A power battery power monitoring method and system for new energy vehicles

Abstract

The present invention relates to the technical field of on-vehicle power supply monitoring, and particularly relates to a power supply monitoring method and system for a power battery of a new energy vehicle. The present invention obtains a neighborhood attenuation degree according to the degree of change in electrical parameters of a battery at each moment and its adjacent moments within a monitoring period and the degree of abnormality at an abnormal moment; obtains a distance adjustment coefficient corresponding to two batteries according to the similarity degree of the change trends of the electrical parameters of any two batteries within the monitoring period and the difference in the attenuation degree, uses it to adjust the difference between the neighborhood attenuation degrees of the corresponding two batteries within the monitoring period, obtains a corrected attenuation distance between the corresponding two batteries between the monitoring periods, and uses it to classify the batteries in the battery pack within the monitoring period, so as to monitor the power battery pack of the new energy vehicle. The present invention accurately measures the performance differences of different batteries through the attenuation corrected distance, improves the battery classification effect within the battery pack, and increases the accuracy of power supply monitoring for the power battery of the new energy vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of on-vehicle power supply monitoring, and particularly to a power supply monitoring method and system for a power battery of a new energy vehicle. Background Art

[0002] Common problems of the power battery of a new energy vehicle are abnormal voltages of individual batteries, including undervoltage, overvoltage, and excessive voltage difference; undervoltage of the battery may not provide the required voltage to the motor, resulting in insufficient power during vehicle acceleration; overvoltage will reduce the charging efficiency of the battery, causing the battery management system to possibly cut off power frequently, affecting the user experience and reducing the driving range; continuously excessive voltage difference of the battery may damage the balance of the battery pack, exacerbating the decline of the overall vehicle performance. Therefore, it is necessary to monitor the power supply of the vehicle power battery to ensure the performance and safety of the vehicle.

[0003] Existing methods classify the batteries in the battery pack into different performance categories by comparing the deviation of the electrical parameters of each individual battery in the power battery pack from the average value of the electrical parameters of the batteries in the pack; however, due to differences in the manufacturing level, usage conditions, and aging degree of different batteries in the battery pack, and the battery management system of the new energy vehicle will transfer the excess power from the battery with high power to the battery with low power through an additional circuit, resulting in the electrical parameter differences of different batteries in the battery pack not being able to accurately measure the performance differences of different batteries, and further causing errors in the classification of abnormal performance batteries, reducing the accuracy of the power supply monitoring of the power battery of the new energy vehicle. Summary of the Invention

[0004] In order to solve the technical problem that the electrical parameter differences of different batteries in the battery pack cannot accurately measure the performance differences of different batteries, resulting in errors in the selection of abnormal performance batteries, the purpose of the present invention is to provide a power supply monitoring method and system for a power battery of a new energy vehicle, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a power supply monitoring method for a power battery of a new energy vehicle, and the method includes:

[0006] Obtain the electrical parameters of each battery in the power battery pack of the new energy vehicle at each moment within a monitoring period;

[0007] Obtain the neighborhood attenuation degree of each battery at each moment within the monitoring period according to the degree of change of the electrical parameters of each battery at each moment within the monitoring period with respect to its adjacent moment, and the degree of abnormality of the adjacent moment;

[0008] Obtain the distance adjustment coefficient of two corresponding batteries according to the similarity degree of the electrical parameter change trends of any two batteries within the monitoring period and the difference in the attenuation degree;

[0009] Adjust the difference between the neighborhood attenuation degrees of two corresponding batteries during the monitoring period according to the distance adjustment coefficient of any two batteries, and obtain the corrected attenuation distance between the two corresponding batteries during the monitoring period; classify the batteries in the battery pack during the monitoring period based on the corrected attenuation distance, and monitor the power battery pack of the new energy vehicle.

[0010] Further, the obtaining of the neighborhood attenuation degree of each battery at each moment during the monitoring period includes:

[0011] Record the ratio of the difference in electrical parameters between each battery at each moment and the next adjacent moment during the monitoring period to the time interval between the two corresponding moments as the instantaneous discharge rate at each moment.

[0012] Record the instantaneous discharge rate and electrical parameters as analysis indicators, calculate the absolute value of the difference between the analysis indicator at each moment and the average value of the analysis indicators of its adjacent moments, and record the instantaneous discharge rate and electrical parameters corresponding to the absolute value of the difference as the adjacent rate difference and adjacent parameter difference at each moment in sequence; use the ratio of the sum of the adjacent parameter difference, the adjacent rate difference, and a preset positive number at each moment of each battery during the monitoring period as the attenuation index.

[0013] Select abnormal moments based on electrical parameters; obtain the neighborhood attenuation degree of each battery at each moment during the monitoring period according to the number of abnormal moments among the adjacent moments of each moment and the attenuation index.

