An adaptive multi-temperature-zone lithium-ion battery capacity prediction method and system

Abstract

This invention discloses an adaptive multi-temperature-domain lithium-ion battery capacity prediction method and system, relating to the field of lithium-ion battery technology. The method includes: setting the ambient temperature of the battery as t0, obtaining the discharge capacity of the battery at temperature t0; and setting different temperatures T, obtaining the discharge capacity Cap of the battery at different temperatures. T Based on the battery electrochemical reaction mechanism, Cap was established at temperatures t0 and T. T The invention employs empirical formulas; solves these formulas to obtain θ1, θ2, and θ3, and establishes a lithium-ion battery capacity prediction model; then, it predicts the lithium-ion battery capacity based on this model. This invention's lithium-ion battery capacity prediction model, based on the battery's electrochemical mechanism, can quickly and accurately predict the battery's discharge capacity at different rates across multiple temperature ranges.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery technology, and specifically to a method and system for predicting the discharge capacity of lithium-ion batteries at different temperatures. Background Technology

[0002] Lithium-ion batteries exhibit varying polarization due to differences in ohmic impedance, charge transfer impedance, and mass transfer impedance at different temperatures. The electrochemical reaction mechanisms at the electrode / electrolyte interface also differ, leading to variations in the discharge performance of the same battery at different temperatures. Particularly, the electrochemical and side reaction mechanisms at the electrode / electrolyte interface are complex across different temperature ranges. Accurately predicting charge-discharge performance across multiple temperature ranges and rates has become a significant challenge in the industry. Chinese invention patent CN114814614A, published on July 29, 2022, discloses a method for predicting the capacity of lithium-ion batteries. The method involves charging the battery to full capacity; collecting the capacity Q of the battery at different discharge rates I; establishing a relationship between discharge rate and capacity; and predicting the capacity of the battery at any discharge rate based on this relationship. The functional expression is lnQ=(1-a)lnI+lnB, where a is the polarization parameter and B is the battery parameter. Application publication number CN112379277A, published on February 19, 2021, discloses a method for predicting the capacity of a lithium-ion battery. The method includes taking A cells whose capacity is to be predicted and performing a complete charge and discharge cycle to derive the discharge capacitance and calculate the average discharge capacity Cm; fully charging each cell whose capacity is to be predicted, and then performing charge and discharge according to the following process: ① performing constant current discharge on the cell whose capacity is to be predicted, setting the cell's discharge cutoff capacity to C; ② continuing to discharge with a small current until the discharge cutoff voltage V1, and calculating the total discharge capacity C1 of steps ① and ②; ③ then charging the cell whose capacity is to be predicted with a small current until the cutoff voltage V2, and calculating the charging capacity C2, and after a certain period of rest, calculating the reverse voltage OCV; inputting C1, C2, and OCV into the capacity prediction formula yields the predicted capacity value of the cell. Authorization announcement number CN112034367B, published on January 15, 2021, discloses a method and system for predicting the capacity of a lithium-ion battery, including: Step 1: discharging from a preset initial SOC to a preset discharge cutoff voltage, and then charging to 100% SOC; Step 2: predicting the battery's discharge capacity Y1 using the interval discharge capacity C1, discharge temperature T1, and charging capacity C2, charging temperature T2; Step 3: performing temperature compensation on the predicted battery discharge capacity Y1 based on the relationship curve between charging temperature T2 and capacity to obtain the battery's capacity at 25℃. The published patent does not describe a mathematical model for predicting the discharge capacity of lithium-ion batteries under multiple temperature ranges or at different rates at the same temperature. Summary of the Invention

[0003] To address the problem that existing technologies suffer from low accuracy in predicting lithium-ion battery capacity under multiple temperature domains and different rate conditions, where the original temperature-based prediction model is not applicable to all temperatures and different rate conditions, this invention provides an adaptive multi-temperature domain lithium-ion battery capacity prediction method and system. Based on the battery electrochemical reaction mechanism, it establishes the Capacity prediction model at temperatures t0 and T. T and Empirical formulas are used to obtain θ1, θ2, and θ3, and an empirical mathematical model is established. The deviation rate between the empirical mathematical model and the measured data under multiple temperature ranges is verified. If the deviation rate is less than the preset value, the empirical mathematical model is determined as the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for temperature ranges with large deviations, new unknown parameters θ1, θ2, and θ3 are solved by fitting the temperature ranges to obtain the lithium-ion battery capacity prediction model for that temperature range. The lithium-ion battery capacity is predicted based on the lithium-ion battery capacity prediction model.

