A method and system for managing battery temperature data

Abstract

The application relates to the technical field of data management, in particular to a battery temperature data management method and system. The method comprises the following steps: based on all battery temperature data in an original data set, at least two continuous battery temperature data of the same process are combined to obtain a pretreatment data group; based on the starting time and the ending time of each pretreatment data group, a first interval duration of each adjacent pretreatment data group is obtained; a preset duration threshold is acquired, and adjacent pretreatment data groups with a first interval duration less than the duration threshold are combined to obtain a target data group; the target data group is used for indicating the classification of the battery temperature data of the same process and continuity. The method can improve the management efficiency of the battery temperature data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, in particular to a battery temperature data management method and system. BACKGROUND

[0002] The battery is one of the core technologies of new energy vehicles, which determines the vehicle's range. Different types of batteries have different temperature requirements for use in different environments. Too high or too low temperature will directly or indirectly affect the charge and discharge performance, safety performance and service life of the battery. Therefore, it is necessary to monitor the temperature data of the battery of the new energy vehicle.

[0003] Generally, the battery temperature data is collected once every first interval, and then the collected battery temperature data is stored in the battery management system. However, during the collection and storage of the battery temperature data, part of the battery temperature data may jump due to network or system reasons.

[0004] For example, during a continuous "charging" process, there are occasional "discharging" and / or "standing" battery temperature data uploads. At this time, a continuous "charging" process includes "charging", "discharging" and / or "standing" battery temperature data. Since the obtained battery temperature data is from different processes, not the full data of the same process, errors are caused when the battery temperature data of the same process is counted or analyzed.

[0005] Therefore, the management efficiency of the battery temperature data in the prior art is low. SUMMARY

[0006] Therefore, a battery temperature data management method and system are provided to improve the management efficiency of the battery temperature data.

[0007] In a first aspect, a battery temperature data management method is provided, which includes:

[0008] Based on all battery temperature data in the original data set, at least two continuous battery temperature data of the same process are combined to obtain a preprocessed data group;

[0009] Based on the start time and end time of each preprocessed data group, a first interval of adjacent preprocessed data groups is obtained;

[0010] A preset time threshold is obtained, and adjacent preprocessed data groups with a first interval not exceeding the time threshold are combined to obtain a target data group;

[0011] The target data group is used to indicate the classification of the same process and continuous battery temperature data.

[0012] With reference to the first aspect, in a first implementation form of the first aspect, the combining the adjacent pre-processing data groups with the first interval time length not exceeding the time length threshold to obtain a target data group comprises:

[0013] determining whether the first interval time length exceeds the time length threshold;

[0014] if yes, inserting a preset first identification number into the subsequent pre-processing data group;

[0015] if no, inserting a preset second identification number into the subsequent pre-processing data group;

[0016] inserting the first identification number into the pre-processing data group with the earliest start time;

[0017] performing accumulation calculation based on the first identification number and / or the second identification number of the current pre-processing data group and all the preceding pre-processing data groups to obtain a third identification number, and inserting the third identification number into the current pre-processing data group;

[0018] combining the pre-processing data groups with the same third identification number to obtain the target data group.

[0019] With reference to the first aspect, in a second implementation form of the first aspect, the method further comprises:

[0020] determining whether the process corresponding to the target data group is ended;

[0021] if yes, storing the corresponding target data group as a target data set;

[0022] if no, using the corresponding target data group to count a target data set of a next period.

[0023] With reference to the second implementation form of the first aspect, in a third implementation form of the first aspect, the step of determining whether the process corresponding to the target data group is ended comprises:

[0024] determining whether the target data group is the last group;

[0025] if no, determining that the process corresponding to the target data group is ended, and inserting a preset fourth identification number into the target data group;

[0026] if yes, obtaining a second interval time length according to an end time of the target data group and a preset end counting time;

[0027] determining whether the second interval time length is greater than or equal to the time length threshold;

[0028] If greater than or equal to, it is judged that the process corresponding to the target data group ends, and the fourth identification number is inserted into the target data group;

[0029] If less than, it is judged that the process corresponding to the target data group does not end, and a preset fifth identification number is inserted into the target data group.

[0030] In combination with the first aspect, in a fourth implementation manner of the first aspect, the step of combining at least two pieces of battery temperature data of the same process to obtain a preprocessing data group comprises:

[0031] Obtaining collection time and battery temperature in the at least two pieces of battery temperature data of the same process;

[0032] According to the time sequence of the collection time, the battery temperatures are sorted to obtain a temperature list;

[0033] In all the collection time, the earliest collection time is taken as a start time of the temperature list, and the latest collection time is taken as an end time of the temperature list;

[0034] According to the start time, the end time, a vehicle identification code, a process and the temperature list, the preprocessing data group is generated.

[0035] In combination with the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the step of obtaining a first interval length of each adjacent preprocessing data group based on a start time and an end time of each preprocessing data group comprises:

[0036] Based on the preprocessing data groups of each process, the preprocessing data groups are classified according to the same process to obtain a preprocessing data set of each process;

[0037] Obtaining a start time and an end time of each preprocessing data group in the preprocessing data set;

[0038] According to the end time of a preprocessing data group in front and the start time of a preprocessing data group behind, the first interval length of each adjacent preprocessing data group is obtained according to the sequence of the start time.

