Charging and discharging safety monitoring system and method for new energy automobile battery

Abstract

The invention provides a charging and discharging safety monitoring system and method for a new energy automobile battery, and the system comprises a cloud server which is used for providing the charging and discharging safety monitoring service of the new energy automobile battery, and a plurality of new energy automobiles which are in communication connection with the cloud server. The new energy automobile uploads real-time charging and discharging data to the cloud server, so that the cloud server carries out charging and discharging safety monitoring on the new energy automobile based on the charging and discharging data, and the cloud server is configured to carry out charging and discharging safety monitoring on the new energy automobile which is connected with the cloud server and has the same or similar internal charging and discharging attributes. According to the method, clustering analysis is carried out on new energy automobile clusters under the same or similar external charging and discharging environment conditions, so that safety anomaly identification is carried out on the charging and discharging process of the new energy automobile according to a clustering analysis result, and the charging and discharging anomaly condition of a new energy automobile battery can be timely and accurately predicted; therefore, personal and property safety of people is fully and effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a charging and discharging safety monitoring system and method for a new energy vehicle battery. Background Art

[0002] With the increasing popularity of new energy vehicles, the safety of new energy vehicles has gradually become one of the important issues that people are concerned about. Compared with traditional fuel vehicles, the power battery of new energy vehicles, as a "new" component of the car, is the focus of people's attention on the safety of new energy vehicles. The main reason is that the thermal stability of the power battery itself has a certain threshold. During the charging and discharging process of new energy vehicles, the temperature of the power battery will continue to rise. If effective heat dissipation measures are not taken, it is easy to cause thermal runaway of the battery, resulting in fire or even explosion. In addition, the charging and discharging process of new energy vehicles is also very important for monitoring the charging and discharging status of the battery. The power battery is usually a battery pack composed of a large number of single cells. The current technical means are difficult to accurately monitor the charging and discharging status of each single cell at the same time. Once the charging and discharging current or voltage of any single cell exceeds the critical value and is not detected in time, it may cause a very serious safety accident. At present, the common safety monitoring method for new energy vehicle batteries is to measure the charging and discharging current and voltage of the power battery of new energy vehicles in real time, and compare them with the configured safety current and safety voltage in real time, so as to stop charging or limit the discharge power when the current or voltage exceeds the safety critical value during the charging and discharging process. However, on the one hand, the critical value of the charging and discharging parameters itself is not a fixed value that does not change, and it will change due to the battery's own characteristics and environmental influences. On the other hand, the charging and discharging parameters of the power battery will also be affected by environmental factors and have unstable characteristics. Therefore, it is not possible to accurately identify the charging and discharging abnormalities of new energy vehicles simply by judging whether the critical value is exceeded. Summary of the invention

[0003] Based on the above problems, the present invention proposes a new energy vehicle battery charging and discharging safety monitoring system and method, which can timely and accurately predict the abnormal charging and discharging conditions of new energy vehicle batteries, thereby fully and effectively protecting people's personal and property safety.

[0004] In view of this, the first aspect of the present invention proposes a new energy vehicle battery charge and discharge safety monitoring system, including a cloud server for providing new energy vehicle battery charge and discharge safety monitoring services, and a number of new energy vehicles communicatively connected to the cloud server. The new energy vehicles upload real-time charge and discharge data to the cloud server, so that the cloud server performs charge and discharge safety monitoring on the new energy vehicles based on the charge and discharge data. The cloud server is configured to perform cluster analysis on a cluster of new energy vehicles connected to it that have the same or similar intrinsic charge and discharge properties and the same or similar external charge and discharge environmental conditions, so as to identify safety anomalies in the charge and discharge process of the new energy vehicles based on the cluster analysis results.

[0005] The second aspect of the present invention provides a method for monitoring the charging and discharging safety of a new energy vehicle battery, comprising: Determine a new energy vehicle to be monitored as a monitoring object, wherein the monitoring object is a new energy vehicle that is undergoing fast charging or fast discharging; Acquiring real-time charge and discharge parameters of the monitored object, wherein the charge and discharge parameters include one or more of a charge and discharge current, a charge and discharge voltage, and a charge and discharge power of a power battery of the monitored object; Determining a cluster object cluster of the monitoring object in the new energy vehicle list, each new energy vehicle in the cluster object cluster has the same or similar intrinsic charging and discharging properties as the monitoring object, and the same or similar external charging and discharging environmental conditions; Acquire the charging and discharging parameters of each new energy vehicle in the cluster object cluster, wherein the charging and discharging parameters are the charging and discharging parameters of the most recent charging and discharging behavior of the new energy vehicles in the cluster object cluster; Clustering the associated charging and discharging parameters of the new energy vehicles in the clustering object cluster; The charging and discharging process of the monitored object is identified for safety anomalies based on the clustering results.

