battery key text to obtain the second battery key text, and output the second battery key text to the multi-dimensional key element collaborative coupling model construction and deep analysis module;

[0054] The multi-dimensional key element collaborative coupling model construction and in-depth analysis module is used to import the second battery key text data into the mathematical model of the battery capacity attenuation coefficient, the mathematical model of the battery internal resistance variation coefficient, the mathematical model of the overcharge risk assessment coefficient, the mathematical model of the short circuit safety assessment coefficient, the mathematical model of the capacity consistency assessment coefficient, the mathematical model of the internal resistance consistency assessment coefficient and the mathematical model of the battery effect comprehensive assessment coefficient, and process and analyze them to obtain a text output value based on the second battery key text, and output the text output value to the battery performance evaluation module;

[0055] The battery performance evaluation module: matches the battery effect comprehensive evaluation coefficient in the text output value of the second battery key text with the battery level according to the preset battery level range, determines the battery performance, and outputs the determination result to the visualization interaction module;

[0056] The visual interaction module displays the battery performance evaluation results through an intuitive graphical interface and provides human-computer interaction functions. Users can conveniently view the battery performance status, input control instructions, and perform personalized settings through the user information terminal.

[0057] Technical effects and advantages of the present invention:

[0058] 1. The present invention can collect data from multiple battery packs numbered 1 to n at the same time through a multi-modal high-precision data automatic collection step, and obtain data by screening feature texts according to different battery packs, thereby avoiding the problem of low test throughput for a single or a few battery samples in the prior art, and closely linking with subsequent steps and automatically performing them, without the need for manual switching between different test equipment and processes, greatly improving test efficiency and reducing test time costs;

[0059] 2. In the step of mining key elements of power battery performance, the present invention deeply mines key information such as battery capacity attenuation, internal resistance change, safety and battery pack consistency, which is more comprehensive than the prior art which only evaluates a single or a few performance indicators. At the same time, the mathematical models constructed by the present invention, such as battery capacity attenuation coefficient, internal resistance change coefficient, overcharge risk assessment coefficient, short circuit safety assessment coefficient, capacity consistency assessment coefficient, internal resistance consistency assessment coefficient and battery effect comprehensive assessment coefficient, can accurately quantify various performance indicators and comprehensively evaluate battery quality.

[0060] 3. In the multi-modal high-precision data automatic collection step, the present invention collects data for different battery groups under complex working conditions such as different ambient temperatures, charge and discharge rates, and charge states, comprehensively covering various working conditions faced by power batteries in actual use, thereby providing an accurate basis for the adaptability of batteries in different application scenarios, and making up for the shortcomings of the prior art in simulating complex and changeable actual working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0062] Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] As attached Figure 1 A power battery testing method for new energy vehicles shown includes S1: multi-modal high-precision data automatic acquisition, S2: power battery performance key factor mining, S3: data preprocessing, S4: multi-dimensional key factor collaborative coupling model construction and in-depth analysis, S5: battery performance evaluation and S6: visual interaction;

[0065] The S1 multi-modal high-precision data automatic acquisition: number the test battery groups and record them as 1, 2, 3...i...n in sequence, filter the power battery feature texts according to different battery groups, and obtain the power battery feature text data;

[0066] The S2 power battery performance key factor mining includes a battery capacity attenuation information mining step, a battery internal resistance change information mining step, a battery safety information mining step, and a battery pack consistency information mining step, which is used to select key texts from the power battery feature text data to obtain the first battery key text;

[0067] The first battery key text in the step of mining the key elements of power battery performance in the preferred technical solution of this application is as follows:

[0068] It should be specifically noted that in this embodiment, the first battery key text specifically refers to: ambient temperature parameter, cycle number parameter, average charge and discharge rate parameter, minimum SOC internal resistance parameter, maximum SOC internal resistance parameter, state of charge SOC parameter, overcharge voltage parameter, overcharge current parameter, overcharge time parameter, short-circuit current parameter, short-circuit temperature rise parameter, short-circuit duration parameter, single cell capacity parameter, average capacity parameter, single cell internal resistance parameter, average internal resistance parameter;