[0004] To address the above problems, the present invention provides a technical solution.

[0005] In a first aspect, the present invention provides an adaptive multi-temperature-domain lithium-ion battery capacity prediction method, comprising the following steps:

[0006] The ambient temperature of the battery is set to t0. The battery is charged and discharged at a current rate of nC at temperature t0 to obtain the discharge capacity of the battery at temperature t0.

[0007] By setting different temperatures T, the battery is discharged at a current rate of nC at each temperature T, and the discharge capacity Cap of the battery at different temperatures is obtained. T ;

[0008] Based on the battery electrochemical reaction mechanism, Cap was established at temperatures t0 and T. T and Empirical formula;

[0009] Solve the empirical formula to obtain θ1, θ2, θ3, and establish an empirical mathematical model;

[0010] The deviation rate between the empirical mathematical model and the measured data under multiple temperature range conditions is verified. If the deviation rate is less than the preset value, the empirical mathematical model is determined as the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for the temperature range with large deviation, the new unknown parameters θ1, θ2, and θ3 are solved by fitting the temperature range to obtain the lithium-ion battery capacity prediction model for the temperature range.

[0011] Predict the capacity of lithium-ion batteries based on lithium-ion battery capacity prediction models.

[0012] Preferably, the battery is charged and discharged at a current rate of nC at temperature t0 to obtain the discharge capacity of the battery at temperature t0. Includes the following steps:

[0013] Set the upper limit voltage of the battery to V. max The lower limit voltage is V min The resting time is t, and the battery discharges at a current of nC to the lower limit voltage V. min Then, let the battery stand for t1.

[0014] The battery is charged to its upper limit voltage V at a current of nC. max Then, use the upper limit voltage V max After constant voltage charging to a current of 0.05C, let the battery rest for t1.

[0015] The battery discharges at an nC current to its lower limit voltage V. min The capacity of the battery at temperature t0 is obtained. Let the battery stand still for t1;

[0016] The battery is charged to its upper limit voltage V at a current of nC. max Then, with voltage V max Charge at constant voltage until the current reaches 0.05C.

[0017] Preferably, the settling time t1 is 10 minutes.

[0018] Preferably, the battery is discharged at a current rate of nC at different temperatures T to obtain the discharge capacity Cap of the battery at different temperatures. T This includes the following steps:

[0019] At temperature T, the battery is left to stand for t2.

[0020] The battery discharges at nC to the lower limit voltage V. min The discharge capacity Cap at temperature T was obtained. T .

[0021] Preferably, the settling time t2 ≥ 6h.

[0022] As a preferred option, the empirical formula is as follows:

[0023]

[0024] In the formula, θ1 is the pre-exponential factor, θ2 is a parameter related to the activation energy of the reaction, and θ3 is related to... Associated parameters.

[0025] As a preferred approach, θ1, θ2, and θ3 are obtained by solving the empirical formula, and an empirical mathematical model is established, including the following steps:

[0026] Transform the empirical formula into an equation:

[0027] Cap T T Substituting these values ​​into the equations yields matrices. Further solving for the unknown parameters θ1, θ2, and θ3, we substitute these parameters into the empirical formula to obtain an empirical mathematical model.

[0028] Preferably, n in the nC ratio is between 0.02 and 10.

[0029] Secondly, the present invention provides an adaptive multi-temperature domain lithium-ion battery capacity prediction system, including an adaptive multi-temperature domain lithium-ion battery capacity prediction method provided in any embodiment of the first aspect. The system includes: a control module, which sets the ambient temperature of the battery to t0, and charges and discharges the battery at a current of nC at temperature t0; and sets different temperatures T, and discharges the battery at a current of nC at different temperatures T.

