Lithium iron phosphate battery health state detection method based on real-time current and voltage signals

Abstract

The invention relates to the technical field of battery state detection, and discloses a lithium iron phosphate battery health state detection method based on real-time current and voltage signals, and the method comprises the steps of data testing, feature extraction, model construction, battery detection and health assessment. The full life cycle performance data of the lithium iron phosphate cell can be effectively obtained through an elaborately designed battery aging test, a practical linear equation is constructed based on extracted key characteristics such as charging platform voltage, discharging platform voltage, voltage difference and capacity fading rate, and accurate quantitative evaluation of the health state of the battery is realized; the whole detection process does not need to depend on deep learning and a complex model, and the application threshold and the calculation complexity are reduced; when being applied to port equipment, the battery health state can be monitored in real time, the health, sub-health or fault state can be accurately judged, and corresponding measures can be taken in time.

Description

Technical Field

[0001] The present invention relates to the technical field of battery status detection, and in particular to a method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals. Background Art

[0002] During actual service, lithium iron phosphate batteries face the dilemma of gradually declining performance with charge and discharge cycles. Take port equipment as an example. Its operation intensity is high and its operation time is long. It relies on lithium iron phosphate batteries for continuous energy supply. However, as the battery is constantly charged and discharged, the cruising range is significantly shortened. Frequent charging not only reduces the operating efficiency of the equipment, but also increases operating costs. What is more serious is that a sudden drop in battery performance may cause sudden power outages in the equipment, resulting in stagnation of cargo loading and unloading, logistics delays, and even port operation safety accidents, endangering the safety of personnel and cargo.

[0003] Looking at the current traditional methods of testing the health status of lithium iron phosphate batteries, their shortcomings are obvious. On the one hand, most existing methods rely on complex and expensive professional instruments. Such equipment is often limited to specific laboratory environments and can only periodically sample a small number of samples. It is difficult to achieve real-time online monitoring of large-scale, dispersed battery packs, such as batteries carried by many equipment in ports. It is impossible to immediately feedback the actual working conditions of the battery and it is difficult to detect potential hidden dangers in a timely manner. On the other hand, conventional detection strategies often focus on a single physical quantity, simply examining voltage or current fluctuations, but ignoring the complex internal linkage between multiple parameters throughout the battery charging and discharging process, causing the test results to deviate from reality and unable to accurately outline the overall health of the battery. In addition, the performance degradation of lithium iron phosphate batteries is interactively affected by multiple factors such as temperature, charge and discharge rate, and use environment. Traditional detection methods are unable to take into account all these factors. Under complex and changeable actual working conditions, the error of battery health assessment increases significantly, and reliability is greatly reduced. Summary of the invention

[0004] The purpose of the present invention is to provide a method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals, thereby solving the technical problems raised in the background technology.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals comprises the following steps:

[0007] Step 1: Data testing:

[0008] The battery aging experiment is used to collect the full life cycle performance data of the lithium iron phosphate battery cell. The full life cycle performance data is included in each charge and discharge cycle test. The current, voltage and time node data of each stage of charging and discharging are recorded in real time, and the charge and discharge amount of each charge and discharge is calculated. Finally, based on the recorded data, the UQ curve of charging and discharging is drawn with the charge or discharge amount as the horizontal axis and the corresponding voltage as the vertical axis;

[0009] Step 2: Feature extraction:

[0010] From the charging UQ curve, select the charge amount and voltage value of adjacent charging time nodes, calculate the slope change rate between them, and find the charging voltage corresponding to the minimum slope change rate, which is the charging platform voltage; similarly, in the discharge UQ curve, use a similar method to calculate the discharge platform voltage, subtract the discharge platform voltage from the charging platform voltage to get the voltage difference, and then calculate the capacity decay rate based on the initial discharge amount and the discharge amount recorded at the specified discharge time node;

[0011] Step 3: Model construction:

[0012] We conducted charge and discharge cycle tests on multiple groups of lithium iron phosphate battery samples in different health states, collected the voltage difference and capacity decay rate of the samples at different cycle times, and also collected the battery health status indicators quantitatively evaluated by battery testing equipment, and then constructed a linear equation based on them;

[0013] Step 4: Battery test:

[0014] According to the data testing process, the current, voltage, time node and power data involved in the charging and discharging process of the lithium iron phosphate battery to be tested are collected in real time. Then, the voltage difference and capacity decay rate of the battery are extracted by means of feature extraction. Finally, the extracted voltage difference and capacity decay rate are substituted into the constructed linear equation to calculate the battery health status index.

