In-situ rapid prediction method for service life of lithium ion battery

Abstract

The invention relates to the field of lithium ion battery health state evaluation, and discloses a lithium ion battery life in-situ rapid prediction method and device, and the method comprises the steps: carrying out a battery capacity calibration test, and obtaining a first calibration capacity Q1 of a battery; installing the battery in a mechanical system of the measuring device, and setting an initial pre-tightening force; standing the mechanical system of the measuring device for a preset standing time, and then testing the capacity calibration of the battery again to obtain a second calibration capacity Q2 of the battery; when the value of Q1 / Q2 is higher than a preset ratio, carrying out charge-discharge circulation on the battery, and in the charge-discharge circulation process of the battery, collecting the pressure change of the battery in the longitudinal direction, namely a pressure change curve of expansion force; and establishing an expansive force change fitting formula in the circulation process according to the pressure change curve of the expansive force, and predicting the service life of the battery according to the relationship between the expansive force and the calibrated capacity of the battery. By using the scheme of the invention, early warning of the advance can be realized, the algorithm is simple, and the applicability is high.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery health status assessment technology, specifically to a method for rapid in-situ prediction of lithium-ion battery life. Background Technology

[0002] As a core component of new energy vehicles and energy storage systems, the lifespan prediction of lithium-ion batteries is crucial for safety and economic efficiency. However, traditional lifespan assessment methods (such as capacity decay methods) require thousands of cycle tests, taking months, and can only issue warnings after significant battery performance degradation, failing to identify early degradation. While electrochemical impedance spectroscopy (EIS) can reflect changes in the battery's internal state, the testing conditions are demanding (requiring fixed state of charge (SOC) and temperature), and the measurement error fluctuates significantly (±15%), making it difficult to promote in practical applications.

[0003] Expansion force monitoring, as an emerging method for assessing battery health, has received widespread attention in recent years. Studies have shown that the expansion behavior of batteries during cycling is closely related to the degradation of internal materials (such as SEI film rupture and negative electrode particle cracking). However, existing technologies mainly rely on the absolute value or first derivative (dF / dv) analysis of expansion force, which lacks sufficient sensitivity and struggles to capture subtle changes in the early stages. The proposed method for monitoring abrupt changes in expansion force has a false alarm rate as high as 40% in actual tests, mainly because noise interference masks the true degradation signals.

[0004] Machine learning methods (such as LSTM neural networks) demonstrate high prediction accuracy (Ri) in laboratory settings. 2 While the accuracy is >0.95, it relies on massive amounts of historical data (typically requiring over 3,000 sets of complete cycle data), and the error increases significantly (>30%) in new material systems (such as silicon-based anodes). Furthermore, the complex computational model is difficult to integrate into a battery management system (BMS), limiting its industrial application. Therefore, developing an accurate in-situ rapid lifetime prediction method is a crucial technical challenge that urgently needs to be addressed by those skilled in the art. Summary of the Invention

[0005] This invention provides a rapid in-situ prediction method for lithium-ion battery life, which has good timeliness and reliability, reduces space occupation, and improves the monitoring range.

[0006] A method for rapid in-situ prediction of lithium-ion battery lifetime, the method comprising: Step 1: Perform battery capacity calibration test to obtain the first calibrated capacity Q1 of the battery; Step 2: Install the battery into the mechanical system of the measuring device and set the initial preload. Step 3: After the mechanical system of the measuring device is left to stand for a predetermined time, the battery capacity calibration test is performed again to obtain the second calibrated capacity Q2 of the battery. Step 4: When the value of Q1 / Q2 is not higher than a predetermined ratio, the battery is charged and discharged in cycles. During the battery charge and discharge cycle, the pressure change curve of the battery in the longitudinal direction, i.e., the pressure change curve of the expansion force, is collected. Step 5: Establish a fitting formula for the change of expansion force during the cycle based on the pressure change curve of the expansion force, and predict the battery life based on the relationship between the expansion force and the battery rated capacity.

[0007] Optionally, in step 1, the battery is charged to the specified charging cutoff condition using a charging regime of I1(A), and then left to stand for a first predetermined time at the ambient temperature to be tested; at the ambient temperature to be tested, the battery is discharged at a constant current of I1 until the battery discharge termination voltage is reached, and the discharge capacity is measured; the above charging and discharging steps are performed a total of 5 times, and if the change in the discharge capacity of the battery in the three consecutive steps does not exceed 3% of the rated capacity of the battery, then the discharge capacity of the third step is taken as the first rated capacity of the battery and recorded as Q1.

