CN116404277A - Multi-dimensional monitoring and early warning method and device for vehicle battery and Internet of vehicles server - Google Patents
Multi-dimensional monitoring and early warning method and device for vehicle battery and Internet of vehicles server Download PDFInfo
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- H01M10/00—Secondary cells; Manufacture thereof
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Abstract
The application relates to the technical field of automobiles and provides a multi-dimensional monitoring and early warning method and device for a vehicle battery and an Internet of vehicles server. The method comprises the following steps: receiving battery data of a target vehicle reported by a vehicle cloud platform at a first preset interval; sequentially carrying out data analysis and standardization processing on battery data; processing the battery data subjected to standardized processing in real time by using a distributed processing engine so as to extract data belonging to each early warning task, and determining whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task; based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered, the battery of the target vehicle is monitored and early warned in multiple dimensions. By adopting the technical means, the problem that the battery of the new energy automobile in the prior art lacks effective monitoring and has low battery abnormality early warning accuracy is solved.
Description
Technical Field
The application relates to the technical field of automobiles, in particular to a multi-dimensional monitoring and early warning method and device for a vehicle battery and an Internet of vehicles server.
Background
The new energy automobile is different from the traditional automobile, and takes a battery as power, so that the battery needs to be monitored in real time and the possible abnormality is early-warned in time. However, the utilization rate of battery data reported by a vehicle end in the prior art is low, the battery is lack of effective monitoring and the battery abnormality early warning accuracy is required to be improved, so that the battery safety accidents of the new energy automobile frequently occur.
Disclosure of Invention
In view of this, the embodiment of the application provides a vehicle battery multidimensional monitoring and early warning method, device and a vehicle networking server, so as to solve the problems that in the prior art, a new energy automobile battery lacks effective monitoring and the battery abnormality early warning accuracy is low.
In a first aspect of an embodiment of the present application, a method for multi-dimensional monitoring and early warning of a vehicle battery is provided, including: receiving battery data of a target vehicle reported by a vehicle cloud platform at a first preset interval; sequentially carrying out data analysis and standardization processing on battery data; processing the battery data subjected to standardized processing in real time by using a distributed processing engine so as to extract data belonging to each early warning task, and determining whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task; based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered, the battery of the target vehicle is monitored and early warned in multiple dimensions.
In a second aspect of the embodiments of the present application, a vehicle battery multidimensional monitoring and early warning device is provided, including: the receiving module is configured to receive battery data of a target vehicle reported by the vehicle cloud platform at a first preset interval; the processing module is configured to sequentially perform data analysis and standardization processing on the battery data; the engine module is configured to process the battery data subjected to standardized processing in real time by utilizing the distributed processing engine so as to extract data belonging to each early warning task, and determine whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task; the early warning module is configured to monitor and early warn the battery of the target vehicle in multiple dimensions based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered or not.
In a third aspect of the embodiments of the present application, there is provided a vehicle networking server comprising a memory, a battery data center and a computer program stored in the memory and operable on the battery data center, the battery data center implementing the steps of the method as described above when executing the computer program.
Compared with the prior art, the beneficial effects of the embodiment of the application at least comprise: according to the embodiment of the application, the distributed processing engine is utilized to process the battery data in real time, and the battery is monitored and pre-warned in various dimensions by utilizing the plurality of pre-warning tasks, so that the problems that in the prior art, the battery of the new energy automobile is lack of effective monitoring and the battery abnormality pre-warning accuracy is low can be solved by adopting the technical means, the battery of the new energy automobile is effectively monitored, and the battery abnormality pre-warning accuracy is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a multi-dimensional monitoring and early warning method for a vehicle battery according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for performing multiple dimension index analysis on a vehicle battery according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a multi-dimensional monitoring and early warning device for a vehicle battery according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an internet of vehicles server according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
Fig. 1 is a schematic flow chart of a multi-dimensional monitoring and early warning method for a vehicle battery according to an embodiment of the present application. The vehicle battery multidimensional monitoring and early warning method of fig. 1 can be executed by a battery data center arranged on a vehicle networking server. Alternatively, the vehicle battery multidimensional monitoring and early warning method of fig. 1 can be executed by a computer or a common server, or software on the computer or the common server. The internet of vehicles server may be regarded as a server that provides services for the internet of vehicles. Taking a battery data center as an execution main body as an example, the vehicle battery multi-dimensional monitoring and early warning method comprises the following steps:
s101, receiving battery data of a target vehicle reported by a vehicle cloud platform at a first preset interval;
s102, sequentially carrying out data analysis and standardization processing on battery data;
s103, processing the battery data subjected to standardized processing in real time by using a distributed processing engine so as to extract data belonging to each early warning task, and determining whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task;
and S104, monitoring and early warning the battery of the target vehicle in multiple dimensions based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered.
