An on-line monitoring method and system for the internal resistance of an electric vehicle power battery

Abstract

The present invention provides an on-line monitoring method and system for the internal resistance of an electric vehicle power battery. The method includes: determining whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and determining whether the correlation coefficient between the voltage and current of the power battery within the set time period meets a set correlation coefficient threshold; if both are satisfied, then based on the voltage value and current value at the current moment within the set time period, a recursive method with a forgetting factor is used to calculate the internal resistance value of the power battery within the set time period. According to the battery voltage and current sampling values within a period of time, the present invention calculates the internal resistance value of the battery by using a weighted recursive algorithm, avoiding the calculation deviation caused by asynchronous sampling of the battery voltage and current, and at the same time avoiding the scenario applicability of the solution and improving the application frequency of the solution.

Description

Technical Field

[0001] The present invention relates to the field of battery internal resistance monitoring, and more specifically, to an on-line monitoring method and system for the internal resistance of a power battery of an electric vehicle. Background Art

[0002] On-line estimation of the internal resistance of a battery is beneficial to accurately evaluate the power attenuation and aging degree of the battery. Currently, the method for estimating the internal resistance at the vehicle end is as follows: capturing the scenario after the vehicle is stationary and starts charging, and calculating the internal resistance of the battery by using the detected changes in the battery voltage value and current value.

[0003] In the above solution, the frequency of occurrence of a specific scenario is not high, the sampling of the battery voltage value and current value is not synchronized, and the current value output by the charging pile at the beginning of charging rises slowly rather than suddenly, so it will lead to calculation deviation.

[0004] In the off-line case, usually the voltage value and current value within a period of time are also obtained, and the least squares method is used to fit the linear relationship between the voltage value and current value. For details, please refer to Figure 1 , and the slope is the resistance of the battery. However, since storing the voltage value and current value within a period of time requires a large amount of memory, it cannot be popularized on a vehicle-mounted controller. Summary of the Invention

[0005] The present invention aims at the technical problems existing in the prior art, and provides an on-line monitoring method and system for the internal resistance of a power battery of an electric vehicle.

[0006] According to a first aspect of the present invention, there is provided an on-line monitoring method for the internal resistance of a power battery of an electric vehicle, including:

[0007] Judging whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and judging whether the correlation coefficient between the voltage and current of the power battery within the set time period meets a set correlation coefficient threshold;

[0008] If both are satisfied, then based on the voltage value and current value at the current moment within the set time period, a recursive method with a forgetting factor is used to calculate the internal resistance value of the power battery within the set time period.

[0009] On the basis of the above technical solution, the present invention can also be improved as follows.

[0010] Optionally, the set time period includes N moments, and there is a recursive relationship between the voltage values and current values of two adjacent moments.

[0011] Optionally, the recursive relationship between the voltage values and current values of two adjacent moments includes:

[0012] Defining the weight coefficients of the voltage value and current value at each moment based on the forgetting factor λ:

[0013] w i = λ N-i ;

[0014] where λ ranges from 0 to 1, N is the total number of time instances within a set time period, and i is the i-th time instance;

[0015] There is the following recursive relationship between the sum of weighted coefficients at the (N - 1)-th time instance and the sum of weighted coefficients at the N-th time instance:

[0016]

[0017] There is the following recursive relationship between the sum of the products of the voltage value, current value, and weighted coefficient at the (N - 1)-th time instance and the sum of the products of the voltage value, current value, and weighted coefficient at the N-th time instance:

[0018]

[0019] There is the following recursive relationship between the sum of the products of the current value and the weighted coefficient at the (N - 1)-th time instance and the sum of the products of the current value and the weighted coefficient at the N-th time instance:

[0020]

[0021] There is the following recursive relationship between the sum of the products of the voltage value and the weighted coefficient at the (N - 1)-th time instance and the sum of the products of the voltage value and the weighted coefficient at the N-th time instance:

[0022]

[0023] There is the following recursive relationship between the sum of the products of the square of the current value and the weighted coefficient at the (N - 1)-th time instance and the sum of the products of the square of the current value and the weighted coefficient at the N-th time instance:

[0024]

[0025] where w i is the weight coefficient of the voltage value and current value at the i-th time instance, I i is the current value at the i-th time instance, V i is the voltage value at the i-th time instance, I is the current value at the current N-th time instance, and V is the voltage value at the current N-th time instance.