[0008] Optionally, the method further includes, after step 1, adjusting the battery state of charge (SOC), adjusting the battery SOC to 0% SOC before installing the mechanical system of the measuring device, charging the battery to the specified charging cutoff condition using an I2(A) current charging regime, and resting it at the temperature to be measured for a second predetermined time; discharging the battery to 0% SOC using an I2(A) current or according to the manufacturer's recommended discharge regime, and resting it for a third predetermined time, at which point the battery voltage is the open-circuit voltage (OCV) under this SOC state.

[0009] Optionally, in step 2, the battery is installed into the mechanical system of the measuring device, the mechanical system of the measuring device is placed in an environmental chamber, and when setting the initial preload, a pressure of a predetermined megapascal is applied to the surface of the battery as the initial condition of constant pressure or constant gap.

[0010] Optionally, in step 3, after the mechanical system of the measuring device is left to stand for a predetermined time at the target ambient temperature, the pressure change of the monitoring system is recorded, so that the pressure change is less than 5%. Optionally, in step 4, if the value of Q1 / Q2 is greater than the predetermined ratio, the process returns to step 2.

[0011] Optionally, in step 5, the expansion force change coefficient k and expansion force change parameter m are calculated according to the battery expansion force change fitting formula (1) for n battery charge-discharge cycles; based on the expansion force parameter k and m, the expansion force value corresponding to the n+x cycles is calculated: Fn=F0+k•n m (1) Fn+x=F0+k•(n+x) m (2) Where Fn represents the expansion force value corresponding to n cycles, F0 represents the initial preload, k represents the slope coefficient of the fitting formula, m represents the power coefficient of the fitting formula, n represents the number of cycles of n cycles, and n+x represents the number of cycles of n+x cycles.

[0012] Optionally, in step 5, based on the relationship between the expansion force change and capacity during the n+x cycle (3), the capacity decay and parameter a are calculated, and Q is solved based on parameter a and the expansion force change in the n+xth cycle. n+x : Q n+x =Q0-α•ΔF n •N(3) Among them, Q n+x Let Q0 represent the initial capacity, α represent the capacity-to-expansion force conversion coefficient, and •ΔF represent the capacity corresponding to the (n+x)th cycle. n • Represents the difference between the maximum pressure of the nth cycle and the maximum pressure of the first cycle; N represents the number of cycles corresponding to the nth cycle.

[0013] A rapid in-situ prediction system for lithium-ion battery life includes an environmental monitoring system, a battery charge-discharge control system, a data acquisition and monitoring system, and a measuring device mechanical system. The system places the battery under test in the environmental monitoring system. After reaching thermal equilibrium, the battery charge-discharge control system performs a capacity calibration test on the battery under test to obtain a first calibrated capacity Q1. The battery under test is then installed into the measuring device mechanical system, which is placed within the environmental monitoring system, and an initial preload is set. After the measuring device mechanical system is allowed to stand for a predetermined time, the battery charge-discharge control system performs another capacity calibration test on the battery under test to obtain a second calibrated capacity Q2. When the ratio of Q1 / Q2 is not higher than a predetermined value, the battery charge-discharge control system performs charge-discharge cycles on the battery under test. During these cycles, the data acquisition and monitoring system collects the pressure change curve of the battery under test in the longitudinal direction, i.e., the pressure change curve of the expansion force. Based on the pressure change curve of the expansion force, a fitting formula for the expansion force change during the cycle is established, and the battery life is predicted based on the relationship between the expansion force and the battery's calibrated capacity.

[0014] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to perform the steps of the in-situ rapid prediction method for lithium-ion battery life.