[0006] Furthermore, the step of determining the clustering object cluster of the monitoring object in the new energy vehicle list specifically includes: Configuring an upper bound on the number of cluster objects and a lower bound on the number of cluster objects for limiting the number of cluster objects in the cluster of cluster objects; Obtain the vehicle type, geographical location, and charging and discharging time of the monitored object; The clustering object cluster is obtained by sequentially screening the new energy vehicle list using the vehicle type, geographical location and charging and discharging time of the monitored object as screening conditions, so that the number of new energy vehicles in the clustering object cluster falls within the range of the number of clustering objects formed by the upper limit of the number of clustering objects and the lower limit of the number of clustering objects.

[0007] Further, the step of sequentially using the vehicle type, geographical location and charging and discharging time of the monitored object as screening conditions to obtain the cluster of clustered objects in the new energy vehicle list specifically includes: Using the model of the monitored object as a screening condition, the new energy vehicle list is screened to obtain a first candidate object list; Determine whether the number of new energy vehicles in the first candidate object list falls within the number range of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects; When the number of new energy vehicles in the first candidate object list is greater than the upper limit of the number of cluster objects, the first candidate object list is screened to obtain a second candidate object list by taking the geographical location of the monitored object as a screening condition; Determine whether the number of new energy vehicles in the second candidate object list falls within the number range of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects; When the number of new energy vehicles in the second candidate object list is greater than the upper limit of the number of clustering objects, a clustering object cluster falling within the range of the number of clustering objects formed by the upper limit of the number of clustering objects and the lower limit of the number of clustering objects is screened based on the deviation between the most recent charging and discharging time of the new energy vehicles in the second candidate object list and the current time.

[0008] Further, the step of selecting a cluster of cluster objects that falls within the range of the number of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects based on the deviation between the most recent charge and discharge time of the new energy vehicle in the second candidate object list and the current time specifically includes: Determining a target number of clustering objects between the upper limit of the number of clustering objects and the lower limit of the number of clustering objects; Calculate the deviation between the most recent charge and discharge time of the new energy vehicle in the second candidate object list and the current time; sorting the new energy vehicles in the second candidate list according to the deviation between the latest charge and discharge time of the new energy vehicles in the second candidate list and the current time from small to large; New energy vehicles whose number is equal to the number of the target clustering objects are intercepted from the reordered second candidate object list to construct the clustering object cluster.

[0009] Furthermore, before the step of clustering the charging and discharging parameters of the new energy vehicles in the clustering object cluster, the method further includes: Determine the key time nodes of the entire charging and discharging process of the new energy vehicle, wherein the key time nodes include the two end nodes of the charging and discharging process; Scaling and aligning the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process based on the key time nodes; Regenerate the time data in the time data series of new energy vehicles.

[0010] Furthermore, the step of scaling and aligning the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process based on the key time node specifically includes: The charging and discharging parameters that the monitored object has uploaded during the current charging and discharging process; Predicting the next critical time node of the monitored object; Based on the known key time nodes of the charging and discharging parameters uploaded by the monitored object and the predicted next key time nodes, the charging and discharging parameters of the monitored object are scaled and aligned with the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process.

[0011] Furthermore, the step of clustering the charging and discharging parameters of the new energy vehicles in the clustering object cluster specifically includes: Get the pre-configured clustering analysis period; Constructing a cluster analysis data set of the cluster object clusters in each cluster analysis cycle; Mapping the charge and discharge parameters in the cluster analysis data set to a vector space; Cluster analysis is performed on the charge and discharge parameters in the cluster analysis data set in the vector space.

[0012] Furthermore, the step of constructing the clustering analysis data set of the clustering object cluster in each clustering analysis cycle specifically includes: In each cluster analysis cycle, a cluster analysis time point is determined to perform the following steps: Acquire a first charge and discharge parameter of the monitored object at the cluster analysis time point; Mapping the cluster analysis time point to the time data of the monitored object after scaling and alignment to determine a standardized analysis time point; Determine the relative position relationship between the standardized analysis time point and the preceding and following key time nodes in the time data after scaling and alignment of the monitoring object; Acquire, according to the relative position relationship, second charging and discharging parameters of other new energy vehicles in the cluster object cluster except the monitoring object corresponding to the standardized analysis time point; The first charge and discharge parameter and the second charge and discharge parameter are combined into a cluster analysis data set.

[0013] Furthermore, the step of identifying safety anomalies in the charging and discharging process of the monitored object according to the clustering result specifically includes: Mapping the first charge and discharge parameter to a vector in the vector space and determining it as a target vector; Determine whether the target vector is a discrete point after clustering in the vector space, or whether the number of other vectors included in the cluster to which the target vector belongs is less than a preset value; When the target vector is a discrete point after clustering in the vector space, or the number of other vectors included in the cluster to which the target vector belongs is less than a preset value, it is determined that a safety anomaly occurs in the charging and discharging process of the monitored object.

