Method for predicting cycle life of lithium iron phosphate battery and application

Abstract

The invention relates to the field of batteries, and discloses a method and application for predicting the cycle life of a lithium iron phosphate battery, and the method comprises the steps: carrying out the performance cycle test processing of a plurality of batteries according to a preset charging and discharging condition, and obtaining the difference value D of the DC internal resistance values of each battery circulating to the first circle and circulating to the (n + 1) th circle, the value G of the characteristic peak II of each battery is further obtained, curve fitting is carried out based on the first discharge rate, the value D and the value G of each battery, and a battery cycle life prediction function is obtained. The method provided by the invention is simple to operate, the obtained prediction function is used for predicting the cycle life of the battery, and the prediction accuracy is high.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a method and application for predicting the cycle life of a lithium iron phosphate battery. Background Art

[0002] Among lithium-ion batteries, lithium iron phosphate batteries have the advantages of low cost, long life and high safety. They are one of the batteries with the highest market share. Their usage scenarios include electric vehicles, grid energy storage and portable electronic devices.

[0003] Lithium iron phosphate batteries have a cycle life of over 2,000 cycles, but this still presents a lifespan shortcoming for some applications, such as large-scale industrial energy storage. This requires continuous improvement of the cycle life of lithium iron phosphate batteries and understanding the sources of their lifespan degradation. However, the evaluation cycle for a cycle life of 2,000-4,000 cycles is relatively long, typically requiring one to two years. To shorten the battery cycle life evaluation period, it is necessary to study the degradation patterns, establish a lifespan model, and expedite the evaluation process.

[0004] CN115047364A discloses a method for predicting the service life of lithium-ion batteries based on an electrochemical model. This method tests the composition and thickness of the solid electrolyte interface of the negative electrode plate at different cycle times, and tests the length and depth of surface cracks in the negative electrode material. The method then uses an electrochemical model and mathematical algorithm to predict the lithium-ion battery's service capacity. This includes analyzing and predicting the formation and growth of the solid electrolyte interface on the surface of the negative electrode material, and predicting the lifespan of the lithium-ion battery in stages throughout its life cycle. While this method significantly shortens prediction time, it requires disassembling the battery and testing the solid electrolyte and negative electrode plate. The testing method is cumbersome, requires numerous conditions, and the calculation process is time-consuming and complex.

[0005] CN110426639A discloses a method and system for predicting the lifespan of lithium-ion batteries based on dynamic impedance spectroscopy. This method performs charge-discharge cycles and dynamic impedance testing on the lithium-ion battery to obtain the dynamic impedance spectrum of the lithium-ion battery at different cycle numbers. The method then determines the parameter values at the same frequency for different cycle numbers in the dynamic impedance spectrum and obtains a relationship between the parameter values and the number of cycles. However, this method only considers the impedance factor, which cannot guarantee accuracy, and does not consider the battery's cycle life at different charge and discharge rates. Summary of the Invention

[0006] The present invention aims to overcome the problems of existing technologies in predicting the cycle life of lithium iron phosphate batteries, such as cumbersome operations or failure to consider cycle life at different charge and discharge rates. The present invention provides a method and application for predicting the cycle life of lithium iron phosphate batteries. This method integrates different discharge rates and utilizes the difference in DC internal resistance and the value of characteristic peak II in the discharge differential voltage curve to obtain a battery life prediction function. The method is simple, easy to operate, and highly accurate.

[0007] In order to achieve the above object, the first aspect of the present invention provides a method for predicting the cycle life of a lithium iron phosphate battery, wherein the method comprises:

[0008] In the first and n+1th rounds, the plurality of batteries are charged and discharged according to the first mode, and in the other rounds, the plurality of batteries are charged and discharged according to the second mode, where n is a natural number;

[0009] The first mode includes a first charging step and a first discharging step, and there is a rest period of t1 min between the first charging step and the first discharging step. The second mode includes a second charging step and a second discharging step. The first discharge rate of the first discharging step and the second discharge rate of the second discharging step corresponding to the same battery are the same, and the first discharge rate corresponding to different batteries is different.