[0015] The in-situ rapid prediction method for lithium-ion battery life provided by this invention, compared with traditional life prediction methods, relies on battery expansion force for in-situ life prediction, offering excellent timeliness and reliability. A single device can be used to test evaluation methods for various abnormal conditions such as rapid capacity decay, reducing space requirements, increasing monitoring range, significantly lowering costs, and facilitating integration into lithium-ion battery thermal management systems. Compared with existing technologies, this invention has the following technical advantages: (1) Early warning lead time: 21.3% earlier than the capacity method on average (test data range 18.7-23.6%). (2) The algorithm is simple, and multiple abnormal warnings of lithium-ion batteries can be realized through simple difference quotient calculation and threshold setting; (3) Strong applicability: Applicable to soft pack / cylindrical batteries, not limited by SOC range. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0017] Figure 1 This is a flowchart of a method for rapid in-situ prediction of lithium-ion battery life in a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a rapid in-situ prediction system for lithium-ion battery life in a specific embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] During cycling, irreversible changes such as lithium-ion intercalation / deintercalation, SEI film growth, and microcracks in electrode materials lead to internal mechanical stress in lithium-ion batteries, causing battery thickness expansion. Changes in expansion force and capacity are inextricably linked. However, obtaining accurate expansion force and utilizing it for lifespan prediction are the core points of this invention. Specifically, this is manifested in: This invention addresses the correlation between expansion force and battery life. First, the battery under test is placed in an expansion force monitoring fixture and a certain initial preload is applied. Then, an environmental chamber is used to control the ambient temperature of the battery under test. The battery cell fixture is controlled in a constant gap mode, and an initial preload is applied to the cell. A high-precision displacement control system maintains a constant cell gap corresponding to this preload condition. The longitudinal pressure change curve of the cell during charging and discharging is collected in real time. Based on the pressure change curve, the expansion force change during subsequent cycles is inferred. Finally, the battery life is predicted based on the relationship between expansion force and capacity.

[0021] In one specific embodiment, the present invention uses the second derivative of the expansion force to predict battery life. For example... Figure 1 The diagram shown is a flowchart of a rapid in-situ prediction method for lithium-ion battery lifetime in an embodiment of the present invention, including the following steps: Step 101: Using a measuring device mechanical system, perform an expansion force capacity test under a certain initial preload. For example... Figure 2 As shown, the mechanical system 10 of the measuring device includes: a threaded locking device 1, a lower end plate 2, a sliding plate 4, a limiting post 3, a sensor fixing plate 5, and a sensor 6. The lower end plate 2 is provided with four evenly distributed slotted threaded fixing limiting posts 3. The four limiting posts 3 are fixed to the lower end plate 2 by the threaded locking device 1. The battery is placed between the lower end plate 2 and the sliding plate 4. The sensor fixing plate 5 is provided with an opening for fixing the sensor 6. The initial force is adjusted by tightening the four limiting posts 3, and then locked vertically by the four threaded locking devices 1.

[0022] Step 102: Charge the battery to the specified charging cutoff condition using a constant current charging regime of I1(A). The I1(A) constant current charging regime means maintaining a constant current of 1 ampere throughout the entire charging phase (or the main charging phase). Let the battery rest at the ambient temperature under test for t1. Then, discharge the battery at a constant current of I1 until the battery discharge termination voltage is reached at the ambient temperature under test, and measure the discharge capacity. Perform the above charge and discharge steps a total of 5 times. If the battery's discharge capacity change in the three consecutive cycles does not exceed 3% of the battery's rated capacity, the third discharge capacity is taken as the battery's calibrated capacity and denoted as Q1. Wherein, I1 range: 0 < I1 ≤ 1 / 3C, where C is the rated capacity; t1 range: 0 < t1 ≤ 2h.

[0023] Step 103: Before installing the test fixture, adjust the battery's State of Charge (SOC) to 0%. Charge it with a constant current of I2(A) until the specified charging cutoff condition is met, i.e., maintain an output current of 2A throughout the entire charging process. Let it rest at the test temperature for t2. Discharge it to 0% SOC using I2(A) current or according to the manufacturer's recommended discharge regime, and let it rest for t3. The range of t2 is: 0 < t2 ≤ 2h; the range of t3 is: 0 < t3 ≤ 2h.

[0024] Step 104: Place the battery with the test fixture installed in the environmental chamber and set the initial preload again: apply a predetermined pressure of megapascals to the surface of the cell as the initial condition for constant pressure or constant gap.

[0025] Step 105: Under the target ambient temperature, the above device is left to stand for t4, and the expansion force is recorded. Pressure changes are such that the pressure change is less than 5%; where t4 ranges from 2h to 24h.

[0026] Step 106, repeat step 102, and record the measured average capacity as Q2. If Q1 / Q2 ≤ 105%, proceed with subsequent cyclic expansion force testing; if... If so, repeat step 104.

[0027] Step 107: Cycle the battery by charging and discharging, and simultaneously collect data on the change of expansion force F with time t in situ during the cycle.

