A method for measuring the health state of a power battery throughout its life cycle

Abstract

The application provides a power battery full life cycle health state evaluation method, which firstly combines experimental data and real vehicle data, obtains an SOH-IC relationship through offline experimental data at a single body level, and further maps the SOH to a real vehicle battery pack. Only the full life cycle cycle experiment of the battery monomer of the same vehicle type needs to be carried out, and the SOH online estimation of a large number of real vehicle batteries of the same vehicle type can be realized. Meanwhile, the SOH-IC relationship can be updated and adjusted by using the data collected in the real vehicle, the cost of the experiment on the real vehicle battery pack to obtain the battery health state is greatly reduced, the problem of inaccurate SOH estimation caused by the inconsistency between the experimental results and the real vehicle operation condition is effectively avoided, and the calculation result of the real vehicle data has high reliability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of new energy vehicle big data, and particularly relates to a method for detecting and evaluating the health state of a power battery in a full life cycle by using the big data of a new energy vehicle. BACKGROUND

[0002] For the health state (SOH) of a power battery, since direct measurement of the SOH is not available, the existing technology mainly indirectly diagnoses the SOH of the battery based on an experimental test method, and it is even more impossible to monitor each battery monomer. However, in order to ensure the accuracy and authenticity of the SOH diagnosis result, it is necessary to obtain as much full life cycle data of the battery in actual working conditions as possible, and to perform comprehensive and large-scale laboratory tests. Whether the battery health state estimation method at the monomer level is applicable to the battery pack, and what kind of data relationship exists in the conversion of relevant parameters, the above problems need to be verified by experimental methods to provide theoretical support for the battery health state estimation at the real vehicle level. This makes the existing experimental process have the shortcomings of time-consuming, labor-intensive and low efficiency. Due to the high diversity and complexity of the actual working conditions of the power battery running on the real vehicle, when the full life cycle obtained from the experimental environment is used as the basis for SOH evaluation, the matching with the real working conditions is not satisfactory, and the coverage of various working conditions is obviously insufficient. Therefore, how to provide a more accurate full life cycle power battery health state evaluation method for real vehicle working conditions on the premise of relying on less laboratory research data is a technical problem that needs to be solved in the current field. SUMMARY

[0003] Therefore, the present application provides a full life cycle health state evaluation method of a power battery, which specifically comprises the following steps:

[0004] Step one, obtaining the specification information of the same battery monomer as the power battery pack used on the real vehicle, and selecting the same specification battery monomer as a battery sample for offline experiments;

[0005] Step two, accelerating the aging of the battery sample through CC-CV (constant current-constant voltage) charge-discharge cycle offline experiments, and calibrating the charge capacity, discharge capacity, current and voltage data in the charge-discharge cycle process until the capacity of the target battery is lower than 80% of its nominal capacity;

[0006] Step three, calculating the incremental capacity value IC in the constant current charging process, extracting the maximum value of IC in each charging as IC i , and the corresponding charge capacity, and calculating the battery health state SOH i corresponding to this charging;

[0007] Step four, for the ICi and the corresponding SOH i establishing a quadratic function relationship between them, and fitting to obtain the SOH-IC mapping curve of the battery sample;

[0008] Step five, according to the actual grouping form of the power battery pack used on the real vehicle, combining the SOH-IC mapping curve of the battery sample to derive the SOH-IC mapping relationship of the power battery pack, and entering the online identification stage of the real vehicle, collecting the capacity, current and voltage data of the power battery pack in the real vehicle operation, calculating the IC maximum value and inputting the derived SOH-IC mapping relationship of the power battery pack, and outputting the SOH of the power battery pack;

[0009] Step six, using the IC maximum value of the real vehicle power battery pack and the corresponding SOH data to periodically update the SOH-IC mapping relationship of the power battery pack.

[0010] Further, the charge and discharge cycle offline experiment in step two specifically includes performing the following steps:

[0011] (1) Initial state calibration of battery sample:

[0012] 1) Discharge the new battery sample at 1C for 1h to ensure emptying, and then stand for 1h.