[0010] Obtaining the DC internal resistance of the first cycle and the (n+1) cycle of each battery, and obtaining the difference ΔD between the two;

[0011] Obtaining a discharge state differential voltage curve of each battery cycled to a first preset number of cycles, and extracting a value G of a characteristic peak II thereof;

[0012] Performing curve fitting based on the first discharge rate, the corresponding ΔD, and the corresponding value G of the characteristic peak II corresponding to each of the batteries to obtain a cycle life prediction function of the battery;

[0013] The cycle life of the battery to be predicted is obtained according to the cycle life prediction function.

[0014] The second aspect of the present invention provides an application of the method according to the first aspect of the present invention in battery cycle life prediction, wherein the battery cycle life is the cycle life during which the capacity decays to no less than 60% of the initial capacity.

[0015] Compared with the prior art, the present invention has the following advantages:

[0016] 1. This invention proposes for the first time to use the value of characteristic peak II in the battery's discharge differential voltage curve and the difference between the DC internal resistance values at two preset cycles to predict the cycle aging life of the battery under different rate conditions. A ternary function model of cycle life, DC internal resistance difference, and the degree of change in the value of characteristic peak II is established.

[0017] 2. The present invention is simple and reliable, and combines the electrochemical data fitting model to complete the cycle life prediction, shortening the product verification cycle, and the prediction results are reliable and the model is stable.

[0018] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a graph showing changes in the DC internal resistance of each battery under a first preset charge and discharge condition as a function of the number of equivalent cycles in a specific embodiment;

[0020] Figure 2 is a graph showing a change in the difference between the DC internal resistance of the 52nd cycle and the 1st cycle of the equivalent cycle of each battery in a specific embodiment and the corresponding first discharge rate;

[0021] Figure 3 is a DV curve corresponding to each battery at a corresponding first discharge rate in a specific embodiment;

[0022] Figure 4 The figure is a curve fitting diagram of the difference ΔD of the DC internal resistance of each battery at the corresponding first discharge rate, the 52nd cycle and the 1st cycle of the equivalent cycle, and the value G of the characteristic peak II in a specific embodiment. DETAILED DESCRIPTION

[0023] The endpoints of the ranges and any values disclosed herein are not limited to the precise ranges or values, and these ranges or values should be understood to include values close to these ranges or values. For numerical ranges, the endpoints of each range, the endpoints of each range and individual point values, and the individual point values can be combined with each other to obtain one or more new numerical ranges, which should be considered to be specifically disclosed herein.

[0024] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0025] The endpoints of the ranges and any values disclosed herein are not limited to the precise ranges or values, and these ranges or values should be understood to include values close to these ranges or values. For numerical ranges, the endpoints of each range, the endpoints of each range and individual point values, and the individual point values can be combined with each other to obtain one or more new numerical ranges, which should be considered to be specifically disclosed herein.

[0026] In addition, the term "and / or" in the specification and claims is used to describe an association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0027] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0028] Existing battery life prediction methods mainly predict the life of lithium-ion batteries through electrochemical models. This is based on the fact that the change in DC resistance of lithium iron phosphate batteries during the initial activation stage is related to the rate, and that batteries experience accelerated aging during actual operation. For example, different charge and discharge rates affect the decline in battery capacity and cycle life. The existence of accelerated aging leads to inaccurate predictions of lithium-ion battery life. Existing life prediction methods are difficult to solve the problem of battery life prediction with accelerated aging.

[0029] In order to solve the above problems, the first aspect of the present invention provides a method for predicting the cycle life of a lithium iron phosphate battery, wherein the method comprises:

[0030] In the first and n+1th rounds, the plurality of batteries are charged and discharged according to the first mode, and in the other rounds, the plurality of batteries are charged and discharged according to the second mode, where n is a natural number;

[0031] The first mode includes a first charging step and a first discharging step, and there is a rest period of t1 min between the first charging step and the first discharging step. The second mode includes a second charging step and a second discharging step. The first discharge rate of the first discharging step and the second discharge rate of the second discharging step corresponding to the same battery are the same, and the first discharge rate corresponding to different batteries is different.

[0032] Obtaining the DC internal resistance of the first cycle and the (n+1) cycle of each battery, and obtaining the difference ΔD between the two;

[0033] Obtaining a discharge state differential voltage curve of each battery cycled to a first preset number of cycles, and extracting a value G of a characteristic peak II thereof;

[0034] Performing curve fitting based on the first discharge rate, the corresponding ΔD, and the corresponding value G of the characteristic peak II corresponding to each of the batteries to obtain a cycle life prediction function of the battery;

[0035] The cycle life of the battery to be predicted is obtained according to the cycle life prediction function.

