Method for predicting internal resistance of power battery, method, system and storage medium for predicting power

Abstract

The present invention discloses a method for predicting the internal resistance of a power battery, a method for predicting power, a system and a storage medium, including: using a ternary power battery internal resistance (or power) table, combined with the electrochemical characteristics of the battery itself, first normalizing the parameters of the battery internal resistance (or power) table, then taking the battery internal resistance (or power) as the dependent variable, taking the battery temperature, battery state of charge, and pulse discharge (or charging) time as independent variables, performing nonlinear regression fitting, establishing a multivariate factor-coupled internal resistance (or power) model, correcting and verifying multiple models, and predicting the internal resistance (or power) of the power battery through the model. The present invention is used to predict the battery internal resistance (power), and the prediction error is less than 10%.

Description

Technical Field

[0001] The present invention belongs to the technical field of power batteries, and in particular relates to a method for predicting the internal resistance of a power battery, a method for predicting the power, a system and a storage medium. Background Art

[0002] The maximum output power and feedback power capability of lithium-ion power batteries, which are the mainstream in the new energy vehicle market, are greatly affected by the ambient temperature and the battery's own state of charge. This is determined by the electrochemical characteristics of the battery itself. Therefore, when designing the power performance of new energy vehicles, it is necessary to combine the actual characteristics and capabilities of the battery. At the same time, the battery power capability is strongly related to the battery's capacity and internal resistance.

[0003] In traditional methods, it is necessary to measure the DC internal resistance and peak power of the battery through methods such as HPPC. The battery is tested for charge and discharge pulses at the maximum current capacity at different temperatures, different charge states, and different charge and discharge times. The test cycle is long and the operability is poor. In addition, because the maximum current that the battery can withstand under different conditions is not easy to determine, and factors such as the test current have a great influence on the test results, the difference in the measured battery internal resistance and peak power is large, and it is difficult to test the true pulse capacity with a small number of tests. Using this electrical performance parameter can easily lead to problems such as inaccurate control strategy, insufficient power, and power interruption in the entire vehicle.

[0004] Therefore, it is necessary to develop a new method for predicting the internal resistance of a power battery, a method, a system and a storage medium for power prediction. Summary of the invention

[0005] The object of the present invention is to provide a method for predicting the internal resistance of a power battery, a method for predicting the power, a system and a storage medium, which can predict the internal resistance or power of the power battery with a prediction error of less than 10%.

[0006] In a first aspect, a method for predicting the internal resistance of a power battery according to the present invention comprises the following steps:

[0007] Step 1: Normalize the parameters of the battery internal resistance table;

[0008] Step 2: Taking the battery internal resistance as the dependent variable, and the battery temperature, battery state of charge, and pulse charge / discharge time as independent variables, perform multivariate nonlinear regression fitting to obtain a multivariate factor-coupled internal resistance model;

[0009] Step 3, judging whether the established multivariable factor coupled internal resistance model conforms to the electrochemical and thermodynamic laws, and testing the credibility of the multivariable factor coupled internal resistance model through statistical analysis;

[0010] Step 4: For power batteries of new models or new material systems, the internal resistance of the battery under multiple characteristic conditions is measured, and the multivariate factor coupling internal resistance model is corrected based on the internal resistance of the battery under multiple characteristic conditions obtained by the test;

[0011] Step 5: Verify the multivariable factor coupled internal resistance model obtained in step 4. If the prediction accuracy meets the preset accuracy value, it means that the multivariable factor coupled internal resistance model is built. If the prediction accuracy does not meet the preset accuracy value, return to step 2.

[0012] Step 6: Use the multivariable factor-coupled internal resistance model built in step 5 to predict the internal resistance of power batteries of new models or new material systems.