[0013] 2) Charge each battery sample at 1 / 3C current, and after CC charging reaches the cut-off voltage, convert CV charging until the current is less than 0.05C, and record the charging capacity Q1;

[0014] 3) Stand for 30min;

[0015] 4) Discharge the battery sample at 1 / 3C constant current to the discharge cut-off voltage, and record the discharge capacity Q2;

[0016] 5) Stand for 30min;

[0017] 6) Repeat steps 2)-5) three times;

[0018] 7) Calculate the average of the three charging capacities Q1 as the initial capacity Q0;

[0019] (2) Accelerated aging of battery sample:

[0020] 1) Charge each battery sample at 1C current, and after CC charging reaches the cut-off voltage, convert CV charging until the current is less than 0.05C;

[0021] 2) Stand for 30min;

[0022] 3) Discharge the battery sample at 1C constant current to the discharge cut-off voltage;

[0023] 4) Stand for 30 min;

[0024] 5) Repeat the above steps 1) - 4) for 5 times;

[0025] 6) Capacity calibration: each battery sample is charged with 1 / 3C current, and after CC charging reaches the cut-off voltage, it is converted to CV charging until the current is less than 0.05C, and the charging capacity Q3 is recorded;

[0026] By repeatedly performing the above battery sample accelerated aging process until the charging capacity Q3 is reduced to 80% of the initial capacity Q0, the charging and discharging cycle is stopped; the corresponding current I and voltage U data are continuously recorded in the charging and discharging cycle.

[0027] Further, the incremental capacity value IC in the constant current charging process in step three is calculated by the following method:

[0028]

[0029] In the formula, the symbol Δ represents the change amount of the corresponding parameter, and t is the time variable;

[0030] The maximum value IC of IC is extracted for the constant current charging process i , IC i = max(IC), and the charging capacity Q i corresponding to IC 3i , then the battery state of health SOH i corresponding to this charging is calculated as:

[0031]

[0032] Further, by fitting IC i and SOH i obtained in the offline experiment stage, the following quadratic function relationship between them is obtained:

[0033] SOH i = a·IC i 2 +b·IC i +c

[0034] Wherein, a, b, c are the corresponding coefficients and constants in the function relationship;

[0035] For the battery pack composed of N series battery monomers and M parallel battery monomers in the power battery pack on the real vehicle, the incremental capacity maximum value IC' is calculated and mapped to the incremental capacity maximum value IC" of the monomer in the battery pack based on the following relationship:

[0036]

[0037] The SOH-IC mapping relationship of the battery pack in the power battery pack on the real vehicle can be derived as:

[0038]

[0039] Therefore, the SOH-IC relationship of the battery pack or the single battery on the real vehicle can more accurately and comprehensively reflect the current health status of the power battery pack.

[0040] The power battery full life cycle health state evaluation method provided by the present application firstly combines experimental data and real vehicle data, obtains the SOH-IC relationship through offline experimental data of the single battery, and further maps the SOH of the real vehicle battery pack. Only the full life cycle cycle experiment of the battery single body of the same vehicle type is needed, and the SOH of a large number of real vehicle batteries of the same vehicle type can be estimated online, and the SOH-IC relationship can be updated and adjusted by using the data collected in the real vehicle. The cost of obtaining the battery health state by experiment on the real vehicle battery pack is greatly reduced, the problem of inaccurate SOH estimation caused by the inconsistency between the experimental results and the real vehicle operation condition is effectively avoided, and the calculation result of the real vehicle data has high reliability. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The flowchart of the method provided by the present application is shown. DETAILED DESCRIPTION

[0042] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0043] A power battery full life cycle health state evaluation method provided by the present application, as shown in Figure 1 The specific steps include the following steps:

[0044] Step 1, obtain the specification information of the same battery single body in the power battery pack used on the real vehicle, and select the same specification battery single body as a battery sample for offline experiment;

[0045] Step 2, accelerate the aging of the battery sample by CC-CV (constant current-constant voltage) charge-discharge cycle offline experiment, and calibrate the charge capacity, discharge capacity, current and voltage data in the charge-discharge cycle process until the capacity of the target battery is lower than 80% of its nominal capacity;

[0046] Step 3, calculate the incremental capacity value IC in the constant current charging process, extract the maximum value of IC in each charging as ICi and the corresponding charging capacity, and calculate the state of health SOH corresponding to the charging i ;

[0047] Step four, for the IC i and the corresponding SOH i establish a quadratic function relationship between them, and fit to obtain the SOH-IC mapping curve of the battery sample;

[0048] Step five, according to the actual grouping form of the power battery pack used on the real vehicle, combining the SOH-IC mapping curve of the battery sample, the SOH-IC mapping relationship of the power battery pack is derived, and the online identification stage of the real vehicle is entered. The capacity, current and voltage data of the power battery pack in the real vehicle running are collected, the IC maximum value is calculated and input into the derived SOH-IC mapping relationship of the power battery pack, and the SOH of the power battery pack is output.