[0014] Further, the method for obtaining the distance adjustment coefficient includes:

[0015] Calculate the cumulative sum of the absolute values of the differences in the neighborhood attenuation degrees of any two batteries at the same position moments during the monitoring period as the overall attenuation difference between the two corresponding batteries during the monitoring period.

[0016] Arrange the electrical parameters of each battery at all moments during the monitoring period in chronological order to obtain an electrical parameter sequence.

[0017] Obtain the distance adjustment coefficient between any two batteries during the monitoring period according to the DTW value of the electrical parameter sequences of the two corresponding batteries and the overall attenuation difference, and both the DTW value and the overall attenuation difference have a positive correlation with the distance adjustment coefficient.

[0018] Further, the method for obtaining the corrected attenuation distance includes:

[0019] Based on the difference between the neighborhood attenuation degrees of any two batteries during the monitoring period, obtain the attenuation distance between the two corresponding batteries during the monitoring period.

[0020] Optionally select a battery as the example battery, and then randomly select a battery within the k-neighborhood of the example battery as the analysis battery. Take the absolute value of the difference between the distance adjustment coefficient of the example battery and the analysis battery and the average value of the distance adjustment coefficients of the example battery and all batteries within its k-neighborhood as the numerator, and use the average value of the distance adjustment coefficients of the example battery and all batteries within its k-neighborhood as the denominator to calculate the ratio, and then perform normalization processing to obtain the abnormality degree of the example battery and the analysis battery;

[0021] Determine whether the abnormality degree of the example battery and the analysis battery is greater than the preset abnormality threshold. If so, use the sum of the abnormality degree and the constant 1 to weight the attenuation distance between the example battery and the analysis battery during the monitoring period to obtain the corrected attenuation distance; if not, use the attenuation distance between the example battery and the analysis battery during the monitoring period as the corrected attenuation distance.

[0022] Further, classifying the batteries in the battery pack during the monitoring period based on the corrected attenuation distance includes:

[0023] Cluster all the batteries in the battery pack during the monitoring period based on the attenuation distance between any two batteries during the monitoring period to obtain two clustering clusters; mark the batteries in the clustering cluster with the largest number of batteries as suspected normal batteries, and mark the batteries in the other clustering cluster as suspected abnormal batteries;

[0024] Select the k batteries corresponding to the largest k corrected attenuation distances from the corrected attenuation distances between the example battery and the remaining batteries during the monitoring period, and count the total number of suspected normal batteries and the total number of suspected abnormal batteries respectively among them; if the total number of suspected normal batteries is the largest, the example battery is a normal battery during the monitoring period; if the total number of suspected abnormal batteries is the largest, the example battery is an abnormal battery during the monitoring period.

[0025] Further, the attenuation distance between the two batteries during the monitoring period is the absolute value of the difference between the average values of the neighborhood attenuation degrees of the two batteries at all times during the monitoring period.

[0026] Further, the number of abnormal times among the neighboring times at each time, the attenuation index, and the neighborhood attenuation degree are all positively correlated.

[0027] Further, the electrical parameters of the abnormal time exceed the preset standard range.

[0028] Further, the method for clustering all the batteries in the battery pack during the monitoring period is the K-means clustering algorithm.

[0029] In a second aspect, another embodiment of the present invention provides a power battery power monitoring system for a new energy vehicle, and the system includes:

[0030] A data acquisition module, configured to obtain the electrical parameters of each battery in the power battery pack of a new energy vehicle at each moment within a monitoring period;

[0031] An attenuation analysis module, configured to obtain the neighborhood attenuation degree of each battery at each moment within the monitoring period according to the degree of change in the electrical parameters of each battery at each moment and its adjacent moments within the monitoring period, and the degree of abnormality of the adjacent moments;

[0032] A distance adjustment analysis module, configured to obtain the distance adjustment coefficient of two corresponding batteries according to the difference between the similarity degree of the change trends of the electrical parameters of any two batteries within the monitoring period and the attenuation degree;

[0033] A battery classification module, configured to adjust the difference between the neighborhood attenuation degrees of two corresponding batteries within the monitoring period according to the distance adjustment coefficient of the two corresponding batteries, so as to obtain the corrected attenuation distance between the two corresponding batteries between the monitoring periods; classify the batteries in the battery pack within the monitoring period based on the corrected attenuation distance, and monitor the power battery pack of the new energy vehicle.