[0026] Optionally, the separately determining whether the current distribution of the power battery within a set time period meets the internal resistance calculation conditions includes:

[0027] Calculate the average current and the mean square deviation of the current within the set time period. When the average current is less than the set current threshold and the mean square deviation of the current is greater than the set variance threshold, the current distribution of the power battery meets the internal resistance calculation condition;

[0028] Judging whether the correlation coefficient of the voltage and current of the power battery within the set time period meets the set correlation coefficient threshold includes:

[0029] Calculate the correlation coefficient of the voltage and current within the set time period. When the correlation coefficient is greater than the set correlation coefficient threshold, the correlation coefficient of the voltage and current of the power battery meets the set correlation coefficient threshold.

[0030] Optionally, calculating the average current and the mean square deviation of the current within the set time period includes:

[0031] The formula for calculating the average current is:

[0032]

[0033] where Avg_Cur wI|N is the average current at the current Nth moment within the set time period, S wI|N is the sum value after multiplying the current value by the weighting coefficient at the current Nth moment, S w|N is the sum value of the weighting coefficients at the current Nth moment;

[0034] The formula for calculating the mean square deviation of the current is:

[0035]

[0036] where Var_Cur wII|N is the mean square deviation of the current at the current Nth moment within the set time period, S wII|N is the sum value after multiplying the square of the current value by the weighting coefficient at the current Nth moment, S w|N is the sum value of the weighting coefficients at the current Nth moment.

[0037] Optionally, calculating the correlation coefficient of the voltage and current within the set time period includes:

[0038]

[0039] where r is the correlation coefficient of the voltage and current, Avg_IV wI|N is the average value of the product of the current value and the voltage value at the current Nth moment, Avg_Cur wI|N is the average current at the current Nth moment, Avg_Vol wI|N is the average voltage at the current Nth moment, Var_Cur wII|N is the current variance value at the current Nth moment, Var_Vol wVV|Nis the voltage variance value at the current Nth moment.

[0040] Optionally, the calculation formula for the voltage mean value is:

[0041]

[0042] The calculation formula for the voltage variance value is:

[0043]

[0044] The calculation formula for the mean value of the product of the current value and the voltage value is:

[0045]

[0046] Optionally, based on the voltage value and the current value at the current moment within a set time period, using a recursive method with a forgetting factor, calculating the internal resistance value of the power battery within the set time period includes:

[0047]

[0048] Wherein, R is the internal resistance value of the power battery, S w|N is the sum value of the weighted coefficients at the current Nth moment, S wIV|N is the sum value after multiplying the current value, voltage value and weighted coefficient at the current moment, S wI|N is the sum value after multiplying the current value and the weighted coefficient at the current Nth moment, S wV|N is the sum value after multiplying the voltage value and the weighted coefficient at the current Nth moment, Var_Cur wII|N is the current mean square deviation at the current Nth moment.

[0049] Optionally, based on the voltage value and the current value at the current moment within a set time period, using a recursive method with a forgetting factor, calculating the internal resistance value of the power battery within the set time period further includes:

[0050] Calculating the internal resistance values of the power battery in multiple set time periods, and calculating the average value of the multiple internal resistance values as the final internal resistance value of the power battery.

[0051] According to the second aspect of the present invention, there is provided an on-line monitoring system for the internal resistance of an electric vehicle power battery, including:

[0052] A judgment module, configured to judge whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and judge whether the correlation coefficient between the voltage and current of the power battery within the set time period meets the set correlation coefficient threshold;

[0053] A calculation module, which, if all are satisfied, calculates the internal resistance value of the power battery within the set time period by using a recursive method with a forgetting factor based on the voltage value and current value at the current moment within the set time period.