[0049] Step six, using the IC maximum value and the corresponding SOH data of the real vehicle power battery pack, the SOH-IC mapping relationship of the power battery pack is periodically updated.

[0050] In a preferred embodiment of the present application, the corresponding single battery is selected as the battery sample for the battery used in a vehicle model of Yutong Bus, and the specific parameters of the real vehicle battery pack are shown in Table 1 as follows:

[0051] Table 1 Battery pack specification parameters

[0052]

[0053] The battery single specification information includes the battery model, electrode material, nominal capacity and the like constituting the battery pack, as shown in Table 2 as follows:

[0054] Table 2 Battery single specification

[0055] Model CATL lithium iron phosphate (rated voltage 3.2 V) Discharge cut-off voltage 2.5V Charge cut-off voltage 3.65V Rated capacity 86 Ah

[0056] The above single battery is used as a battery sample to perform an offline experiment of charging and discharging cycle, which specifically includes the following steps:

[0057] (1) Initial state calibration of battery sample:

[0058] 1) Discharge the new battery sample at 1C for 1h to ensure emptying, and then stand for 1h.

[0059] 2) Each battery sample is charged at 1 / 3C current, and the CC charging is converted to CV charging until the current is less than 0.05C after reaching the cut-off voltage. Record the charging capacity Q1;

[0060] 3) Stand for 30min;

[0061] 4) discharge the battery sample at 1 / 3C constant current to the discharge cut-off voltage, record the discharge capacity Q2;

[0062] 5) stand for 30min;

[0063] 6) repeat the above steps 2)-5) for three times;

[0064] 7) calculate the average value of the three charging capacities Q1 as the initial capacity Q0;

[0065] (2) battery sample accelerated aging:

[0066] 1) CC-CV charge each battery sample at 1C current, after CC charging reaches the cut-off voltage, CV charging until the current is less than 0.05C;

[0067] 2) stand for 30min;

[0068] 3) discharge the battery sample at 1C constant current to the discharge cut-off voltage;

[0069] 4) stand for 30min;

[0070] 5) repeat the above steps 1)-4) for five times;

[0071] 6) capacity calibration: CC-CV charge each battery sample at 1 / 3C current, after CC charging reaches the cut-off voltage, CV charging until the current is less than 0.05C, record the charging capacity Q3;

[0072] By repeatedly performing the above battery sample accelerated aging process until the charging capacity Q3 is reduced to 80% of the initial capacity Q0, stop the charge-discharge cycle; continuously record the corresponding current I and voltage U data in the charge-discharge cycle.

[0073] In a preferred embodiment of the present application, the incremental capacity value IC in the constant current charging process in step three is calculated by the following method:

[0074]

[0075] In the formula, the symbol Δ represents the change amount of the corresponding parameter, t is the time variable;

[0076] Extract the maximum value IC of IC for the constant current charging process i , IC i = max(IC), and the charging capacity Q i corresponding to IC 3i , then the battery state of health SOH i corresponding to this charging is calculated as:

[0077]

[0078] In a preferred embodiment of the present application, the IC i is obtained by fitting the SOH i obtained in the offline experiment stage to obtain the following quadratic function relationship between the two:

[0079] SOH i = a · IC i 2 + b · IC i + c

[0080] wherein a, b, c are the corresponding coefficients and constants in the function relationship, respectively;

[0081] For a battery pack composed of N series-connected battery monomers and M parallel-connected battery monomers in a real vehicle power battery pack, the incremental capacity maximum value IC' is calculated and mapped to the incremental capacity maximum value IC" of the monomers in the battery pack based on the following relationship:

[0082]

[0083] Then the SOH-IC mapping relationship of the battery pack in the real vehicle power battery pack can be derived as:

[0084]

[0085] Thus, the SOH-IC relationship of the real vehicle battery pack or monomers can be more accurate and comprehensive to reflect the current health status of the power battery pack.

[0086] Those skilled in the art should know based on the teachings of the present application that various forms of monomer combination and connection form used for the battery pack can achieve the mapping between the real vehicle battery pack and the monomers by the above method.