[0039] In combination with the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the step of obtaining a first interval length of each adjacent preprocessing data group according to the sequence of the start time, the end time of a preprocessing data group in front and the start time of a preprocessing data group behind comprises:

[0040] Adding a first mark bit in the preprocessing data group;

[0041] According to the order of the starting time of each pre-processing data group, the ending time of the previous pre-processing data group is inserted into the first mark bit of the subsequent pre-processing data group, wherein the ending time of the previous pre-processing data group corresponding to the pre-processing data group with the earliest starting time is empty.

[0042] According to the ending time of the previous pre-processing data group and the starting time of the subsequent pre-processing data group, the first interval duration of each adjacent pre-processing data group is obtained.

[0043] With reference to the seventh implementation manner of the first aspect, in a seventh implementation manner of the first aspect, the pre-processing data group further comprises a second mark bit and a third mark bit, wherein the second mark bit is used to carry the first identification number or the second identification number, and the third mark bit is used to carry the third identification number.

[0044] With reference to the third implementation manner of the first aspect, in an eighth implementation manner of the first aspect, the pre-processing data group further comprises a fourth mark bit, wherein the fourth mark bit is used to carry the fourth identification number or the fifth identification number.

[0045] In a second aspect, a battery temperature data management system is provided, and the system comprises:

[0046] a data merging module, configured to merge at least two pieces of continuous battery temperature data of the same process based on all the battery temperature data in the original data set, to obtain a pre-processing data group;

[0047] an interval duration obtaining module, configured to obtain a first interval duration of each adjacent pre-processing data group based on the starting time and the ending time of each pre-processing data group;

[0048] a data group merging module, configured to obtain a preset duration threshold, and merge the adjacent pre-processing data groups whose first interval duration does not exceed the duration threshold, to obtain a target data group;

[0049] The target data group is used to indicate the classification of the battery temperature data of the same process and continuity.

[0050] The battery temperature data management method and system can be applied to new energy vehicles. Specifically, based on all battery temperature data in an original data set, at least two continuous battery temperature data of the same process are combined to obtain a pre-processed data group. Based on the start time and end time of all pre-processed data groups of the same process, a first interval duration of each adjacent pre-processed data group is obtained. A preset duration threshold is obtained, and adjacent pre-processed data groups with a first interval duration less than the duration threshold are combined to obtain a target data group. The target data group is used to indicate the classification of battery temperature data of the same process and continuity. It can be seen that when the jump data of other processes is included in the multiple continuous battery temperature data of the same process, the jump data can be filtered out, and the multiple discontinuous battery temperature data of the same process can be combined into a continuous target data group. The jump phenomenon of the battery temperature data in the prior art is improved, and the management efficiency of the battery temperature data is improved. On the other hand, by using the target data group, the user or the vehicle enterprise can count the process times and calculate the average battery temperature, the maximum / minimum battery temperature and the battery temperature variance, etc. The repeated calculation, omission and miscalculation caused by the jump phenomenon can be improved, and the calculation accuracy is improved. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A flowchart of a battery temperature data management method in an embodiment;

[0052] Figure 2 A flowchart of a battery temperature data management method in another embodiment;

[0053] Figure 3 A block diagram of a battery temperature data management system in an embodiment;

[0054] Figure 4 A block diagram of a battery temperature data management system in another embodiment;

[0055] Figure 5 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0057] It should be noted that the diagrams provided in the embodiments are only schematic and that all related components are not necessarily shown in the figures. The size of the components, the shapes of the components, the relative positioning of the components, and the like are not intended to be limiting. The drawings are intended to conceptualize the basic principles of the application.

[0058] The structures, proportions, sizes, etc. shown in the drawings attached to the present specification are only used to cooperate with the content disclosed in the present specification, so that those skilled in the art can understand and read, and are not intended to limit the conditions that can be implemented by the present application. Therefore, any modification of the structure, change of the proportional relationship, or adjustment of the size, which does not affect the effects and purposes that can be achieved by the present application, should still fall within the scope of the technical content disclosed by the present application.

[0059] The orientations or positional relationships indicated by the terms such as "upper", "lower", "left", "right", "intermediate", "vertical", "horizontal", "inner", "outer", "radial", "circumferential", etc. are based on the orientations or positional relationships shown in the drawings, and are only used to simplify the description and are not intended to indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0060] The battery temperature data management method provided by the present application can be applied to the application environment of the vehicle end and the server or cloud end. Among them, the vehicle end communicates with the server through the network. A temperature sensor can be arranged on the vehicle end, which collects and reports the battery temperature at a certain frequency. The server or cloud end receives the battery temperature, generates battery temperature data based on the battery temperature, and then stores it in the database. As shown in Table 1, the battery temperature data includes the collection time, the vehicle identification code (Vehicle Identification Number, VIN), the process, and the battery temperature.

[0061] In order to improve the management efficiency of the battery temperature data, the server or cloud end can filter out the battery temperature data of other processes existing in the plurality of continuous battery temperature data of the same process, and combine the plurality of discontinuous battery temperature data belonging to the same process into a continuous target data group, thereby improving the jump phenomenon of the battery temperature data in the prior art. On the other hand, the user or the vehicle enterprise can use the target data group to count the process times and calculate the average battery temperature, the maximum / minimum battery temperature, and the battery temperature variance, etc. The repeated calculation, omission, and miscalculation caused by the jump phenomenon can be improved, thereby improving the calculation accuracy.