[0036] In the discharge differential voltage (DV) curve, as the discharge capacity increases, its dV / dQ value first decreases and then increases, decreases again after reaching the peak, increases again after reaching the bottom, decreases again after reaching the peak, then increases again, and decreases again after reaching the peak. During this change process, the first characteristic peak I is formed between the first increase and the subsequent decrease, and the peak reached during the second increase is the characteristic peak II.

[0037] Generally speaking, in order to improve the accuracy of the prediction function, the number of batteries and their corresponding first discharge rates is four or more.

[0038] The purpose of standing between charging and discharging is to make the battery in a steady state, so that the extracted DC internal resistance value is stable and can reflect the internal state of the battery. There is no special requirement for the standing time, as long as the battery is stable, such as 20 minutes, 25 minutes, 30 minutes, 40 minutes, etc.

[0039] There is no specific requirement for the first preset cycle number, and it can be selected according to the smoothness of the characteristic peak II. For each corresponding first discharge rate, generally a relatively smooth characteristic peak can be selected.

[0040] As the discharge rate increases, the actual cycle capacity of the battery gradually decreases, and the gap between the battery cycle capacity and the reversible capacity is also gradually widening, indicating that the battery polarization is constantly intensifying. For example, when the discharge rate is 5C, the actual cycle capacity of the battery may be between 60-70mAh; when the discharge rate is 10C, the actual cycle capacity of the battery may be less than 20mAh. Since the total amount of charge experienced by batteries with different discharge rates in the same cycle period varies greatly, in order to make the prediction results more accurate, before performing curve fitting, the selected number of circles, such as the n+1th circle, the first preset number of weeks, is generally converted into an equivalent number of cycles. The result predicted by the prediction function is the equivalent number of cycles corresponding to the life span, so the result is more practical.

[0041] Preferably, the first charging step comprises: charging each of the batteries to an upper limit of a preset voltage window according to a preset mode at a preset temperature and a preset charging rate, standing for t1 min, and then discharging according to the first discharging step;

[0042] The first discharge step includes: discharging to a preset intermediate voltage at the first discharge rate, standing for t2 min, and then discharging to a lower limit of the preset voltage window at a third discharge rate, and standing for t3 min.

[0043] To ensure more stable battery discharge, discharging at the first discharge rate to a preset intermediate voltage and then resting for t2 minutes can be repeated multiple times until the voltage reaches the preset intermediate voltage. Similarly, discharging at the third discharge rate to the lower limit of the preset voltage window and then resting for t3 minutes can be repeated multiple times until the voltage reaches the lower limit of the preset voltage window. There is no specific restriction on the values of t2 and t3, as long as the battery is in a stable state. For example, 5 minutes can be sufficient.

[0044] Preferably, the second charging step comprises: charging each of the batteries to an upper limit of a preset voltage window according to a preset mode at a preset temperature and a preset charging rate;

[0045] The second discharging step includes: discharging to a lower limit of a preset voltage window at the second discharge rate.

[0046] The first mode is the pulse charge and discharge mode, and the second mode is the normal cycle mode of the battery (discharge is performed immediately after charging, and there is no special requirement for whether to stand still between the two, and generally no standing treatment is performed). There are no special requirements for its specific parameter conditions, as long as the performance cycle treatment conditions commonly used for lithium iron phosphate batteries are sufficient, such as the preset temperature can be 0-45°C, the preset charge rate can be 0.33C, the preset mode can be CC-CV mode, the upper limit of the window voltage can be 3.6-3.7V, and the lower limit can be 2-2.5V, etc.

[0047] Preferably, the step of separately obtaining the DC internal resistance values of each battery for the first cycle and the (n+1) cycle includes separately obtaining the DC internal resistance values of each battery for the first cycle and the (n+1) cycle after discharge at the corresponding first discharge rate for t4 seconds. To maintain a steady state, it is necessary to obtain the DC internal resistance value after a certain discharge time, thereby improving the accuracy of the prediction. t seconds can be 25-35 seconds, with 30 seconds being generally selected.

[0048] Preferably, 50≤n≤100. During the cycling process, the battery is in the activation stage for the first 100 cycles. During the second stage, a pulse charge and discharge is performed to obtain a stable DC internal resistance value, thereby improving the accuracy of the prediction.