[0030] The capacity calculation module obtains the battery's discharge capacity at temperature t0. The discharge capacity Cap of the battery at different temperatures was obtained. T ;

[0031] The data processing module, based on the battery electrochemical reaction mechanism, establishes Cap at temperatures t0 and T. T and Empirical formula;

[0032] Solve the empirical formula to obtain θ1, θ2, θ3, and establish an empirical mathematical model;

[0033] The capacity prediction module predicts the capacity of lithium-ion batteries based on a lithium-ion battery capacity prediction model.

[0034] Preferably, the data processing module is also used to verify the deviation rate between the measured data and the empirical mathematical model under multiple temperature range conditions. If the deviation rate is less than the preset value, the empirical mathematical model is determined to be the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for the temperature range with large deviation, the new unknown parameters θ1, θ2, and θ3 are solved by temperature zone fitting to obtain the lithium-ion battery capacity prediction model for the temperature range.

[0035] The beneficial effects of this invention: Based on the electrochemical mechanism of batteries, this invention establishes Cap at temperatures t0 and T. T and Empirical formulas are used to solve for θ1, θ2, and θ3, establishing an empirical mathematical model. The deviation rate between the empirical mathematical model and measured data under multiple temperature ranges is verified. If the deviation rate is less than a preset value, the empirical mathematical model is determined as the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for temperature ranges with large deviations, new unknown parameters θ1, θ2, and θ3 are solved by fitting the data across different temperature zones, obtaining the lithium-ion battery capacity prediction model for that temperature range. The lithium-ion battery capacity is predicted based on this model, enabling rapid and accurate prediction of battery discharge capacity at different rates under multiple temperature ranges. Attached Figure Description

[0036] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating an adaptive multi-temperature-domain lithium-ion battery capacity prediction method according to the present invention.

[0039] Figure 2 This is a schematic diagram of the structure of an adaptive multi-temperature-domain lithium-ion battery capacity prediction system according to the present invention;

[0040] Figure 3 This is a schematic diagram comparing the predicted capacity of the model with the measured value according to the present invention.

[0041] Icons: 1-Control module, 2-Capacity calculation module, 3-Data processing module, 4-Capacity prediction module. Detailed Implementation

[0042] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0043] In the description of this application, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0044] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installed," "equipped with," "sleeved / connected," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0045] Please see Figure 1 , Figure 1 This is a flowchart illustrating an adaptive multi-temperature-domain lithium-ion battery capacity prediction method according to this application. A preferred embodiment of this application, an adaptive multi-temperature-domain lithium-ion battery capacity prediction method, includes the following steps:

[0046] S1: Set the ambient temperature of the battery to t0. Charge and discharge the battery at a current rate of nC at temperature t0 to obtain the discharge capacity of the battery at temperature t0.

[0047] In this step, the upper limit voltage of the battery is set to V. max The lower limit voltage is V min The resting time is t, and the battery discharges at a current of nC to the lower limit voltage V. min Then, let the battery rest for t1; charge the battery with a current of nC until the battery's upper limit voltage V. max Then, use the upper limit voltage V max After constant voltage charging to a current of 0.05C, the battery is left to rest for t1; the battery is then discharged at an nC current until the battery's lower limit voltage V. min The capacity of the battery at temperature t0 is obtained. Let the battery rest for t1; charge the battery with a current of nC to the battery's upper limit voltage V. max Then, with voltage V max Charge at constant voltage until the current reaches 0.05C.

[0048] S2: Set different temperatures T, and discharge the battery at a current rate of nC at each temperature T to obtain the battery's discharge capacity Cap at different temperatures. T ;

[0049] In this step, the battery is left to stand at temperature T for t2.

[0050] The battery discharges at nC to the lower limit voltage V. min The discharge capacity Cap at temperature T was obtained. T .

[0051] S3: Based on the battery electrochemical reaction mechanism, establish Cap at temperatures t0 and T. T and Empirical formula;

[0052] In this step, the empirical formula is as follows:

[0053]

[0054] In the formula, θ1 is the pre-exponential factor, θ2 is a parameter related to the activation energy of the reaction, and θ3 is related to... Associated parameters.

[0055] S4: Solve the empirical formula to obtain θ1, θ2, θ3, and establish an empirical mathematical model;

[0056] In this step, the empirical formula is transformed into an equation:

[0057] Cap T T Substituting these values ​​into the equations yields matrices. Further solving for the unknown parameters θ1, θ2, and θ3, we substitute these parameters into the empirical formula to obtain an empirical mathematical model.