[0014] The present invention proposes a charging and discharging safety monitoring system and method for new energy vehicle batteries. The charging and discharging safety monitoring system includes a cloud server for providing charging and discharging safety monitoring services for new energy vehicle batteries, and a plurality of new energy vehicles connected to the cloud server for communication. The new energy vehicles upload real-time charging and discharging data to the cloud server, so that the cloud server performs charging and discharging safety monitoring on the new energy vehicles based on the charging and discharging data. The cloud server is configured to perform cluster analysis on a cluster of new energy vehicles connected thereto that have the same or similar intrinsic charging and discharging properties and the same or similar external charging and discharging environmental conditions, so as to perform safety anomaly identification on the charging and discharging process of the new energy vehicle according to the cluster analysis results, and can timely and accurately predict the charging and discharging anomalies of the new energy vehicle batteries, thereby fully and effectively protecting people's personal and property safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of a charging and discharging safety monitoring system for a new energy vehicle battery provided by an embodiment of the present invention; Figure 2 It is a flow chart of a method for monitoring the charging and discharging safety of a new energy vehicle battery provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] In the description of the present invention, the term "multiple" refers to two or more. Unless otherwise clearly defined, the orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation of the present invention. The terms "connection", "installation", "fixation", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", etc. can explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "multiple" is two or more.

[0019] In the description of this specification, the description of the terms "one embodiment", "some implementations", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0020] A new energy vehicle battery charge and discharge safety monitoring system and method provided according to some embodiments of the present invention will be described below with reference to the accompanying drawings.

[0021] like Figure 1 As shown, the first aspect of the present invention proposes a new energy vehicle battery charge and discharge safety monitoring system, including a cloud server for providing new energy vehicle battery charge and discharge safety monitoring services, and a number of new energy vehicles communicatively connected to the cloud server, the new energy vehicles upload real-time charge and discharge data to the cloud server, so that the cloud server performs charge and discharge safety monitoring on the new energy vehicles based on the charge and discharge data, the cloud server is configured to perform cluster analysis on a cluster of new energy vehicles connected to it that have the same or similar intrinsic charge and discharge properties, and the same or similar external charge and discharge environmental conditions, so as to identify safety anomalies in the charge and discharge process of the new energy vehicles according to the cluster analysis results.

[0022] Specifically, the cloud server is a server device that provides cloud services to the Internet, and a charging and discharging safety monitoring service program that provides charging and discharging safety monitoring services for the connected new energy vehicles runs on the cloud server. The new energy vehicle accesses the Internet through 5G or other Internet of Things communication networks to establish a communication connection with the cloud server.

[0023] In the technical solutions of some embodiments of the present invention, after the new energy vehicle establishes a communication connection with the cloud server, it registers in the charging and discharging safety monitoring service program to obtain an identity assigned by the cloud server to uniquely represent the new energy vehicle.

[0024] In the technical solutions of other embodiments of the present invention, the identity identifier is a unique identity identifier pre-configured in the storage module of the new energy vehicle before leaving the factory. The new energy vehicle interacts with the charging and discharging safety monitoring service program in the cloud server based on the unique identity identifier without the need for registration, allocation and binding after leaving the factory.

[0025] The intrinsic charge and discharge properties are intrinsic properties related to the charge and discharge of new energy vehicle batteries, including but not limited to one or more of the rated capacity, rated power and rated voltage of new energy vehicle batteries. When we say that two new energy vehicles have the same intrinsic charge and discharge properties, it means that all the intrinsic charge and discharge properties of the two new energy vehicles are the same. When we say that two new energy vehicles have similar intrinsic charge and discharge properties, it means that some of the intrinsic charge and discharge properties of the two new energy vehicles are the same, and the difference between the different intrinsic charge and discharge properties is less than a preset threshold.

[0026] The external charging and discharging environmental conditions are external environmental conditions related to the charging and discharging of new energy vehicles, including but not limited to one or more of geographical location, ambient temperature and air humidity, wherein the geographical location is used to reflect the power grid status, charging equipment status and other external environmental conditions related to charging and discharging that are difficult to detect and quantify at the location of the new energy vehicle. Similarly, when we say that two new energy vehicles have the same external charging and discharging environmental conditions, it means that all external charging and discharging environmental conditions of the two new energy vehicles are the same. When we say that two new energy vehicles have similar external charging and discharging environmental conditions, it means that some of the external charging and discharging environmental conditions of the two new energy vehicles are the same, and the difference between the different external charging and discharging environmental conditions is less than a preset threshold.

[0027] In the technical solution of the above-mentioned implementation mode, when the monitoring object is a new energy vehicle that is undergoing fast charging, the so-called latest charging and discharging behavior of the new energy vehicle refers to the latest fast charging behavior of the new energy vehicle. Similarly, when the monitoring object is a new energy vehicle that is undergoing fast discharging, the so-called latest charging and discharging behavior of the new energy vehicle refers to the latest fast discharging behavior of the new energy vehicle.

[0028] Furthermore, the cloud server in the new energy vehicle battery charge and discharge safety monitoring system is configured to implement the new energy vehicle battery charge and discharge safety monitoring method provided in the second aspect of the present invention.