[0028] Step 108: Calculate the expansion force variation coefficient k and expansion force variation parameter m according to the fitting formula (1) for the expansion force change during cycle n. Based on the expansion force parameters k and m, calculate the corresponding expansion force value at cycle n+x. F n =F0+k•N m (1) F nx =F0+k•(N+x) m (2) Among them, F n This represents the expansion force value corresponding to n cycles, F0 represents the initial preload, k represents the slope coefficient of the fitting formula, m represents the power coefficient of the fitting formula, N represents the number of cycles for n cycles, and N+x represents the range of n: 100≤n≤200; x range: 100≤x≤1000.

[0029] Step 109, based on the relationship between the expansion force change and capacity during the n+x cycle (3), calculate the capacity decay and parameter a, and solve for Qn+m based on parameter a and the expansion force change in the n+xth cycle: Q n+x= Q0-α•ΔF•N(3) Among them, Q n+x Let Q0 represent the capacity corresponding to the (n+x)th cycle, α represent the capacity-expansion force conversion coefficient, and •ΔFn• represent the difference between the maximum pressure of the nth cycle and the maximum pressure of the first cycle; N represents the number of cycles corresponding to the nth cycle.

[0030] The methods described above are applicable to soft-pack lithium-ion batteries and prismatic lithium-ion batteries.

[0031] The following are several specific embodiments of the application of the present invention.

[0032] Example 1 (a1) Capacity calibration test: At 25℃, a 35Ah battery with ternary cathode material and graphite anode material is charged at a constant current of 0.33C to a voltage of 4.2V, then charged at a constant voltage of 4.2V to a current of 1 / 20C. After standing for 30 minutes, the battery is discharged at a constant current of 0.33C to a voltage of 2.8V. The battery is subjected to 5 cycles of charge-discharge test. If the change in discharge capacity of the battery in three consecutive cycles does not exceed 3% of the rated capacity, the discharge capacity of the third cycle is taken as the calibration capacity and recorded as Q1.

[0033] (a2) SOC adjustment: Before installing the test fixture, adjust the SOC to 0% SOC state, charge with a current of 0.33C to the specified charging cutoff condition, and let it stand at 25℃ for 0.5h; discharge with a current of 0.33C to 0% SOC, let it stand for 0.5h, and record the voltage as OCV under this SOC state.

[0034] (a3) Place the battery with the test fixture installed in the environmental chamber and set the initial preload again: apply a pressure of 0.5 MPa to the surface of the cell as the initial condition for constant pressure, and the displacement gap is 10.22 mm; (a4) At an ambient temperature of 25°C, the above device was left to stand for 24 hours, and the pressure change of the monitoring system was recorded to ensure that the pressure change was less than 5%.

[0035] (a5) Repeat step (a1), and record the measured average capacity as Q2. If Under these circumstances, subsequent cyclic expansion force tests should be conducted; if If so, repeat step (a3).

[0036] (a6) Charge the battery at a constant current of 1C to a voltage of 4.2V, then charge it at a constant voltage of 4.2V to a current of 1 / 20C. After standing for 30 minutes, discharge the battery at a constant current of 1C to a voltage of 2.8V. Cycle the battery charge and discharge, and collect data on the change of expansion force F with time t in situ during the cycle.

[0037] (a7) Calculate the expansion force variation coefficient k and expansion force variation parameter m according to the fitting formula (1) for the expansion force variation during a 100-cycle process. Calculate n+x based on the expansion force parameters k and m.

[0038] Fn=F0+k•N m (1) Fn+x=F0+k•(N+x) m (2).

[0039] (a8) Based on the relationship between the expansion force change and capacity during the 500-cycle process (3), calculate the capacity decay and parameter a, and solve Qn+m based on parameter a and the expansion force change in the (n+x)th cycle. See Table 1 for specific data.

[0040] Qn+x=Q0-α•ΔF•N (3).

[0041] (a9) The capacity at 200 cycles is predicted to be 33.25 Ah using the above formula. The capacity at 200 cycles is 33.86 Ah. The deviation between the predicted and actual values ​​is 1.8%, and the displacement gap after cycling is 10.24 mm.

[0042] Example 2 (b1) Capacity calibration test: At 25℃, a 35Ah battery with ternary cathode material and graphite anode material is charged at a constant current of 0.33C to a voltage of 4.2V, then charged at a constant voltage of 4.2V to a current of 1 / 20C. After standing for 30 minutes, the battery is discharged at a constant current of 0.33C to a voltage of 2.8V. The battery is subjected to 5 cycles of charge-discharge test. If the change in discharge capacity of the battery in three consecutive cycles does not exceed 3% of the rated capacity, the discharge capacity of the third cycle is taken as the calibration capacity and recorded as Q1.