[0058] S5: Verify the deviation rate between the empirical mathematical model and the measured data under multiple temperature range conditions. If the deviation rate is less than the preset value, the empirical mathematical model is determined as the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for the temperature range with large deviation, the new unknown parameters θ1, θ2, and θ3 are solved by fitting the temperature range to obtain the lithium-ion battery capacity prediction model for the temperature range.

[0059] S6: Predict the capacity of lithium-ion batteries based on the lithium-ion battery capacity prediction model.

[0060] Please see Figure 2 , Figure 2This is a schematic diagram of an adaptive multi-temperature-domain lithium-ion battery capacity prediction system. In a preferred embodiment of this application, the adaptive multi-temperature-domain lithium-ion battery capacity prediction system includes an adaptive multi-temperature-domain lithium-ion battery capacity prediction method provided in any embodiment of this invention. The system includes: a control module connected to a capacity calculation module, a capacity calculation module connected to a data processing module, and a data processing module connected to a capacity prediction module; the control module sets the ambient temperature of the battery to t0, and charges and discharges the battery at an nC rate current at temperature t0; it also sets different temperatures T, and discharges the battery at an nC rate current at different temperatures T.

[0061] The capacity calculation module obtains the battery's discharge capacity at temperature t0. The discharge capacity Cap of the battery at different temperatures was obtained. T ;

[0062] The data processing module, based on the battery electrochemical reaction mechanism, establishes Cap at temperatures t0 and T. T and Empirical formula;

[0063] Solve the empirical formula to obtain θ1, θ2, θ3, and establish an empirical mathematical model;

[0064] The capacity prediction module predicts the capacity of lithium-ion batteries based on a lithium-ion battery capacity prediction model.

[0065] The data processing module is also used to verify the deviation rate between the measured data and the empirical mathematical model under multiple temperature range conditions. If the deviation rate is less than the preset value, the empirical mathematical model is determined to be the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for the temperature range with large deviation, the new unknown parameters θ1, θ2, and θ3 are solved by temperature zone fitting to obtain the lithium-ion battery capacity prediction model for the temperature range.

[0066] Example 1

[0067] This invention provides an adaptive multi-temperature-domain lithium-ion battery capacity prediction method, comprising the following steps:

[0068] S101: Take 25 packs of 78Ah batteries, place 5 packs of batteries in a temperature chamber, set the temperature of the temperature chamber to 25℃, and charge and discharge the batteries at a 1C rate. The upper limit voltage of the batteries is 4.25V and the lower limit voltage is 2.7V. The specific steps are as follows:

[0069] ① The battery is discharged at a 1C rate to the lower limit voltage of 2.7V;

[0070] ② Let stand for 10 minutes;

[0071] ③ Charge the battery at a 1C rate to its upper limit voltage of 4.25V, and maintain the voltage at a constant current of 3.9A;

[0072] ④ Let stand for 10 minutes;

[0073] ⑤ Discharge the battery at a 1C rate to its lower limit voltage of 2.7V, obtaining the capacity (Cap) of 5 battery packs at 25℃. 25℃ .

[0074] ⑥ Let stand for 10 minutes;

[0075] ⑦ Charge the battery at a 1C rate to its upper limit voltage of 4.25V, and maintain the voltage at a constant current of 3.9A;

[0076] S201: Take four temperature chambers and set the chamber temperatures to 18℃, 21℃, 27℃, and 30℃ respectively. From the remaining 20 battery packs in step S101, take 5 battery packs from each chamber and place them in the temperature chamber. After standing for 6 hours, discharge them at a 1C rate to the lower limit voltage of 2.7V to obtain the discharge capacity Cap at different temperatures T. T .

[0077] S301: Based on the battery electrochemical reaction mechanism, establish Capacitance at 25℃ and different temperatures T. T and Cap 25℃ Empirical formula;

[0078] In this step, the different temperatures T are 18℃, 21℃, 27℃, and 30℃, and the empirical formula is as follows:

[0079]

[0080] In the formula, θ1 is the pre-exponential factor, θ2 is a parameter related to the activation energy of the reaction, and θ3 is related to... Associated parameters.