[0034] The present invention has the following beneficial effects:

[0035] First aspect: Considering that due to differences in the manufacturing levels, usage conditions, and aging degrees of different batteries, the reference value of analyzing the battery performance differences based on the electrical parameter differences of different batteries in the battery pack for classifying battery performance is relatively small. According to the characteristic that the electrical parameters of the battery will show a downward trend as the battery power is consumed during the vehicle driving process, the neighborhood attenuation degree is obtained through the degree of change in the electrical parameters of the battery at each moment and its adjacent moments within the monitoring period and the degree of abnormality of the adjacent moments, presenting the performance status of the battery at a local time.

[0036] Second aspect: Considering that the battery management system of the new energy vehicle will perform power balancing adjustment during the vehicle driving process, weakening the attenuation trend of the battery electrical parameters. According to the degree of consistency of the voltage attenuation trends of two batteries within the monitoring period, the difference in the performance degradation of the two corresponding batteries is analyzed, and the difference in the attenuation trends of the two batteries is adjusted to obtain the distance adjustment coefficient. The similarity degree of the change trends of the electrical parameters of two batteries within the monitoring period and the difference in the attenuation degree analyze the consistency of the electrical parameter attenuation trends of the corresponding batteries within the monitoring period from the overall perspective and the local perspective in sequence. Comprehensive analysis can improve the accuracy of analyzing the consistency of the electrical parameter attenuation trends of the two batteries.

[0037] Third aspect: The difference in neighborhood attenuation degrees of different batteries within the monitoring period measures the attenuation distance between different batteries; classifying the batteries in the battery pack within the monitoring period by correcting the attenuation distance can dynamically strip the interference of the power balance management of the battery management system on the attenuation trend of electrical parameters, quantify the true performance differences between batteries, classify the batteries more accurately, and improve the accuracy of power source monitoring for power batteries of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of the steps of a method for monitoring the power source of a power battery of a new energy vehicle provided by an embodiment of the present invention;

[0040] Figure 2 It is a system structure diagram of a system for monitoring the power source of a power battery of a new energy vehicle provided by an embodiment of the present invention;

[0041] Figure 3 It is a schematic diagram of a computer device of a device for monitoring the power source of a power battery of a new energy vehicle provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the drawings and preferred embodiments, describe in detail the specific implementation manners, structures, features, and effects of a method and system for monitoring the power source of a power battery of a new energy vehicle proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0044] The following will specifically describe the specific solutions of a method and system for monitoring the power source of a power battery of a new energy vehicle provided by the present invention in conjunction with the drawings.

[0045] Embodiment 1:

[0046] The present invention proposes a method for monitoring the power source of a power battery of a new energy vehicle. Please refer toFigure 1 , which shows a step flowchart of a power battery power monitoring method provided by an embodiment of the present invention. The method includes:

[0047] Step S1: Obtain the electrical parameters of each battery in the power battery pack of the new energy vehicle at each moment within the monitoring period.

[0048] Install electrical sensors on each battery in the power battery pack of the new energy vehicle. During the vehicle's driving process, collect the voltage of each battery in the battery pack at each moment within the monitoring period through the electrical sensors, and record it as the electrical parameter; use the data transmission device connected to the electrical sensors to transmit the electrical parameters of the battery pack to the cloud platform, so as to monitor the power of the vehicle's power battery.

[0049] In an implementation manner of the embodiment of the present invention, the data acquisition frequency of the electrical sensors is set to once every 5 seconds; the duration of the monitoring period is set to 10 minutes.

[0050] It should be noted that the data acquisition frequencies of the electrical sensors on all batteries in the battery pack are equal.

[0051] Step S2: Obtain the neighborhood attenuation degree of each battery at each moment within the monitoring period according to the degree of change in the electrical parameters of each battery at each moment and its adjacent moments within the monitoring period, and the degree of abnormality of the adjacent moments.

[0052] Because there are differences in the manufacturing levels, usage conditions, and aging degrees of different batteries in the battery pack, the battery powers in the battery pack may be inconsistent. Moreover, after multiple charge and discharge cycles, long-term use, and overcharge and over-discharge, etc., the power differences of the batteries in the battery pack may be amplified. The power condition of the battery can be judged through its voltage. From the perspective of the battery service life, the performance of the battery will decline after normal charge and discharge cycles, but there are significant differences in its performance changes when the battery is overcharged or over-discharged. Therefore, analyzing the battery performance differences based on the electrical parameter differences of different batteries in the battery pack has little reference value for classifying the battery performance.