[0062] Table 1 Partial original data set of a certain vehicle

[0063] Time of acquisition Vehicle identification number Process Battery temperature 2023-04-0110:20:00 VIN1 Charging 24.1 2023-04-0110:20:05 VIN1 Charging 24.2 2023-04-0110:20:10 VIN1 Charging 24.4 2023-04-0110:20:15 VIN1 Charging 24.6 2023-04-0110:20:20 VIN1 Charging 24.7 2023-04-0110:20:25 VIN1 Charging 24.9 2023-04-0110:20:30 VIN1 Charging 25.0 2023-04-0110:20:35 VIN1 Discharging 25.4 2023-04-0110:20:40 VIN1 Charging 25.5 2023-04-0110:20:45 VIN1 Charging 25.6 2023-04-0110:20:50 VIN1 Charging 25.6 2023-04-0110:20:55 VIN1 Charging 25.7 2023-04-0110:21:00 VIN1 Charging 25.5 2023-04-0110:21:05 VIN1 Charging 25.4 2023-04-0110:21:10 VIN1 Resting 24.7 2023-04-0123:58:50 VIN1 Charging 25.1 2023-04-0123:58:55 VIN1 Charging 25.2 2023-04-0123:59:00 VIN1 Charging 25.4 2023-04-0123:59:05 VIN1 Charging 25.6 2023-04-0123:59:10 VIN1 Charging 25.7 2023-04-0123:59:15 VIN1 Charging 25.9 2023-04-0123:59:20 VIN1 Discharging 26.0 2023-04-0123:59:25 VIN1 Charging 26.4 2023-04-0123:59:30 VIN1 Charging 26.5 2023-04-0123:59:35 VIN1 Charging 26.6 2023-04-0123:59:40 VIN1 Charging 26.6 2023-04-0123:59:45 Figure 1 Figure 2 26.7 2023-04-0123:59:50 Figures 1-2 Figures 1-2 26.5

[0064] It should be noted that "jump" refers to: in the process of collecting or uploading battery temperature to the server or cloud, due to network delay or other reasons, there may be battery temperature data of other processes in multiple continuous battery temperature data belonging to the same process. For example, Table 1 is a partial original data set of a certain vehicle with vehicle identification code VIN1, and there are battery temperature data of discharge process and / or static process in multiple continuous battery temperature data belonging to charging process.

[0065] In one embodiment, as shown in Figure 3 A battery temperature data management method is provided, which is applied to a server or cloud for example, and includes the following steps:

[0066] S101: Based on all battery temperature data in the original data set, at least two continuous battery temperature data of the same process are combined to obtain a preprocessed data set.

[0067] It should be noted that a temperature sensor can be provided in the battery system at the vehicle end, and the temperature sensor can be set with a collection frequency; the temperature sensor collects battery temperature at regular intervals according to the collection frequency, and then uploads the collected battery temperature to the server or cloud; after the server or cloud receives the battery temperature uploaded by the temperature sensor, a battery temperature data is generated, and the battery temperature data is stored in the database.

[0068] As shown in Table 1, one battery temperature data specifically includes the collection time of the battery temperature collected by the temperature sensor, the vehicle identification code, the process, and the battery temperature; the original temperature data set is used to indicate a collection of multiple battery temperature data; the vehicle identification code in the battery temperature data is used to identify the vehicle to which the battery temperature data belongs; the process can be charging, discharging, and static, etc.

[0069] In one embodiment, a database can be provided for each vehicle to store the battery temperature data of each vehicle; in another embodiment, two or more vehicles can share one database to store the battery temperature data of each vehicle. If the second embodiment is adopted, i.e., two or more vehicles share one database, before performing the steps of the method described in the present application, the battery temperature data of each vehicle in the database can be grouped according to the vehicle identification code, and the battery temperature data with the same vehicle identification code can be classified into the same data set, and then the steps of the method described in the present application can be performed on each vehicle data set.

[0070] As a specific implementation manner of the above step, the step of combining the battery temperature data of at least two continuous same processes to obtain a preprocessed data set comprises: acquiring the acquisition time and the battery temperature in the battery temperature data of at least two continuous same processes; sorting each battery temperature according to the time sequence of the acquisition time to obtain a temperature list; taking the earliest acquisition time as the starting time of the temperature list and taking the latest acquisition time as the ending time of the temperature list among all the acquisition times; and generating the preprocessed data set according to the starting time, the ending time, the vehicle identification code, the process and the temperature list.

[0071] An example is described by taking the local raw data set shown in Table 1 as an example. After the above combining step is performed on the battery temperature data in the local raw data set shown in Table 1, a local first data set of a certain vehicle can be obtained as shown in Table 2, wherein the first data set is used to indicate a set of preprocessed data sets. The battery temperature data in Table 1 is combined, and the corresponding preprocessed data set shown in Table 2 can be obtained. Each preprocessed data set is arranged from top to bottom in Table 2 as follows: a preprocessed data set of a charging process, a preprocessed data set of a discharging process, a preprocessed data set of a charging process, a preprocessed data set of a standing process, a preprocessed data set of a charging process, a preprocessed data set of a discharging process and a preprocessed data set of a charging process.

[0072] Table 2: Local first data set of a certain vehicle

[0073]

[0074] It should be noted that when the above combining step is performed, a parallel time sequence control mode or a serial time sequence control mode can be used, and the application does not limit this; but from the perspective of improving the data statistical rate, the parallel time sequence control mode is used in this embodiment, that is, the battery temperature data of continuous processes belonging to the same process is combined at the same time; in other embodiments, the serial time sequence control mode can also be used, which is not described here.