[0043] (b2) SOC adjustment: Before installing the test fixture, adjust the SOC to 0% SOC state, charge with a current of 0.33C to the specified charging cutoff condition, and let it stand at 25℃ for 0.5h; discharge with a current of 0.33C to 0% SOC, let it stand for 0.5h, and record the voltage as OCV under this SOC state.

[0044] (b3) Place the battery with the test fixture installed in the environmental chamber and set the initial preload again: apply a pressure of 0.5 MPa to the surface of the cell as the initial condition for constant pressure, and the displacement gap is 10.19 mm; (b4) At an ambient temperature of 25°C, the above device was left to stand for 24 hours, and the pressure change of the monitoring system was recorded to ensure that the pressure change was less than 5%.

[0045] (b5) Repeat step (b2), and record the measured average capacity as Q2. If Under these circumstances, subsequent cyclic expansion force tests should be conducted; if If so, repeat step (b3).

[0046] (b6) Charge the battery at a constant current of 2C to a voltage of 4.2V, then charge it at a constant voltage of 4.2V to a current of 1 / 20C. After standing for 30 minutes, discharge the battery at a constant current of 2C to a voltage of 2.8V. Cycle the battery charge and discharge, and collect data on the change of expansion force F with time t in situ during the cycle.

[0047] (b7) Calculate the expansion force variation coefficient k and expansion force variation parameter m according to the fitting formula (1) for the expansion force variation during a 100-cycle process. Calculate n+x based on the expansion force parameters k and m.

[0048] Fn=F0+k•N m (1) Fn+x=F0+k•(N+x) m (2).

[0049] (b8) Based on the relationship between the expansion force change and capacity during the 200-cycle process (3), calculate the capacity decay and parameter a, and solve Qn+m based on parameter a and the expansion force change in the (n+x)th cycle. See Table 1 for specific data.

[0050] Qn+x=Q0-α•ΔF•N (3).

[0051] (b9) The capacity at 200 cycles is predicted to be 28.31 Ah using the above formula. The capacity at 200 cycles is 29.12 Ah. The deviation between the predicted and actual values ​​is 2.8%, and the displacement gap after cycling is 10.34 mm.

[0052] Comparative Example (c1) Capacity calibration test: At 25℃, a 35Ah battery with ternary cathode material and graphite anode material is charged at a constant current of 0.33C to a voltage of 4.2V, then charged at a constant voltage of 4.2V to a current of 1 / 20C. After resting for 30 minutes, the battery is discharged at a constant current of 0.33C to a voltage of 2.8V. The battery is subjected to 5 cycles of charge-discharge test. If the change in discharge capacity of the battery in three consecutive cycles does not exceed 3% of the rated capacity, the discharge capacity of the third cycle is taken as the calibration capacity and recorded as Q1.

[0053] (c2) SOC adjustment: Before installing the test fixture, adjust the SOC to 0% SOC state, charge with a current of 0.33C to the specified charging cutoff condition, and let it stand at 25℃ for 0.5h; discharge with a current of 0.33C to 0% SOC, let it stand for 0.5h, and record the voltage as OCV under this SOC state.

[0054] (c3) Battery expansion force monitoring was performed using a comparative fixture (ordinary type, without double limiting device); the battery with the test fixture installed was placed in an environmental chamber, and the initial preload was set again: a pressure of 0.5 MPa was applied to the surface of the cell as the initial condition for constant pressure, and the displacement gap was 10.21 mm. (c4) At an ambient temperature of 25°C, the above device is left to stand for 24 hours, and the pressure change of the monitoring system is recorded to ensure that the pressure change is less than 5%.

[0055] (c5) Repeat step (c2), and record the measured average capacity as Q2. If Under these circumstances, subsequent cyclic expansion force tests should be conducted; if If so, repeat step (c3).

[0056] (c6) Charge the battery at a constant current of 1C to a voltage of 4.2V, then charge it at a constant voltage of 4.2V to a current of 1 / 20C. After standing for 30 minutes, discharge the battery at a constant current of 1C to a voltage of 2.8V. Cycle the battery charge and discharge, and collect data on the change of expansion force F with time t in situ during the cycle.