[0029] like Figure 2 As shown, the second aspect of the present invention proposes a charging and discharging safety monitoring method for a new energy vehicle battery, comprising: Determine a new energy vehicle to be monitored as a monitoring object, wherein the monitoring object is a new energy vehicle that is undergoing fast charging or fast discharging; Acquiring real-time charge and discharge parameters of the monitored object, wherein the charge and discharge parameters include one or more of a charge and discharge current, a charge and discharge voltage, and a charge and discharge power of a power battery of the monitored object; Determining a cluster object cluster of the monitoring object in the new energy vehicle list, each new energy vehicle in the cluster object cluster has the same or similar intrinsic charging and discharging properties as the monitoring object, and the same or similar external charging and discharging environmental conditions; Acquire the charging and discharging parameters of each new energy vehicle in the cluster object cluster, wherein the charging and discharging parameters are the charging and discharging parameters of the most recent charging and discharging behavior of the new energy vehicles in the cluster object cluster; Clustering the associated charging and discharging parameters of the new energy vehicles in the clustering object cluster; The charging and discharging process of the monitored object is identified for safety anomalies based on the clustering results.

[0030] Specifically, the new energy vehicle uploads the charging and discharging data to the cloud server in real time during the charging and discharging process. The cloud server determines whether the new energy vehicle is undergoing fast charging or fast discharging based on the charging and discharging data. The new energy vehicle is undergoing fast charging, which means that the new energy vehicle is charged using the fast charging mode, that is, the process of fast charging with a large charging current through a high-power charging pile. The new energy vehicle is undergoing fast discharging, which means that the new energy vehicle is discharged with a large output power in order to obtain greater output power. The high power, large charging current, and large output power mentioned above are all judged using pre-configured empirical values.

[0031] In the technical solution of the above implementation mode, since the risk factor of low-power charging or low-power discharging is low, the safety monitoring system of the new energy vehicle itself is sufficient to cope with it, so the cloud server only performs safety monitoring on the fast charging and fast discharging of the new energy vehicle.

[0032] Preferably, the charging and discharging parameters include but are not limited to one or more of the charging and discharging current, charging and discharging voltage and charging and discharging power of each single cell in the power battery of the new energy vehicle.

[0033] Furthermore, the step of obtaining the real-time charging and discharging parameters of the monitored object specifically includes: Determining a current charging and discharging behavior of the monitored object, wherein the charging and discharging behavior includes a fast charging behavior and a fast discharging behavior; Acquiring charging and discharging data of the current charging and discharging behavior of the monitored object, wherein the charging and discharging data includes charging and discharging parameters of the charging and discharging behavior and corresponding sampling time; The charge and discharge data of the monitored object are constructed as a time data sequence consisting of discrete charge and discharge parameters and their sampling times.

[0034] The new energy vehicle list is a collection of new energy vehicles that are bound to the cloud server and are assigned unique identities. The new energy vehicles in the new energy vehicle list implement charge and discharge safety monitoring through the charge and discharge safety monitoring service program running in the cloud server. The cluster of clustered objects is a cluster of new energy vehicles that includes the monitoring object, and the cluster of clustered objects is a subset of the new energy vehicle list.

[0035] Furthermore, the step of determining the clustering object cluster of the monitoring object in the new energy vehicle list specifically includes: Configuring an upper bound on the number of cluster objects and a lower bound on the number of cluster objects for limiting the number of cluster objects in the cluster of cluster objects; Obtain the vehicle type, geographical location, and charging and discharging time of the monitored object; The clustering object cluster is obtained by sequentially screening the new energy vehicle list using the vehicle type, geographical location and charging and discharging time of the monitored object as screening conditions, so that the number of new energy vehicles in the clustering object cluster falls within the range of the number of clustering objects formed by the upper limit of the number of clustering objects and the lower limit of the number of clustering objects.

[0036] Specifically, the upper bound of the number of cluster objects and the lower bound of the number of cluster objects together constitute a cluster object number range including the upper bound of the number of cluster objects and the lower bound of the number of cluster objects, and the number of new energy vehicles in the cluster object cluster falls within the cluster object number range.

[0037] Further, the step of sequentially using the vehicle type, geographical location and charging and discharging time of the monitored object as screening conditions to obtain the cluster of clustered objects in the new energy vehicle list specifically includes: Using the model of the monitored object as a screening condition, the new energy vehicle list is screened to obtain a first candidate object list; Determine whether the number of new energy vehicles in the first candidate object list falls within the cluster object number range formed by the cluster object number upper bound and the cluster object number lower bound; When the number of new energy vehicles in the first candidate object list is greater than the upper limit of the number of cluster objects, the first candidate object list is screened to obtain a second candidate object list by taking the geographical location of the monitored object as a screening condition; Determine whether the number of new energy vehicles in the second candidate object list falls within the cluster object number range formed by the cluster object number upper bound and the cluster object number lower bound; When the number of new energy vehicles in the second candidate object list is greater than the upper limit of the number of clustering objects, a clustering object cluster falling within the range of the number of clustering objects formed by the upper limit of the number of clustering objects and the lower limit of the number of clustering objects is screened based on the deviation between the most recent charging and discharging time of the new energy vehicles in the second candidate object list and the current time.