[0013] Optionally, in step 2, if the pulse charging time is used as an independent variable, the internal resistance of the battery under the multiple characteristic conditions measured in step 4 is specifically:

[0014] (1) Internal resistance of the battery at 50% SOC, -10°C, and charging for 10 seconds;

[0015] (2) Internal resistance of the battery at 50% SOC, 10°C, and charging for 10 seconds;

[0016] (3) Internal resistance of the battery at 50% SOC, 25°C, and charging for 10 seconds;

[0017] (4) Internal resistance of the battery at 50% SOC, 40°C, and charging for 10 seconds;

[0018] (5) Internal resistance of the battery at 50% SOC, 25°C, and charging for 30 seconds;

[0019] (6) Internal resistance of the battery at 20% SOC, 0°C, and charging for 10 seconds;

[0020] (7) Internal resistance of the battery at 20% SOC, 25°C, and charging for 10 seconds;

[0021] (8) Internal resistance of the battery at 80% SOC, 25°C, and charging for 10 seconds;

[0022] (9) Internal resistance of the battery at 5% SOC, -25°C, and charging for 30 seconds.

[0023] The present invention can obtain true and accurate characteristic internal resistance parameters by measuring the battery internal resistance parameters under nine characteristic conditions and performing constant power charging test verification.

[0024] Optionally, in step 2, if the pulse discharge time is used as an independent variable, the internal resistance of the battery under the multiple characteristic conditions measured in step 4 is specifically:

[0025] (1) Internal resistance of the battery at 50% SOC, -10°C, and discharge for 10 seconds;

[0026] (2) Internal resistance of the battery at 50% SOC, 10°C, and discharge for 10 seconds;

[0027] (3) Internal resistance of the battery at 50% SOC, 25°C, and discharge for 10 seconds;

[0028] (4) Internal resistance of the battery at 50% SOC, 40°C, and discharge for 10 seconds;

[0029] (5) Internal resistance of the battery at 50% SOC, 25°C, and discharge for 30 seconds;

[0030] (6) Internal resistance of the battery at 20% SOC, 0°C, and discharge for 10 seconds;

[0031] (7) Internal resistance of the battery at 20% SOC, 25°C, and discharge for 10 seconds;

[0032] (8) Internal resistance of the battery at 80% SOC, 25°C, and discharge for 10 seconds;

[0033] (9) Internal resistance of the battery at 5% SOC, -25°C, and discharge for 30 seconds.

[0034] The present invention can obtain true and accurate characteristic internal resistance parameters by measuring the internal resistance parameters of the battery under nine characteristic conditions and performing constant power discharge test verification.

[0035] In a second aspect, a method for predicting power of a power battery according to the present invention comprises the following steps:

[0036] Step 1: normalize the parameters of the battery power meter;

[0037] Step 2: Taking battery power as the dependent variable, battery temperature, battery state of charge, and pulse charge / discharge time as independent variables, perform multivariate nonlinear regression fitting to obtain a multivariate factor coupling power model;

[0038] Step 3: determine whether the established multivariable factor coupling power model conforms to the laws of electrochemistry and thermodynamics, and test the credibility of the multivariable factor coupling power model through statistical analysis;

[0039] Step 4: For power batteries of new models or new material systems, the battery power under multiple characteristic conditions is measured, and the multivariable factor coupling power model is corrected based on the battery power under multiple characteristic conditions obtained from the test;

[0040] Step 5: Verify the multivariable factor coupling power model obtained in step 4. If the prediction accuracy meets the preset accuracy value, it means that the multivariable factor coupling power model is built. If the prediction accuracy does not meet the preset accuracy value, return to step 2.

[0041] Step 6: Use the multivariable factor coupling power model built in step 5 to predict the power of power batteries of new models or new material systems.

[0042] Optionally, in step 2, if the pulse charging time is used as an independent variable, the battery power values ​​measured under multiple characteristic conditions in step 4 are specifically:

[0043] (1) The power value of the battery at 50% SOC, -10°C, and charging for 10 seconds;

[0044] (2) The power value of the battery at 50% SOC, 10°C, and charging for 10 seconds;

[0045] (3) The power value of the battery at 50% SOC, 25°C, and charging for 10 seconds;

[0046] (4) Battery power at 50% SOC, 40°C, and charging for 10 seconds;

[0047] (5) Battery power at 50% SOC, 25°C, and charging for 30 seconds;

[0048] (6) Battery power value at 20% SOC, 0°C, and charging for 10 seconds;

[0049] (7) Battery power value at 20% SOC, 25°C, and charging for 10 seconds;

[0050] (8) Battery power at 80% SOC, 25°C, charging for 10 seconds;

[0051] (9) Battery power at 5% SOC, -25°C, charging for 30 seconds.