[0075] If a user or a vehicle enterprise needs to count the battery temperature data of a specified process, other processes except the specified process can be collectively referred to as other processes without distinguishing specific processes when the above combining step is performed. For example, if a user or a vehicle enterprise needs to count the battery temperature data of a charging process, the discharging process and the standing process can be collectively referred to as other processes.

[0076] S102: Based on the starting time and the ending time of each preprocessed data set, a first interval length of each adjacent preprocessed data set is obtained.

[0077] As a specific implementation of the above step, the step of obtaining the first interval duration of each adjacent preprocessing data group based on the start time and the end time of each preprocessing data group comprises: classifying the preprocessing data groups of each process according to the same process principle to obtain a preprocessing data set of each process; obtaining the start time and the end time of each preprocessing data group in the preprocessing data set; and obtaining the first interval duration of each adjacent preprocessing data group according to the end time of the preprocessing data group in front and the start time of the preprocessing data group behind in the order of the start time.

[0078] For example, the step of classifying the preprocessing data groups of each process according to the same process principle to obtain a preprocessing data set of each process refers to the above-mentioned local first data set of a certain vehicle shown in Table 2. In Table 2, one preprocessing data group of the discharge process, one preprocessing data group of the standing process and one preprocessing data group of the discharge process are sequentially inserted in the four preprocessing data groups of the charging process; therefore, the preprocessing data groups belonging to the same process can be classified into the same preprocessing data set according to the process; for example, the preprocessing data groups of the discharge process and the preprocessing data groups of the standing process are screened, and the four preprocessing data groups of the discharge process are classified into a preprocessing data set, which is shown in the local second data set of a certain vehicle in Table 3. The second data set is a preprocessing data set of the same process.

[0079] It should be noted that the preprocessing data group in front refers to the preprocessing data group arranged in front in each adjacent two preprocessing data groups; similarly, the preprocessing data group behind refers to the preprocessing data group arranged behind in each adjacent two preprocessing data groups. When the above-mentioned classification step is performed, parallel time sequence control mode or serial time sequence control mode can be used, which is not limited in the present application; however, from the perspective of improving the data statistical rate, the parallel time sequence control mode is used in the present embodiment, that is, the preprocessing data groups belonging to the same process are classified into the same preprocessing data set at the same time; in other embodiments, the serial time sequence control mode can also be used, which is not described herein.

[0080] Table 3 Local second data set of a certain vehicle

[0081]

[0082] Further, the step of obtaining the first interval duration of each adjacent preprocessing data group according to the end time of the previous preprocessing data group and the start time of the subsequent preprocessing data group in the order of the start times comprises: adding a first mark bit in the preprocessing data group; inserting the end time of the previous preprocessing data group into the first mark bit of the subsequent preprocessing data group in the order of the start times, wherein the end time of the previous preprocessing data group corresponding to the preprocessing data group with the earliest start time is empty; and obtaining the first interval duration of each adjacent preprocessing data group according to the end time of the previous preprocessing data group and the start time of the subsequent preprocessing data group.

[0083] For example, the partial second data set of the vehicle shown in Table 3 is taken as an example to illustrate that after the end time of the previous preprocessing data group is inserted, the partial third data set of the vehicle shown in Table 4 can be obtained, wherein the third data set is the preprocessing data set after the end time of the previous preprocessing data group is inserted in Table 3.

[0084] For example, the partial third data set of the vehicle shown in Table 4 is taken as an example to illustrate that the step of obtaining the first interval duration of each adjacent preprocessing data group according to the end time of the previous preprocessing data group and the start time of the subsequent preprocessing data group means that in Table 4, according to the time of the first mark bit and the start time in the second group of preprocessing data groups, the first interval duration of the first group of preprocessing data groups and the second group of preprocessing data groups is 20 seconds; by analogy, the first interval duration of the second group of preprocessing data groups and the third group of preprocessing data groups is 13 hours 37 minutes and 45 seconds, and the first interval duration of the third group of preprocessing data groups and the fourth group of preprocessing data groups is 10 seconds.

[0085] Table 4 partial third data set of the vehicle

[0086]

[0087] It should be noted that the first mark bit can be located at any position in Table 4, that is, the end time of the previous preprocessing data group can be inserted at any position in the subsequent preprocessing data group, and the position of the first mark bit shown in Table 4 for carrying the end time of the previous preprocessing data group is only an example, which is not limited in the present application.

[0088] S103: obtaining a preset duration threshold, merging adjacent preprocessing data groups with the first interval duration not exceeding the duration threshold to obtain a target data group; wherein the target data group is used to indicate the classification of the same process and continuous battery temperature data.

[0089] As a specific implementation manner of the above step, the step of combining the adjacent pre-processing data groups with the first interval time length not exceeding the time length threshold to obtain a target data group comprises: judging whether the first interval time length exceeds the time length threshold; if yes, inserting a preset first identification number into the subsequent pre-processing data group; if no, inserting a preset second identification number into the subsequent pre-processing data group; inserting the first identification number into the pre-processing data group with the earliest starting time; based on the first identification number and / or the second identification number of the current pre-processing data group and all the preceding pre-processing data groups, performing accumulation calculation to obtain a third identification number, and inserting the third identification number into the current pre-processing data group; combining the pre-processing data groups with the same third identification number to obtain the target data group.

