Method and System for Detecting Remaining Battery Charge

Abstract

The present invention provides a method and system for detecting the remaining power of a battery pack, which relates to the technical field of detecting the remaining power of a battery pack. By detecting the real-time operation data of battery cells, battery cells with good consistency and those with poor consistency are classified into different clusters; the calculation weight is increased for the high-consistency cluster and decreased for the low-consistency cluster; cluster-based calculation is beneficial to reducing the calculation amount, and the finally output SOC value is more accurate; in addition, a signal can be sent to the BMS for the battery cells classified into the low-consistency cluster, and the output of the battery cells is controlled by the BMS to decrease, so as to avoid situations such as EV limitation caused by the discharge of the battery cells, which affects safety.

Description

Technical Field

[0001] The present invention relates to the technical field of battery pack remaining power detection technology, and more specifically, to a battery pack remaining power detection method and system. Background Art

[0002] Lithium iron phosphate batteries are widely used in electric vehicles due to their low cost and high safety. SOC refers to the remaining power of the battery; for lithium iron phosphate batteries, when the SOC is 100%, all lithium ions have moved from the positive electrode to the negative electrode and are embedded in the carbon of the negative electrode. The discharge process is also the process of lithium ions moving from the negative electrode to the positive electrode. When all the lithium ions in the negative electrode move to the positive electrode, the SOC is 0%.

[0003] During discharge, no matter what the SOC value is, the voltage of the positive electrode of lithium iron phosphate is 3.45V, and the voltage of the negative electrode of lithium iron phosphate is a variable value. When the SOC is equal to 0%, the negative electrode voltage value is about 1.5V, but when the SOC is close to 100%, the voltage value is only about 0.08V. Therefore, the OCV range is 1.95V-3.37V, and its OCV-SOC curve is as follows Figure 4 As shown in the figure, we can see that the SOC curve is too flat when the SOC is between 20% and 80%, and the extreme value is only about 0.1V, which leads to a problem: SOC is easy to be inaccurately measured within this range;

[0004] Different manufacturers have different methods of measuring SOC. A common solution is to estimate the remaining battery capacity by calculating the integral of current and time. This method is very dependent on the accuracy of the sensor. If it is not fully calibrated for a long time, the error will continue to accumulate and become larger. In addition, low temperature will also cause the battery capacity to decrease, which will also affect the measurement accuracy of this method.

[0005] Poor consistency of lithium iron phosphate refers to the poor consistency of remaining power between different lithium iron phosphate cells. This is because the internal resistance of lithium iron phosphate batteries is high, and the internal resistance of each battery cell is different. Part of the power is consumed by the internal resistance during charging and discharging (equivalent to several bottles of the same size leaking water, but the volume of the leak is different). Over time, the difference in power between batteries will become larger and larger. Another is that divalent iron may be oxidized or reduced to trivalent iron and iron element during the redox reaction, which may also change the internal resistance of the battery. In addition to the problem of inaccurate SOC measurement, overcharging or over-discharging of a single cell will also affect the internal resistance of the battery. In the end, it is possible that the actual SOC of 4 batteries is 80%, 70%, 50%, and 30%. This is a problem of poor consistency of lithium iron phosphate;

[0006] The battery pack of new energy vehicles is composed of modules composed of individual batteries (cells) connected in series or in parallel, and then multiple modules are combined to form a battery pack. The cycle number of lithium iron phosphate single cells can reach 3000-4000 times, but the life of the entire battery pack is affected by the worst single cell, and the overall cycle may not reach 4000 times or even less than 3000 times. This is the barrel effect of the battery.

[0007] Assume there are 4 cells with SOC of 80%, 80%, 80%, and 30%. Since the difference between the OCV voltages of 30% and 80% is not big, the system may calculate the overall SOC as 75%.

[0008] When 30% of the cells are out of power, the SOC of the four batteries is 50%, 50%, 50%, and 0%.

[0009] At this time, the battery management system protects the worst battery, which will trigger the battery's over-discharge protection and the system's overall SOC will be displayed as 0%.