[0052] The present invention can obtain true and accurate characteristic power parameters by measuring battery power parameters under nine characteristic conditions and performing constant power charging test verification.

[0053] Optionally, in step 2, if the pulse discharge time is used as an independent variable, the battery power values ​​measured under multiple characteristic conditions in step 4 are specifically:

[0054] (1) The power value of the battery at 50% SOC, -10°C, and discharge for 10 seconds;

[0055] (2) The power value of the battery at 50% SOC, 10°C, and discharge for 10 seconds;

[0056] (3) The power value of the battery at 50% SOC, 25°C, and discharge for 10 seconds;

[0057] (4) The power value of the battery at 50% SOC, 40°C, and discharge for 10 seconds;

[0058] (5) The power value of the battery at 50% SOC, 25°C, and discharge for 30 seconds;

[0059] (6) The power value of the battery at 20% SOC, 0°C, and discharge for 10 seconds;

[0060] (7) The power value of the battery at 20% SOC, 25°C, and discharge for 10 seconds;

[0061] (8) The power value of the battery at 80% SOC, 25°C, and discharge for 10 seconds;

[0062] (9) Battery power at 5% SOC, -25°C, and discharge for 30 seconds.

[0063] The present invention can obtain true and accurate characteristic power parameters by measuring battery power parameters under nine characteristic conditions and performing constant power discharge test verification.

[0064] In a third aspect, a system for predicting the internal resistance of a power battery described in the present invention comprises a memory and a controller, wherein the memory stores a computer-readable program, and when the computer-readable program is called by the controller, it can execute the steps of the method for predicting the internal resistance of a power battery described in the present invention.

[0065] In a fourth aspect, a system for predicting power battery power described in the present invention comprises a memory and a controller, wherein the memory stores a computer-readable program, and when the computer-readable program is called by the controller, it can execute the steps of the method for predicting power battery power described in the present invention.

[0066] In a fifth aspect, a storage medium according to the present invention stores a computer-readable program therein, and when the computer-readable program is called, it can execute the steps of the method for predicting the internal resistance of a power battery according to the present invention.

[0067] In a sixth aspect, a storage medium according to the present invention stores a computer-readable program therein, and when the computer-readable program is called, it can execute the steps of the method for predicting power of a power battery according to the present invention.

[0068] The present invention has the following advantages:

[0069] (1) The multivariable factor coupled resistance (or power) model constructed by the present invention is used to predict the internal resistance (or power) value of a new system battery model, with a prediction error of less than 10%.

[0070] (2) The multivariable factor coupled power model of battery internal resistance (or power) established by this method can reflect the universality and universal laws behind the internal resistance and power of power batteries, and realize the quantification and high-precision prediction of battery internal resistance and power.

[0071] In summary, the present invention can effectively save battery testing and matching application development cycles, avoid quality problems during use caused by inaccurate battery internal resistance and power measurement, and can be used to guide and support battery design and application development. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a flow chart of this embodiment;

[0073] Figure 2 1 is a diagram of the model building process and key factor analysis in this embodiment;

[0074] Figure 3 It is the residual graph of the model in this embodiment. DETAILED DESCRIPTION

[0075] The present invention will be described in detail below with reference to the accompanying drawings.

[0076] Embodiment 1

[0077] like Figure 1 As shown, in this embodiment, a method for predicting the internal resistance of a power battery includes the following steps:

[0078] Step 1: Normalize the parameters of the battery internal resistance table.

[0079] Step 2: Taking the battery internal resistance as the dependent variable, and the battery temperature, battery state of charge, and pulse charge / discharge time as independent variables, the normalized internal resistance data is subjected to multivariate nonlinear regression fitting by the least squares method to obtain a multivariate factor-coupled internal resistance model.