[0053] During the driving process of new energy vehicles, the battery is mainly in a discharging state. The voltage of the battery will show a downward trend as the power is consumed. Moreover, the attenuation of battery performance will cause a more rapid decline in voltage. Therefore, the overall change trend of the battery voltage data is mainly affected by the attenuation of battery performance, and the local change is affected by external factors such as the environment and user driving behavior. The degree of change in electrical parameters between each moment and its adjacent moments can reflect the voltage change intensity within a local time period. The voltage change intensity presents the degree of voltage attenuation within the neighborhood time period of each moment. The degree of abnormality of the battery at the adjacent moments of each moment indicates the degree of performance attenuation of the battery at that moment. By comprehensively analyzing the above two factors, the voltage attenuation situation of the battery at each moment is obtained, and the neighborhood attenuation degree is obtained. The neighborhood attenuation degree presents the battery performance status of the battery within a local time period.

[0054] In one implementation manner of the embodiment of the present invention, the 4 adjacent moments before and after each moment are respectively recorded as the adjacent moments of each moment.

[0055] Step S3: Obtain the distance adjustment coefficient corresponding to two batteries according to the similarity degree of the change trend of electrical parameters and the difference in attenuation degree of the two batteries during the monitoring period.

[0056] The battery management system of new energy vehicles will transfer the excess power from the battery with high power to the battery with low power through additional circuits such as battery swapping and energy transfer, so as to improve the battery discharge efficiency and extend the battery life. However, the balanced adjustment of battery power will weaken the power attenuation trend of a single battery, reducing the power difference caused by factors such as multiple charge and discharge cycles of the battery. Therefore, it is more accurate to judge the performance of a battery with an obvious voltage attenuation trend, while there is a large error in judging the performance of a battery with an unobvious voltage attenuation trend, and further analysis is required.

[0057] Different batteries are regulated by the battery management system to different degrees, resulting in different voltage attenuation trends of different batteries during the monitoring period. If the voltage attenuation trends of two batteries are more inconsistent during the monitoring period, it indicates that there is an obvious attenuation trend difference between the two batteries after being regulated by the battery management system, and the difference in the performance degradation of the two batteries is more obvious. Then the possibility of abnormal conditions occurring in the two batteries is greater. In order to improve the accuracy of battery performance classification, the difference in the attenuation trends of the two batteries, that is, the distance between the two batteries, should be increased. The similarity degree of the change trend of electrical parameters and the difference in attenuation degree of the two batteries during the monitoring period are used to analyze the consistency of the voltage attenuation trends of the corresponding batteries during the monitoring period from the overall and local perspectives in turn, so as to determine the adjustment of the distance between the two batteries and obtain the distance adjustment coefficient.

[0058] Step S4: Adjust the difference in the neighborhood attenuation degrees between any two batteries during the monitoring period according to the distance adjustment coefficient of the two batteries, and obtain the corrected attenuation distance between the corresponding two batteries during the monitoring period; classify the batteries in the battery pack during the monitoring period based on the corrected attenuation distance, and monitor the power battery pack of the new energy vehicle.

[0059] The neighborhood attenuation degree presents the battery performance status of the battery at a local time, and the difference in the neighborhood attenuation degrees between different batteries during the monitoring period measures the attenuation distance between different batteries; classifying the batteries in the battery pack during the monitoring period through the corrected attenuation distance can dynamically strip the interference of the charge equalization management of the battery management system on the attenuation trend of electrical parameters, quantify the true performance differences between batteries, classify the batteries more accurately, and obtain batteries with abnormal performance, that is, batteries with more significant attenuation of electrical parameters and batteries with normal performance, that is, batteries with weaker attenuation of electrical parameters, ensuring the safety and reliability of new energy vehicles during use. Then, use the classified batteries to timely warn of the abnormal conditions of the power battery pack of the new energy vehicle during the monitoring period, and complete the real-time monitoring of the operating state of the power battery power supply of the new energy vehicle. Among them, the warning methods for the power battery packs of new energy vehicles in all monitoring periods are the same.

[0060] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the neighborhood attenuation degree includes: taking the ratio of the difference in electrical parameters between each battery at each moment and the next adjacent moment during the monitoring period to the time interval between the corresponding two moments as the discharge instantaneous rate at each moment; taking the discharge instantaneous rate and electrical parameters as analysis indicators, calculating the absolute value of the difference between the analysis indicator at each moment and the mean value of the analysis indicators at its adjacent moments, and sequentially recording the absolute values of the differences in the discharge instantaneous rate and electrical parameters as the adjacent rate difference and adjacent parameter difference at each moment; taking the ratio of the sum of the adjacent parameter difference, adjacent rate difference and a preset positive number at each moment of each battery during the monitoring period as the attenuation index; selecting abnormal moments based on electrical parameters; and obtaining the neighborhood attenuation degree of each battery at each moment during the monitoring period according to the number of abnormal moments and the attenuation index among the adjacent moments of each moment.