[0054] According to the third aspect of the present invention, there is provided an electronic device including a memory and a processor, and the processor is configured to implement the steps of the method for on-line monitoring of the internal resistance of an electric vehicle power battery when executing a computer management program stored in the memory.

[0055] According to the fourth aspect of the present invention, there is provided a computer-readable storage medium, on which a computer management program is stored, and the computer management program implements the steps of the method for on-line monitoring of the internal resistance of an electric vehicle power battery when executed by a processor.

[0056] A method and system for on-line monitoring of the internal resistance of an electric vehicle power battery provided by the present invention calculates the internal resistance of the battery by using a weighted recursive algorithm according to the battery voltage and current sampling values within a period of time, avoids the calculation deviation caused by the asynchronous sampling of the battery voltage and current, and at the same time avoids the scenario applicability of the solution, and improves the application frequency of the solution. Description of the Drawings

[0057] Figure 1 It is a schematic diagram of fitting the linear relationship between the voltage value and the current value by the least squares method;

[0058] Figure 2 It is a flowchart of a method for on-line monitoring of the internal resistance of an electric vehicle power battery provided by the present invention;

[0059] Figure 3 It is a schematic structural diagram of a system for on-line monitoring of the internal resistance of an electric vehicle power battery provided by the present invention;

[0060] Figure 4 It is a schematic hardware structure diagram of a possible electronic device provided by the present invention;

[0061] Figure 5 It is a schematic hardware structure diagram of a possible computer-readable storage medium provided by the present invention. Detailed Embodiments

[0062] The following will further describe in detail the specific embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0063] The traditional method for calculating the internal resistance of a power battery is obtained by fitting using the method of linear regression. First, the voltage values and current values within a period of time are stored, (I 1 , V 1 ), (I 2 , V 2), (I 3 , V 3 ), ……(I n , V n ), the method of linear regression is used to fit k and b in the equation y = kx + b. As Figure 1 shown, where k is the internal resistance of the battery, unit: Ω, and b is the OCV of the battery under the current state.

[0064] Among them, the calculation formulas for k and b are:

[0065]

[0066]

[0067] Adopting this method requires storing the voltage values and current values within a period of time. Storing the voltage values and current values within a period of time consumes a large amount of memory, so it cannot be popularized on vehicle-mounted controllers.

[0068] Based on this, the present invention provides an on-line monitoring method for the internal resistance of a power battery, which is applied to an electric vehicle. See Figure 2 , the on-line monitoring method for the internal resistance of a power battery mainly includes the following steps:

[0069] S1, judging whether the current distribution of the power battery within the set time period meets the internal resistance calculation condition, and judging whether the correlation coefficient between the voltage and current of the power battery within the set time period meets the set correlation coefficient threshold.

[0070] As an embodiment, the step of respectively judging whether the current distribution of the power battery within the set time period meets the internal resistance calculation condition includes: calculating the current mean value and the current mean square deviation within the set time period. When the current mean value is less than the set current threshold and the current mean square deviation is greater than the set variance threshold, the current distribution of the power battery meets the internal resistance calculation condition; the step of judging whether the correlation coefficient between the voltage and current of the power battery within the set time period meets the set correlation coefficient threshold includes: calculating the correlation coefficient between the voltage and current within the set time period. When the correlation coefficient is greater than the set correlation coefficient threshold, the correlation coefficient between the voltage and current of the power battery meets the set correlation coefficient threshold.

[0071] It can be understood that before calculating the internal resistance value of the power battery, it is necessary to determine whether the battery internal resistance is normal. Specifically, it can be determined whether the current distribution of the power battery within a period of time meets the internal resistance calculation conditions. For example, when the absolute value of the current mean value within a period of time is less than 5A and the current mean square deviation is greater than 100A, it can be determined that the current distribution of the power battery within a period of time meets the internal resistance calculation conditions. Then, it is determined whether the correlation coefficient between the voltage and current of the power battery during this period is greater than the set value. For example, the correlation coefficient > 0.99, then the correlation coefficient between the voltage and current of the power battery during this period meets the conditions.