[0015] Step 5: Health Assessment:

[0016] The battery health status index calculated for the lithium iron phosphate battery to be tested is compared with a preset battery health status threshold, and the current health status of the lithium iron phosphate battery to be tested is determined.

[0017] As a further solution of the present invention: when the lithium iron phosphate battery cell sample is subjected to a charge and discharge cycle test, the lithium iron phosphate battery cell sample is placed in a constant temperature environment box.

[0018] As a further solution of the present invention: the data testing method is as follows:

[0019] Step S1.1, performing a charge-discharge cycle test on a lithium iron phosphate battery cell sample;

[0020] The charge and discharge cycle test is carried out until the discharge capacity of the battery cell drops to 80% of the initial discharge capacity;

[0021] i.e. QD p ≤80%×QD 0 , where p is the number of cycles when the discharge capacity of the cell drops to 80% of the initial discharge capacity, QD 0 is the initial discharge capacity;

[0022] Step S1.2, during each charge and discharge cycle, the charging current, charging voltage and charging time node are recorded in real time, as well as the test data corresponding to the discharge current, discharge voltage and discharge time node are recorded in real time; and during each charge and discharge cycle, the charging current and charging voltage are recorded in real time at each charging time node as LC t1，k ,UC t1，k , and the discharge current and discharge voltage are recorded in real time at each discharge time node, respectively as LD t2，k UD t2，k ;

[0023] Wherein, t1=1, 2, ... e1, e1 represents the number of charging time nodes, t2=1, 2, charge and discharge cycles ... e2, e2 represents the number of discharge time nodes, k=1, 2, ... p, p represents the number of charge and discharge cycle processes;

[0024] Step S1.3, simultaneously by:

[0025] Calculate the charge capacity QC of each charging process k ;

[0026] and through:

[0027] Calculate the charge amount QD of each charging process k ;

[0028] Step S1.4: In each charge and discharge cycle, the voltage data and the corresponding capacity data in the charge test or discharge test are sorted out, and a charge UQ curve and a discharge UQ curve are drawn.

[0029] As a further solution of the present invention: the arrangement method in step S1.4 is as follows:

[0030] During the charging process, the initial charge is started and the charge amount QC t1，k The increase of charging voltage UC t1，k The charging UQ curve is gradually drawn with the charging amount as the horizontal axis and the charging voltage as the vertical axis;

[0031] Among them, QCt1，k Refers to the charge amount recorded at the t1th charging time node in the kth charging cycle;

[0032] During the discharge process, starting with the initial discharge amount, as the discharge amount QD t2，k The increase of discharge voltage UD is recorded. t2，k The discharge UQ curve is gradually drawn with the discharge amount as the horizontal axis and the discharge voltage as the vertical axis;

[0033] Among them, QD t2，k Refers to the discharge amount recorded at the t2th discharge time node in the kth discharge cycle.

[0034] As a further solution of the present invention: the feature extraction method is as follows:

[0035] Step T1.1, charging platform voltage:

[0036] In the charging UQ curve, obtain the charging amount QC at adjacent charging time nodes t1-1，k , QC t1，k and QC t1+1，k , and the charging voltage value UC at the adjacent charging time node t1-2，k , U.C. t1，k and UC t1+1，k ;

[0037] Then through:

[0038] Calculate the slope change rate R between the charging amount and the charging voltage value at adjacent charging time nodes t1 ;

[0039] Then in all R t1 The slope change rate with the minimum value is obtained, and the charging voltage value at the corresponding charging time node t1 is obtained as the charging platform voltage, and is recorded as Upc;

[0040] Step T1.2, discharge platform voltage:

[0041] In the discharge UQ curve, obtain the discharge amount QD at adjacent discharge time nodes t2-1，k , QD t2，k and QD t2+1，k , and the discharge voltage value UD at the adjacent discharge time node t2-2，k , U.D. t2，k and UD t2+1，k ;

[0042] Then through:

[0043] Calculate the slope change rate R between the discharge amount and the discharge voltage value at adjacent discharge time nodes t2 ;

[0044] Then in all R t2 The minimum slope change rate is obtained, and the discharge voltage value at the corresponding discharge time node t2 is obtained as the discharge platform voltage, which is recorded as Upd;

[0045] Step T1.3, voltage difference:

[0046] The voltage difference is calculated by subtracting the discharge platform voltage from the charge platform voltage and recorded as UW;

[0047] The formula is: UW = Upc-Upd;

[0048] Step T1.4, capacity decay rate:

[0049] pass:

[0050] Calculate the capacity decay rate SJ of the lithium iron phosphate battery;

[0051] Where, QD 0 is the initial discharge capacity, QD e2，p Refers to the discharge amount recorded at the e2th discharge time node in the pth discharge cycle.

[0052] As a further solution of the present invention: the model is constructed as follows:

[0053] Step M1.1, by performing charge-discharge cycle tests on multiple groups of lithium iron phosphate battery samples in different health states, the voltage difference and capacity attenuation rate of these samples at different charge-discharge cycle times are collected and recorded as UW g and S.J. g , g = 1, 2, ... v, v represents the number of different charge and discharge cycles;

[0054] At the same time, the battery health status indicator of the corresponding lithium iron phosphate battery is recorded and marked as HK g ;

[0055] Among them, the battery health status indicators are quantitatively evaluated through battery testing equipment;

[0056] Step M1.2, formulate a linear equation based on the voltage difference, capacity decay rate and battery health status index;

[0057] The linear equation is: HK = UW × β 1 +SJ×β 2 +β 0 ;

[0058] In the formula, HK, UW and SJ are the input values ​​of voltage difference, capacity attenuation rate and battery health status index respectively, β 0 , β 1 , β 2 is the weight coefficient obtained by fitting the test data in step M1.1;

[0059] Step M1.3: Substitute the UW obtained in step M1.1 g , SJ g and HK g Substitute into the linear equations proposed in step M1.2 and obtain the linear equation system:

[0060] Then through the least squares method;

[0061] Let the error sum of squares Minimum;

[0062] Then, β 0 , β 1 , β 2 Find the partial derivative and set it equal to 0:

[0063] The equations are as follows:

[0064] By solving the above equations, we can get the weight coefficient β 0 , β 1 , β 2 The value of .

[0065] As a further solution of the present invention: the battery detection method is as follows:

[0066] Step X1.1, in accordance with the data testing method, real-time collection of the charging current, charging voltage, charging time node, charging amount, discharging current, discharging voltage, discharging time node and discharging amount of the lithium iron phosphate battery to be tested;

[0067] Step X1.2, extracting the voltage difference and capacity attenuation rate of the lithium iron phosphate battery to be tested in a feature extraction manner;

[0068] Step X1.3, substitute the voltage difference and capacity attenuation rate of the lithium iron phosphate battery to be tested into the linear equation, and obtain the battery health status index of the lithium iron phosphate battery to be tested.

[0069] As a further solution of the present invention: the health assessment method is as follows:

[0070] Compare the battery health status value of the lithium iron phosphate battery to be tested with the preset battery health status threshold:

[0071] When HK1≥0.8×HKy, it means the battery is in a healthy state;

[0072] When 0.5×HKy<HK1<0.8×HKy, it means the battery is in a sub-healthy state;

[0073] When HK1≤0.5×HKy, it means the battery is in a fault state;

[0074] Among them, HK1 is the battery health status value of the lithium iron phosphate battery to be tested, and HKy is the preset battery health status threshold.

[0075] Beneficial effects of the present invention:

[0076] Comprehensive use of data: Through battery aging experiments, the performance data of lithium iron phosphate batteries throughout their life cycle is collected, covering the current, voltage, and time node data at each stage of charging and discharging. The charging and discharging amount is calculated, and the UQ curves of charging and discharging are drawn. Starting from multi-dimensional data, a comprehensive and solid data foundation is laid for the subsequent accurate detection of the battery health status, avoiding detection errors caused by missing or one-sided data.