[0061] It should be noted that the instantaneous discharge rate can quantify the voltage change intensity of the battery per unit time, presenting the real-time condition of the battery performance; the adjacent rate difference and the adjacent parameter difference can be regarded as the mean values of the absolute values of the differences between the instantaneous discharge rate and the electrical parameters of each moment and all its adjacent moments respectively. The ratio of the sum of the adjacent parameter difference, the adjacent rate difference and a preset positive number at each moment can measure the voltage change intensity of the battery under the unit discharge rate. The larger the ratio, that is, the attenuation index, the higher the degree of attenuation of the battery performance in the neighborhood period of each moment, and the greater the neighborhood attenuation degree. Among them, the role of the preset positive number is to prevent the ratio from being meaningless due to the denominator being zero. In this embodiment, the preset positive number is set to 0.01, and the implementer can set it according to the specific situation. The attenuation of the battery performance will cause the vehicle battery voltage to exceed the standard range for a longer time, and its influence is persistent and irreversible. The voltage anomalies caused by external factors of the battery such as the environment and driving behavior are mostly short-term or intermittent phenomena; the number of abnormal moments among the adjacent moments at each moment reflects the abnormal degree of the adjacent moments at that moment. The more the number of abnormal moments, the greater the abnormal degree, the more serious the performance attenuation of the battery at that moment, and the greater the neighborhood attenuation degree. Therefore, the number of abnormal moments and the attenuation index among the adjacent moments at each moment are both positively correlated with the neighborhood attenuation degree.

[0062] In the embodiment of the present invention, the product of the number of abnormal moments and the attenuation index among the adjacent moments at each moment of each battery within the monitoring period is normalized to obtain the neighborhood attenuation degree at each moment. It should be noted that in the embodiment of the present invention, the maximum-minimum normalization is used for normalization, and normalization methods such as function transformation and Norm function can also be selected.

[0063] In an implementation manner of the embodiment of the present invention, the abnormal moment is the moment when the electrical parameter exceeds the preset standard range. Among them, the preset standard range is the standard voltage range of the batteries in the battery pack, which is obtained from the instruction manual of the new energy vehicle. For example, if the standard voltage range of the power battery of Geely Emgrand EV450 is between 11 volts and 14 volts, then the preset standard range of the battery is between 11 volts and 14 volts.

[0064] In another embodiment of the present invention, the electrical parameters of each battery at all moments within the monitoring period are curve-fitted to obtain a fitted curve, and the slope of the corresponding position of each moment on the fitted curve is used as the instantaneous discharge rate at each moment.

[0065] It should be noted that the instantaneous discharge rates of each battery at the last two moments within the monitoring period are the same; during the calculation of the instantaneous discharge rate, the difference in electrical parameters between each moment and the next adjacent moment is equal to the absolute value of the difference between the electrical parameters of the corresponding two moments.

[0066] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the distance adjustment coefficient includes: calculating the sum of absolute values of differences in neighborhood attenuation degrees at the same position of any two batteries at the same moment within the monitoring period as the overall attenuation difference between the corresponding two batteries within the monitoring period; arranging the electrical parameters of each battery at all moments within the monitoring period in chronological order to obtain an electrical parameter sequence; obtaining the distance adjustment coefficient between the corresponding two batteries within the monitoring period according to the DTW value of the electrical parameter sequences of any two batteries and the overall attenuation difference. It should be noted that the moments at the same position of any two batteries within the monitoring period have the same ranking within the corresponding monitoring period. The DTW value and the overall attenuation degree of the electrical parameter sequences of any two batteries analyze the consistency of the voltage attenuation trends of the two batteries within the monitoring period from the overall and local perspectives respectively. If both the DTW value and the overall attenuation degree are larger, it indicates that the voltage attenuation trends of the two batteries within the monitoring period are more inconsistent, and the possibility of abnormal conditions occurring in the two batteries is greater. It is necessary to increase the difference in the voltage attenuation trends of the two batteries to improve the accuracy of battery performance classification, so the distance adjustment coefficient is larger. Therefore, both the DTW value and the overall attenuation difference are positively correlated with the distance adjustment coefficient.

[0067] In the embodiments of the present invention, the product of the DTW value of the electrical parameter sequences of any two batteries within the monitoring period and the overall attenuation difference is normalized to obtain the distance adjustment coefficient between the corresponding two batteries within the monitoring period. It should be noted that in the embodiments of the present invention, the Norm function is used for normalization processing, and normalization methods such as function transformation and maximum-minimum normalization can also be selected.