[0072] When the above two conditions are met, the internal resistance value of the power battery can be calculated.

[0073] S2, if both are satisfied, then based on the voltage value and current value at the current moment within the set time period, a recursive method with a forgetting factor is used to calculate the internal resistance value of the power battery within the set time period.

[0074] It can be understood that in the process of calculating the internal resistance value of the power battery in the present invention, a weighted recursive algorithm is adopted. Among them, there is a certain recursive relationship between the voltage value and current value at two adjacent moments within a period of time. Therefore, the relevant parameters of the voltage value and current value at the next moment and the relevant parameters of the voltage value and current value at the previous moment can be obtained from each other through recursion.

[0075] Therefore, when using the traditional linear regression algorithm to fit and calculate the internal resistance value of the power battery at the current moment, only the voltage value and current value at the current moment, as well as the relevant parameters at the previous moment, need to be obtained, and then the internal resistance value of the power battery at the current moment can be calculated, without storing the voltage values and current values within a period of time, thus avoiding the occupation of the memory resources of the controller by storing the voltage values and current values within a period of time.

[0076] The present invention uses the recursive least squares method with a forgetting factor to replace the traditional least squares method. For the recursive least squares method with a forgetting factor, the solution formulas for k and b are:

[0077]

[0078]

[0079] Among them, the weight coefficients of the voltage value and current value at each moment can be defined based on the forgetting factor λ:

[0080] w i =λ N-i ;

[0081] Among them, λ ranges from 0 to 1, N is the total number of moments within the set time period, and i is the i-th moment. It can be seen that the weight coefficients corresponding to the voltage value and current value at each moment are different.

[0082] There is the following recursive relationship between the relevant parameters of the voltage value and current value at two adjacent moments:

[0083] There is the following recursive relationship between the sum value of the weighting coefficients at the (N - 1)-th moment and the sum value of the weighting coefficients at the N-th moment:

[0084]

[0085] There is the following recursive relationship between the sum value after multiplying the voltage value, current value, and weighting coefficient at the (N - 1)-th moment and the sum value after multiplying the voltage value, current value, and weighting coefficient at the N-th moment:

[0086]

[0087] There is the following recursive relationship between the sum value after multiplying the current value and the weighting coefficient at the (N - 1)-th moment and the sum value after multiplying the current value and the weighting coefficient at the N-th moment:

[0088]

[0089] There is the following recursive relationship between the sum value after multiplying the voltage value and the weighting coefficient at the (N - 1)-th moment and the sum value after multiplying the voltage value and the weighting coefficient at the N-th moment:

[0090]

[0091] There is the following recursive relationship between the sum value after multiplying the square of the current value and the weighting coefficient at the (N - 1)-th moment and the sum value after multiplying the square of the current value and the weighting coefficient at the N-th moment:

[0092]

[0093] Among them, w i is the weight coefficient of the voltage value and current value at the i-th moment, I i is the current value at the i-th moment, V i is the voltage value at the i-th moment, I is the current value at the current N-th moment, and V is the voltage value at the current N-th moment.

[0094] Based on the above recursive relationships, it can be understood that by only obtaining the voltage value and current value at the current moment, as well as the relevant parameters at the previous moment, the relevant parameters at the current moment can be calculated.

[0095] Among them, the recursive method can be used to calculate the mean and variance values of the voltage and current. The formula for calculating the current mean is:

[0096]

[0097] Among them, Avg_Cur wI|N is the current mean at the current Nth moment within the set time period, S wI|N is the sum value after multiplying the current value by the weighting coefficient at the current Nth moment, S w|N is the sum value of the weighting coefficients at the current Nth moment.

[0098] The formula for calculating the current mean square deviation is:

[0099]

[0100] Among them, Var_Cur wII|N is the current mean square deviation at the current Nth moment within the set time period, S wII|N is the sum value after multiplying the square of the current value by the weighting coefficient at the current Nth moment, S w|N is the sum value of the weighting coefficients at the current Nth moment.