[0077] Accurate feature extraction: Carefully select characteristic data from the charging and discharging UQ curves, such as calculating the charging platform voltage and the discharging platform voltage, and then obtaining the voltage difference, as well as calculating the capacity decay rate based on the discharge amount recorded at the initial and specified discharge time nodes. These characteristic parameters can effectively reflect the changes in the internal performance of the battery and provide a key basis for accurately judging the health status of the battery.

[0078] Build a reasonable model: Conduct a large number of charge and discharge cycle tests on multiple groups of lithium iron phosphate battery cell samples in different health conditions, collect relevant key data under different cycle numbers to build a linear equation, and use the sample data to fit the weight coefficient. This allows the model to more accurately output battery health status indicators based on the input voltage difference and capacity decay rate, thereby improving the scientificity and accuracy of the detection.

[0079] Full-cycle consideration: During the charge and discharge cycle test, the test continues until the discharge amount of the battery cell drops to 80% of the initial discharge amount, completely covering the stage from when the battery is new to when it begins to show a certain degree of performance degradation. This full-cycle data collection and consideration can more reliably reflect the changing patterns of the battery's health status under different degrees of use, and ensure that the test results can truly reflect the actual situation of the battery.

[0080] Constant temperature environment guarantee: When the lithium iron phosphate battery cell samples are subjected to charge and discharge cycle tests, they are placed in a constant temperature environment box, eliminating the interference of ambient temperature fluctuations on battery performance and test data, making the collected data more stable and reliable, thereby improving the credibility of the entire health status detection.

[0081] Real-time detection capability: It can collect multiple types of key data involved in the charging and discharging process of the lithium iron phosphate battery to be tested in real time according to the established process, and quickly calculate the battery health status indicators through feature extraction, model substitution and other steps. It can provide timely feedback on the current health status of the battery, which is convenient for real-time monitoring and maintenance management of the battery in actual application scenarios, such as electric vehicles, energy storage systems and other fields that use lithium iron phosphate batteries.

[0082] Clear health assessment: Clear and definite health assessment standards are set. The battery health status value of the lithium iron phosphate battery to be tested is compared with the preset battery health status threshold. It can intuitively determine whether the battery is in a healthy, sub-healthy or faulty state, which is convenient for users to quickly understand the battery condition and take corresponding countermeasures. For example, maintenance can be arranged in advance for batteries in sub-healthy states, which improves the safety and economy of the entire battery use process. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The present invention will be further described below in conjunction with the accompanying drawings.

[0084] Figure 1 It is a system block diagram of the lithium iron phosphate battery health status detection method based on real-time current and voltage signals of the present invention. DETAILED DESCRIPTION

[0085] 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.

[0086] Embodiment 1

[0087] See also Figure 1 As shown, the present invention is a method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals, comprising the following steps:

[0088] Step 1: Data testing:

[0089] In this embodiment, since there is no historical battery data at present, the performance data of the lithium iron phosphate battery cell in the whole life cycle is obtained through the battery aging experiment, and during the battery aging experiment, the lithium iron phosphate battery cell sample is placed in a constant temperature environment box to keep the temperature stable so as to eliminate the influence of temperature on battery performance;

[0090] Experimental process:

[0091] Step S1.1, performing a charge-discharge cycle test on a lithium iron phosphate battery cell sample;

[0092] The charge and discharge cycle test is performed until the discharge capacity of the battery cell is significantly attenuated. In this embodiment, the discharge capacity of the battery cell is reduced to a certain proportion of 80% of the initial discharge capacity.

[0093] i.e. QD p ≤80%×QD 0 , where p is the number of cycles when significant decay occurs, QD 0 is the initial discharge capacity;

[0094] Step S1.2, during each charge and discharge cycle, the charging current, charging voltage and charging time node are recorded in real time, as well as the test data corresponding to the discharge current, discharge voltage and discharge time node are recorded in real time; and during each charge and discharge cycle, the charging current and charging voltage are recorded in real time at each charging time node as LC t1，k ,UC t1，k , and the discharge current and discharge voltage are recorded in real time at each discharge time node, respectively as LD t2，k UD t2，k ;

[0095] Wherein, t1=1, 2, ... e1, e1 represents the number of charging time nodes, t2=1, 2, charge and discharge cycles ... e2, e2 represents the number of discharge time nodes, k=1, 2, ... p, p represents the number of charge and discharge cycle processes;

[0096] Step S1.3, simultaneously by:

[0097] Calculate the charge capacity QC of each charging process k ;

[0098] and through:

[0099] Calculate the charge amount QD of each charging process k ;

[0100] Step S1.4, in each charge and discharge cycle, the voltage data and the corresponding capacity data during the charge test or discharge test are sorted;

[0101] The arrangement is as follows:

[0102] During the charging process, the initial charge is started and the charge amount QC t1，k The increase of charging voltage UC t1，k The charging UQ curve is gradually drawn with the charging amount as the horizontal axis and the charging voltage as the vertical axis;

[0103] Among them, QC t1，kRefers to the charge amount recorded at the t1th charging time node in the kth charging cycle;

[0104] During the discharge process, starting with the initial discharge amount, as the discharge amount QD t2，k The increase of discharge voltage UD is recorded. t2，k The discharge UQ curve is gradually drawn with the discharge amount as the horizontal axis and the discharge voltage as the vertical axis;

[0105] Among them, QD t2，k Refers to the discharge amount recorded at the t2th discharge time node in the kth discharge cycle;

[0106] Step 2: Feature extraction:

[0107] Step T1.1, charging platform voltage:

[0108] In the charging UQ curve, obtain the charging amount QC at adjacent charging time nodes t1-1，k , QC t1，k and QC t1+1，k , and the charging voltage value UC at the adjacent charging time node t1-2，k , U.C. t1，k and UC t1+1，k ;

[0109] Then through:

[0110] Calculate the slope change rate R between the charging amount and the charging voltage value at adjacent charging time nodes t1 ;

[0111] Then in all R t1 The slope change rate with the minimum value is obtained, and the charging voltage value at the corresponding charging time node t1 is obtained as the charging platform voltage, and is recorded as Upc;

[0112] Step T1.2, discharge platform voltage:

[0113] In the discharge UQ curve, obtain the discharge amount QD at adjacent discharge time nodes t2-1，k , QD t2，k and QD t2+1，k , and the discharge voltage value UD at the adjacent discharge time node t2-2，k , U.D. t2，k and UD t2+1，k ;

[0114] Then through:

[0115] Calculate the slope change rate R between the discharge amount and the discharge voltage value at adjacent discharge time nodes t2 ;

[0116] Then in all R t2 The minimum slope change rate is obtained, and the discharge voltage value at the corresponding discharge time node t2 is obtained as the discharge platform voltage, which is recorded as Upd;

[0117] Step T1.3, voltage difference:

[0118] The voltage difference is calculated by subtracting the discharge platform voltage from the charge platform voltage and recorded as UW;

[0119] In this embodiment, the voltage difference can reflect the performance changes such as the polarization degree of the lithium iron phosphate battery cell;

[0120] The formula is: UW = Upc-Upd;

[0121] Step T1.4, capacity decay rate:

[0122] pass:

[0123] Calculate the capacity decay rate SJ of the lithium iron phosphate battery;

[0124] Where, QD 0 is the initial discharge capacity, QD e2，p Refers to the discharge amount recorded at the e2th discharge time node in the pth discharge cycle;

[0125] Step 3: Model construction:

[0126] Step M1.1, by performing charge-discharge cycle tests on multiple groups of lithium iron phosphate battery samples in different health states, the voltage difference and capacity attenuation rate of these samples at different charge-discharge cycle times are collected and recorded as UW g and S.J. g , g = 1, 2, ... v, v represents the number of different charge and discharge cycles;

[0127] At the same time, the battery health status indicator of the corresponding lithium iron phosphate battery is recorded and marked as HK g ;

[0128] Among them, the battery health status indicators are quantitatively evaluated through battery testing equipment;

[0129] Step M1.2, formulate a linear equation based on the voltage difference, capacity decay rate and battery health status index;

[0130] The linear equation is: HK = UW × β 1 +SJ×β 2 +β 0 ;

[0131] In the formula, HK, UW and SJ are the input values ​​of voltage difference, capacity attenuation rate and battery health status index respectively, β 0 , β 1 , β 2 is the weight coefficient obtained by fitting the test data in step M1.1;

[0132] Step M1.3: Substitute the UW obtained in step M1.1 g , SJ g and HK g Substitute into the linear equations proposed in step M1.2 and obtain the linear equation system:

[0133] Then through the least squares method;

[0134] Let the error sum of squares Minimum;

[0135] Then, β 0 , β 1 , β 2 Find the partial derivative and set it equal to 0:

[0136] The equations are as follows:

[0137] By solving the above equations, we can get the weight coefficient β 0 , β 1 , β 2 The value of

[0138] This embodiment solves the problem of no historical battery data. Through battery aging experiments and placing in a constant temperature environment, the performance data of the lithium iron phosphate battery cell throughout its life cycle is accurately obtained, temperature interference is eliminated, and the accuracy and reliability of the data are ensured, providing a solid foundation for subsequent precise detection. In the data collection process, multiple types of data in each link of charging and discharging are carefully recorded, including current, voltage, time nodes and power. At the same time, the charging and discharging UQ curve is drawn to fully capture the characteristics of battery performance changes, creating conditions for in-depth analysis of the battery status. Key features are extracted from the UQ curve, such as the charging platform voltage, the discharging platform voltage, the voltage difference and the capacity decay rate. These features are closely related to the battery polarization degree and capacity decay, effectively reflecting the changes in the intrinsic performance of the battery, and providing strong support for building an accurate model.

[0139] Embodiment 2

[0140] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes the steps of battery detection and health assessment;

[0141] The battery detection method is as follows:

[0142] Step X1.1, in accordance with the data testing method, during the operation of the port equipment, the charging current, charging voltage, charging time node, charging amount, discharge current, discharge voltage, discharge time node and discharge amount of the lithium iron phosphate battery to be tested are collected in real time;

[0143] Step X1.2: Extract the voltage difference and capacity attenuation rate of the lithium iron phosphate battery to be tested in a feature extraction manner:

[0144] Step X1.3, substitute the voltage difference and capacity decay rate of the lithium iron phosphate battery to be tested into the linear equation, and obtain the battery health status index of the lithium iron phosphate battery to be tested:

[0145] The health assessment is as follows:

[0146] Compare the battery health status value of the lithium iron phosphate battery to be tested with the preset battery health status threshold:

[0147] When HK1≥0.8×HKy, it means that the battery is in a healthy state, indicating that the battery performance is good and can normally meet the operating requirements of the port equipment;

[0148] When 0.5×HKy<HK1<0.8×HKy, it means that the battery is in a sub-healthy state. At this time, the battery performance begins to decline to a certain extent, and it may need to be monitored more closely.

[0149] When HK1≤0.5×HKy, it means that the battery is in a faulty state, which may cause the port equipment to malfunction and need to be replaced or repaired in time;

[0150] Among them, HK1 is the battery health status value of the lithium iron phosphate battery to be tested, and HKy is the preset battery health status threshold.

[0151] Based on the first embodiment, this embodiment adds battery detection and health assessment links to realize the complete process from data collection, feature extraction to real-time monitoring. All kinds of data of batteries in port equipment operation are collected in real time, features are extracted according to existing methods, and the battery health status indicators are substituted into linear equations to meet the needs of real-time battery monitoring in actual scenarios; clear health assessment standards are provided, and the battery is quickly judged to be healthy, sub-healthy or faulty based on the comparison of the battery health status value with the preset threshold, so that port operators can know the battery status in time and take corresponding measures in advance to ensure the stable operation of port equipment and improve the safety and efficiency of operations.

[0152] Embodiment 3

[0153] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0154] This embodiment combines the advantages of the first and second embodiments. It not only has the comprehensive and accurate data collection and feature extraction capabilities of the first embodiment, laying a solid foundation for model construction, but also has the real-time detection and health assessment functions of the second embodiment, forming a complete closed-loop battery health status detection system; it is suitable for a wider range of scenarios, whether it is data accumulation and model optimization in the early battery research and development stage, or real-time monitoring in actual application scenarios such as ports in the later stage, it can play an excellent role, comprehensively guarantee the reliable operation of lithium iron phosphate batteries at different stages, and promote the efficient development of related industries.