[0010] At this time, only 30% of the electricity will be used, and the power display will drop directly from 75% to 0%, which is a power jump. If the power drops directly to 0%, the EV will be restricted, affecting the normal driving of new energy vehicles and easily causing safety accidents.

[0011] Therefore, there is an urgent need for an accurate solution for detecting the remaining battery capacity of a lithium iron phosphate power battery pack. Summary of the invention

[0012] In order to solve the above technical problems, the present invention provides a method and system for detecting the remaining power of a battery pack.

[0013] According to one aspect of the present invention, a method for detecting the remaining power of a battery pack is provided, comprising the following specific steps:

[0014] S1: Real-time collection of physical parameters and historical data of each battery cell in the battery pack;

[0015] S2: Calculate the voltage standard deviation, internal resistance difference rate and temperature gradient between each battery cell; generate a comprehensive index of inconsistency;

[0016] S3: Divide the cells into high consistency clusters and low consistency clusters based on the comprehensive inconsistency index;

[0017] S4: Use fuzzy control algorithm to dynamically adjust the calculation weights of high consistency clusters and low consistency clusters;

[0018] S5: Establish an SOC calculation model; take the voltage data of the high consistency cluster and the low consistency cluster and the corresponding weight coefficients as input, and output the SOC calculation value.

[0019] Further, the physical parameters collected for the battery cells include voltage, current, and temperature data.

[0020] Further, the historical data of the battery cells collected in step S1 includes the historical cycle times and the reference internal resistance value.

[0021] Further, in step S2, different weight coefficients are set for three parameters: voltage standard deviation, internal resistance difference rate, and temperature gradient; the weight coefficients are calibrated through experimental calculations;

[0022] After normalizing the three parameters, they are weighted and summed to calculate the normalized parameter.

[0023] Further, in step S3, the judgment criteria are set based on actual experiments; those higher than the judgment criteria are classified into the high-consistency cluster, otherwise into the low-consistency cluster;

[0024] Among them, the weight is increased for the high-consistency cluster; the weight is suppressed for the low-consistency cluster;

[0025] Further, for the battery cells that have been classified into the high-consistency cluster or the low-consistency cluster, classification conditions are set; they are further divided into multiple independently calculated clusters and processed separately.

[0026] Further, in step S4, the input variables of the fuzzy control algorithm are the comprehensive inconsistency index and the current SOC estimated value;

[0027] The output variables are the calculation weights of the high-consistency cluster and the low-consistency cluster.

[0028] Further, in step S5, an SOC calculation model is established based on the Kalman filter; the SOC means of the high-consistency cluster and the low-consistency cluster are calculated respectively, and weighted fusion is performed to output the final SOC calculated value.

[0029] The second aspect of the present invention provides a battery pack remaining power detection system, which uses the above method for detection, including: a data acquisition module, a data processing module, a weight calculation module, and an SOC calculation module;

[0030] The data acquisition module collects the voltage, current, temperature data, historical cycle times, and reference internal resistance values of each battery cell in the battery pack in real time through the BMS;

[0031] The data processing module processes the collected data, calculates the voltage standard deviation, internal resistance difference rate, and temperature gradient between each battery cell; generates a comprehensive inconsistency index; and according to the pre-set judgment criteria, those higher than the judgment criteria are classified into the high-consistency cluster, otherwise into the low-consistency cluster;

[0032] The weight calculation module dynamically adjusts the calculation weights of the high-consistency cluster and the low-consistency cluster by using the fuzzy control algorithm;

[0033] The SOC calculation module is built with an SOC calculation model based on a Kalman filter. It calculates the weighted fusion of the SOC means of the high-consistency clusters and the low-consistency clusters, combined with the corresponding calculation weights, and outputs the final SOC calculation value.

[0034] The third aspect of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the above-mentioned method for detecting the remaining power of the battery pack.