[0069] The ambient temperature parameter refers to the ambient temperature T; the cycle number parameter refers to the cycle number N; the average charge and discharge rate parameter refers to the average charge and discharge rate C; the minimum SOC internal resistance parameter refers to the minimum SOC internal resistance R min ; The maximum SOC internal resistance parameter refers to the maximum SOC internal resistance R max ; The state of charge SOC parameter refers to the state of charge SOC parameter s; The overcharge voltage parameter refers to the overcharge voltage V OC ; The overcharge current parameter refers to the overcharge current I OC ; The overcharge time parameter refers to the overcharge time tOC ; The short-circuit current parameter refers to the short-circuit current I SC ; The short-circuit temperature rise parameter refers to the short-circuit temperature rise ΔT SC ; The short circuit duration parameter refers to the short circuit duration t SC The single cell capacity parameter refers to the actual capacity C of the ith single cell in the battery pack. i ; The average capacity parameter refers to the average capacity The internal resistance parameter of the single cell refers to the internal resistance R of the i-th single cell in the battery pack. i ; The average internal resistance parameter refers to the average internal resistance ;

[0070] It should be specifically explained in this embodiment that the method for collecting the ambient temperature parameter is as follows:

[0071] Use high-precision temperature sensors placed near the battery to monitor and record ambient temperature data in real time;

[0072] The acquisition method of the cycle number parameter is as follows:

[0073] The battery management system BMS records the number of battery charge and discharge cycles;

[0074] The average charge and discharge rate refers to the average value of the ratio of the charge and discharge current to the rated capacity of the battery within a period of time or in a specific charge and discharge process. The charge and discharge current I is measured by a high-precision ammeter. If the rated capacity C of the battery is known, rated ,but = ;

[0075] The minimum SOC internal resistance parameter specifically refers to the internal resistance at the minimum SOC, which is obtained by discharging the battery to the lowest allowable SOC value and then measuring its internal resistance;

[0076] The maximum SOC internal resistance parameter specifically refers to the internal resistance at the maximum SOC, which is obtained by measuring the internal resistance when the battery is fully charged and reaches the maximum SOC;

[0077] The overcharge voltage specifically refers to the value of the battery exceeding its normal rated charge termination voltage when it is charged. The battery charging voltage is monitored in real time by using a high-precision voltmeter. When it exceeds the rated charge termination voltage, the excess value is recorded as the overcharge voltage.

[0078] The overcharge current specifically refers to the current flowing through the battery during the overcharge process;

[0079] The overcharge time specifically refers to the duration of the battery being in an overcharge state, starting from when the battery voltage exceeds the rated charge termination voltage until the overcharge test is stopped or a dangerous situation occurs, and the recorded time is the overcharge time;

[0080] The short-circuit current specifically refers to the large current generated instantly when the positive and negative electrodes of the battery are directly short-circuited. By connecting a high-precision current sensor in series in the short-circuit test loop, the magnitude and change of the short-circuit current are measured and recorded at the moment of short circuit;

[0081] The short-circuit temperature rise specifically refers to the increase in battery temperature relative to the initial temperature after a short circuit occurs. The temperature rise after the short circuit is calculated by attaching thermistors to key positions on the battery surface, recording the initial temperature before the short circuit, monitoring the temperature change in real time during the short circuit, and calculating the temperature rise after the short circuit.

[0082] The short circuit duration specifically refers to the time interval from the occurrence of the short circuit to the circuit being cut off or the test being stopped. The specific collection method is to start timing from the moment the short circuit occurs and stop timing when the short circuit protection device is activated or the test is manually terminated to obtain the short circuit duration;

[0083] The S3 data preprocessing is used to perform standardized processing and analysis on the first battery key text to obtain the second battery key text;

[0084] It should be specifically explained in this embodiment that the standardization process specifically refers to Z-score standardization process, which converts the original data into a standardized Z-Score value by calculating the mean and standard deviation of the original data.