[0049] Preferably, the first discharge rate corresponding to each of the batteries is independently any value between 1C-10C.

[0050] Preferably, the first discharge rate corresponding to each of the batteries is independently any value between 1C and 5C.

[0051] Preferably, the preset temperature is 20-30°C.

[0052] Preferably, the step of obtaining the cycle life of the battery to be predicted according to the cycle life prediction function includes:

[0053] The battery to be predicted is charged and discharged according to the first mode in the first cycle and the (n+1) cycle, and is charged and discharged according to the second mode in other cycles. The second mode of the battery to be predicted corresponds to a second discharge rate of any value between 1C and 5C.

[0054] Obtaining the DC internal resistance values of the first cycle and the (n+1) cycle of the battery to be predicted, and obtaining the difference ΔD' between the two;

[0055] Obtaining a discharge state differential voltage curve of the battery to be predicted when it cycles to a second preset number of cycles, and extracting a value G' of the characteristic peak II, wherein the second preset number of cycles is any one of the 300th to 500th cycles;

[0056] According to the ΔD′ and G′, the cycle life of the battery to be predicted is obtained by using the battery cycle life prediction function.

[0057] Since the battery will experience serious polarization after exceeding 5C, the battery aging regularity will decrease and the accuracy of the prediction result will be reduced. Therefore, the discharge rate used in the prediction of the present invention is preferably 1C-5C.

[0058] The battery in the 300-500th cycle is in the early cycle stage and has gone through the activation stage. It is in the stable charge and discharge stage. The characteristic peak intensity is obvious and relatively smooth, and the capacity has not yet significantly decayed, which is conducive to the realization of life prediction. When predicting the battery to be predicted, generally speaking, in order to make the obtained characteristic peak II value more representative and to make the established function model more accurate, during the cycle test process, the discharge state differential voltage curve is obtained every 100 or so, and then one of the smoothest characteristic peaks II in the 300-500th cycle is selected as the value G for extracting its characteristic peak II for curve fitting. This can not only reduce the complexity of the operation and the time required for prediction, but also better predict its life.

[0059] Preferably, the method further comprises: performing curve fitting based on the difference ΔD between the DC internal resistance values of the first and n+1th cycles of each battery and the corresponding first discharge rate to obtain a battery cycle rate prediction function. For batteries whose operating conditions are unclear, the battery cycle rate prediction function provided by the present invention can predict their operating conditions, i.e., the discharge rate.

[0060] The second aspect of the present invention provides an application of the method according to the first aspect of the present invention in battery cycle life prediction, wherein the battery cycle life is the cycle life during which the capacity decays to no less than 60% of the initial capacity.

[0061] Preferably, the battery cycle life is the cycle life at which the capacity decays to 60-80% of the initial capacity. When the present invention is used to predict the capacity decay to within 80% of the initial capacity, it has a good prediction effect, and for the cycle life of the battery with a capacity decay of 60-80% of the initial capacity, its prediction accuracy is also very high.

[0062] The present invention proposes for the first time to use the value of characteristic peak II in the battery's discharge differential voltage curve and the difference between the DC internal resistance value at a preset number of cycles to predict the cycle aging life of the battery under different rate conditions, and establishes a ternary function model of cycle life-DC internal resistance difference-characteristic peak II value change degree.

[0063] The present invention is simple and reliable, combines electrochemical data fitting model to complete cycle life prediction, shortens product verification cycle, and has reliable prediction results and stable model.

[0064] The present invention also proposes for the first time to use the DC resistance change during the cycle of lithium iron phosphate batteries to predict the battery cycle rate, and establishes a DC internal resistance change-discharge rate change degree model, which can be used to predict the battery cycle rate.

[0065] The present invention will be further described below through specific examples.

[0066] In the following examples, the cycle performance of the battery was tested by a Xinwei high-precision battery tester. The lithium iron phosphate battery used in the test was a soft-pack laminated battery with a nominal capacity of 110 mAh, a positive electrode active material of lithium iron phosphate, a nominal specific capacity of 149.4 mAh / g, a ratio of 96.9%, and a negative electrode active material of graphite, with a nominal specific capacity of 344 mAh / g, a ratio of 95.55%, and N / P1.09.