[0155] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0156] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals, characterized in that: The following steps are involved: Step 1: Data testing: The battery aging experiment is used to collect the full life cycle performance data of the lithium iron phosphate battery cell. The full life cycle performance data is included in each charge and discharge cycle test. The current, voltage and time node data of each stage of charging and discharging are recorded in real time, and the charge and discharge amount of each charge and discharge is calculated. Finally, based on the recorded data, the UQ curve of charging and discharging is drawn with the charge or discharge amount as the horizontal axis and the corresponding voltage as the vertical axis; Step 2: Feature extraction: From the charging UQ curve, select the charge amount and voltage value of adjacent charging time nodes, calculate the slope change rate between them, and find the charging voltage corresponding to the minimum slope change rate, which is the charging platform voltage; similarly, in the discharge UQ curve, use a similar method to calculate the discharge platform voltage, subtract the discharge platform voltage from the charging platform voltage to obtain the voltage difference characteristic, and then calculate the capacity decay rate characteristic based on the initial discharge amount and the discharge amount recorded at the specified discharge time node; Step 3: Model construction: We conducted charge and discharge cycle tests on multiple groups of lithium iron phosphate battery samples in different health states, collected the voltage difference and capacity decay rate of the samples at different cycle times, and also collected the battery health status indicators quantitatively evaluated by battery testing equipment, and then constructed a linear equation based on them; Step 4: Battery test: According to the data testing process, the current, voltage, time node and power data involved in the charging and discharging process of the lithium iron phosphate battery to be tested are collected in real time. Then, the voltage difference and capacity decay rate of the battery are extracted by means of feature extraction. Finally, the extracted voltage difference and capacity decay rate are substituted into the constructed linear equation to calculate the battery health status index. Step 5: Health Assessment: The battery health status index calculated for the lithium iron phosphate battery to be tested is compared with a preset battery health status threshold, and the current health status of the lithium iron phosphate battery to be tested is determined.

2. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 1, characterized in that: The data testing method is as follows: Step S1.1, performing a charge-discharge cycle test on a lithium iron phosphate battery cell sample; The charge and discharge cycle test is carried out until the discharge capacity of the battery cell drops to 80% of the initial discharge capacity; i.e. QD p ≤80%×QD0, where p is the number of cycles when the discharge capacity of the battery cell drops to 80% of the initial discharge capacity, and QD0 is the initial discharge capacity; Step S1.2, during each charge and discharge cycle, the charging current, charging voltage and charging time node are recorded in real time, and the test data corresponding to the discharge current, discharge voltage and discharge time node are recorded in real time; During each charge and discharge cycle, the charging current and charging voltage are recorded in real time at each charging time node and recorded as LC t1，k ,UC t1，k , and at each discharge time node, the discharge current and discharge voltage are recorded in real time as LD t2，k UD t2，k ; Wherein, t1=1, 2, ... e1, e1 represents the number of charging time nodes, t2=1, 2, charge and discharge cycles ... e2, e2 represents the number of discharge time nodes, k=1, 2, ... p, p represents the number of charge and discharge cycle processes; Step S1.3, simultaneously by: Calculate the charge capacity QC of each charging process k ; and through: Calculate the charge amount QD of each charging process k ; Step S1.4: In each charge and discharge cycle, the voltage data and the corresponding capacity data in the charge test or discharge test are sorted out, and a charge UQ curve and a discharge UQ curve are plotted.

3. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 2, characterized in that: The arrangement in step S1.4 is as follows: During the charging process, the initial charge is started and the charge amount QC t1，k The increase of charging voltage UC t1，k The charging UQ curve is gradually drawn with the charging amount as the horizontal axis and the charging voltage as the vertical axis; Among them, QC t1，k Refers to the charge amount recorded at the t1th charging time node in the kth charging cycle; During the discharge process, starting with the initial discharge amount, as the discharge amount QD t2，k The increase of discharge voltage UD is recorded. t2，k The discharge UQ curve is gradually drawn with the discharge amount as the horizontal axis and the discharge voltage as the vertical axis; Among them, QD t2，k Refers to the discharge amount recorded at the t2th discharge time node in the kth discharge cycle.

4. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 3, characterized in that: The feature extraction method of voltage difference feature is as follows: Step T1.1, charging platform voltage: In the charging UQ curve, obtain the charging amount QC at adjacent charging time nodes t1-1，k , QC t1，k and QC t1+1，k , and the charging voltage value UC at the adjacent charging time node t1-2，k , U.C. t1，k and UC t1+1，k ; Then through: Calculate the slope change rate R between the charging amount and the charging voltage value at adjacent charging time nodes t1 ; Then in all R t1 The slope change rate with the minimum value is obtained, and the charging voltage value at the corresponding charging time node t1 is obtained as the charging platform voltage, and is recorded as Upc; Step T1.2, discharge platform voltage: In the discharge UQ curve, obtain the discharge amount QD at adjacent discharge time nodes t2-1，k , QD t2，k and QD t2+1，k , and the discharge voltage value UD at the adjacent discharge time node t2-2，k , U.D. t2，k and UD t2+1，k ; Then, according to the calculation method of the slope change rate between the charge amount and the charge voltage value at the adjacent charge time node, the slope change rate between the discharge amount and the discharge voltage value at the adjacent discharge time node is calculated and recorded as R t2 ; Then in all R t2 The minimum slope change rate is obtained, and the discharge voltage value at the corresponding discharge time node t2 is obtained as the discharge platform voltage, which is recorded as Upd; Step T1.3, voltage difference: The voltage difference is calculated by subtracting the discharge platform voltage from the charge platform voltage and is recorded as UW.

5. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 4, characterized in that: The feature extraction method of capacity decay rate feature is as follows: pass: Calculate the capacity decay rate SJ of the lithium iron phosphate battery; Where QD0 is the initial discharge capacity, QD e2，p Refers to the discharge amount recorded at the e2th discharge time node in the pth discharge cycle.

6. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 1, characterized in that: The model is constructed as follows: Step M1.1, by performing charge-discharge cycle tests on multiple groups of lithium iron phosphate battery samples in different health states, the voltage difference and capacity attenuation rate of these samples at different charge-discharge cycle times are collected and recorded as UW g and S.J. g , g = 1, 2, ... v, v represents the number of different charge and discharge cycles; At the same time, the battery health status indicator of the corresponding lithium iron phosphate battery is recorded and marked as HK g ; Among them, the battery health status indicators are quantitatively evaluated through battery testing equipment; Step M1.2, formulate a linear equation based on the voltage difference, capacity decay rate and battery health status index; The linear equation is: HK = UW × β1 + SJ × β2 + β0; Where HK, UW and SJ are the input values ​​of voltage difference, capacity attenuation rate and battery health status index respectively, and β0, β1 and β2 are weight coefficients obtained by fitting the test data in step M1.

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7. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 6, characterized in that: The fitting method of weight coefficients β0, β1, and β2 is as follows: The UW obtained in step M1.1 g , SJ g and HK g Substitute into the linear equations proposed in step M1.2 and obtain the linear equation system: Then through the least squares method; Let the error sum of squares Minimum; Then calculate the partial derivatives of β0, β1, and β2 respectively, and set the partial derivatives equal to 0: The equations are as follows: By solving the above equations, the values ​​of weight coefficients β0, β1, and β2 are obtained.

8. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 6, characterized in that: The battery detection method is as follows: Step X1.1, in accordance with the data testing method, real-time collection of the charging current, charging voltage, charging time node, charging amount, discharging current, discharging voltage, discharging time node and discharging amount of the lithium iron phosphate battery to be tested; Step X1.2, extracting the voltage difference and capacity attenuation rate of the lithium iron phosphate battery to be tested in a feature extraction manner; Step X1.3, substitute the voltage difference and capacity attenuation rate of the lithium iron phosphate battery to be tested into the linear equation, and obtain the battery health status index of the lithium iron phosphate battery to be tested.

9. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 1, characterized in that: The health assessment is as follows: Compare the battery health status value of the lithium iron phosphate battery to be tested with the preset battery health status threshold: When HK1≥0.8×HKy, it means the battery is in a healthy state; When 0.5×HKy<HK1<0.8×HKy, it means the battery is in a sub-healthy state; When HK1≤0.5×HKy, it means the battery is in a fault state; Among them, HK1 is the battery health status value of the lithium iron phosphate battery to be tested, and HKy is the preset battery health status threshold.

10. The method for detecting the health status of a lithium iron phosphate battery based on real-time current and voltage signals according to claim 1, characterized in that: When performing charge and discharge cycle tests on lithium iron phosphate battery cell samples, the lithium iron phosphate battery cell samples are placed in a constant temperature environment box.

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