[0081] S401: Solve the empirical formula to obtain θ1, θ2, θ3, and establish an empirical mathematical model;

[0082] In this step, the empirical formula is transformed into the equation: lnCap T =lnθ1-θ 2 / T+θ3lnCap 25℃ ;

[0083] Cap T T, Cap 25℃ Substituting these values ​​into the equations yields matrices. Further solving for the unknown parameters θ1, θ2, and θ3, we obtain values ​​of 4.06, 266.55, and 0.88. Substituting these values ​​into the empirical formula, we obtain an empirical mathematical model for temperatures ranging from 18℃ to 30℃.

[0084]

[0085] S501: Verify the deviation rate between the empirical mathematical model and the measured data under the temperature range of 18℃~30℃. If the deviation rate is less than 2%, then determine the empirical mathematical model as the lithium-ion battery capacity prediction model for this temperature range. Otherwise, for the temperature range with large deviation, repeat steps S101 to S401 in different temperature zones to fit and solve for new unknown parameters θ1, θ2, θ3, and obtain the lithium-ion battery capacity prediction model for this temperature range.

[0086] S601: Predict the lithium-ion battery capacity based on the lithium-ion battery capacity prediction model. The measured lithium-ion battery capacities are shown in the table below:

[0087] Battery number 18℃ 21℃ 25℃ 27℃ 30℃ 1 77.60 78.38 79.30 80.01 80.70 2 77.33 78.21 79.04 79.79 80.37 3 77.21 78.09 78.82 79.65 80.28 4 78.14 78.75 79.63 80.65 80.65 5 78.43 79.12 79.93 80.91 80.91

[0088] Please see Figure 3 , Figure 3 This is a schematic diagram comparing the predicted and measured values ​​of the lithium-ion battery capacity of the present invention. The vertical axis is Cap (Ah), and the horizontal axis is Temp (K), where K is Kelvin, Kelvin = Celsius + 273.15℃. As can be seen from the figure, the predicted value of the present invention is closer to the measured value at 20℃ to 26℃, while the predicted value deviates significantly from the measured value at 18℃ to 20℃ and 26℃ to 30℃.

[0089] In this embodiment, for the temperature ranges with large deviations, specifically the ranges of 18℃~20℃ and 26℃~30℃, steps S101 to S401 are repeated for each of these two ranges to fit and solve for new unknown parameters θ1, θ2, and θ3, thus obtaining lithium-ion battery capacity prediction models for the 18℃~20℃ and 26℃~30℃ ranges. Then, these three lithium-ion battery capacity prediction models for the 18℃~20℃, 20℃~26℃, and 26℃~30℃ ranges adaptively predict the battery capacity for the 18℃~30℃ range.

[0090] Example 2

[0091] Three packs of 78Ah lithium-ion batteries were selected and divided into three groups. Each group was placed in an incubator at 25°C. The three groups were charged at 1C, 1.5C, and 2C rates, respectively, yielding the following charging capacities: 66.89Ah at 1C, 63.21Ah at 1.5C, and 60.45Ah at 2C. Three more packs of 78Ah lithium-ion batteries were then selected and divided into three groups. Each group was placed in an incubator at 25°C, and the three groups were discharged at 1C to the lower voltage limit of 2.7V. After resting for 10 minutes, the batteries were charged at a 1C rate to the upper limit voltage of 4.25V, and then charged at a constant voltage to a current of 3.9A. After resting for 10 minutes, the three groups of batteries were discharged at 1C, 1.5C, and 2C respectively to obtain the discharge capacity at different rates. The discharge capacity of the battery at 1C rate was 79.28Ah, the charging capacity of the battery at 1.5C rate was 77.61Ah, and the charging capacity of the battery at 2C rate was 78.44Ah. The battery capacity at different charge and discharge rates was predicted according to the lithium-ion battery capacity prediction model of the present invention, and the predicted battery capacity values ​​at different charge and discharge rates were compared with the measured battery capacity values ​​at different charge and discharge rates.

[0092] The above are merely preferred embodiments of this application; however, the scope of protection of this application is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in this application, based on the technical solution and its improved concept, should be covered within the scope of protection of this application.