[0067] In a specific embodiment, a method for predicting the cycle life of a lithium iron phosphate battery is provided, the method comprising:

[0068] S1, charging and discharging six lithium iron phosphate batteries according to a first mode (referred to as pulse discharge) in the first and 52nd cycles, and charging and discharging the batteries according to a second mode (referred to as cyclic discharge) in the remaining cycles, specifically:

[0069] The specific steps of pulse discharge include: in the first and 52nd cycles, at 25°C, with a preset window voltage of 2.5-3.65V, first charging to 3.65V in CC-CV mode at 0.33C (1C=110mA), standing for 30min (t1), and then discharging to 3V at the first discharge rate (in order to ensure stable discharge of the battery, this process generally needs to be repeated multiple times, each discharge for 60s, and standing for 5min after each discharge (t2), until the voltage is 3V), and then discharging to 2.5V at 0.2C (in order to ensure stable discharge of the battery, this process generally needs to be repeated multiple times, each discharge for 90s, and standing for 5min after each discharge (t3), until the voltage is 2.5V);

[0070] The specific steps of cyclic discharge include: in other cycles (except the first and 51st cycles), at 25°C, the preset window voltage is 2.5-3.65V, first charging to 3.65V at 0.33C (1C=110mA) in CC-CV mode, and then discharging to 2.5V at the second discharge rate.

[0071] The first discharge rate of the first discharge step and the second discharge rate of the second discharge step corresponding to the same battery are the same, and the first discharge rates corresponding to each battery are different, that is, the first discharge rate of one battery during pulse discharge is 1C, and the second discharge rate during cyclic discharge is also 1C, the first discharge rate of the second battery during pulse discharge is 1.5C, and the second discharge rate during cyclic discharge is also 1.5C, the first discharge rate of the third battery during pulse discharge is 2C, and the second discharge rate during cyclic discharge is also 2C, and so on. The first discharge rates corresponding to the six batteries are 1C, 1.5C, 2C, 3C, 5C and 10C respectively (to improve the accuracy of the data, multiple batteries can be selected to repeat the test three times at each rate and the average value is taken).

[0072] The DC internal resistance value after discharging for 30 seconds at the corresponding first discharge rate is obtained from the pulse cycle curve (i.e., the first and 51st cycles). The DC internal resistance value is calculated as follows:

[0073] DCIR=(V t1 -V t0 ) / (I t1 -I t0 ), where V t0 is the instantaneous voltage before discharge, V t1 is the instantaneous voltage at the end of discharge, I t0 is the current at time t0, I t1 is the current at time t1. When pulse discharge is performed, t0 = 0, t1 = 30s.

[0074] S2, respectively obtaining the DC internal resistance of each battery at the 1st cycle and the 52nd cycle, and obtaining the difference ΔD between the two, as follows:

[0075] The DC internal resistance value of each battery when discharged at the corresponding first discharge rate for 30 seconds after the 52nd cycle and the DC internal resistance value of the battery when discharged at the corresponding first discharge rate for 30 seconds after the first cycle were obtained, and the difference ΔD between the two was calculated. The results are shown in Table 1.

[0076] S3, performing curve fitting based on the difference ΔD between each of the first discharge rates and the corresponding DC internal resistance values to obtain a battery cycle rate prediction function, which is as follows:

[0077] The obtained cycle number of each battery is replaced by the equivalent cycle number, and a curve diagram of the change of the equivalent cycle number and the DC internal resistance value is drawn. The results are as follows: Figure 1 shown.

[0078] The conversion formula between the test cycle number and the equivalent cycle is as follows:

[0079] Equivalent cycle number = Ah total / 2C0, where Ah total is the total charge experienced by the battery, Ah total C0 is equal to current multiplied by time. C0 is the nominal capacity of the battery. Since the battery goes through several stages during pulse discharge and cycle discharge, when calculating the total charge, it is necessary to calculate the charge experienced in each stage and sum them up, which is the total charge.

[0080] Based on each of the first discharge rates R, and the difference D between the DC internal resistance value at the 52nd cycle of discharge for 30s and the DC internal resistance value at the 1st cycle of discharge for 30s under each of the first discharge rates (including 1C, 1.5C, 2C, 3C, 5C and 10C), a linear fitting is performed. The results are as follows: Figure 2 As shown, the battery cycle rate prediction function P(R, D) is obtained, D = 0.34628-0.00681×R. The obtained P(R, D) can be used to predict the cycle rate of 1C-10C cycle conditions.