[0080] Step 3: Determine whether the established multivariate factor coupled internal resistance model conforms to the laws of electrochemistry and thermodynamics, and test the credibility of the multivariate factor coupled internal resistance model through statistical analysis.

[0081] Step 4: For power batteries of new models or new material systems, measure the battery internal resistance under multiple characteristic conditions, and correct the multivariate factor coupled internal resistance model based on the battery internal resistance under multiple characteristic conditions obtained from the tests.

[0082] Step 5: Verify the multivariable factor coupled internal resistance model obtained in step 4. Specifically, compare the predicted internal resistance value with the actual internal resistance value. If the prediction errors are both less than 10%, it means that the multivariable factor coupled internal resistance model is completed. Otherwise, return to step 2.

[0083] Step 6: Use the multivariable factor-coupled internal resistance model built in step 5 to predict the internal resistance of power batteries of new models or new material systems.

[0084] In this embodiment, the method for predicting the internal resistance of the power battery fully considers the characteristics of the battery itself, and establishes a multivariate factor coupling power model of battery power to battery temperature, state of charge, and pulse charge / discharge time, so as to achieve the effect of improving the accuracy of battery internal resistance parameter estimation and save battery testing and matching application development cycle.

[0085] In this embodiment, the multivariable factor coupling resistance model is:

[0086] ln(y DCR )=c0+c1×x SOC +c2×x T +c3×x t +c 11 ×(x SOC ) 2 +c 22 ×(x T ) 2 +c 33 ×(x t ) 2 +c 12 × SOC × T +c 13 × SOC × t

[0087] Where: y DCR is the calculated DC internal resistance of the power battery, in mΩ;

[0088] x SOC It is the state of charge of the power battery, unit is 1; its boundary range is 0% to 100%.

[0089] x T It is the power battery temperature, unit is ℃; its boundary range is 30℃~55℃.

[0090] x t It is the pulse discharge (or charge) time of the power battery, in seconds; its boundary range is 0s to 60s.

[0091] c0, c1, c2, c3, c 11 , c 22 , c 33 , c 12 , c 13 is a constant term, where c1, c2, c 33 , c 13 Is a negative number.

[0092] The multivariable factor coupled resistance model of a certain type of power battery is as follows:

[0093] ln(y DCR )=38.41-12.000×x SOC -0.21109×x T +0.05188×x t +3.353×(x SOC ) 2 +0.000287×(x T ) 2 -0.000712×(x t ) 2 +0.02705×x SOC × T -0.02283×x SOC × t

[0094] Among them, the model fit R-sq = 97.26%; the model significance P < 0.001.

[0095] The multivariate factor coupling internal resistance model (hereinafter referred to as the model) is a composite function of logarithmic function and power function. With the increase of dependent variable polynomials in the model, the model fit R-sq gradually increases. The final model fit reached 97.26%, with a significant P < 0.001, which meets the credibility test requirements. Figure 2 (a). By analyzing the key influencing factors of the model, the influence degree of the battery internal resistance is ranked as follows: ambient temperature (x T )>Battery state of charge (x SOC )>Pulse charge / discharge time(x t ),See Figure 2 (b). Figure 2 (c) indicates variables not covered by the unseen model. Figure 3 It shows that the residual value randomly falls on both sides of zero, there is no abnormal independent variable factor value in the model, there is no unequal variation, and the model assumption is reasonable. In the model, as far as the influence of a single factor is concerned, the change of the battery internal resistance with temperature conforms to the Arrhenius law (the natural logarithm of the internal resistance is proportional to the inverse of the temperature); the change of the battery internal resistance with the state of charge conforms to the law of change of the potential in the battery charge / discharge curve (analogous to the Nernst equation); the change of the battery internal resistance with the pulse charge / discharge time is approximately linear in the early stage, and tends to an upper limit value in the later stage.