[0068] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the corrected attenuation distance includes: obtaining the attenuation distance between the corresponding two batteries within the monitoring period based on the difference in neighborhood attenuation degrees between any two batteries within the monitoring period; arbitrarily selecting one battery as the example battery, and arbitrarily selecting one battery from the k-neighborhood of the example battery as the analysis battery. Taking the absolute value of the difference between the distance adjustment coefficient between the example battery and the analysis battery and the average value of the distance adjustment coefficients between the example battery and all batteries within its k-neighborhood as the numerator, and the average value of the distance adjustment coefficients between the example battery and all batteries within its k-neighborhood as the denominator, and normalizing the obtained ratio to obtain the abnormality degree between the example battery and the analysis battery; determining whether the abnormality degree between the example battery and the analysis battery is greater than a preset abnormality threshold. If so, weighting the attenuation distance between the example battery and the analysis battery within the monitoring period with the sum of the abnormality degree and the constant 1 to obtain the corrected attenuation distance; if not, taking the attenuation distance between the example battery and the analysis battery within the monitoring period as the corrected attenuation distance.

[0069] It should be noted that the batteries within the k-neighborhood of the example battery are the batteries corresponding to the k largest attenuation distances among the attenuation distances between the example battery and all the other batteries during the monitoring period. Since the average value of the neighborhood attenuation degrees of the battery at all times during the monitoring period represents the overall performance status of the battery during the monitoring period, the absolute value of the difference between the average values of the neighborhood attenuation degrees of two batteries at all times during the monitoring period is used as the attenuation distance between the two batteries during the monitoring period. The abnormality degree between the example battery and the analysis battery measures the deviation degree of the distance adjustment coefficient between the example battery and the analysis battery relative to the overall level of the distance adjustment coefficients between the example battery and the batteries within its k-neighborhood. If the abnormality degree is larger, it indicates that the distance adjustment coefficient between the example battery and the analysis battery is more inconsistent with the above overall level, and the greater the possibility that the voltage attenuation trends of the example battery and the analysis battery are abnormal. In this embodiment, it is considered that the abnormality degree greater than the preset abnormality threshold corresponds to the abnormal voltage attenuation trends of the two batteries, and then it is necessary to increase the attenuation distance between the two batteries corresponding to the abnormality degree greater than the preset abnormality threshold. The larger the obtained corrected attenuation distance. In the embodiment of the present invention, the Norm function is used for normalization processing, and other normalization methods can also be selected, which are not limited herein.

[0070] In an implementation manner of the embodiment of the present invention, k is set to 21.

[0071] In an implementation manner of the embodiment of the present invention, the K-means clustering algorithm is selected to cluster all the batteries in the battery pack during the monitoring period, where K is equal to 2.

[0072] In an implementation manner of the embodiment of the present invention, the preset abnormality threshold is set to 0.92.

[0073] It should be noted that the method for obtaining the corrected attenuation distance between every two batteries in the battery pack during the monitoring period is the same as the method for obtaining the corrected attenuation distance between the example battery and the analysis battery during the monitoring period.

[0074] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for battery classification includes: clustering all the batteries in the battery pack during the monitoring period based on the attenuation distance between any two batteries during the monitoring period to obtain two clustering clusters; marking the batteries in the clustering cluster with the largest number of batteries in the cluster as suspected normal batteries, and marking the batteries in the other clustering cluster as suspected abnormal batteries; selecting the k batteries corresponding to the largest k corrected attenuation distances from the corrected attenuation distances between the example battery and the remaining batteries during the monitoring period, and counting the total number of suspected normal batteries and the total number of suspected abnormal batteries respectively among them; if the total number of suspected normal batteries is the largest, the example battery is a normal battery during the monitoring period; if the total number of suspected abnormal batteries is the largest, the example battery is an abnormal battery during the monitoring period. It should be noted that in actual situations, during the driving of new energy vehicles, the performance of most batteries is normal, and the performance of some batteries is abnormal. The batteries with suspected normal and abnormal performance can be initially divided based on the number of batteries in the cluster. The batteries within the k-neighborhood of the example battery are re-determined based on the corrected attenuation distances between the example battery and the remaining batteries, and the k-nearest neighbor classification algorithm is used to finally determine whether the example battery belongs to an abnormal battery or a normal battery.

[0075] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for monitoring the power battery pack of a new energy vehicle includes: recording the proportion of abnormal batteries in the battery pack during the monitoring period in the batteries in the battery pack as the power supply abnormality degree; judging whether the power supply abnormality degree is greater than a preset power supply abnormality threshold. If so, the battery power supply of the new energy vehicle is abnormal during the monitoring period; if not, the battery power supply of the new energy vehicle is normal during the monitoring period. It should be noted that the larger the number of abnormal batteries in the battery pack, the greater the possibility that the battery power supply of the new energy vehicle is abnormal during the monitoring period, and the greater the power supply abnormality degree. The battery power supply situation of the new energy vehicle during the monitoring period is determined by the size relationship between the power supply abnormality degree and the preset power supply abnormality threshold.