[0035] Compared with the prior art, the method and system for detecting the remaining power of the battery pack provided by the present invention divide the cells with good consistency and the cells with poor consistency into different clusters by using the real-time operation data of the detected cells; increase the calculation weight for the high-consistency clusters and decrease the corresponding weight for the low-consistency clusters; the cluster calculation is beneficial to reducing the calculation amount, and the output final SOC value is more accurate; in addition, for the cells divided into the low-consistency clusters, a signal can be sent to the BMS, and the BMS can control to reduce the output of these cells to avoid situations such as EV limitation caused by the discharge of the cells, which may affect safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0037] Figure 1 It is a flowchart of the method for detecting the remaining power of the battery pack according to an embodiment of the present invention.

[0038] Figure 2 It is a block diagram of the system for detecting the remaining power of the battery pack according to an embodiment of the present invention.

[0039] Figure 3 It shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present invention.

[0040] Figure 4 It is the OCV-SOC curve of a lithium iron phosphate battery in the prior art. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0042] As mentioned in the background technology above, the poor consistency of lithium iron phosphate is due to the high internal resistance of lithium iron phosphate batteries. The internal resistance of each battery cell is different. Part of the power is consumed by the internal resistance during charging and discharging. Over time, the power difference between each battery will become larger and larger. Another reason is that divalent iron may be oxidized or reduced to trivalent iron and iron during the redox reaction, which may also change the internal resistance of the battery. In addition to the problem of inaccurate SOC measurement, it is necessary to accurately measure the remaining power of the battery pack to reduce the risk of stalling of new energy electric vehicles.

[0043] Figure 1 FIG. 1 is a flow chart of a method for detecting the remaining power of a battery pack according to an embodiment of the present invention. Figure 1 As shown, a method for detecting the remaining power of a battery pack includes the following specific steps:

[0044] S1: Real-time collection of physical parameters and historical data of each battery cell in the battery pack; specifically: synchronously measuring the physical parameters and historical data of each battery cell at a sampling rate of 100Hz;

[0045] Among them, the physical parameters of the collected battery cells include voltage V （i） , Current I (i) And temperature data T (i) ; In step S1, the historical data of the battery cells are collected, including the historical cycle times C (i) And the reference internal resistance R 0(i) ; ( i Take 1, 2, ..., N ), N is the total number of cells;

[0046] S2: Calculate the voltage standard deviation, internal resistance difference rate and temperature gradient between each battery cell; generate an inconsistency comprehensive index; in step S2, set different weight coefficients for the three parameters of voltage standard deviation, internal resistance difference rate and temperature gradient; the weight coefficient is calibrated after experimental calculation;

[0047] The normalized parameters are calculated by normalizing the three parameters and taking the weighted sum.

[0048] The specific steps are as follows:

[0049] The voltage standard deviation is calculated as follows:

[0050]

[0051] In the formula, i is the serial number of the battery cell, N is the total number of cells; V（i） is the voltage of the i-th battery cell, the average voltage of the battery cells; is the standard deviation of the voltage;

[0052] The internal resistance difference rate is calculated according to the following formula:

[0053]

[0054] In the formula, i is the serial number of the battery cell, N is the total number of battery cells; is the internal resistance value of the i -th battery cell estimated online based on the AC impedance method; R 0(i) is the i -th reference internal resistance value of the battery cell; is the i -th difference rate of the battery cell, is the average difference rate of the battery cells in the battery pack.

[0055] The temperature gradient is calculated according to the following formula:

[0056]

[0057] In the formula, is the value of the temperature gradient, which is calculated by the difference between the maximum temperature and the minimum temperature of the i-th battery cell.

[0058] The comprehensive inconsistency index is calculated according to the following formula:

[0059]

[0060] α, β, and γ are the weight coefficients of the voltage standard deviation, the internal resistance difference rate, and the temperature gradient respectively; in the formula, , , are the maximum values of the voltage standard deviation, the internal resistance difference rate, and the temperature gradient in the collected historical data respectively.

[0061] The weight coefficients are calibrated through experiments. Among them, α takes values of 0.5 - 0.7, β takes values of 0.2 - 0.3, and γ takes values of 0.1 - 0.2.