[0096] Figure 2 Discharge in it means discharge;

[0097] Increase in R-Squared% indicates the increment of goodness of fit;

[0098] R-Squared% indicates goodness of fit;

[0099] R-Squared (adjusted)% indicates goodness of fit (adjusted);

[0100] In this embodiment, in order to ensure the versatility and accuracy of the model, a characteristic value calibration model of the battery internal resistance is introduced for specific battery models and different material systems. The battery internal resistance parameters under 9 characteristic conditions are measured and verified by constant power charge / discharge test to obtain the true and accurate characteristic internal resistance parameters. The characteristic values ​​of the internal resistance parameters are then imported into the model, the constants of the model are corrected, and the final optimized multivariable factor coupled internal resistance model is obtained.

[0101] In this embodiment, in step 2, if the pulse charging time is used as an independent variable, the internal resistance of the battery under multiple (for example, 9) characteristic conditions measured in step 4 is specifically:

[0102] (1) Internal resistance of the battery at 50% SOC, -10°C, and charging for 10 seconds;

[0103] (2) Internal resistance of the battery at 50% SOC, 10°C, and charging for 10 seconds;

[0104] (3) Internal resistance of the battery at 50% SOC, 25°C, and charging for 10 seconds;

[0105] (4) Internal resistance of the battery at 50% SOC, 40°C, and charging for 10 seconds;

[0106] (5) Internal resistance of the battery at 50% SOC, 25°C, and charging for 30 seconds;

[0107] (6) Internal resistance of the battery at 20% SOC, 0°C, and charging for 10 seconds;

[0108] (7) Internal resistance of the battery at 20% SOC, 25°C, and charging for 10 seconds;

[0109] (8) Internal resistance of the battery at 80% SOC, 25°C, and charging for 10 seconds;

[0110] (9) Internal resistance of the battery at 5% SOC, -25°C, and charging for 30 seconds.

[0111] In this embodiment, in step 2, if the pulse discharge time is used as an independent variable, the internal resistance of the battery under the multiple characteristic conditions measured in step 4 is specifically:

[0112] (1) Internal resistance of the battery at 50% SOC, -10°C, and discharge for 10 seconds;

[0113] (2) Internal resistance of the battery at 50% SOC, 10°C, and discharge for 10 seconds;

[0114] (3) Internal resistance of the battery at 50% SOC, 25°C, and discharge for 10 seconds;

[0115] (4) Internal resistance of the battery at 50% SOC, 40°C, and discharge for 10 seconds;

[0116] (5) Internal resistance of the battery at 50% SOC, 25°C, and discharge for 30 seconds;

[0117] (6) Internal resistance of the battery at 20% SOC, 0°C, and discharge for 10 seconds;

[0118] (7) Internal resistance of the battery at 20% SOC, 25°C, and discharge for 10 seconds;

[0119] (8) Internal resistance of the battery at 80% SOC, 25°C, and discharge for 10 seconds;

[0120] (9) Internal resistance of the battery at 5% SOC, -25°C, and discharge for 30 seconds.

[0121] In this embodiment, by measuring the internal resistance parameters of the battery under nine characteristic conditions and performing constant power discharge test verification, it is possible to obtain true and accurate characteristic internal resistance parameters.

[0122] In this embodiment, a system for predicting the internal resistance of a power battery includes a memory and a controller. The memory stores a computer-readable program. When the computer-readable program is called by the controller, it can execute the steps of the method for predicting the internal resistance of a power battery as described in this embodiment.

[0123] In this embodiment, a storage medium stores a computer-readable program, and when the computer-readable program is called, the steps of the method for predicting the internal resistance of a power battery as described in this embodiment can be executed.

[0124] Embodiment 2

[0125] In this embodiment, a method for predicting power of a power battery includes the following steps:

[0126] Step 1: normalize the parameters of the battery power meter;

[0127] Step 2: Taking battery power as the dependent variable, battery temperature, battery state of charge, and pulse charge / discharge time as independent variables, perform multivariate nonlinear regression fitting to obtain a multivariate factor coupling power model;

[0128] Step 3: determine whether the established multivariable factor coupling power model conforms to the laws of electrochemistry and thermodynamics, and test the credibility of the multivariable factor coupling power model through statistical analysis;

[0129] Step 4: For power batteries of new models or new material systems, the battery power under multiple characteristic conditions is measured, and the multivariable factor coupling power model is corrected based on the battery power under multiple characteristic conditions obtained from the test;

[0130] Step 5: Verify the multivariable factor coupling power model obtained in step 4. If the prediction accuracy meets the preset accuracy value, it means that the multivariable factor coupling power model is built. If the prediction accuracy does not meet the preset accuracy value, return to step 2.