[0101] Through these two formulas, the current mean and the variance value of the current can be calculated, which can be used to determine whether the current mean and the variance value of the current meet the internal resistance calculation conditions in the above step S1.

[0102] And the formula for calculating the correlation coefficient between the voltage and the current within the set time period is:

[0103]

[0104] Among them, r is the correlation coefficient between the voltage and the current, Avg_IV wI|N is the mean value of the product of the current value and the voltage value at the current Nth moment, Avg_Cur wI|N is the current mean at the current Nth moment, Avg_Vol wI|N is the voltage mean at the current Nth moment, Var_Cur wII|N is the variance value of the current value at the current Nth moment, Var_VoI wVV|N is the variance value of the voltage at the current Nth moment.

[0105] According to the calculated correlation coefficient between the voltage and the current, it is possible to determine the condition for whether the internal resistance value of the power battery can be calculated in step S1.

[0106] The formula for calculating the voltage mean is:

[0107]

[0108] The formula for calculating the voltage variance value is:

[0109]

[0110] The calculation formula for the average value of the product of the current value and the voltage value is:

[0111]

[0112] Then, the calculation formulas for the slope (internal resistance value) and the intercept of the power battery with a forgetting factor of the present invention are:

[0113]

[0114]

[0115] Wherein, R is the internal resistance value of the power battery, OCV is the intercept, and according to OCV, it can be judged whether the internal resistance value of the power battery is normal or abnormal, S w|N is the sum value of the weighted coefficients at the current Nth moment, S wIV|N is the sum value after multiplying the current value of the current moment, the voltage value and the weighted coefficient, S wI|N is the sum value after multiplying the current value and the weighted coefficient at the current Nth moment, S wV|N is the sum value after multiplying the voltage value and the weighted coefficient at the current Nth moment, Var_Cur wII|N is the current mean square deviation of the current at the current Nth moment.

[0116] It should be noted that since the recursive least squares method with a forgetting factor is used in the present invention to replace the traditional least squares method, only the voltage value and the current value at the current moment, and the parameters at the previous moment of the current moment are required to calculate the internal resistance value at the current moment. Therefore, for the current moment, only the voltage value and the current value at the current moment, and the parameters at the previous moment need to be stored to calculate the internal resistance value at the current moment. Therefore, the occupation of the memory resources of the controller by storing the voltage value and the current value for a period of time is avoided.

[0117] In addition, in order to improve the accuracy of the internal resistance value of the power battery, the vehicle-end controller calculates the internal resistance value of the power battery within multiple set time periods through the above recursive method, and obtains the average value of the internal resistance values obtained multiple times as the final internal resistance value. For example, the average of the internal resistance values obtained from the last 5 calculations is used to obtain the final internal resistance value of the battery.

[0118] Figure 3 is the structure diagram of an on-line monitoring system for the internal resistance of an electric vehicle power battery provided by an embodiment of the present invention. As Figure 3 shown, an on-line monitoring system for the internal resistance of an electric vehicle power battery includes a judgment module 31 and a calculation module 32, wherein:

[0119] A judgment module 31, configured to judge whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and judge whether the correlation coefficient between the voltage and current of the power battery within the set time period meets a set correlation coefficient threshold;

[0120] A calculation module 32, configured to, if both are satisfied, calculate the internal resistance value of the power battery within the set time period by using a recursive method with a forgetting factor based on the voltage value and current value at the current moment within the set time period.

[0121] It can be understood that an on-line monitoring system for the internal resistance of an electric vehicle power battery provided by the present invention corresponds to the on-line monitoring method for the internal resistance of an electric vehicle power battery provided in the foregoing embodiments. The relevant technical features of the on-line monitoring system for the internal resistance of an electric vehicle power battery can refer to the relevant technical features of the on-line monitoring method for the internal resistance of an electric vehicle power battery, and will not be elaborated herein.

[0122] Please refer to Figure 4 , Figure 4 , which is a schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As Figure 4 shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored on the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: judge whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and judge whether the correlation coefficient between the voltage and current of the power battery within the set time period meets a set correlation coefficient threshold; if both are satisfied, calculate the internal resistance value of the power battery within the set time period by using a recursive method with a forgetting factor based on the voltage value and current value at the current moment within the set time period.