[0090] Further, the pre-processing data group further comprises a second marker bit and a third marker bit, wherein the second marker bit is used to carry the first identification number or the second identification number, and the third marker bit is used to carry the third identification number.

[0091] For example, the time length threshold can be set according to the battery temperature change corresponding to each process, for example, the time length threshold can be set to 20 seconds during the charging process. The first identification number is used to identify that the first interval time length exceeds the time length threshold, and the second identification number is used to identify that the first interval time length does not exceed the time length threshold. For example, the first identification number can be set to "1", and the second identification number can be set to "0". In other embodiments, the first identification number and the second identification number can also be set to other numerical values as long as they can be distinguished, which is not limited in the present application, and will not be exemplified one by one here.

[0092] Taking the partial third data set of a certain vehicle shown in Table 4 as an example, since the first interval time length of the first group of pre-processing data groups and the second group of pre-processing data groups is 10 seconds, which does not exceed 20 seconds, "0" is inserted into the second marker bit of the second group of pre-processing data groups. In turn, "1" is inserted into the second marker bit of the third group of pre-processing data groups, and "0" is inserted into the second marker bit of the fourth group of pre-processing data groups. It can be considered that the first group of pre-processing data groups is the battery temperature collected during the first charging of the current charging process, and therefore, the first interval time length thereof exceeds the time length threshold of the last charging process, and the first identification number with the numerical value "1" is inserted into the second marker bit thereof. After the above steps are performed, the partial fourth data set of a certain vehicle shown in Table 5 can be obtained, wherein the fourth data set is used to indicate the collection of pre-processing data groups after the first identification number or the second identification number and the third identification number are inserted.

[0093] Table 5 Partial fourth data set of a certain vehicle

[0094]

[0095] Taking the first identification number or the second identification number of the second marked bit in Table 5 as an example, based on the first identification number and / or the second identification number of the current preprocessing data set and all previous preprocessing data sets, a third identification number is obtained by cumulative calculation, and the step of inserting the third identification number into the current preprocessing data set means that the second marked bit in the first preprocessing data set is the first identification number with a value of "1", and the first preprocessing data set has no previous preprocessing data set, so the value of the third identification number is "1", and the third identification number with a value of "1" is inserted into the third marked bit of the first preprocessing data set.

[0096] The second marked bit in the second preprocessing data set is the second identification number with a value of "0", so the first identification number with a value of "1" of the first preprocessing data set and the second identification number with a value of "0" of the second preprocessing data set are added to obtain the third identification number with a value of "1", and the third identification number with a value of "1" is inserted into the third marked bit of the second preprocessing data set.

[0097] By analogy, the first identification number of the first preprocessing data set, the second identification number of the second preprocessing data set, and the first identification number of the third preprocessing data set are added to obtain the third identification number of the third preprocessing data set with a value of "2", and the third identification number with a value of "2" is inserted into the third marked bit of the third preprocessing data set.

[0098] The first identification number of the first preprocessing data set, the second identification number of the second preprocessing data set, the first identification number of the third preprocessing data set, and the second identification number of the fourth preprocessing data set are added to obtain the third identification number of the fourth preprocessing data set with a value of "2", and the third identification number with a value of "2" is inserted into the third marked bit of the fourth preprocessing data set.

[0099] It should be noted that the second marked bit and the third marked bit can be located at any position in Table 5, that is, the first identification number or the second identification number, and the third identification number can be inserted into any position in the preprocessing data set, and the positions of the second marked bit for carrying the first identification number or the second identification number and the third marked bit for carrying the third identification number shown in Table 5 are only examples, and the present application does not limit this.

[0100] Taking the local fourth data set of a certain vehicle shown in Table 5 as an example, the preprocessing data sets with the same third identification number are combined to obtain a collection of part of the target data set of a certain vehicle as shown in Table 6.

[0101] Table 6 Collection of part of the target data set of a certain vehicle

[0102]

[0103] The step specifically refers to: the third identification numbers of the first group of preprocessed data groups and the second group of preprocessed data groups are the same, and they are merged to obtain the corresponding target data group, wherein the end time of the second group of preprocessed data groups is taken as the end time of the target data group, and the battery temperatures in the temperature list of the second group of preprocessed data groups are sequentially arranged at the end of the temperature list of the first group of preprocessed data groups to form the list temperature of the target data group; the third identification numbers of the third group of preprocessed data groups and the fourth group of preprocessed data groups are the same, and they are merged to obtain the corresponding target data group, wherein the end time of the fourth group of preprocessed data groups is taken as the end time of the target data group, and the battery temperatures in the temperature list of the fourth group of preprocessed data groups are sequentially arranged at the end of the temperature list of the third group of preprocessed data groups to form the list temperature of the target data group.

[0104] Since the first mark bit and the second mark bit are used to assist in obtaining the target data group, when the preprocessed data groups are merged according to the third identification number, the contents of the first mark bit and the second mark bit can be removed. According to the above-mentioned merging step, it can be considered that the target data group obtained by merging the first group of preprocessed data groups and the second group of preprocessed data groups, and the target data group obtained by merging the third group of preprocessed data groups and the fourth group of preprocessed data groups, are respectively a set of battery temperatures continuous in a charging process.