[0076] In one implementation manner of the embodiments of the present invention, the preset power supply abnormality threshold is set to 0.3.

[0077] So far, the present invention is completed.

[0078] Embodiment 2:

[0079] The present invention provides a power battery power supply monitoring system for a new energy vehicle. Please refer to Figure 2 , which shows the system structure diagram of a power battery power supply monitoring system for a new energy vehicle provided by an embodiment of the present invention. The system includes:

[0080] A data acquisition module 510, which is used to acquire the electrical parameters of each battery in the power battery pack of the new energy vehicle at each moment during the monitoring period.

[0081] An attenuation analysis module 520 is configured to obtain the neighborhood attenuation degree of each battery at each moment within a monitoring period according to the degree of change in electrical parameters of each battery between each moment and its adjacent moment within the monitoring period, as well as the degree of abnormality at the adjacent moment.

[0082] A distance adjustment analysis module 530 is configured to obtain a distance adjustment coefficient for two corresponding batteries according to the similarity degree of the change trends of electrical parameters of any two batteries within a monitoring period and the difference in attenuation degrees.

[0083] A battery classification module 540 is configured to adjust the difference in neighborhood attenuation degrees of two corresponding batteries within a monitoring period according to the distance adjustment coefficient of any two batteries, and obtain a corrected attenuation distance between the two corresponding batteries during the monitoring period; classify the batteries in the battery pack during the monitoring period based on the corrected attenuation distance, and monitor the power battery pack of a new energy vehicle.

[0084] It should be noted that: for the device provided in the above embodiment, only the above division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the embodiment of a new energy vehicle power battery power monitoring system and the embodiment of a new energy vehicle power battery power monitoring method provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0085] Embodiment 3:

[0086] Figure 3 The following is a schematic diagram of a computer device of a new energy vehicle power battery power monitoring device provided by an embodiment of the present invention. Exemplarily, as Figure 3 shown, the computer device includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602. When the processor 602 executes the computer program 603, the computer device can execute any one of the new energy vehicle power battery power monitoring methods introduced above.

[0087] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor. Among them, the memory stores executable program code, and the processor is used to call and execute the executable program code to execute a new energy vehicle power battery power monitoring method provided by an embodiment of the present application.

[0088] In this embodiment, the device can be divided into functional modules according to the above method examples. For example, it can correspond to each functional module, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0089] It should be understood that the device provided in this embodiment is used to execute the above-mentioned method for monitoring the power supply of the power battery of a new energy vehicle, so the same effect as the above implementation method can be achieved.

[0090] In the case of adopting an integrated unit, the device can include a processing module and a storage module. Among them, when the device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute mutual program codes, etc.

[0091] Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules, and circuits included in the disclosure of this application. The processor can also be a combination that realizes computing functions, such as including a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory.

[0092] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0094] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring power supply of a power battery of a new energy vehicle, characterized in that: The method includes: Obtain the electrical parameters of each battery in the power battery pack of the new energy vehicle at each moment in the monitoring cycle; Obtaining the neighborhood attenuation of each battery at each moment in the monitoring cycle according to the degree of change of the electrical parameters of each battery at each moment in the monitoring cycle and its adjacent moments, and the degree of abnormality at the adjacent moments; According to the similarity of the electrical parameter change trends of any two batteries within the monitoring period and the difference in the attenuation degree, obtaining the distance adjustment coefficients corresponding to the two batteries; According to the distance adjustment coefficient of any two batteries, the difference between the neighborhood attenuation degrees of the corresponding two batteries in the monitoring cycle is adjusted to obtain a corrected attenuation distance between the corresponding two batteries in the monitoring cycle; based on the corrected attenuation distance, the batteries in the battery pack are classified in the monitoring cycle, and the power battery pack of the new energy vehicle is monitored; The method for obtaining the distance adjustment coefficient includes: Calculate the cumulative sum of the absolute values ​​of the differences in the neighborhood attenuation degrees of any two batteries at the same position during the monitoring period as the overall attenuation difference of the corresponding two batteries during the monitoring period; Arrange the electrical parameters of each battery at all times during the monitoring period in time sequence to obtain an electrical parameter sequence; According to the DTW value of the electrical parameter sequence of any two batteries and the overall attenuation difference, obtaining the distance adjustment coefficient of the corresponding two batteries in the monitoring period, wherein the DTW value and the overall attenuation difference are both positively correlated with the distance adjustment coefficient; The method for obtaining the corrected attenuation distance includes: Based on the difference between the neighborhood attenuation degrees of any two batteries within a monitoring period, obtaining the attenuation distance between the corresponding two batteries between the monitoring periods; Any battery is selected as a sample battery, and any battery in the k-neighborhood of the sample battery is selected as an analysis battery. The absolute value of the difference between the distance adjustment coefficient between the sample battery and the analysis battery and the mean of the distance adjustment coefficient between the sample battery and all the batteries in its k-neighborhood are used as the numerator, and the mean of the distance adjustment coefficient between the sample battery and all the batteries in its k-neighborhood are used as the denominator to obtain the ratio, and the abnormality of the sample battery and the analysis battery is obtained; Determine whether the abnormality of the example battery and the analysis battery is greater than a preset abnormality threshold. If so, weight the attenuation distance between the example battery and the analysis battery between the monitoring cycles using the sum of the abnormality and the constant 1 to obtain a corrected attenuation distance; if not, use the attenuation distance between the example battery and the analysis battery between the monitoring cycles as the corrected attenuation distance; Classifying the batteries in the battery pack during the monitoring period based on the corrected attenuation distance includes: Based on the attenuation distance between any two batteries during the monitoring cycle, all batteries in the battery pack are clustered during the monitoring cycle to obtain two clusters; the batteries in the cluster with the largest number of batteries are recorded as suspected normal batteries, and the batteries in the other cluster are recorded as suspected abnormal batteries; Select the k batteries corresponding to the largest corrected attenuation distances from the example battery and the remaining batteries during the monitoring period, and count the total number of suspected normal batteries and the total number of suspected abnormal batteries; if the total number of suspected normal batteries is the largest, the example battery is a normal battery during the monitoring period; if the total number of suspected abnormal batteries is the largest, the example battery is an abnormal battery during the monitoring period.