Battery data, comprising: charge state, cell current, cell temperature, slow charge state, slow charge gun connection state, fast charge gun connection state, apparent power, charge end reason, cell number, cell voltage value, temperature sensor value, cell voltage maximum, cell voltage minimum, highest voltage cell code, lowest voltage cell code, charge very low temperature, lowest temperature probe number, charge very high temperature, highest temperature probe number, charge average temperature, module temperature polling number, vehicle speed, total mileage, ambient temperature, acquisition time, acquisition period, battery total power, battery drive power limit, battery feedback power limit, cell voltage, battery state of health, battery power limit, battery state of charge the battery pack comprises a single charged electric quantity, an engine rotating speed, a current electric quantity, a national standard insulation resistance value, a high-voltage interlocking state, an insulation state, a relay state, an anti-theft authentication state, a charging request, a heating request, a BMS system fault level, a high-voltage request under fault, a quick charging positive electrode relay control, a quick charging negative electrode relay control, a vehicle state, a battery pack total voltage, a battery pack relay outside voltage, a battery energy state, a maximum charging electric quantity value, a battery reserved heating state, a charging mode, a national standard charging state, a DC system state, a gear, a VCU state signal, a rear electric control state control, a front electric control state control, a BMS state control, an accelerator pedal position, a charging connection state, a brake pedal state and the like.
The BMS system is commonly called as a battery nurse or a battery manager, and is mainly used for intelligently managing and maintaining each battery unit, preventing the battery from being overcharged and overdischarged, prolonging the service life of the battery, and monitoring the state of the battery. DC is a direct current converter and VCU is a voltage control unit. The single cell is the smallest battery, a plurality of single cells form a battery pack, and the plurality of battery packs are the whole battery of the target vehicle.
And the vehicle cloud platform collects battery data of the target vehicle and then reports the battery data to the battery data center. It should be noted that, the battery data center needs to process battery data of a plurality of vehicles, and the formats of the battery data of different types of vehicles may be different, so the battery data needs to be subjected to data analysis and standardization processing. The data analysis is to convert the battery data into data which can be identified by the battery data center, the standardization process is to perform mapping conversion on the analyzed battery data, and different signal names are converted into standard signal names, for example, vco is uniformly used for representing the vehicle speed. The distributed processing engine may be a Flink engine.
Optionally, when the battery data is analyzed, the battery data can be screened, and data required by a plurality of early warning tasks and a plurality of service analysis tasks can be screened.
The target number bin may be a Hive number bin and the query language may be the SQL query language.
According to the technical scheme provided by the embodiment of the application, the battery data of the target vehicle reported by the vehicle cloud platform at a first preset interval is received; sequentially carrying out data analysis and standardization processing on battery data; processing the battery data subjected to standardized processing in real time by using a distributed processing engine so as to extract data belonging to each early warning task, and determining whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task; based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered, the battery of the target vehicle is monitored and early warned in multiple dimensions. According to the embodiment of the application, the distributed processing engine is utilized to process the battery data in real time, and the battery is monitored and pre-warned in various dimensions by utilizing the plurality of pre-warning tasks, so that the problems that in the prior art, the battery of the new energy automobile is lack of effective monitoring and the battery abnormality pre-warning accuracy is low can be solved by adopting the technical means, the battery of the new energy automobile is effectively monitored, and the battery abnormality pre-warning accuracy is improved.