[0057] (c7) Calculate the expansion force variation coefficient k and expansion force variation parameter m according to the fitting formula (1) for the expansion force variation during a 100-cycle process. Calculate n+x based on the expansion force parameters k and m.

[0058] F n =m+k•N(1) F n+x =m+k•(N+x)(2).

[0059] (c8) Based on the relationship between the expansion force change and capacity during the 200-cycle process (3), calculate the capacity decay and parameter a, and solve Qn+X based on parameter a and the expansion force change in the (n+x)th cycle. See Table 1 for specific data.

[0060] Qn+x=Q0-α•ΔF•(n+x) (3).

[0061] (c9) The capacity at 200 cycles is predicted to be 26.25 Ah using the above formula. If the capacity of the battery is further cycled to 200 cycles, it will be 32.67 Ah. The deviation between the predicted value and the actual value is 19.74%, and the displacement gap after cycling is 12.89 mm.

[0062] Table 1 Key parameters for Examples 1, 2 and Comparative Examples

[0063] Accordingly, embodiments of the present invention also provide a rapid in-situ prediction system for lithium-ion battery life, such as... Figure 2 The diagram shown is a structural schematic of the system. This in-situ rapid prediction system for lithium-ion battery life includes an environmental monitoring system 9, a battery charge / discharge control system 7, a data acquisition and monitoring system 8, and a measuring device mechanical system 10. The battery under test is placed in the environmental monitoring system 9. After reaching thermal equilibrium, the battery charge / discharge control system 7 performs a capacity calibration test on the battery under test to obtain the battery's first calibrated capacity Q1. The battery under test is then installed into the measuring device mechanical system 10, which is placed within the environmental monitoring system 9, and an initial preload is set. The measuring device mechanical system 10 is then left to stand for a predetermined time. Then, the battery charge-discharge control system 7 is used again to test the capacity calibration of the battery under test, and the second calibrated capacity Q2 of the battery is obtained. When the value of Q1 / Q2 is not higher than a predetermined ratio, the battery charge-discharge control system 7 is used to perform charge-discharge cycles on the battery under test. During the charge-discharge cycle of the battery under test, the acquisition and monitoring system 8 is used to acquire the pressure change curve of the battery under test in the longitudinal direction, that is, the pressure change curve of the expansion force. Based on the pressure change curve of the expansion force, a fitting formula for the expansion force change during the cycle is established, and the battery life is predicted based on the relationship between the expansion force and the battery calibrated capacity.

[0064] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0065] The present invention also provides a storage medium, which is a computer-readable storage medium storing a computer program thereon, the computer program being executable when it is run. Figure 1 The method shown may include some or all of the steps. The storage medium may include read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. The storage medium may also include non-volatile memory or non-transitory memory, etc.

[0066] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data provider to another website, computer, server, or data provider via wired or wireless means.

[0067] The embodiments of the present invention have been described in detail above. Specific implementation methods have been used to illustrate the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and systems of the present invention, and are merely some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention, and the content of this specification should not be construed as a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for in-situ rapid prediction of lithium-ion battery lifetime, characterized in that, The method comprises: Step 1, performing a battery capacity calibration test to obtain a first calibration capacity Q1 of the battery; Step 2, installing the battery into a mechanical system of a measuring device and setting an initial pre-tightening force; Step 3, after the mechanical system of the measuring device is left to stand for a predetermined standing time, performing a battery capacity calibration test again to obtain a second calibration capacity Q2 of the battery; Step 4, when the value of Q1 / Q2 is not higher than a predetermined ratio, performing a charge-discharge cycle on the battery, and collecting a pressure change curve of the battery in the longitudinal direction, i.e. a pressure change curve of the swelling force during the charge-discharge cycle of the battery; Step 5, establishing a fitting formula of the change of the swelling force during the cycle according to the pressure change curve of the swelling force, and predicting the service life of the battery according to the relationship between the swelling force and the calibration capacity of the battery.

2. The method for in-situ fast prediction of Li-ion battery lifetime according to claim 1, wherein, In step 1, the battery is charged to a specified charge cut-off condition under a I1(A) current charging system, and left to stand for a first predetermined time length at a to-be-measured ambient temperature; The battery is discharged to a battery discharge termination voltage at the to-be-measured ambient temperature under a I1 constant current, and the discharge capacity is measured; The above charge-discharge steps are performed for a total of 5 times, and the discharge capacity of the battery changes by not more than 3% of the rated capacity of the battery for three consecutive times, and then the discharge capacity of the third time is taken as the first calibration capacity of the battery, which is recorded as Q1.