[0105] In summary, when the server or the cloud generates and stores the battery temperature data received by the battery temperature, if there are multiple continuous battery temperature data belonging to the same process, and the battery temperature data of other processes jumps, through the battery temperature data management method of the present application, the jumping data of other processes can be filtered out, and multiple discontinuous battery temperature data belonging to the same process can be merged into a continuous target data group. The jumping phenomenon of the battery temperature data in the prior art is improved, and the management efficiency of the battery temperature data is improved. On the other hand, using the target data group, the user or the vehicle enterprise can count the process times and calculate the average battery temperature, the maximum / minimum battery temperature and the battery temperature variance, etc., which can improve the repeated calculation, omission and miscalculation phenomena caused by the jumping phenomenon, thereby improving the calculation accuracy.

[0106] In another embodiment, as shown in Figure 4 a preferred battery temperature data management method is provided, which is applied to the server or the cloud as an example for illustration, comprising the following steps:

[0107] S201: based on all battery temperature data in the original data set, at least two continuous battery temperature data of the same process are merged to obtain a preprocessed data group;

[0108] S202: Obtain a first interval duration of each adjacent preprocessed data group based on the start time and the end time of each preprocessed data group;

[0109] S203: Obtain a preset duration threshold, and combine adjacent preprocessed data groups with a first interval duration not exceeding the duration threshold to obtain a target data group; wherein the target data group is used to indicate the classification of battery temperature data of the same process and continuity;

[0110] S204: Determine whether the process corresponding to the target data group is ended;

[0111] S2051: If ended, store the corresponding target data group as a target data set;

[0112] S2052: If not ended, use the corresponding target data group to count the target data set of the next period.

[0113] The specific implementation of steps S201-S203 is the same as that of steps S101-S103, and will not be repeated here. Please refer to the foregoing description.

[0114] As a specific implementation of step S204, the step of determining whether the process corresponding to the target data group is ended includes: determining whether the target data group is the last group; if not, determining that the process corresponding to the target data group is ended, and inserting a preset fourth identification number into the target data group; if yes, obtaining a second interval duration according to the end time of the target data group and a preset end counting time; determining whether the second interval duration is greater than or equal to the duration threshold; if yes, determining that the process corresponding to the target data group is ended, and inserting the fourth identification number into the target data group; if no, determining that the process corresponding to the target data group is not ended, and inserting a preset fifth identification number into the target data group.

[0115] It should be noted that if the second interval duration is less than the duration threshold, it cannot be determined whether the process is ended, and in this case, the target data group is used for data counting of the next period. Further, the preprocessed data group further includes a fourth flag bit, wherein the fourth flag bit is used to carry the fourth identification number or the fifth identification number. For example, the fourth identification number can be set to "1", and the fifth identification number can be set to "0". In other embodiments, the fourth identification number and the fifth identification number can be other numerical values, as long as they are distinguished. The present application does not limit this, and will not be exemplified one by one. The preset end counting time can be set to 24:00:00.

[0116] Taking the collection of partial target data sets of a certain vehicle shown in Table 6 as an example, in Table 6, the first target data set is not the last target data set, and therefore the fourth identification number with a value of "1" is inserted into the first target data set; the second target data set is the last target data set, the end time of the target data set is 23:59:50, the second interval time from the end time to the end of the statistics is 10 seconds, and the second interval time is less than the time threshold with a value of 20 seconds, and therefore the fifth identification number with a value of "0" is inserted into the second target data set.

[0117] According to the above steps, the partial fifth data set of a certain vehicle shown in Table 7 can be obtained, wherein the fifth data set is used to indicate the collection of target data sets after the insertion of the third identification number or the fourth identification number.

[0118] Table 7 Partial fifth data set of a certain vehicle

[0119]

[0120] It should be noted that the fourth mark bit can be located at any position in Table 7, that is, the fourth identification number or the fifth identification number can be inserted into any position in the target data set, and the position of the fourth mark bit shown in Table 7 for carrying the fourth identification number or the fifth identification number is only an example, which is not limited in the present application.

[0121] Based on the partial fifth data set of a certain vehicle shown in Table 7, it is considered that the charging process corresponding to the first target data set has ended, and the first target data set is stored as a target data set, as shown in Table 8, the partial target data set of a certain vehicle; the charging process corresponding to the second target data set has not ended, and the second target data set is used to count the target data set of the next period.

[0122] Through the partial target data set of a certain vehicle shown in Table 8, the charging times, the average battery temperature, and the highest / lowest battery temperature of the vehicle can be calculated, so as to analyze the health status of the battery. For example, the target data set shown in Table 8 corresponds to the charging times of 1, the average battery temperature of 25.02, the highest battery temperature of 25.7, and the lowest battery temperature of 24.1.

[0123] Table 8 Partial target data set of a certain vehicle

[0124]

[0125] In summary, the battery temperature data management method described in the embodiment not only can improve the jump phenomenon described in the above embodiment, but also further improves the management efficiency of the battery temperature data; it can also judge whether the process of the data across the statistical period is ended, and use the target data set formed by the target data which is judged to be ended to calculate the average battery temperature, the maximum / minimum battery temperature and the battery temperature variance, etc. by the user or the vehicle enterprise, so as to ensure that the battery temperature data of the process which is not ended is not calculated in advance, not calculated repeatedly and not missed, etc., thereby further improving the calculation accuracy.