[0131] Step 6: Use the multivariable factor coupling power model built in step 5 to predict the power of power batteries of new models or new material systems.

[0132] In this embodiment, the method for predicting power of a power battery fully considers the characteristics of the battery itself, and establishes a multivariate factor coupling power model of battery power to battery temperature, state of charge, and pulse charge / discharge time, so as to improve the accuracy of battery power parameter estimation and save battery testing and matching application development cycles.

[0133] In this embodiment, the multivariable factor coupling power model is:

[0134] ln(y DCR )=c0+c1×x SOC +c2×x T +c3×x t +c 11 ×(x SOC ) 2 +c 22 ×(x T ) 2 +c 33 ×(x t ) 2 +c 12 × SOC × T +c 13 × SOC × t

[0135] Where: y DCR is the calculated power of the power battery, in W;

[0136] x SOC It is the state of charge of the power battery, unit is 1; its boundary range is 0% to 100%.

[0137] xT is the power battery temperature, unit K; its boundary range is 243.15K~328.15K (-30℃~55℃).

[0138] x t It is the pulse discharge (or charge) time of the power battery, in seconds; its boundary range is 0s to 60s.

[0139] c0, c1, c2, c3, c 11 , c 22 , c 33 , c 12 , c 13 is a constant term, where: c1, c2, c 33 , c 13 Is a negative number.

[0140] To ensure the universality and accuracy of the model, a characteristic value calibration model for battery power is introduced for specific battery models and different material systems. By measuring the battery power parameters under nine characteristic conditions and verifying them through constant power charge / discharge tests, the true and accurate characteristic power parameters are obtained. The characteristic values ​​of the power parameters are then imported into the model, the constants of the model are corrected, and the final optimized multivariable factor coupling power model is obtained.

[0141] In this embodiment, in step 2, if the pulse charging time is used as an independent variable, the battery power values ​​measured under multiple characteristic conditions in step 4 are specifically:

[0142] (1) The power value of the battery at 50% SOC, -10°C, and charging for 10 seconds;

[0143] (2) The power value of the battery at 50% SOC, 10°C, and charging for 10 seconds;

[0144] (3) The power value of the battery at 50% SOC, 25°C, and charging for 10 seconds;

[0145] (4) Battery power at 50% SOC, 40°C, and charging for 10 seconds;

[0146] (5) Battery power at 50% SOC, 25°C, and charging for 30 seconds;

[0147] (6) Battery power value at 20% SOC, 0°C, and charging for 10 seconds;

[0148] (7) Battery power value at 20% SOC, 25°C, and charging for 10 seconds;

[0149] (8) Battery power at 80% SOC, 25°C, charging for 10 seconds;

[0150] (9) Battery power at 5% SOC, -25°C, charging for 30 seconds.

[0151] In this embodiment, in step 2, if the pulse discharge time is used as an independent variable, the battery power values ​​measured under multiple characteristic conditions in step 4 are specifically:

[0152] (1) The power value of the battery at 50% SOC, -10°C, and discharge for 10 seconds;

[0153] (2) The power value of the battery at 50% SOC, 10°C, and discharge for 10 seconds;

[0154] (3) The power value of the battery at 50% SOC, 25°C, and discharge for 10 seconds;

[0155] (4) The power value of the battery at 50% SOC, 40°C, and discharge for 10 seconds;

[0156] (5) The power value of the battery at 50% SOC, 25°C, and discharge for 30 seconds;

[0157] (6) The power value of the battery at 20% SOC, 0°C, and discharge for 10 seconds;

[0158] (7) The power value of the battery at 20% SOC, 25°C, and discharge for 10 seconds;

[0159] (8) The power value of the battery at 80% SOC, 25°C, and discharge for 10 seconds;

[0160] (9) Battery power at 5% SOC, -25°C, and discharge for 30 seconds.