[0038] In the technical solution of the above-mentioned implementation mode, the new energy vehicles in the first candidate object list are new energy vehicles of the same model as the monitored object.

[0039] Further, after the step of determining whether the number of new energy vehicles in the first candidate object list falls within the range of the number of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects, the method further includes: When the number of new energy vehicles in the first candidate object list is less than the lower limit of the number of cluster objects, it is determined that the monitored object does not have the conditions for identifying safety anomalies in its charging and discharging process through cluster analysis.

[0040] In the technical solutions of other embodiments of the present invention, when the number of new energy vehicles in the first candidate object list is less than the lower bound of the number of cluster objects, new energy vehicles with the same type of power battery as the monitored object can be determined in the new energy vehicle list and added to the first candidate object list. However, in addition to the power battery itself, other components of new energy vehicles such as the electrical system and the power system also constitute intrinsic factors that affect the safety of its charging and discharging. Therefore, when the new energy vehicles with the same type of power battery as the monitored object are added to the first candidate object list and cluster analysis is performed, the reliability of the analysis results obtained for identifying safety anomalies in the charging and discharging process of new energy vehicles is low.

[0041] Further, after the step of determining whether the number of new energy vehicles in the second candidate object list falls within the range of the number of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects, the method further includes: When the number of new energy vehicles in the second candidate object list is less than the lower limit of the number of clustering objects, a clustering object cluster falling within the range of the number of clustering objects formed by the upper limit of the number of clustering objects and the lower limit of the number of clustering objects is screened based on the deviation between the most recent charging and discharging time of the new energy vehicles in the first candidate object list and the current time.

[0042] In the technical solution of the above-mentioned implementation mode, when the monitoring object is a new energy vehicle that is undergoing fast charging, the so-called latest charging and discharging time of the new energy vehicle refers to the time of the latest fast charging of the new energy vehicle. Similarly, when the monitoring object is a new energy vehicle that is undergoing fast discharging, the so-called latest charging and discharging time of the new energy vehicle refers to the time of the latest fast discharging of the new energy vehicle.

[0043] Further, the step of selecting a cluster of cluster objects that falls within the range of the number of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects based on the deviation between the most recent charge and discharge time of the new energy vehicle in the first candidate object list and the current time specifically includes: Determining a target number of clustering objects between the upper limit of the number of clustering objects and the lower limit of the number of clustering objects; Calculating the difference between the number of the target cluster objects and the number of new energy vehicles in the second candidate object list; Using the new energy vehicles in the first candidate list that are not included in the second candidate list to construct a third candidate list; Based on the deviation between the most recent charge and discharge time of the new energy vehicles in the third candidate object list and the current time, a number of new energy vehicles equal to the difference is screened to construct a fourth candidate object list; The new energy vehicles in the fourth candidate object list and the second candidate object list are merged into the cluster object cluster.

[0044] Preferably, the target number of clustering objects is an average value of an upper limit of the number of clustering objects and a lower limit of the number of clustering objects.

[0045] Furthermore, based on the deviation between the most recent charge and discharge time of the new energy vehicles in the third candidate object list and the current time, the step of selecting new energy vehicles of a number equal to the difference to construct the fourth candidate object list specifically includes: Calculate the deviation between the most recent charge and discharge time of the new energy vehicle in the third candidate object list and the current time; Sort the new energy vehicles in the third candidate list according to the deviation between the latest charge and discharge time of the new energy vehicles in the third candidate list and the current time from small to large; New energy vehicles whose number is equal to the difference are intercepted from the reordered third candidate object list to construct the fourth candidate object list.

[0046] Further, the step of selecting a cluster of cluster objects that falls within the range of the number of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects based on the deviation between the most recent charge and discharge time of the new energy vehicle in the second candidate object list and the current time specifically includes: Determining a target number of clustering objects between the upper limit of the number of clustering objects and the lower limit of the number of clustering objects; Calculate the deviation between the most recent charge and discharge time of the new energy vehicle in the second candidate object list and the current time; sorting the new energy vehicles in the second candidate list according to the deviation between the latest charge and discharge time of the new energy vehicles in the second candidate list and the current time from small to large; New energy vehicles whose number is equal to the number of the target clustering objects are intercepted from the reordered second candidate object list to construct the clustering object cluster.

[0047] Furthermore, the step of obtaining the charging and discharging parameters of each new energy vehicle in the cluster object cluster specifically includes: Determining a current charging and discharging behavior of the monitored object, wherein the charging and discharging behavior includes a fast charging behavior and a fast discharging behavior; Traverse each new energy vehicle in the cluster object cluster to perform the following steps: Determine the currently traversed new energy vehicle as the target new energy vehicle; Acquire charging and discharging data of the target new energy vehicle having the same charging and discharging behavior as the current charging and discharging behavior of the monitored object, wherein the charging and discharging data includes charging and discharging parameters of the charging and discharging behavior and corresponding sampling time; The charging and discharging data of the target new energy vehicle is constructed as a time data sequence consisting of discrete charging and discharging parameters and their sampling times.