An early warning task comprising: a battery overcurrent early warning task, a battery overtemperature early warning task, a thermal runaway abnormal early warning task, a dynamic pressure difference abnormal early warning task, a voltage outlier abnormal early warning task and the like. The data of the battery overcurrent early warning task is data about battery current in a charging process, wherein the battery comprises a plurality of single units; the data of the battery over-temperature early warning task is data about the temperatures of a plurality of monomers; the data of the thermal runaway abnormality pre-warning task is data on temperatures and voltages of a plurality of monomers; the data of the dynamic pressure difference abnormality early warning task is data about output voltages of a plurality of monomers in a discharge state; the data of the voltage outlier warning task is data about open circuit voltages of a plurality of monomers.
The current of the single body comprises a charging current and a discharging current; the voltage of the single body comprises output voltage, input voltage and open circuit voltage; the temperature of the cell includes the temperature of the cell when it is in charge, discharge, open circuit, and short circuit.
Further, determining whether to trigger the pre-warning corresponding to the pre-warning task based on the data of each pre-warning task includes: when the battery current is larger than a first threshold value in the data of the battery overcurrent early-warning task, recording the times that the current exceeds the threshold value, and when the accumulated times that the current exceeds the threshold value are larger than a second threshold value, determining to trigger an alarm corresponding to the battery overcurrent early-warning task; recording the times that the temperature exceeds a threshold value when the highest temperature of any monomer in the data of the battery over-temperature early-warning task exceeds a third threshold value, and determining to trigger the alarm of the battery over-temperature early-warning task when the accumulated temperature exceeds the threshold value and the times is larger than a fourth threshold value; and calculating the comprehensive scores of the temperatures and the voltages of the monomers by using an entropy weight method on the temperatures and the voltages of the plurality of monomers in the data of the thermal runaway abnormal early-warning task, and determining whether the early-warning results corresponding to the early-warning tasks corresponding to the thermal runaway abnormal early-warning task are triggered or not when the difference between the comprehensive score of the temperatures or the voltages of at least one monomer and the average value of the comprehensive scores of all the monomers is larger than a fifth threshold value.
The battery overcurrent early warning task corresponding early warning is battery overcurrent early warning; the battery overtemperature early warning task corresponding early warning is a battery overtemperature early warning; the corresponding early warning of the thermal runaway abnormal early warning task is thermal runaway abnormal early warning; the dynamic differential pressure abnormality early warning task corresponds to the early warning of different grades and is dynamic differential pressure abnormality early warning; the early warning corresponding to the voltage outlier abnormality early warning task is voltage outlier abnormality early warning.
For example, the dynamic differential pressure abnormality pre-warning task can be divided into three levels, and when the difference of maximum voltages is in a first interval and the duration is longer than a first duration, the primary differential pressure abnormality pre-warning is triggered; when the difference of the maximum voltages is in the first interval and the duration is longer than the second duration, triggering a secondary differential pressure abnormality early warning; when the difference of the maximum voltages is in the second interval and the duration is longer than the second duration, triggering three-stage differential pressure abnormality early warning.
Based on the data of each early warning task and whether to trigger the early warning result corresponding to each early warning task, the battery of the target vehicle is monitored and early warned in multiple dimensions, comprising: processing the data of each early warning task and whether triggering the early warning result corresponding to each early warning task by utilizing the Web back end; based on the processing result of the Web back end, performing page rendering by using the Web front end to obtain an intelligent early warning chart; and monitoring and early warning of multiple dimensions are carried out on the battery of the target vehicle based on the intelligent early warning chart.