[0123] Please refer to Figure 5 , Figure 5 , which is a schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. As Figure 5 shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented: judge whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and judge whether the correlation coefficient between the voltage and current of the power battery within the set time period meets a set correlation coefficient threshold; if both are satisfied, calculate the internal resistance value of the power battery within the set time period by using a recursive method with a forgetting factor based on the voltage value and current value at the current moment within the set time period.

[0124] An online monitoring method and system for the internal resistance of an electric vehicle power battery provided by an embodiment of the present invention uses the recursive least squares method with a forgetting factor to replace the traditional least squares method. Only the voltage value and current value at the current moment, as well as the parameters at the previous moment of the current moment, are required to calculate the internal resistance value at the current moment. For the current moment, only the voltage value and current value at the current moment and the parameters at the previous moment need to be stored to calculate the internal resistance value at the current moment. Therefore, the occupation of the memory resources of the controller by storing the voltage values and current values for a period of time is avoided.

[0125] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0128] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, causing a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks. Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0130] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An online monitoring method for the internal resistance of a power battery of an electric vehicle, characterized in that, it includes: judging whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and judging whether the correlation coefficient between the voltage and current of the power battery within the set time period meets the set correlation coefficient threshold; if both are satisfied, then based on the voltage value and current value at the current moment within the set time period, a recursive method with a forgetting factor is used to calculate the internal resistance value of the power battery within the set time period; the set time period includes N moments, and there is a recursive relationship between the voltage values and current values of adjacent two moments, and N is a positive integer; the recursive relationship between the voltage values and current values of adjacent two moments includes: defining the weight coefficients of the voltage value and current value at each moment based on the forgetting factor λ: w i = λ N-i ; where λ ranges from [0, 1], N is the total number of moments within the set time period, and i is the i-th moment; there is the following recursive relationship between the sum of the weighted coefficients at the (N - 1)-th moment and the sum of the weighted coefficients at the N-th moment: there is the following recursive relationship between the sum of the products of the voltage value, current value, and weighted coefficient at the (N - 1)-th moment and the sum of the products of the voltage value, current value, and weighted coefficient at the N-th moment: there is the following recursive relationship between the sum of the products of the current value and weighted coefficient at the (N - 1)-th moment and the sum of the products of the current value and weighted coefficient at the N-th moment: there is the following recursive relationship between the sum of the products of the voltage value and weighted coefficient at the (N - 1)-th moment and the sum of the products of the voltage value and weighted coefficient at the N-th moment: there is the following recursive relationship between the sum of the products of the square of the current value and weighted coefficient at the (N - 1)-th moment and the sum of the products of the square of the current value and weighted coefficient at the N-th moment: Among them, w i is the weight coefficient of the voltage value and current value at time i, I i is the current value at time i, V i is the voltage value at time i, I is the current value at the current Nth moment, and V is the voltage value at the current Nth moment.

2. The method according to claim 1, characterized in that, the judgment of whether the current distribution of the power battery within the set time period meets the internal resistance calculation condition includes: calculating the current mean value and current mean square deviation within the set time period, and when the current mean value is less than the set current threshold and the current mean square deviation is greater than the set variance threshold, the current distribution of the power battery meets the internal resistance calculation condition; the judgment of whether the correlation coefficient between the voltage and current of the power battery within the set time period meets the set correlation coefficient threshold includes: calculating the correlation coefficient between the voltage and current within the set time period, and when the correlation coefficient is greater than the set correlation coefficient threshold, the correlation coefficient between the voltage and current of the power battery meets the set correlation coefficient threshold.

3. The method according to claim 2, characterized in that, calculating the current mean value and current mean square deviation within the set time period includes: the calculation formula for the current mean value is: Among them, Avg_Cur wI|N is the average current value at the current Nth moment within the set time period, S wI|N is the sum value after multiplying the current value by the weighting coefficient at the current Nth moment, S w|N is the sum value of the weighting coefficients at the current Nth moment; the calculation formula for the current mean square deviation is: Among them, Var_Cur wII|N is the mean square error of the current at the current Nth moment within the set time period, S wII|N is the sum value after multiplying the square of the current value at the current Nth moment by the weighting coefficient, S w|N is the sum value of the weighting coefficients at the current Nth moment.