[0062] S3: Divide the battery cells into a high-consistency cluster and a low-consistency cluster based on the comprehensive inconsistency index; set the judgment criterion based on actual experiments in step S3; those higher than the judgment criterion are classified into the high-consistency cluster, otherwise into the low-consistency cluster;

[0063] The judgment criterion is and ; If other conditions are met, it is classified into a high - consistency cluster; otherwise, it is classified into a low - consistency cluster. Among them, the weight of the high - consistency cluster is increased; the weight of the low - consistency cluster is suppressed.

[0064] For the battery cells that have been classified into high - consistency clusters or low - consistency clusters, set classification conditions; further divide them into multiple independently calculated clusters and process them separately; set initial weights for the battery cells classified into different consistency clusters according to the quantity; for the battery cells in the low - consistency cluster, further set classification conditions for classification processing to reduce the computational complexity and improve the computational efficiency.

[0065] S4: Dynamically adjust the calculation weights of the high - consistency cluster and the low - consistency cluster using a fuzzy control algorithm.

[0066] In step S4, the input variables of the fuzzy control algorithm are the comprehensive index of inconsistency and the current SOC estimated value; the output variables are the calculation weights of the high - consistency cluster and the low - consistency cluster.

[0067] 1: Define input and output variables

[0068] Input variables:

[0069] Comprehensive index of inconsistency (ICI): Range 0 - 1 (0 means completely consistent, 1 means severely inconsistent).

[0070] Current SOC estimated value: Range 0% - 100% (sensitive to consistency at low battery levels).

[0071] Output variables:

[0072] Weight of the high - consistency cluster W H : Range 0.5 - 1.0 (the weight of the low - consistency cluster is ).

[0073] 2: Design membership functions

[0074] Fuzzy set partition of ICI:

[0075] Low (L): Triangular function, vertex at ICI = 0.2;

[0076] Medium (M): Triangular function, vertex at ICI = 0.5;

[0077] High (H): Triangular function, vertex at ICI = 0.8.

[0078] Fuzzy set partition of SOC:

[0079] Low battery level (L): Trapezoidal function, covering 0% - 20%;

[0080] Medium battery level (M): Trapezoidal function, covering 15% - 80%;

[0081] High battery level (H): Trapezoidal function, covering 70% - 100%.

[0082] Fuzzification of output variable

[0083] High - consistency cluster weight W H :

[0084] Low weight (LW): Triangular function, vertex at 0.5;

[0085] Medium weight (MW): Triangular function, vertex at 0.7;

[0086] High weight (HW): Triangular function, vertex at 1.0.

[0087] 3: Construct the fuzzy rule base

[0088] Based on expert experience and experimental data, design the following rules:

[0089]

[0090] 4: Use the centroid method for defuzzification to obtain the calculated weight value.

[0091] S5: Establish the SOC calculation model; Use the voltage data and corresponding weight coefficients of high - consistency clusters and low - consistency clusters as inputs, and output the SOC calculation value.

[0092] Step S5 establishes the SOC calculation model based on the Kalman filter; Calculate the SOC means of high - consistency clusters and low - consistency clusters respectively, and perform weighted fusion to output the final SOC calculation value.

[0093]

[0094] SOC final is the final calculated value; W H is the high - consistency cluster weight, SOC H、 SOC L are the SOC estimated values of high - consistency clusters and low - consistency clusters respectively.

[0095] The following uses a specific case to introduce the solution of this embodiment

[0096] Suppose the power battery pack of a new energy vehicle is a lithium iron phosphate battery, and the number of battery cells is 10; They are numbered 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 respectively; Through the BMS system, collect the physical parameters and historical data of the power battery cells during operation, and estimate that the SOC value is 45%;

[0097] Collect the physical parameters and historical data of each battery cell in the battery pack in real time; specifically: synchronously measure the physical parameters and historical data of each battery cell at a sampling rate of 100 Hz;

[0098] Among them, the physical parameters collected for the battery cells include voltage V （i） 、current I (i) and temperature data T (i) ; The historical data of the battery cells collected in step S1 includes the historical cycle count C (i) and the reference internal resistance value R 0(i) ; ( i Take 1, 2, ……, N ), N is the total number of battery cells;

[0099] Calculate the voltage standard deviation, internal resistance difference rate, and temperature gradient between each battery cell; generate a comprehensive inconsistency index; in step S2, set different weight coefficients for the three parameters of voltage standard deviation, internal resistance difference rate, and temperature gradient; the weight coefficients are calibrated through experimental calculations;

[0100] Normalize the three parameters and then perform weighted summation to calculate the normalized parameter.