[0081] S4, respectively obtaining the discharge state differential voltage curve of each battery cycled to a first preset number of cycles, and extracting the value G of its characteristic peak II, as follows:

[0082] For each of the batteries, starting from the 300th cycle of the equivalent cycle, a discharge differential voltage (DV) curve is extracted every 100 cycles, and the extraction is terminated at the 500th cycle of the equivalent cycle. The value of the smoothest characteristic peak II is selected from the extracted DV curves as the subsequent curve fitting to obtain the G value. In this example, the discharge differential voltage curves of each of the batteries at 1C, 1.5C, 2C, 3C and 5C are shown as follows: Figure 3 As shown, they correspond to the 300th, 500th, 700th, 900th and 1300th cycles of the equivalent cycle respectively.

[0083] S5, performing curve fitting based on the first discharge rate of each battery, the corresponding difference ΔD of the DC internal resistance value, and the corresponding value G of the characteristic peak II to obtain a battery cycle life prediction function, specifically as follows:

[0084] The difference ΔD of the DC internal resistance and the value G of the characteristic peak II of each battery at the corresponding first discharge rate are shown in Table 1.

[0085] Table 1

[0086] magnification △D,Ω G,V / Ah 1C 0.34348 4.31449 1.5C 0.3371 4.92302 2C 0.32902 5.390306 3C 0.32283 5.977725 5C 0.31339 7.008331

[0087] Fit the data in Table 1, as shown in Figure 4As shown, the battery cycle life prediction function Q(N,D,G) is obtained as follows:

[0088] N = -4960.5659 × G' - 46431.1607 × △ D' + 142.8568 × G'^2 + 11043.7959 × G' × △ D' - 36365.6895 × △ D'^2 + 22915.349, where N is the predicted number of cycles, △ D' is the difference between the DC internal resistance of the battery to be predicted at the 52nd and 1st cycles of the equivalent cycle, G' is the value of characteristic peak II in the DV curve of one of the 300th to 500th cycles of the equivalent cycle, and the determination coefficient R of the prediction function is . 2 =0.98076.

[0089] S6, the lithium iron phosphate battery to be predicted is charged and discharged in the same mode as the battery in S1, specifically:

[0090] The specific steps of pulse discharge include: in the first and 52nd cycles, at 25°C, with a preset window voltage of 2.5-3.65V, first charging to 3.65V in CC-CV mode at 0.33C (1C=110mA), standing for 30min (t1), and then discharging to 3V at the first discharge rate of 4C (in order to ensure stable discharge of the battery, this process generally needs to be repeated multiple times, each discharge for 60s, and standing for 5min after each discharge (t2), until the voltage is 3V), and then discharging to 2.5V at 0.2C (in order to ensure stable discharge of the battery, this process generally needs to be repeated multiple times, each discharge for 90s, and standing for 5min after each discharge (t3), until the voltage is 2.5V);

[0091] The specific steps of cyclic discharge include: in other cycles (except the first and 51st cycles), at 25°C, the preset window voltage is 2.5-3.65V, first charging to 3.65V at 0.33C (1C=110mA) in CC-CV mode, and then discharging to 2.5V at a second discharge rate of 4C.

[0092] The DC internal resistance value of the lithium iron phosphate battery to be predicted after the 52nd cycle is obtained, and the difference between the DC internal resistance value and the DC internal resistance value of the first cycle is calculated. In this example, ΔD′=0.31845.

[0093] When ΔD'=0.31845 is input into the P(R,D) function, the predicted R is 4.08C. The error between this value and the actual cycle rate is very small, indicating that the rate prediction function model established by the present invention has high prediction accuracy.

[0094] Starting from the 300th cycle of the equivalent cycle, a DV curve is extracted every 100 cycles until the 500th cycle of the equivalent cycle. The value of the smoothest characteristic peak II is selected as the predicted G' value. In this example, G' = 6.587146 (corresponding to the 500th cycle of the equivalent cycle).

[0095] Input △D'==0.31845 and G'=6.587146 into Q(N,D,G), and obtain the cycle life of the lithium iron phosphate battery to be predicted under 4C conditions as 1130 cycles.

[0096] The actual cycle life of the lithium iron phosphate battery to be predicted when it is actually cycled to the end of its life (capacity decay is 78% of the initial capacity) is 1100 cycles. The error between the predicted life and the actual cycle life is only 2.7%, and the prediction accuracy is very high.