4. The method according to claim 2, characterized in that, the calculation of the correlation coefficient between the voltage and current within the set time period includes: where r is the correlation coefficient of voltage and current, Avg_IV wI|N is the mean value of the product of the current value and the voltage value at the current Nth moment, Avg_Cur wI|N is the mean value of the current at the current Nth moment, Avg_Vol wI|N is the mean value of the voltage at the current Nth moment, Var_Cur wII|N is the variance value of the current at the current Nth moment, Var_Vol wVV|N is the variance value of the voltage at the current Nth moment.

5. The method according to claim 4, characterized in that, the calculation formula for the voltage mean value is: the calculation formula for the voltage variance value is: the calculation formula for the mean value of the product of the current value and voltage value is:

6. The method according to claim 2, It is characterized in that Based on the voltage value and current value at the current moment within a set time period, using a recursive method with a forgetting factor to calculate the internal resistance value of the power battery within the set time period, including: Wherein, R is the internal resistance value of the power battery, S w|N is the summation value of the weighted coefficients at the current Nth moment, S wIV|N is the summation value after multiplying the current moment current value, voltage value and weighted coefficients, S wI|N is the summation value after multiplying the current Nth moment current value and weighted coefficients, S wV|N is the summation value after multiplying the current Nth moment voltage value and weighted coefficients, Var_Cur wII|N is the current mean square error at the current Nth moment.

7. The method according to claim 1, It is characterized in that Based on the voltage value and current value at the current moment within a set time period, using a recursive method with a forgetting factor to calculate the internal resistance value of the power battery within the set time period, further includes: Calculating the internal resistance values of the power battery in multiple set time periods, and calculating the average value of the multiple internal resistance values as the final internal resistance value of the power battery.

8. An on-line monitoring system for the internal resistance of an electric vehicle power battery, It is characterized in that Including: A judgment module, used to judge whether the current distribution of the power battery within a set time period meets the internal resistance calculation condition, and judge whether the correlation coefficient between the voltage and current of the power battery within the set time period meets the set correlation coefficient threshold; A calculation module, used to, if both are satisfied, based on the voltage value and current value at the current moment within the set time period, use a recursive method with a forgetting factor to calculate the internal resistance value of the power battery within the set time period; The set time period includes N moments, and there is a recursive relationship between the voltage values and current values of two adjacent moments, and N is a positive integer; The recursive relationship between the voltage values and current values of two adjacent moments includes: Defining the weight coefficients of the voltage value and current value at each moment based on the forgetting factor λ: w i = λ N-i ; Where λ ranges from [0, 1], N is the total number of moments within the set time period, and i is the i-th moment; There is the following recursive relationship between the sum of the weighted coefficients at the (N - 1)-th moment and the sum of the weighted coefficients at the N-th moment: There is the following recursive relationship between the sum of the products of the voltage value, current value, and weighted coefficient at the (N - 1)-th moment and the sum of the products of the voltage value, current value, and weighted coefficient at the N-th moment: There is the following recursive relationship between the sum of the products of the current value and weighted coefficient at the (N - 1)-th moment and the sum of the products of the current value and weighted coefficient at the N-th moment: There is the following recursive relationship between the sum of the products of the voltage value and weighted coefficient at the (N - 1)-th moment and the sum of the products of the voltage value and weighted coefficient at the N-th moment: There is the following recursive relationship between the sum of the products of the square of the current value and weighted coefficient at the (N - 1)-th moment and the sum of the products of the square of the current value and weighted coefficient at the N-th moment: Among them, w i is the weight coefficient of the voltage value and the current value at time i, I i is the current value at time i, V i is the voltage value at time i, I is the current value at the current Nth moment, and V is the voltage value at the current Nth moment.

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