[0101] The specific steps are as follows:

[0102] The voltage standard deviation is calculated according to the following formula:

[0103]

[0104] In the formula, i is the serial number of the battery cell, N is the total number of battery cells; V （i） is the voltage of the i-th battery cell, the average voltage of the battery cells; is the standard deviation of the voltage;

[0105] The internal resistance difference rate is calculated according to the following formula:

[0106]

[0107] In the formula, i is the serial number of the battery cell, N is the total number of battery cells; is the internal resistance value of the i battery cell estimated online based on the AC impedance method; R 0(i) is the i reference internal resistance value of the is the difference rate of the i th cell, is the average difference rate of the cells in the battery pack.

[0108] The temperature gradient is calculated according to the following formula:

[0109] In the formula, is the value of the temperature gradient, which is calculated by the difference between the maximum temperature and the minimum temperature of the

[0110] The comprehensive index of inconsistency is calculated according to the following formula:

[0111]

[0112] α, β, and γ are the weight coefficients of the voltage standard deviation, the internal resistance difference rate, and the temperature gradient respectively; in the formula, , , are the maximum values of the voltage standard deviation, the internal resistance difference rate, and the temperature gradient in the collected historical data respectively.

[0113] The weight coefficients are calibrated through experiments, where α ranges from 0.5 to 0.7, β ranges from 0.2 to 0.3, and γ ranges from 0.1 to 0.2.

[0114] Table 1 BMS data acquisition table at a certain moment

[0115]

[0116] Calculate the voltage standard deviation, the internal resistance difference rate, and the temperature gradient respectively according to the above steps; and further calculate the comprehensive index of inconsistency ICI.

[0117] The calculation results are as follows:

[0118] = 3.222V, = 0.085V, = 20.6%, ΔT = 9°C; ICI = 0.751

[0119] S3: Divide the cells into a high consistency cluster and a low consistency cluster based on the comprehensive index of inconsistency; the judgment criteria are set based on actual experiments in step S3; those higher than the judgment criteria are classified into the high consistency cluster, otherwise they are classified into the low consistency cluster;

[0120] The judgment criteria are and ; those meeting other conditions are classified into the high consistency cluster, otherwise they are classified into the low consistency cluster; among them, the weights of the high consistency cluster are increased; the weights of the low consistency cluster are suppressed;

[0121] According to the preset judgment criteria, cells 1, 2, 3, 5, 6, 7, 9, and 10 are classified into the high consistency cluster, and cells 4 and 8 are classified into the low consistency cluster;

[0122] For the battery cells that have been classified into the high consistency cluster or the low consistency cluster, the classification conditions are set; they are further divided into multiple independently calculated clusters and processed separately; for the battery cells classified into different consistency clusters, the initial weights are set according to the number. Taking this embodiment as an example, there are 10 groups of battery cell data in this embodiment, of which 8 groups are classified into the high consistency cluster and 2 groups are classified into the low consistency cluster; therefore, the initial weight for the high consistency cluster can be set to 0.8, and the initial weight for the low consistency cluster can be set to 0.2. In this embodiment, since the number of battery cells is too small, the use of this method will affect the accuracy of the final weight, so this rule is not used; for the battery cells in the low consistency cluster, the classification conditions can be further set and the classification processing can be carried out to reduce the complexity of the calculation and improve the calculation efficiency; since there are only two battery cells in the low consistency cluster in this experiment, they are not further divided.