3. The method for in-situ fast prediction of Li-ion battery lifetime according to claim 1, wherein, The method further comprises, after step 1, adjusting the state of charge SOC of the battery, adjusting the state of charge SOC of the battery to 0% SOC state before installation in the mechanical system of the measuring device, and charging the battery to a specified charge cut-off condition under a I2(A) current charging system, and left to stand for a second predetermined time length at a to-be-measured temperature; The battery is discharged to 0% SOC under a I2(A) current or according to a discharge system recommended by the enterprise, and left to stand for a third predetermined time length, at which time the battery voltage is the open circuit voltage OCV under this SOC state.

4. The method for in-situ fast prediction of Li-ion battery lifetime according to claim 1, wherein, In step 2, the battery is installed into the mechanical system of the measuring device, the mechanical system of the measuring device is placed in an environmental chamber, and a predetermined megapascal pressure is applied to the surface of the battery as a constant pressure or constant gap initial condition when the initial pre-tightening force is set.

5. The method for in-situ rapid prediction of Li-ion battery lifetime according to claim 1, wherein, In step 3, after the mechanical system of the measuring device is left to stand for a predetermined standing time at a target ambient temperature, the pressure change of the monitoring system is recorded, so that the pressure change is less than 5%.

6. The method for in-situ rapid prediction of Li-ion battery lifetime according to claim 1, wherein, In step 4, when the value of Q1 / Q2 is greater than the predetermined ratio, the process returns to step 2.

7. The method for in-situ fast prediction of Li-ion battery lifetime according to claim 1, wherein, In step 5, the swelling force change coefficient k and the swelling force change parameter m are calculated according to the battery swelling force change fitting formula (1) during n cycles of charge-discharge cycle of the battery; and the swelling force value corresponding to the cycle of n+x is calculated according to the swelling force parameters k and m: Fn = F0 + k • n m (1) Fn+x = F0+ k • (n + x) m (2) Wherein, Fn represents the swelling force value corresponding to the cycle of n, F0 represents the initial pre-tightening force, k represents the slope coefficient of the fitting formula, m represents the power coefficient of the fitting formula, n represents the cycle number of the cycle of n, and n+x represents the cycle number of the cycle of n+x.

8. The method for in-situ fast prediction of Li-ion battery lifetime according to claim 7, characterized in that, In step 5, according to the n+x cycle process expansion force change and capacity relationship formula (3), the capacity attenuation and parameter a are calculated, and the Q is solved according to the parameter a and the n+xth cycle expansion force change n+x : Q n+x = Q0- a • AF n • N(3) Wherein, Q n+x represents the capacity corresponding to the nth+x cycle, Q0 represents the initial capacity, a represents the capacity and expansion force conversion coefficient, • ΔF n represents the difference value between the maximum pressure of the n cycle and the maximum pressure of the first cycle; N represents the cycle number corresponding to the n cycle.

9. A system for in-situ rapid prediction of lithium-ion battery lifetime, characterized in that, The system comprises an environment monitoring system, a battery charging and discharging control system, a collection and monitoring system and a measuring device mechanical system. The battery to be tested is placed in the environment monitoring system, and after reaching thermal equilibrium, the battery charging and discharging control system is used to perform capacity calibration test on the battery to be tested to obtain a first calibration capacity Q1 of the battery. The battery to be tested is installed in the measuring device mechanical system, which is placed in the environment monitoring system, and an initial pre-tightening force is set. After the measuring device mechanical system is left for a predetermined standing time, the battery charging and discharging control system is used again to perform capacity calibration test on the battery to be tested to obtain a second calibration capacity Q2 of the battery. When Q1 / Q2 is not higher than a predetermined ratio, the battery charging and discharging control system is used to perform charging and discharging cycle on the battery to be tested. In the charging and discharging cycle of the battery to be tested, the collection and monitoring system is used to collect the pressure change curve of the expansion force of the battery to be tested in the longitudinal direction. A fitting formula of the change of the expansion force in the cycle is established according to the pressure change curve of the expansion force, and the battery life is predicted according to the relationship between the expansion force and the battery calibration capacity.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is run by a processor to perform the steps of the lithium ion battery life in-situ rapid prediction method according to any one of claims 1 to 8.

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