[0126] It should be understood that, although Figure 5 the steps in the flowchart are shown in sequential order, such that one step necessarily occurs before any other step in some of the embodiments, the steps do not have to be performed in the precise order shown in the figures. Unless explicitly stated, the steps can be performed in any order, and the steps can be performed in other sequences than those shown in the figures. Moreover, Figure 5 at least some of the steps in the flowchart can include multiple sub-steps or multiple stages, which can not necessarily be performed at the same time, but can be performed at different times, and the order of the sub-steps or stages can not necessarily be sequential, but can be performed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0127] In one embodiment, as shown in ​ a battery temperature data management system is provided, which is applied to a server or a cloud, and the system comprises:

[0128] a data merging module, configured to merge at least two pieces of continuous battery temperature data of the same process based on all the battery temperature data in the original data set, to obtain a preprocessed data group;

[0129] an interval duration obtaining module, configured to obtain a first interval duration of each adjacent preprocessed data group based on the starting time and the ending time of each preprocessed data group;

[0130] a data combination module, configured to obtain a preset duration threshold, and combine the adjacent preprocessed data groups whose first interval durations do not exceed the duration threshold, to obtain a target data group;

[0131] wherein, the target data group is used to indicate the classification of the battery temperature data of the same process and continuity.

[0132] Specifically, the data combination module performs the step of combining the adjacent pre-processing data groups with the first interval time length not exceeding the time length threshold to obtain a target data group, including: judging whether the first interval time length exceeds the time length threshold; if yes, inserting a preset first identification number into the subsequent pre-processing data group; if no, inserting a preset second identification number into the subsequent pre-processing data group; inserting the first identification number into the pre-processing data group with the earliest starting time; based on the first identification number and / or the second identification number of the current pre-processing data group and all the previous pre-processing data groups, performing accumulation calculation to obtain a third identification number, and inserting the third identification number into the current pre-processing data group; combining the pre-processing data groups with the same third identification number to obtain the target data group.

[0133] In another embodiment, as shown in FIG. 4, the system further includes a judgment module, wherein the judgment module is configured to judge whether the process corresponding to the target data group is ended; if yes, storing the corresponding target data group as a target data set; if no, using the corresponding target data group to count the target data set of the next period. ​

[0134] Specifically, the judgment module performs the step of judging whether the process corresponding to the target data group is ended, including: judging whether the target data group is the last group; if not, judging that the process corresponding to the target data group is ended, and inserting a preset fourth identification number into the target data group; if yes, obtaining a second interval time length according to the ending time of the target data group and a preset ending counting time; judging whether the second interval time length is greater than or equal to the time length threshold; if yes, judging that the process corresponding to the target data group is ended, and inserting the fourth identification number into the target data group; if no, judging that the process corresponding to the target data group is not ended, and inserting a preset fifth identification number into the target data group.

[0135] Specifically, the data combination module performs the step of combining the battery temperature data of at least two continuous same processes to obtain a pre-processing data group, including: obtaining the collection time and the battery temperature in the battery temperature data of at least two continuous same processes; sorting each battery temperature according to the time sequence of the collection time to obtain a temperature list; taking the earliest collection time as the starting time of the temperature list and taking the latest collection time as the ending time of the temperature list among all the collection times; generating the pre-processing data group according to the starting time, the ending time, the vehicle identification code, the process and the temperature list.

[0136] ​Specifically, the interval duration obtaining module performs the step of obtaining the first interval duration of each adjacent preprocessing data group based on the starting time and the ending time of each preprocessing data group, including: classifying the preprocessing data groups of each process according to the same process principle to obtain preprocessing data sets of each process; obtaining the starting time and the ending time of each preprocessing data group in the preprocessing data set; and obtaining the first interval duration of each adjacent preprocessing data group according to the ending time of the preceding preprocessing data group and the starting time of the subsequent preprocessing data group in the order of the starting time.

[0137] Further, the interval duration obtaining module performs the step of obtaining the first interval duration of each adjacent preprocessing data group according to the ending time of the preceding preprocessing data group and the starting time of the subsequent preprocessing data group in the order of the starting time, including: adding a first mark bit in the preprocessing data group; inserting the ending time of the preceding preprocessing data group into the first mark bit of the subsequent preprocessing data group in the order of the starting time, wherein the ending time of the preceding preprocessing data group corresponding to the preprocessing data group with the earliest starting time is empty; and obtaining the first interval duration of each adjacent preprocessing data group according to the ending time of the preceding preprocessing data group and the starting time of the subsequent preprocessing data group.

[0138] Specifically, the preprocessing data group further includes a second mark bit and a third mark bit, wherein the second mark bit is used to carry the first identification number or the second identification number, and the third mark bit is used to carry the third identification number.

[0139] Specifically, the preprocessing data group further includes a fourth mark bit, wherein the fourth mark bit is used to carry the fourth identification number or the fifth identification number.

[0140] The specific definition of the battery temperature data management system can refer to the definition of the battery temperature data management method in the above, which will not be repeated here. Each module in the above battery temperature data management system can be realized by software, hardware and their combination in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0141] In one embodiment, a computer device, which can be a server, is provided, and the internal structure diagram thereof can be as shown in FIG. 1. ​As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store battery temperature data and the like. The network interface of the computer device is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to implement a battery temperature data management method.

[0142] Those skilled in the art can understand that, ​ The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0143] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM) and the like.

[0144] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of technical features in the above embodiments are described, but as long as the combination of technical features does not exist, it should be considered as the scope of the present application.