[0097] Of course, the method provided in this application can also be used for lithium iron phosphate batteries of other capacities and other types. This is easy to implement in the art and will not be described in detail here.

[0098] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited thereto. Within the technical concept of the present invention, various simple variations of the technical solution of the present invention may be made, including combining the various technical features in any other appropriate manner. These simple variations and combinations should also be regarded as disclosed in the present invention and fall within the scope of protection of the present invention.

Claims

1. A method for predicting the cycle life of a lithium iron phosphate battery, characterized in that: The method comprises: In the first and n+1th rounds, the plurality of batteries are charged and discharged according to the first mode, and in the other rounds, the plurality of batteries are charged and discharged according to the second mode, where n is a natural number; The first mode includes a first charging step and a first discharging step, and there is a rest period of t1 min between the first charging step and the first discharging step. The second mode includes a second charging step and a second discharging step. The first discharge rate of the first discharging step and the second discharge rate of the second discharging step corresponding to the same battery are the same, and the first discharge rate corresponding to different batteries is different. Obtaining the DC internal resistance of the first cycle and the (n+1) cycle of each battery, and obtaining the difference ΔD between the two; Obtaining a discharge state differential voltage curve of each battery cycled to a first preset number of cycles, and extracting a value G of a characteristic peak II thereof; Performing curve fitting based on the first discharge rate, the corresponding ΔD, and the corresponding value G of the characteristic peak II corresponding to each of the batteries to obtain a cycle life prediction function of the battery; The cycle life of the battery to be predicted is obtained according to the cycle life prediction function.

2. The method according to claim 1, wherein The first charging step includes: charging each of the batteries to the upper limit of a preset voltage window according to a preset mode at a preset temperature and a preset charging rate, standing for t1 min, and then discharging according to the first discharging step; The first discharge step includes: discharging to a preset intermediate voltage at the first discharge rate, standing for t2 min, and then discharging to a lower limit of the preset voltage window at a third discharge rate, and standing for t3 min.

3. The method according to claim 2, wherein: The step of respectively obtaining the DC internal resistance values of the first cycle and the (n+1) cycle of each battery includes respectively obtaining the DC internal resistance values of each battery after discharging for t4 seconds at the corresponding first discharge rate during the first cycle and the (n+1) cycle of each battery.

4. The method according to any one of claims 1 to 3, wherein: The second charging step includes: charging each of the batteries to an upper limit of a preset voltage window according to a preset mode at a preset temperature and a preset charging rate; The second discharging step includes: discharging to a lower limit of a preset voltage window at the second discharge rate.

5. The method according to claim 4, wherein 50≤n≤60。 6. The method according to claim 2 or 3, wherein: The first discharge rate corresponding to each of the batteries is independently any value between 1C and 10C, preferably any value between 1C and 5C; And / or, the preset temperature is 20-30°C.

7. The method according to claim 6, wherein: The method further includes: performing curve fitting based on the difference ΔD between the DC internal resistance values of the first cycle and the (n+1) cycle of each battery and the corresponding first discharge rate to obtain a battery cycle rate prediction function.

8. The method according to claim 4, wherein: The step of obtaining the cycle life of the battery to be predicted according to the cycle life prediction function includes: The battery to be predicted is charged and discharged according to the first mode in the first cycle and the (n+1) cycle, and is charged and discharged according to the second mode in other cycles. The second mode of the battery to be predicted corresponds to a second discharge rate of any value between 1C and 5C. Obtaining the DC internal resistance values of the first cycle and the (n+1) cycle of the battery to be predicted, and obtaining the difference ΔD' between the two; Obtaining a discharge state differential voltage curve of the battery to be predicted when it cycles to a second preset number of cycles, and extracting a value G' of the characteristic peak II, wherein the second preset number of cycles is any one of the 300th to 500th cycles; According to the ΔD′ and G′, the cycle life of the battery to be predicted is obtained by using the battery cycle life prediction function.

9. An application of the method according to any one of claims 1 to 8 in battery cycle life prediction, characterized in that: The battery cycle life is the cycle life during which the capacity decays to no less than 60% of the initial capacity.

10. The use according to claim 9, wherein: The battery cycle life is the cycle life during which the capacity decays to 60-80% of the initial capacity.

Images