[0123] S4: Use fuzzy control algorithm to dynamically adjust the calculation weights of high consistency clusters and low consistency clusters;

[0124] The input variables of the fuzzy control algorithm in step S4 are the inconsistency comprehensive index and the current SOC estimation value; the output variables are the calculation weights of the high consistency cluster and the low consistency cluster;

[0125] Input fuzzification

[0126] ICI=0.751: Membership calculation:

[0127] ICI=H (high): membership degree = 0.8 (because 0.751 is close to the 0.8 vertex);

[0128] ICI=M (medium): membership degree = 0.2.

[0129] SOC=45%: Membership calculation:

[0130] SOC=M (medium charge): membership degree=1.0.

[0131] Fuzzy rule matching

[0132] Matching rule 8: If ICI=H and SOC=M, the output weight adjustment coefficient is 0.5;

[0133] Matching rule 6: If ICI=M and SOC=H, but SOC is actually M, this rule is not activated

[0134] 4: Use the centroid method to defuzzify and obtain the calculated weight value.

[0135] Only rule 8 is activated, with output weight W H= 0.5.

[0136] S5: Establish an SOC calculation model; use the voltage data and corresponding weight coefficients of the high-consistency cluster and the low-consistency cluster as inputs to output the SOC calculation value.

[0137] Step S5 establishes an SOC calculation model based on a Kalman filter; calculates the SOC means of the high-consistency cluster and the low-consistency cluster respectively, and performs weighted fusion to output the final SOC calculation value.

[0138]

[0139] SOC final is the final calculated value; W H is the weight of the high-consistency cluster, SOC H、 SOC L are the SOC estimated values of the high-consistency cluster and the low-consistency cluster respectively.

[0140] Estimate the remaining capacity of the battery by calculating the integral of current and time SOC H is 44%, SOC L is 35%;

[0141] Then the final SOC value calculated by weighted fusion is 39.5%.

[0142] In this embodiment, by using the real-time operation data of the detected battery cells, the battery cells with good consistency and those with poor consistency are divided into different clusters; the calculation weight is increased for the high-consistency cluster and the corresponding weight is decreased for the low-consistency cluster; clustering calculation is beneficial to reducing the calculation amount, and the output final SOC value is more accurate; in addition, for the battery cells divided into the low-consistency cluster, a signal can be sent to the BMS, and the BMS is controlled to reduce the output of this battery cell to prevent the battery cell from being discharged and causing situations such as EV limitation, which may affect safety.

[0143] Embodiment 2

[0144] The second aspect of the present invention provides a battery pack remaining power detection system, which uses the above method for detection, including: a data acquisition module, a data processing module, a weight calculation module, and an SOC calculation model;

[0145] The data acquisition module uses the BMS to collect the voltage, current, temperature data, historical cycle times, and reference internal resistance values of each battery cell in the battery pack in real time;

[0146] The data processing module processes the collected data, calculates the voltage standard deviation, internal resistance difference rate, and temperature gradient between each battery cell; generates a comprehensive inconsistency index; and according to a preset determination criterion, classifies those higher than the determination criterion as high-consistency clusters, otherwise as low-consistency clusters.

[0147] The weight calculation module dynamically adjusts the calculation weights of the high-consistency clusters and low-consistency clusters by using a fuzzy control algorithm.

[0148] The SOC calculation module has a built-in SOC calculation model based on a Kalman filter, performs weighted fusion through the SOC means of the high-consistency clusters and low-consistency clusters, and combines the corresponding calculation weights to output the final SOC calculation value.

[0149] Embodiment 3

[0150] Figure 4 The figure shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present invention.

[0151] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitations to the functions and usage scope of the embodiments of the present invention.

[0152] In this embodiment, the computer system includes a central processing unit 401, which can execute various appropriate actions and processes according to the program stored in the read-only memory 402 or the program loaded from the storage section 408 into the random access memory 403, such as executing the multi-spectral imaging-based deformed blind nut detection system and method described in the above embodiments. In the random access memory 403, various programs and data required for system operation are also stored. The central processing unit 401, the read-only memory 402, and the random access memory 403 are connected to each other through a bus 404. The input / output interface 405 is also connected to the bus 404.