[0161] In this embodiment, by measuring the battery power parameters under nine characteristic conditions and performing constant power discharge test verification, real and accurate characteristic power parameters can be obtained.

[0162] The rest is the same as the embodiment.

[0163] In this embodiment, a system for predicting power battery power includes a memory and a controller. The memory stores a computer-readable program. When the computer-readable program is called by the controller, it can execute the steps of the method for predicting power battery power as described in this embodiment.

[0164] In this embodiment, a storage medium stores a computer-readable program, and when the computer-readable program is called, the steps of the method for predicting power of a power battery as described in this embodiment can be executed.

[0165] The above embodiments are preferred implementation modes of the method of the present invention, but the implementation modes of the method of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement modes and shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the internal resistance of a power battery, characterized in that : It includes the following steps: Step 1: Normalize the parameters of the battery internal resistance table; Step 2: Taking the battery internal resistance as the dependent variable, and the battery temperature, battery state of charge, and pulse charge / discharge time as independent variables, perform multivariate nonlinear regression fitting to obtain a multivariate factor-coupled internal resistance model; Step 3, judging whether the established multivariable factor coupled internal resistance model conforms to the electrochemical and thermodynamic laws, and testing the credibility of the multivariable factor coupled internal resistance model through statistical analysis; Step 4: For power batteries of new models or new material systems, the internal resistance of the battery under multiple characteristic conditions is measured, and the multivariate factor coupling internal resistance model is corrected based on the internal resistance of the battery under multiple characteristic conditions obtained by the test; Step 5: Verify the multivariable factor coupled internal resistance model obtained in step 4. If the prediction accuracy meets the preset accuracy value, it means that the multivariable factor coupled internal resistance model is built. If the prediction accuracy does not meet the preset accuracy value, return to step 2. Step 6: Use the multivariable factor-coupled internal resistance model built in step 5 to predict the internal resistance of power batteries of new models or new material systems.

2. The method for predicting the internal resistance of a power battery according to claim 1, characterized in that: In step 2, if the pulse charging time is used as an independent variable, the internal resistance of the battery under the multiple characteristic conditions measured in step 4 is specifically: (1) Internal resistance of the battery at 50% SOC, -10°C, and charging for 10 seconds; (2) Internal resistance of the battery at 50% SOC, 10°C, and charging for 10 seconds; (3) Internal resistance of the battery at 50% SOC, 25°C, and charging for 10 seconds; (4) Internal resistance of the battery at 50% SOC, 40°C, and charging for 10 seconds; (5) Internal resistance of the battery at 50% SOC, 25°C, and charging for 30 seconds; (6) Internal resistance of the battery at 20% SOC, 0°C, and charging for 10 seconds; (7) Internal resistance of the battery at 20% SOC, 25°C, and charging for 10 seconds; (8) Internal resistance of the battery at 80% SOC, 25°C, and charging for 10 seconds; (9) Internal resistance of the battery at 5% SOC, -25°C, and charging for 30 seconds.

3. The method for predicting the internal resistance of a power battery according to claim 1, characterized in that: In step 2, if the pulse discharge time is used as an independent variable, the internal resistance of the battery under the multiple characteristic conditions measured in step 4 is specifically: (1) Internal resistance of the battery at 50% SOC, -10°C, and discharge for 10 seconds; (2) Internal resistance of the battery at 50% SOC, 10°C, and discharge for 10 seconds; (3) Internal resistance of the battery at 50% SOC, 25°C, and discharge for 10 seconds; (4) Internal resistance of the battery at 50% SOC, 40°C, and discharge for 10 seconds; (5) Internal resistance of the battery at 50% SOC, 25°C, and discharge for 30 seconds; (6) Internal resistance of the battery at 20% SOC, 0°C, and discharge for 10 seconds; (7) Internal resistance of the battery at 20% SOC, 25°C, and discharge for 10 seconds; (8) Internal resistance of the battery at 80% SOC, 25°C, and discharge for 10 seconds; (9) Internal resistance of the battery at 5% SOC, -25°C, and discharge for 30 seconds.