2. A method for monitoring power supply of a power battery of a new energy vehicle according to claim 1, characterized in that: The obtaining of the neighborhood attenuation of each battery at each moment in the monitoring period includes: The ratio of the difference between the electrical parameters of each battery at each moment and the next adjacent moment in the monitoring cycle to the time interval between the two corresponding moments is recorded as the instantaneous discharge rate at each moment; Record the instantaneous discharge rate and electrical parameters as analysis indicators, calculate the absolute value of the difference between the analysis indicator at each moment and the average value of the analysis indicator at its adjacent moments, and record the absolute values ​​of the difference corresponding to the instantaneous discharge rate and electrical parameters as the adjacent rate difference and adjacent parameter difference at each moment in turn; and take the ratio of the adjacent parameter difference, the adjacent rate difference and the sum of a preset positive number at each moment of each battery within the monitoring period as the attenuation indicator; The abnormal moment is selected based on the electrical parameters; and the neighborhood attenuation degree of each battery at each moment in the monitoring period is obtained according to the number of abnormal moments in the neighborhood of each moment and the attenuation index.

3. A method for monitoring power supply of a power battery of a new energy vehicle according to claim 1, characterized in that: The attenuation distance between the two batteries during the monitoring period is the absolute value of the difference between the neighborhood attenuation mean values ​​of the two batteries at all times during the monitoring period.

4. A method for monitoring power supply of a power battery of a new energy vehicle according to claim 2, characterized in that: The number of abnormal moments in the adjacent moments of each moment and the attenuation index are both positively correlated with the neighborhood attenuation degree.

5. A method for monitoring power supply of a power battery of a new energy vehicle according to claim 2, characterized in that: The electrical parameters at the abnormal moment exceed the preset standard range.

6. A method for monitoring power supply of a power battery of a new energy vehicle according to claim 1, characterized in that: The method for clustering all batteries in the battery pack during the monitoring period is a K-means clustering algorithm.

7. A new energy vehicle power battery power monitoring system, using a new energy vehicle power battery power monitoring method as claimed in any one of claims 1 to 6, characterized in that: The system includes: A data acquisition module is used to obtain the electrical parameters of each battery in the power battery pack of the new energy vehicle at each moment in the monitoring cycle; An attenuation analysis module, used to obtain the neighborhood attenuation degree of each battery at each moment in the monitoring cycle according to the degree of change of the electrical parameters of each battery at each moment in the monitoring cycle and its adjacent moments, and the degree of abnormality of the adjacent moments; A distance adjustment analysis module, used to obtain the distance adjustment coefficients of the corresponding two batteries according to the similarity of the electrical parameter change trends of any two batteries within a monitoring period and the difference in the attenuation degree; The battery classification module is used to adjust the difference between the neighborhood attenuation degrees of the corresponding two batteries during the monitoring period according to the distance adjustment coefficient of any two batteries, and obtain the corrected attenuation distance between the corresponding two batteries during the monitoring period; classify the batteries in the battery pack during the monitoring period based on the corrected attenuation distance, and monitor the power battery pack of the new energy vehicle.

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