[0153] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as required. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 410 as required so that a computer program read therefrom is installed into the storage section 408 as required.

[0154] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product including a computer program carried on a computer-readable medium, the computer program including a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit 401, various functions defined in the system of the present invention are executed.

[0155] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0156] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0157] The units involved in the embodiments of the present invention can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation on the units themselves in some cases.

[0158] According to one aspect of the present invention, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to execute the methods provided in the above various alternative implementations.

[0159] As another aspect, the present invention further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the tailless nut deformation detection system and method based on multispectral imaging described in the above embodiments.

[0160] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present invention, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0161] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented in software, or in a manner of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the methods according to the embodiments of the present invention.

[0162] After considering the specification and practicing the disclosed embodiments herein, those skilled in the art will readily conceive of other embodiments of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention.

[0163] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for detecting the remaining power of a battery pack, characterized in that, It includes the following specific steps: S1: Real-time collect the physical parameters and historical data of each cell in the battery pack; S2: Calculate the voltage standard deviation, internal resistance difference rate, and temperature gradient between each cell; Generate an inconsistency comprehensive index; S3: Divide the cells into a high-consistency cluster and a low-consistency cluster based on the inconsistency comprehensive index; S4: Dynamically adjust the calculation weights of the high-consistency cluster and the low-consistency cluster using a fuzzy control algorithm; S5: Establish an SOC calculation model; Use the SOC estimated values of the high-consistency cluster and the low-consistency cluster as inputs, and output the SOC calculated value; In step S1, the physical parameters of the cells collected include voltage, current, and temperature data; The historical data of the cells collected in step S1 includes the historical cycle times and the reference internal resistance value; In step S2, different weight coefficients are set for the three parameters of voltage standard deviation, internal resistance difference rate, and temperature gradient; The weight coefficients are calibrated after experimental calculation; Normalize the three parameters and calculate the normalized parameter by weighted summation; In step S3, set the determination criterion based on actual experiments; Classify those higher than the determination criterion into the high-consistency cluster, otherwise into the low-consistency cluster; Among them, the weight is increased for the high-consistency cluster; The weight is suppressed for the low-consistency cluster.

2. The method for detecting the remaining power of the battery pack according to claim 1, characterized in that, For the cells that have been classified into the high-consistency cluster or the low-consistency cluster, set classification conditions; Further divide them into multiple independently calculated clusters and process them separately.

3. The method for detecting the remaining power of the battery pack according to claim 1, wherein In step S4, the input variables of the fuzzy control algorithm are the inconsistency comprehensive index and the current SOC estimated value; The output variable is the calculation weights of the high-consistency cluster and the low-consistency cluster.

4. The method for detecting the remaining power of the battery pack according to claim 1, wherein Step S5 establishes an SOC calculation model based on the Kalman filter; Calculate the SOC means of the high-consistency cluster and the low-consistency cluster respectively, and perform weighted fusion to output the final SOC calculated value.

5. A remaining battery power detection system for a battery pack, which performs detection by using the method described in any one of claims 1-4, characterized in that, It includes: A data acquisition module, a data processing module, a weight calculation module, and an SOC calculation module; The data acquisition module real-time collects the voltage, current, temperature data, historical cycle times, and reference internal resistance values of each cell in the battery pack through the BMS; The data processing module processes the collected data, calculates the voltage standard deviation, internal resistance difference rate, and temperature gradient between each cell; Generate an inconsistency comprehensive index; And according to the pre-set determination criterion, classify those higher than the determination criterion into the high-consistency cluster, otherwise into the low-consistency cluster; The weight calculation module dynamically adjusts the calculation weights of the high-consistency cluster and the low-consistency cluster using a fuzzy control algorithm; The SOC calculation module has an SOC calculation model based on the Kalman filter built-in, and performs weighted fusion through the SOC means of the high-consistency cluster and the low-consistency cluster and combines the corresponding calculation weights to output the final SOC calculated value.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the battery pack remaining power detection method described in any one of claims 1-4.

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