[0087] It should be understood that the size of the serial number of each step in the embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0088] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the health status of a power battery throughout its entire life cycle, characterized in that: Specifically, the following steps are included: Step 1: Obtain the specification information of the same battery cells as those used in the power battery pack in the actual vehicle, and select the same specification battery cells as battery samples for offline experiments. Step 2: Accelerate the aging of the battery samples by performing CC-CV charge-discharge cycle offline experiments, and calibrate the charging capacity, discharging capacity, current and voltage data during the charge-discharge cycle until the capacity of the target battery is lower than 80% of its nominal capacity. Step 3: Calculate the incremental capacity value IC during the constant current charging process, and extract the maximum value of IC in each charge as IC. i And the corresponding charging capacity, and calculate the battery health state (SOH) for this charging. i ; Step 4: For the IC i and the corresponding SOH i A quadratic function relationship is established between the two, and the SOH-IC mapping curve of the battery sample is obtained by fitting. Step 5: Based on the actual assembly form of the power battery pack used in the actual vehicle, and combined with the SOH-IC mapping curve of the battery sample, derive the SOH-IC mapping relationship of the power battery pack, and proceed to the online identification stage of the actual vehicle. Collect the capacity, current and voltage data of the power battery pack during the operation of the actual vehicle, calculate the maximum IC value and input the derived SOH-IC mapping relationship of the power battery pack, and output the SOH of the power battery pack. Step 6: Use the maximum IC value of the actual vehicle power battery pack and the corresponding SOH data to periodically update the SOH-IC mapping relationship of the power battery pack.

2. The method as described in claim 1, characterized in that: The offline charge-discharge cycle experiment described in step two specifically includes the following steps: (1) Initial state calibration of battery sample: 1) Discharge the new battery sample at 1C for 1 hour to ensure it is completely discharged, and then let it stand for 1 hour; 2) Each battery sample is charged with CC-CV at a current of 1 / 3C. After CC charging reaches the cutoff voltage, it is switched to CV charging until the current is less than 0.05C. The charging capacity Q1 is recorded. 3) Let stand for 30 minutes; 4) Discharge the battery sample at a constant current of 1 / 3C until the discharge cutoff voltage, and record the discharge capacity Q2; 5) Let stand for 30 minutes; 6) Repeat steps 2)-5) above three times; 7) Calculate the average of the three charging capacities Q1 as the initial capacity Q0; (2) Accelerated aging of battery samples: 1) Charge each battery sample with a 1C current using CC-CV charging. After CC charging reaches the cutoff voltage, switch to CV charging until the current is less than 0.05C. 2) Let stand for 30 minutes; 3) Discharge the battery sample at a constant current of 1C until the discharge cutoff voltage; 4) Let stand for 30 minutes; 5) Repeat steps 1)-4) above 5 times; 6) Capacity calibration: Each battery sample is charged with CC-CV at a current of 1 / 3C. After CC charging reaches the cutoff voltage, it is switched to CV charging until the current is less than 0.05C. The charging capacity Q3 is recorded. The aging process of the battery sample is accelerated by repeating the above process until the charging capacity Q3 is reduced to 80% of the initial capacity Q0, at which point the charge-discharge cycle is stopped; the corresponding current I and voltage U data are continuously recorded during the charge-discharge cycle.

3. The method as described in claim 2, characterized in that: The incremental capacity value IC in step three during the constant current charging process is calculated in the following way: In the formula, the symbol Δ represents the change in the corresponding parameter, and t is the time variable; Extracting the maximum value of IC during constant current charging process i IC i =max(IC), and IC i The corresponding charging capacity Q 3i The battery health status (SOH) corresponding to this charging is... i The calculation is as follows:

4. The method as described in claim 3, characterized in that: ICs obtained during the offline experimental phase i With SOH i By fitting the data, the following quadratic function relationship between the two is obtained: SOH i =a·IC i 2 +b·IC i +c Where a, b, and c are the corresponding coefficients and constants in the functional relationship, respectively; For a battery pack consisting of N series-connected battery cells and M parallel-connected battery cells in a real vehicle, calculate its maximum incremental capacity IC' and map it to the maximum incremental capacity IC" of the individual cells in the battery pack based on the following relationship: The SOH-IC mapping relationship of the battery pack in the actual vehicle's power battery pack can be derived as follows:

Images