[0085] The S4 multi-dimensional key element collaborative coupling model construction and in-depth analysis: used to import the second battery key text data into the mathematical model of battery capacity attenuation coefficient, the mathematical model of battery internal resistance variation coefficient, the mathematical model of overcharge risk assessment coefficient, the mathematical model of short circuit safety assessment coefficient, the mathematical model of capacity consistency assessment coefficient, the mathematical model of internal resistance consistency assessment coefficient and the mathematical model of battery effect comprehensive assessment coefficient, and process and analyze them to obtain a text output value based on the second battery key text;

[0086] It should be specifically noted that in this embodiment, the text output value includes a battery capacity attenuation coefficient, a battery internal resistance variation coefficient, an overcharge risk assessment coefficient, a short circuit safety assessment coefficient, a capacity consistency assessment coefficient, an internal resistance consistency assessment coefficient, and a battery effect comprehensive assessment coefficient;

[0087] It should be specifically explained in this embodiment that the mathematical model of the battery capacity attenuation coefficient is as follows:

[0088] ,in Refers to the battery capacity attenuation coefficient, T refers to the ambient temperature, N refers to the number of cycles, C refers to the average charge and discharge rate, exp refers to the exponential function with e as the base, and j and k refer to constants;

[0089] It should be specifically explained in this embodiment that the mathematical model of the battery internal resistance variation coefficient is as follows:

[0090] + ,in Refers to the battery internal resistance variation coefficient, R min Refers to the minimum SOC internal resistance, R max refers to the maximum SOC internal resistance, s refers to the state of charge SOC parameter, and q refers to the constant;

[0091] It should be specifically explained in this embodiment that the mathematical model of the overcharge risk assessment coefficient is as follows:

[0092] ,in Refers to the overcharge risk assessment coefficient, V OC Refers to the overcharge voltage, I OC Refers to the overcharge current, t OC Refers to the overcharge time, r, b, t, u, v, w refer to constants;

[0093] It should be specifically explained in this embodiment that the mathematical model of the short-circuit safety assessment coefficient is as follows:

[0094] = ,in Refers to the short circuit safety assessment factor, I SC Refers to short-circuit current, ΔT SC Refers to short circuit temperature rise, t SC Refers to the short circuit duration, , , , , Refers to a constant;

[0095] It should be specifically explained in this embodiment that the mathematical model of the capacity consistency evaluation coefficient is as follows:

[0096] = ,in Refers to the capacity consistency assessment coefficient, C i Refers to the actual capacity of the ith single cell in the battery pack, Refers to the average capacity;

[0097] It should be specifically noted in this embodiment that i and n in the mathematical model of the capacity consistency evaluation coefficient are not equivalent to the battery pack number. In the mathematical model of the capacity consistency evaluation coefficient, i refers to the i-th single cell in the battery pack, and n refers to the number of single cells in the battery pack;

[0098] It should be specifically explained in this embodiment that the mathematical model of the internal resistance consistency evaluation coefficient is as follows:

[0099] = ,in Refers to the internal resistance consistency evaluation coefficient, R i Refers to the internal resistance of the ith single cell in the battery pack, Refers to the battery pack Internal resistance of a single cell, Refers to the average internal resistance;

[0100] It should be specifically explained in this embodiment that i and n in the mathematical model of the internal resistance consistency evaluation coefficient are not equivalent to the battery pack number. In the mathematical model of the internal resistance consistency evaluation coefficient, i refers to the i-th single cell in the battery pack, and n refers to the number of single cells in the battery pack. Refers to the battery pack Single battery;

[0101] It should be specifically explained in this embodiment that the mathematical model of the comprehensive evaluation coefficient of the battery effect is as follows:

[0102] + + + + + ,in Refers to the comprehensive evaluation coefficient of battery effect, Refers to the battery capacity attenuation coefficient, Refers to the battery internal resistance variation coefficient, Refers to the overcharge risk assessment coefficient, Refers to the short-circuit safety assessment factor, Refers to the capacity consistency assessment coefficient, Refers to the internal resistance consistency evaluation coefficient;

[0103] S5 battery performance evaluation: matching the battery effect comprehensive evaluation coefficient in the text output value of the second battery key text with the battery level according to the preset battery level range to determine the battery performance;

[0104] It should be specifically explained in this embodiment that the preset battery level range includes:

[0105] Excellent E ≥ 0.8: When E reaches 0.8 or above, it means that the battery performs well in capacity retention, internal resistance control, safety and consistency. In terms of capacity, the capacity decay rate is low, which can ensure a long driving range; the internal resistance is small, the charging and discharging efficiency is high and the heat generation is low; the risk of overcharging and short circuit is low, and the safety is reliable; the single cells in the battery pack have good consistency in both capacity and internal resistance, which is beneficial to the overall performance and life of the battery pack. This type of battery is suitable for high-end new energy vehicles with extremely high performance requirements or special application scenarios with extremely high reliability requirements;