[0145] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these are all within the scope of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A method for managing battery temperature data, characterized in that, include: Based on all battery temperature data in the original dataset, the battery temperature data from at least two consecutive processes of the same process are merged to obtain a preprocessed data set. The original dataset includes: among multiple consecutive battery temperature data belonging to the same process, there are battery temperature jump data of other processes, including charging, discharging and resting; Obtain the start time and end time of each of the preprocessed data groups; according to the order of the start times, and based on the end time of the earlier preprocessed data group and the start time of the later preprocessed data group, obtain the first interval duration of each adjacent preprocessed data group. Obtain a preset duration threshold, and combine adjacent preprocessed data whose first interval duration does not exceed the duration threshold to obtain a target data group; The target data group is used to classify the battery temperature data that are continuous and indicate the same process.

2. The method for managing battery temperature data according to claim 1, characterized in that, The step of combining adjacent preprocessed data with a first interval duration not exceeding the duration threshold to obtain a target data group includes: Determine whether the duration of the first interval exceeds the duration threshold; If so, insert the preset first identifier into the subsequent preprocessed data group; If not, insert the preset second identifier into the subsequent preprocessed data group; Insert the first identifier into the preprocessed data group with the earliest start time; Based on the first identifier and / or second identifier of the preprocessed data group described in this group and all the previous preprocessed data groups, a third identifier is obtained by cumulative calculation, and the third identifier is inserted into the preprocessed data group described in this group. The preprocessed data with the same third identifier are combined to obtain the target data group.

3. The method for managing battery temperature data according to claim 1, characterized in that, The method further includes: Determine whether the process corresponding to the target data set has ended; If the process ends, the corresponding target data group will be stored as the target dataset. If the process is not completed, the corresponding target data group will be used to statistically analyze the target dataset for the next period.

4. The method for managing battery temperature data according to claim 3, characterized in that, The step of determining whether the process corresponding to the target data set has ended includes: Determine whether the target data group is the last group; If it is not the last group, the process corresponding to the target data group is determined to be over, and the preset fourth identifier is inserted into the target data group; If it is the last group, the second interval duration is obtained based on the end time of the target data group and the preset end statistics time; Determine whether the second interval duration is greater than or equal to the duration threshold; If so, determine that the process corresponding to the target data group has ended, and insert the fourth identifier into the target data group; If not, it is determined that the process corresponding to the target data group has not ended, and the preset fifth identifier is inserted into the target data group.

5. The method for managing battery temperature data according to claim 1, characterized in that, The step of merging the battery temperature data from at least two consecutive processes of the same time to obtain a preprocessed data set includes: Acquire the acquisition time and battery temperature from at least two consecutive battery temperature data points of the same process; The temperatures of each battery are sorted according to the chronological order of the data collection times to obtain a temperature list; Of all the acquisition times, the earliest acquisition time is taken as the start time of the temperature list, and the latest acquisition time is taken as the end time of the temperature list. The preprocessed data set is generated based on the start time, the end time, the vehicle identification number, the process, and the temperature list.

6. The method for managing battery temperature data according to claim 5, characterized in that, The step of obtaining the first interval duration of each adjacent preprocessed data group based on the start and end times of each preprocessed data group includes: Based on the preprocessed data groups of each process, they are classified according to the principle of the same process to obtain the preprocessed datasets of each process; Obtain the start time and end time of each preprocessed data group in the preprocessed dataset; Based on the order of the start times of each of the preprocessed data groups, the first interval duration of each adjacent preprocessed data group is obtained according to the end time of the earlier preprocessed data group and the start time of the later preprocessed data group.

7. The method for managing battery temperature data according to claim 6, characterized in that, The step of obtaining the first interval duration of each adjacent preprocessed data group according to the order of the start times of each preprocessed data group and the start time of the earlier preprocessed data group includes: Add a first flag bit to the preprocessed data group; According to the order of the start times, the end time of the earlier preprocessed data group is inserted into the first flag bit of the later preprocessed data group, wherein the end time of the earlier preprocessed data group corresponding to the earliest start time is empty. The first interval duration of each adjacent preprocessed data group is obtained based on the end time of the preceding preprocessed data group and the start time of the subsequent preprocessed data group.

8. The method for managing battery temperature data according to claim 2, characterized in that, The preprocessed data group further includes a second marker bit and a third marker bit, wherein the second marker bit is used to carry the first identifier or the second identifier, and the third marker bit is used to carry the third identifier.

9. The method for managing battery temperature data according to claim 4, characterized in that, The preprocessed data group further includes a fourth marker bit, wherein the fourth marker bit is used to carry the fourth identifier or the fifth identifier.

10. A management system for battery temperature data, characterized in that, The system includes: The data merging module is used to merge at least two consecutive battery temperature data points belonging to the same process from all battery temperature data in the original dataset to obtain a preprocessed data set; the original dataset includes: among multiple consecutive battery temperature data points belonging to the same process, there are battery temperature jump data from other processes, the processes including charging, discharging, and resting; An interval duration acquisition module is used to acquire the start time and end time of each of the preprocessed data groups; according to the order of the start times, and based on the end time of the earlier preprocessed data group and the start time of the later preprocessed data group, the first interval duration of each adjacent preprocessed data group is obtained. The data combination module is used to obtain a preset duration threshold, and combine adjacent preprocessed data whose first interval duration does not exceed the duration threshold to obtain a target data group; The target data group is used to classify the battery temperature data that are continuous and indicate the same process.

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