[0048] Specifically, the real-time charging and discharging data reported by the new energy vehicle to the cloud server during the charging and discharging process includes the value of its charging and discharging parameters and the sampling time corresponding to each data. In the technical solution of the above-mentioned implementation mode, the step of obtaining the charging and discharging data of the target new energy vehicle having the same charging and discharging behavior as the current charging and discharging behavior of the monitored object specifically includes obtaining the charging and discharging parameters of the target new energy vehicle corresponding to the charging and discharging behavior, and the sampling time corresponding to each value of the charging and discharging parameters.

[0049] The time data sequence is composed of several data elements, each of which is composed of a discrete charge and discharge parameter value and its corresponding sampling time. Exemplarily, during a rapid discharge process of the target new energy vehicle, a total of n discrete discharge power values ​​are collected, which correspond to n sampling times. Therefore, the time data sequence corresponding to the rapid discharge process of the target new energy vehicle is composed of n data elements, each of which includes a key-value pair consisting of a discharge power value and its sampling time.

[0050] Furthermore, before the step of clustering the charging and discharging parameters of the new energy vehicles in the clustering object cluster, the method further includes: Determine the key time nodes of the entire charging and discharging process of the new energy vehicle, wherein the key time nodes include the two end nodes of the charging and discharging process; Scaling and aligning the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process based on the key time nodes; Regenerate the time data in the time data series of new energy vehicles.

[0051] Specifically, when the charging and discharging process is a fast charging process, it has several fixed key time nodes, including the constant current charging start node for switching from pre-charging to constant current charging, the constant voltage charging start node for switching from constant current charging to constant voltage charging, the floating charging start node for switching from constant voltage charging to floating charging, etc., and also includes the charging start node and charging end node in the entire charging process. Since the discharge parameters usually do not have a fixed change pattern during the discharge process, when the charging and discharging process is a fast discharge process, it only includes two fixed key time nodes, the discharge start node and the discharge end node of the entire discharge process.

[0052] Furthermore, the step of scaling and aligning the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process based on the key time node specifically includes: Determining fixed key time nodes in the charging and discharging process; The charging parameters between two adjacent fixed time nodes are scaled respectively so as to align the key time nodes in the charging and discharging process of the new energy vehicles in the cluster.

[0053] Before scaling and alignment, the time data in the time data sequence corresponding to the charging and discharging parameters of new energy vehicles is the sampling time corresponding to each charging and discharging parameter value, that is, it is a natural time with information such as year, month, day, hour, minute, and second. Of course, according to different accuracy requirements, it can be accurate to milliseconds or other time units.

[0054] After scaling and alignment, the time data regenerated in the time data sequence of the new energy vehicle is the standardized time data, which can be a time series calculated from 0 in milliseconds. After scaling and alignment, the time data in the charging and discharging parameters of all new energy vehicles in the cluster object cluster have the same start time and end time. When the charging and discharging process is a fast charging process, the time corresponding to several fixed key time nodes in the middle is also the same.

[0055] Furthermore, the step of scaling and aligning the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process based on the key time node specifically includes: The charging and discharging parameters that the monitored object has uploaded during the current charging and discharging process; Predicting the next critical time node of the monitored object; Based on the known key time nodes of the charging and discharging parameters uploaded by the monitored object and the predicted next key time nodes, the charging and discharging parameters of the monitored object are scaled and aligned with the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process.

[0056] Furthermore, in the step of predicting the next critical time node of the monitored object, the charge and discharge parameters of the monitored object are predicted by an artificial intelligence model based on deep learning, so as to determine the next critical time node of the monitored object according to the predicted charge and discharge parameters. More specifically, the historical charge and discharge data of the monitored object or other new energy vehicles can be used as sample data, and the artificial intelligence prediction model for predicting charge and discharge parameters can be trained by deep learning technology.

[0057] In the technical solution of the above-mentioned implementation mode, the known key time node is a key time node included in the charge and discharge parameters of the monitored object that have been uploaded to the cloud server. The next key time node is a key time node that is not included in the charge and discharge parameters of the monitored object that have been uploaded to the cloud server after the last key time node in the known key time node, and can be identified from the charge and discharge parameters predicted by the artificial intelligence model.

[0058] Furthermore, the step of clustering the charging and discharging parameters of the new energy vehicles in the clustering object cluster specifically includes: Get the pre-configured clustering analysis period; Constructing a cluster analysis data set of the cluster object clusters in each cluster analysis cycle; Mapping the charge and discharge parameters in the cluster analysis data set to a vector space; Cluster analysis is performed on the charge and discharge parameters in the cluster analysis data set in the vector space.

[0059] In the technical scheme of the above-mentioned implementation mode, according to specific implementation needs, based on considerations such as the number of monitored new energy vehicles, the probability of abnormal charging and discharging of new energy vehicles, and the performance and load pressure of the cloud server, the clustering analysis cycle can be configured to an appropriate length.