4. A method for predicting power of a power battery, characterized in that : It includes the following steps: Step 1: normalize the parameters of the battery power meter; Step 2: Taking battery power as the dependent variable, battery temperature, battery state of charge, and pulse charge / discharge time as independent variables, perform multivariate nonlinear regression fitting to obtain a multivariate factor coupling power model; Step 3: determine whether the established multivariable factor coupling power model conforms to the electrochemical and thermodynamic laws, and test the credibility of the multivariable factor coupling power model through statistical analysis; Step 4: For power batteries of new models or new material systems, measure the battery power under multiple characteristic conditions, and modify the multivariable factor coupling power model based on the battery power under multiple characteristic conditions obtained from the tests; Step 5: Verify the multivariable factor coupling power model obtained in step 4. If the prediction accuracy meets the preset accuracy value, it means that the multivariable factor coupling power model is built. If the prediction accuracy does not meet the preset accuracy value, return to step 2. Step 6: Use the multivariable factor coupling power model built in step 5 to predict the power of power batteries of new models or new material systems.

5. The method for predicting power battery power according to claim 4, characterized in that: In step 2, if the pulse charging time is used as an independent variable, the battery power values ​​measured under multiple characteristic conditions in step 4 are specifically: (1) The power value of the battery at 50% SOC, -10°C, and charging for 10 seconds; (2) Power value of the battery at 50% SOC, 10°C, and charging for 10 seconds; (3) Power value of the battery at 50% SOC, 25°C, and charging for 10 seconds; (4) Power value of the battery at 50% SOC, 40°C, and charging for 10 seconds; (5) Battery power value at 50% SOC, 25°C, and charging for 30 seconds; (6) Battery power value at 20% SOC, 0°C, and charging for 10 seconds; (7) Battery power value at 20% SOC, 25°C, and charging for 10 seconds; (8) Battery power value at 80% SOC, 25°C, and charging for 10 seconds; (9) Battery power at 5% SOC, -25°C, and charging for 30 seconds.

6. The method for predicting power battery power according to claim 4, characterized in that: In step 2, if the pulse discharge time is used as an independent variable, the battery power values ​​measured under multiple characteristic conditions in step 4 are specifically: (1) The power value of the battery at 50% SOC, -10°C, and discharge for 10 seconds; (2) The power value of the battery at 50% SOC, 10°C, and discharge for 10 seconds; (3) The power value of the battery at 50% SOC, 25°C, and discharge for 10 seconds; (4) Power value of the battery at 50% SOC, 40°C, and discharge for 10 seconds; (5) The power value of the battery at 50% SOC, 25°C, and discharge for 30 seconds; (6) The power value of the battery at 20% SOC, 0°C, and discharge for 10 seconds; (7) The power value of the battery at 20% SOC, 25°C, and discharge for 10 seconds; (8) Power value of the battery at 80% SOC, 25°C, and discharge for 10 seconds; (9) Battery power value at 5% SOC, -25°C, and discharge for 30 seconds.

7. A system for predicting the internal resistance of a power battery, characterized in that: It comprises a memory and a controller, wherein the memory stores a computer-readable program, and when the computer-readable program is called by the controller, the steps of the method for predicting the internal resistance of a power battery as claimed in any one of claims 1 to 3 can be executed.

8. A system for predicting power of a power battery, characterized in that: It comprises a memory and a controller, wherein the memory stores a computer-readable program, and when the computer-readable program is called by the controller, it can execute the steps of the method for predicting power battery power as claimed in any one of claims 4 to 6.

9. A storage medium, characterized in that: A computer-readable program is stored therein, and when the computer-readable program is called, the steps of the method for predicting the internal resistance of a power battery as described in any one of claims 1 to 3 can be executed.

10. A storage medium, characterized in that: A computer-readable program is stored therein, and when the computer-readable program is called, the steps of the method for predicting power of a power battery as described in any one of claims 4 to 6 can be executed.

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