[0106] Good 0.6≤E<0.8: If E is between 0.6 and 0.8, the battery performance is at a good level, the capacity has a certain degree of attenuation but is still within an acceptable range, the internal resistance is slightly larger but will not have a serious impact on the charge and discharge performance, there is a small risk in terms of safety, and the consistency is also good. This type of battery can be used for ordinary household new energy vehicles or electric equipment for general industrial use;

[0107] Medium 0.4≤E<0.6: When E is between 0.4 and 0.6, the battery performance is moderate, the capacity decay is more obvious, and more frequent charging or battery replacement may be required; the internal resistance is large, which will lead to increased energy loss and heat problems during charging and discharging, and a better thermal management system is needed; the safety risk is increased, and it is necessary to strengthen monitoring during charging and use; the battery pack consistency is average, which may affect the overall balance and life of the battery pack. This type of battery can be used in some occasions that are more cost-sensitive and do not have particularly high performance requirements, such as low-speed electric vehicles or some small power tools;

[0108] Poor 0.2≤E<0.4: If E is between 0.2 and 0.4, the battery performance is poor, the capacity decay is serious, and the endurance is greatly reduced; the internal resistance is too large, which may cause low charging and discharging efficiency, serious heat generation and even affect the battery life; there are major safety risks, and failures are prone to occur during overcharging or short circuit; the battery pack has poor consistency, which will lead to increased imbalance between the single cells in the battery pack, further reducing the overall performance. This type of battery is generally not suitable for new equipment manufacturing, but can be used in some scenarios with extremely low performance requirements and strict safety protection measures, such as some temporary emergency power supplies or backup power systems, and requires close monitoring and regular maintenance;

[0109] Poor E<0.2: When E is less than 0.2, the overall performance of the battery is very poor and can hardly meet the requirements of normal use. The capacity is seriously insufficient, the internal resistance is extremely high, the safety is extremely low, and the consistency is also very poor. This type of battery should be eliminated or deeply repaired and modified before it can be considered for use again.

[0110] The S6 visual interaction: displays the battery performance evaluation results through an intuitive graphical interface and provides human-computer interaction functions. Users can easily view the battery performance status, input control commands and make personalized settings through the user information terminal;

[0111] It should be specifically explained in this embodiment that the user information terminal is used to receive the battery performance evaluation result to help the user screen out the battery packs whose performance does not meet the requirements.

[0112] As attached Figure 2 A power battery test system for new energy vehicles shown in the figure includes: a system central processor module, a system operation database and a user information terminal, a multi-modal high-precision data automatic acquisition module, a power battery performance key factor mining module, a data preprocessing module, a multi-dimensional key factor collaborative coupling model construction and in-depth analysis module, a battery performance evaluation module, and a visualization interaction module;

[0113] The system operation database includes all data texts of a power battery test system for new energy vehicles, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module. The user information terminal is an information output device for receiving a power battery test system for new energy vehicles;

[0114] The multimodal high-precision data automatic acquisition module: numbers the test battery groups and records them as 1, 2, 3...i...n in sequence, screens the power battery feature texts according to different battery groups, obtains the power battery feature text data, and outputs the power battery feature text data to the power battery performance key factor mining module;

[0115] The power battery performance key factor mining module includes a battery capacity attenuation information mining unit, a battery internal resistance change information mining unit, a battery safety information mining unit, and a battery pack consistency information mining unit, which is used to select key texts from the power battery feature text data to obtain a first battery key text, and output the first battery key text to the data preprocessing module;

[0116] The data preprocessing module is used to perform standardized processing and analysis on the first battery key text to obtain the second battery key text, and output the second battery key text to the multi-dimensional key element collaborative coupling model construction and deep analysis module;