Claims

1. An adaptive multi-temperature domain lithium-ion battery capacity prediction method, characterized in that, Includes the following steps: Set the ambient temperature of the battery to ,exist The battery was charged and discharged at a current of nC at a temperature to obtain the temperature. The discharge capacity of the battery described below ; By setting different temperatures T, the battery is discharged at a current rate of nC at each temperature T to obtain the discharge capacity of the battery at different temperatures. ; Based on the battery electrochemical reaction mechanism, establish and at temperature T and Empirical formula; Solving the empirical formula yields , , Establish empirical mathematical models; The deviation rate between the empirical mathematical model and the measured data under multiple temperature ranges is verified. If the deviation rate is less than a preset value, the empirical mathematical model is determined to be the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for temperature ranges with large deviations, new unknown parameters are solved by temperature-zone fitting. , , Thus, a lithium-ion battery capacity prediction model for the specified temperature range is obtained; Predict the capacity of lithium-ion batteries based on lithium-ion battery capacity prediction models. Among them, The battery was charged and discharged at a current of nC at a temperature to obtain the temperature. The discharge capacity of the battery described below This includes the following steps: Set the upper limit voltage of the battery to The lower limit voltage is The settling time is The battery discharges to its lower limit voltage at a current of nC. Then, let the battery sit. ; The battery is charged to its upper limit voltage at a current of nC. Then, use the upper limit voltage. After charging at a constant voltage to a current of 0.05C, allow the battery to rest. ; The battery discharges to its lower limit voltage at an nC current. The battery is obtained in Capacity at temperature Let the battery sit ; The battery is charged to its upper limit voltage at a current of nC. Then, with voltage Constant voltage charging until current reaches 0.05C; The battery was discharged at a current rate of nC at different temperatures T to obtain the discharge capacity of the battery at different temperatures. This includes the following steps: Let the battery stand at temperature T. ; The battery discharges at nC to the lower limit voltage. The discharge capacity at temperature T was obtained. ; The empirical formula is as follows: ； In the formula, Pre-exponential factor, These are parameters related to the activation energy of the reaction. To and Associated parameters.

2. The adaptive multi-temperature domain lithium-ion battery capacity prediction method according to claim 1, characterized in that, resting time It lasts for 10 minutes.

3. The adaptive multi-temperature domain lithium-ion battery capacity prediction method according to claim 1, characterized in that, resting time ≥6h.

4. The adaptive multi-temperature domain lithium-ion battery capacity prediction method according to claim 3, characterized in that, Solving the empirical formula yields , , Establishing an empirical mathematical model includes the following steps: Transform the empirical formula into an equation: ; Will T Substituting the values ​​into the equation yields the matrix, which is then used to solve for the unknown parameters. , , Then, the unknown parameters , , Substituting the empirical formula into the given formula yields the empirical mathematical model.

5. The adaptive multi-temperature-domain lithium-ion battery capacity prediction method according to claim 3, characterized in that, In the nC ratio, n = 0.02~10.

6. An adaptive multi-temperature-domain lithium-ion battery capacity prediction system, characterized in that, The system includes an adaptive multi-temperature-domain lithium-ion battery capacity prediction method according to any one of claims 1 to 5, wherein the system includes a control module and sets the ambient temperature of the battery to be... ,exist The battery is charged and discharged at a current of nC at a temperature. Different temperatures T are set, and the battery is discharged at a current of nC at different temperatures T; The capacity calculation module obtains the temperature. The discharge capacity of the battery described below The discharge capacity of the battery at different temperatures was obtained. ; The data processing module, based on the battery electrochemical reaction mechanism, establishes... and at temperature T and Empirical formula; Solving the empirical formula yields , , Establish empirical mathematical models; The capacity prediction module predicts the capacity of lithium-ion batteries based on a lithium-ion battery capacity prediction model.

7. An adaptive multi-temperature-domain lithium-ion battery capacity prediction system according to claim 6, characterized in that, The data processing module is also used to verify the deviation rate between the empirical mathematical model and the measured data under multiple temperature range conditions. If the deviation rate is less than a preset value, the empirical mathematical model is determined to be the lithium-ion battery capacity prediction model for that temperature range. Otherwise, for the temperature range with large deviations, new unknown parameters are solved by temperature zone fitting. , , A lithium-ion battery capacity prediction model for the specified temperature range is obtained.

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