[0060] The cluster analysis data set is a data set consisting of the charging and discharging parameters of each new energy vehicle in the cluster object cluster at a specific time point within a cluster analysis cycle.

[0061] The vector space is a multidimensional vector space, and the number of its dimensions is related to the number of charge and discharge parameters collected during the charge and discharge process. Taking the fast charging process as an example, the charge and discharge parameters are composed of three parameters: charging power, charging voltage, and charging current. The vector space is a three-dimensional vector space composed of three dimensions: charging power dimension, charging voltage dimension, and charging current dimension.

[0062] Furthermore, the step of constructing the clustering analysis data set of the clustering object cluster in each clustering analysis cycle specifically includes: In each cluster analysis cycle, a cluster analysis time point is determined to perform the following steps: Acquire a first charge and discharge parameter of the monitored object at the cluster analysis time point; Mapping the cluster analysis time point to the time data of the monitored object after scaling and alignment to determine a standardized analysis time point; Determine the relative position relationship between the standardized analysis time point and the preceding and following key time nodes in the time data after scaling and alignment of the monitored object; Acquire, according to the relative position relationship, second charging and discharging parameters of other new energy vehicles in the cluster object cluster except the monitoring object corresponding to the standardized analysis time point; The first charge and discharge parameter and the second charge and discharge parameter are combined into a cluster analysis data set.

[0063] Specifically, the cluster analysis time point can be any time point during a cluster analysis cycle, and the starting time point of each cluster analysis cycle can usually be used as the cluster analysis time point. Similarly, the cluster analysis time point is a natural time, and the standardized analysis time point is the cluster analysis time point mapped to the corresponding time point in the standardized time data after scaling the object.

[0064] Furthermore, in the step of determining the relative position relationship between the standardized analysis time point and the previous and next key time nodes in the time data after scaling and alignment of the monitored object, the relative position relationship can be the ratio of the absolute value of the difference between the standardized analysis time point and the previous and next key time nodes.

[0065] Furthermore, the step of identifying safety anomalies in the charging and discharging process of the monitored object according to the clustering result specifically includes: Mapping the first charge and discharge parameter to a vector in the vector space and determining it as a target vector; Determine whether the target vector is a discrete point after clustering in the vector space, or whether the number of other vectors included in the cluster to which the target vector belongs is less than a preset value; When the target vector is a discrete point after clustering in the vector space, or the number of other vectors included in the cluster to which the target vector belongs is less than a preset value, it is determined that a safety anomaly occurs in the charging and discharging process of the monitored object.

[0066] Specifically, when the target vector cannot be classified into the same family as the vector mapped in the vector space by the second charging and discharging parameter of any new energy vehicle in the clustering object cluster, the target vector is regarded as a discrete point.

[0067] The other vectors referred to in the above implementation manner refer to the vectors mapped in the vector space by the second charging and discharging parameters of other new energy vehicles in the clustering object cluster except the monitoring object.

[0068] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0069] According to the embodiments of the present invention as described above, these embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made based on the above description. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can make good use of the present invention and the modified use based on the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A new energy vehicle battery charge and discharge safety monitoring system, characterized in that: It includes a cloud server for providing charging and discharging safety monitoring services for new energy vehicle batteries, and a number of new energy vehicles that are communicatively connected to the cloud server. The new energy vehicles upload real-time charging and discharging data to the cloud server, so that the cloud server performs charging and discharging safety monitoring on the new energy vehicles based on the charging and discharging data. The cloud server is configured to perform cluster analysis on new energy vehicle clusters connected to it that have the same or similar intrinsic charging and discharging properties and the same or similar external charging and discharging environmental conditions, so as to identify safety anomalies in the charging and discharging process of the new energy vehicles based on the cluster analysis results.

2. A method for monitoring the charging and discharging safety of a new energy vehicle battery, characterized in that: include: Determine a new energy vehicle to be monitored as a monitoring object, wherein the monitoring object is a new energy vehicle that is undergoing fast charging or fast discharging; Acquiring real-time charge and discharge parameters of the monitored object, wherein the charge and discharge parameters include one or more of a charge and discharge current, a charge and discharge voltage, and a charge and discharge power of a power battery of the monitored object; Determining a cluster object cluster of the monitoring object in the new energy vehicle list, each new energy vehicle in the cluster object cluster has the same or similar intrinsic charging and discharging properties as the monitoring object, and the same or similar external charging and discharging environmental conditions; Acquire the charging and discharging parameters of each new energy vehicle in the cluster object cluster, wherein the charging and discharging parameters are the charging and discharging parameters of the most recent charging and discharging behavior of the new energy vehicles in the cluster object cluster; Clustering the associated charging and discharging parameters of the new energy vehicles in the clustering object cluster; The charging and discharging process of the monitored object is identified for safety anomalies based on the clustering results.

3. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 2, characterized in that: The step of determining the cluster object cluster of the monitoring object in the new energy vehicle list specifically includes: Configuring an upper bound on the number of cluster objects and a lower bound on the number of cluster objects for limiting the number of cluster objects in the cluster of cluster objects; Obtain the vehicle type, geographical location, and charging and discharging time of the monitored object; The clustering object cluster is obtained by sequentially screening the new energy vehicle list using the vehicle type, geographical location and charging and discharging time of the monitored object as screening conditions, so that the number of new energy vehicles in the clustering object cluster falls within the range of the number of clustering objects formed by the upper limit of the number of clustering objects and the lower limit of the number of clustering objects.

4. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 3, characterized in that: The steps of sequentially selecting the model, geographical location and charging and discharging time of the monitored object in the new energy vehicle list to obtain the cluster of clustered objects specifically include: Using the model of the monitored object as a screening condition, the new energy vehicle list is screened to obtain a first candidate object list; Determine whether the number of new energy vehicles in the first candidate object list falls within the cluster object number range formed by the cluster object number upper bound and the cluster object number lower bound; When the number of new energy vehicles in the first candidate object list is greater than the upper limit of the number of cluster objects, the first candidate object list is screened to obtain a second candidate object list by taking the geographical location of the monitored object as a screening condition; Determine whether the number of new energy vehicles in the second candidate object list falls within the cluster object number range formed by the cluster object number upper bound and the cluster object number lower bound; When the number of new energy vehicles in the second candidate object list is greater than the upper limit of the number of clustering objects, a clustering object cluster falling within the range of the number of clustering objects formed by the upper limit of the number of clustering objects and the lower limit of the number of clustering objects is screened based on the deviation between the most recent charging and discharging time of the new energy vehicles in the second candidate object list and the current time.

5. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 4, characterized in that: The step of selecting a cluster of cluster objects that falls within the range of the number of cluster objects formed by the upper limit of the number of cluster objects and the lower limit of the number of cluster objects based on the deviation between the most recent charge and discharge time of the new energy vehicle in the second candidate object list and the current time specifically includes: Determining a target number of clustering objects between the upper limit of the number of clustering objects and the lower limit of the number of clustering objects; Calculate the deviation between the most recent charge and discharge time of the new energy vehicle in the second candidate object list and the current time; sorting the new energy vehicles in the second candidate list according to the deviation between the latest charge and discharge time of the new energy vehicles in the second candidate list and the current time from small to large; New energy vehicles whose number is equal to the number of the target clustering objects are intercepted from the reordered second candidate object list to construct the clustering object cluster.

6. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 2, characterized in that: Before the step of clustering the charging and discharging parameters of the new energy vehicles in the clustering object cluster, the step further includes: Determine the key time nodes of the entire charging and discharging process of the new energy vehicle, wherein the key time nodes include the two end nodes of the charging and discharging process; Scaling and aligning the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process based on the key time nodes; Regenerate the time data in the time data series of new energy vehicles.

7. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 6, characterized in that: The step of scaling and aligning the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process based on the key time node specifically includes: The charging and discharging parameters that the monitored object has uploaded during the current charging and discharging process; Predicting the next critical time node of the monitored object; Based on the known key time nodes of the charging and discharging parameters uploaded by the monitored object and the predicted next key time nodes, the charging and discharging parameters of the monitored object are scaled and aligned with the charging and discharging parameters of the new energy vehicles in the cluster during the charging and discharging process.

8. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 7, characterized in that: The step of clustering the charging and discharging parameters of the new energy vehicles in the clustering object cluster specifically includes: Get the pre-configured clustering analysis period; Constructing a cluster analysis data set of the cluster object clusters in each cluster analysis cycle; Mapping the charge and discharge parameters in the cluster analysis data set to a vector space; Cluster analysis is performed on the charge and discharge parameters in the cluster analysis data set in the vector space.

9. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 8, characterized in that: The steps of constructing the clustering analysis data set of the cluster object cluster in each clustering analysis cycle specifically include: In each cluster analysis cycle, a cluster analysis time point is determined to perform the following steps: Acquire a first charge and discharge parameter of the monitored object at the cluster analysis time point; Mapping the cluster analysis time point to the time data of the monitored object after scaling and alignment to determine a standardized analysis time point; Determine the relative position relationship between the standardized analysis time point and the preceding and following key time nodes in the time data after scaling and alignment of the monitoring object; Acquire, according to the relative position relationship, second charging and discharging parameters of other new energy vehicles in the cluster object cluster except the monitoring object corresponding to the standardized analysis time point; The first charge and discharge parameter and the second charge and discharge parameter are combined into a cluster analysis data set.

10. The method for monitoring the charging and discharging safety of a new energy vehicle battery according to claim 9, characterized in that: The step of identifying safety anomalies in the charging and discharging process of the monitored object according to the clustering result specifically includes: Mapping the first charge and discharge parameter to a vector in the vector space and determining it as a target vector; Determine whether the target vector is a discrete point after clustering in the vector space, or whether the number of other vectors included in the cluster to which the target vector belongs is less than a preset value; When the target vector is a discrete point after clustering in the vector space, or the number of other vectors included in the cluster to which the target vector belongs is less than a preset value, it is determined that a safety anomaly occurs in the charging and discharging process of the monitored object.

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