[0117] The multi-dimensional key element collaborative coupling model construction and in-depth analysis module is used to import the second battery key text data into the mathematical model of the battery capacity attenuation coefficient, the mathematical model of the battery internal resistance variation coefficient, the mathematical model of the overcharge risk assessment coefficient, the mathematical model of the short circuit safety assessment coefficient, the mathematical model of the capacity consistency assessment coefficient, the mathematical model of the internal resistance consistency assessment coefficient and the mathematical model of the battery effect comprehensive assessment coefficient, and process and analyze them to obtain a text output value based on the second battery key text, and output the text output value to the battery performance evaluation module;

[0118] The battery performance evaluation module: matches the battery effect comprehensive evaluation coefficient in the text output value of the second battery key text with the battery level according to the preset battery level range, determines the battery performance, and outputs the determination result to the visualization interaction module;

[0119] The visual interaction module: displays the battery performance evaluation results through an intuitive graphical interface and provides human-computer interaction functions. Users can conveniently view the battery performance status, input control instructions and perform personalized settings through the user information terminal;

[0120] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0121] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for testing a power battery for a new energy vehicle, characterized in that: include: S1: Multi-modal high-precision data automatic acquisition: number the test battery groups and record them as 1, 2, 3…i…n in sequence, filter the power battery feature text according to different battery groups, and obtain the power battery feature text data; S2: Mining key elements of power battery performance: including the steps of mining battery capacity attenuation information, mining battery internal resistance change information, mining battery safety information and mining battery pack consistency information, for selecting key texts from the power battery feature text data to obtain the first battery key text; S3: Data preprocessing: used to perform standardized processing and analysis on the first battery key text to obtain the second battery key text; S4: Construction and in-depth analysis of the collaborative coupling model of multi-dimensional key elements: used to import the second battery key text data into the mathematical model of battery capacity attenuation coefficient, the mathematical model of battery internal resistance variation coefficient, the mathematical model of overcharge risk assessment coefficient, the mathematical model of short circuit safety assessment coefficient, the mathematical model of capacity consistency assessment coefficient, the mathematical model of internal resistance consistency assessment coefficient and the mathematical model of battery effect comprehensive assessment coefficient, and process and analyze them to obtain the text output value based on the second battery key text; S5: battery performance evaluation: matching the battery effect comprehensive evaluation coefficient in the text output value of the second battery key text with the battery level according to the preset battery level range to determine the battery performance; S6: Visual interaction: The battery performance evaluation results are displayed through an intuitive graphical interface, and human-computer interaction functions are provided. Users can easily view the battery performance status, input control commands, and make personalized settings through the user information terminal.

2. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The first battery key text specifically refers to: ambient temperature parameter, cycle number parameter, average charge and discharge rate parameter, minimum SOC internal resistance parameter, maximum SOC internal resistance parameter, state of charge SOC parameter, overcharge voltage parameter, overcharge current parameter, overcharge time parameter, short circuit current parameter, short circuit temperature rise parameter, short circuit duration parameter, single cell capacity parameter, average capacity parameter, single cell internal resistance parameter, average internal resistance parameter; The ambient temperature parameter refers to the ambient temperature T; The cycle number parameter refers to the cycle number N; The average charge and discharge rate parameter refers to the average charge and discharge rate C; The minimum SOC internal resistance parameter refers to the minimum SOC internal resistance R min ; The maximum SOC internal resistance parameter refers to the maximum SOC internal resistance R max ; The state of charge SOC parameter refers to the state of charge SOC parameter s; The overcharge voltage parameter refers to the overcharge voltage V OC ; The overcharge current parameter refers to the overcharge current I OC ; The overcharge time parameter refers to the overcharge time t OC ; The short-circuit current parameter refers to the short-circuit current I SC ; The short-circuit temperature rise parameter refers to the short-circuit temperature rise ΔT SC ; The short circuit duration parameter refers to the short circuit duration t SC ; The single cell capacity parameter refers to the actual capacity C of the ith single cell in the battery pack. i ; The average capacity parameter refers to the average capacity ; The single cell internal resistance parameter refers to the internal resistance R of the i-th single cell in the battery pack. i ; The average internal resistance parameter refers to the average internal resistance .

3. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The mathematical model of the battery capacity attenuation coefficient is specifically as follows: ,in It refers to the battery capacity attenuation coefficient, T refers to the ambient temperature, N refers to the number of cycles, C refers to the average charge and discharge rate, exp refers to the exponential function with e as the base, and j and k refer to constants.

4. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The mathematical model of the battery internal resistance variation coefficient is as follows: + ,in Refers to the battery internal resistance variation coefficient, R min Refers to the minimum SOC internal resistance, R max It refers to the maximum SOC internal resistance, s refers to the state of charge SOC parameter, and q refers to the constant.

5. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The mathematical model of the overcharge risk assessment coefficient is as follows: ,in Refers to the overcharge risk assessment coefficient, V OC Refers to the overcharge voltage, I OC Refers to the overcharge current, t OC Refers to the overcharge time, r, b, t, u, v, and w are constants.

6. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The mathematical model of the short-circuit safety assessment coefficient is as follows: = ,in Refers to the short circuit safety assessment factor, I SC Refers to short-circuit current, ΔT SC Refers to short circuit temperature rise, t SC Refers to the short circuit duration, , , , , Refers to a constant.

7. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The mathematical model of the capacity consistency evaluation coefficient is as follows: = ,in Refers to the capacity consistency assessment coefficient, C i Refers to the actual capacity of the ith single cell in the battery pack. Refers to the average capacity.

8. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The mathematical model of the internal resistance consistency evaluation coefficient is as follows: = ,in Refers to the internal resistance consistency evaluation coefficient, R i Refers to the internal resistance of the ith single cell in the battery pack, Refers to the battery pack Internal resistance of a single cell, Refers to the average internal resistance.

9. A method for testing a power battery for a new energy vehicle according to claim 1, characterized in that: The mathematical model of the comprehensive evaluation coefficient of the battery effect is as follows: + + + + + ,in Refers to the comprehensive evaluation coefficient of battery effect, Refers to the battery capacity attenuation coefficient, Refers to the battery internal resistance variation coefficient, Refers to the overcharge risk assessment coefficient, Refers to the short circuit safety assessment factor, Refers to the capacity consistency assessment coefficient, Refers to the internal resistance consistency evaluation coefficient.

10. A power battery testing system for new energy vehicles, characterized in that: include: System central processor module, system operation database and user information terminal, multi-modal high-precision data automatic acquisition module, power battery performance key factor mining module, data preprocessing module, multi-dimensional key factor collaborative coupling model construction and in-depth analysis module, battery performance evaluation module and visual interaction module; The system operation database includes all data texts of a power battery test system for new energy vehicles, and collects information texts output by each module in real time. The system central processor module is used to centrally control the information text instructions output by each module. The user information terminal is an information output device for receiving a power battery test system for new energy vehicles; The multimodal high-precision data automatic acquisition module: numbers the test battery groups and records them as 1, 2, 3...i...n in sequence, screens the power battery feature texts according to different battery groups, obtains the power battery feature text data, and outputs the power battery feature text data to the power battery performance key factor mining module; The power battery performance key factor mining module includes a battery capacity attenuation information mining unit, a battery internal resistance change information mining unit, a battery safety information mining unit, and a battery pack consistency information mining unit, which is used to select key texts from the power battery feature text data to obtain a first battery key text, and output the first battery key text to the data preprocessing module; The data preprocessing module is used to perform standardized processing and analysis on the first battery key text to obtain the second battery key text, and output the second battery key text to the multi-dimensional key element collaborative coupling model construction and deep analysis module; The multi-dimensional key element collaborative coupling model construction and in-depth analysis module is used to import the second battery key text data into the mathematical model of the battery capacity attenuation coefficient, the mathematical model of the battery internal resistance variation coefficient, the mathematical model of the overcharge risk assessment coefficient, the mathematical model of the short circuit safety assessment coefficient, the mathematical model of the capacity consistency assessment coefficient, the mathematical model of the internal resistance consistency assessment coefficient and the mathematical model of the battery effect comprehensive assessment coefficient, and process and analyze them to obtain a text output value based on the second battery key text, and output the text output value to the battery performance evaluation module; The battery performance evaluation module: matches the battery effect comprehensive evaluation coefficient in the text output value of the second battery key text with the battery level according to the preset battery level range, determines the battery performance, and outputs the determination result to the visualization interaction module; The visual interaction module displays the battery performance evaluation results through an intuitive graphical interface and provides human-computer interaction functions. Users can conveniently view the battery performance status, input control instructions, and perform personalized settings through the user information terminal.

Images