One early warning task corresponds to monitoring and early warning of one dimension, and the Web back end can perform operations such as data query and aggregation. The Web back end aggregates the data of one early warning task according to the data category to obtain a processing result related to the early warning task, and then the Web front end interacts with the Web back end to render pages according to the processing result to obtain an intelligent early warning chart of the early warning task; or the Web back end aggregates the data of the plurality of early warning tasks according to the data types to obtain processing results of the plurality of early warning tasks, and then the Web front end interacts with the Web back end to render pages according to the processing results to obtain intelligent early warning charts of the plurality of early warning tasks. According to the embodiment of the application, the monitoring and early warning of various dimensions of the battery are realized through the Web back end and the Web front end by utilizing a plurality of early warning tasks.
In an alternative embodiment, battery data which is reported by the vehicle cloud platform at a first preset interval and subjected to standardized processing is stored in a target number bin; scheduling each early warning task through an offline computing engine so as to process the data in the target number bin by using the query language and obtain the data of each early warning task; based on the data obtained by the distributed processing engine and the off-line computing engine, the battery of the target vehicle is monitored and pre-warned in multiple dimensions.
For example, a 10-point 10-split cloud platform reports a battery data, after the battery data is subjected to data analysis and standardization processing in sequence, the battery data subjected to standardization processing is processed by using a distributed processing engine to obtain data of each early warning task and an early warning result corresponding to each early warning task or not, and the battery data subjected to standardization processing is processed by using an offline computing engine to obtain the data of each early warning task. It should be noted that, the data of each early warning task obtained by the processing of the distributed processing engine and the off-line computing engine are different, so that the data obtained by the processing of the off-line computing engine can be used as the supplement of the data obtained by the processing of the distributed processing engine, thereby realizing more effective monitoring and accurate early warning of the battery of the target vehicle. The embodiment of the application utilizes the processing result of the offline computing engine to assist the processing result of the distributed processing engine to monitor and early warn the battery in various dimensions.
Fig. 2 is a flowchart of a method for performing multiple dimension index analysis on a vehicle battery according to an embodiment of the present application, which is performed by a battery data center, as shown in fig. 2, and includes:
for each target event, data that is an event start point and an event end point of the target event is respectively noted as first data and second data:
s201, storing battery data which is reported by a vehicle cloud platform at a first preset interval and subjected to standardized processing into a target number bin until reaching a second preset interval, and performing the following processing;
s202, scheduling each business analysis task through an offline computing engine so as to process the data in the target number bin by using the query language and obtain the business data of each business analysis task;
s203, performing index analysis of multiple dimensions on the battery of the target vehicle based on the service data of each service analysis task.
For example, the first preset interval is 10 minutes, the second preset interval is one day, and the second preset interval is equal to 144 first preset intervals. And reporting the battery data once every 10 minutes by the vehicle cloud platform, and storing the battery data subjected to standardized processing into a target number bin each time until one day of battery data is stored in the target number bin, namely a second preset interval is reached, scheduling each service analysis task through an offline computing engine, and processing the data in the target number bin by each service analysis task by using a query language to obtain own service data.
A business analysis task comprising: a charging process monomer voltage task, a discharging process monomer temperature change task, a charging interval day task and a charging overcurrent task; service data of a charging process monomer voltage task, comprising: a slow charge state, a slow charge charging gun connection state, a fast charge charging gun connection state, and a monomer voltage; service data of a single temperature change task in a discharging process comprises the following steps: a slow charge state, a slow charge gun connection state, a fast charge gun connection state, and a temperature; service data of the charging interval days task charging interval days, comprising: a slow charge state, a slow charge gun connection state, a fast charge gun connection state, and mileage; service data of the charging overcurrent task, including: slow charge state, slow charge charging gun connection state, fast charge charging gun connection state, current electric quantity and single current.
Based on the business data of each business analysis task, carrying out index analysis of multiple dimensions on the battery of the target vehicle, wherein the index analysis comprises the following steps: processing the service data of each service analysis task by utilizing the Web back end; based on the processing result of the Web back end, performing page rendering by using the Web front end to obtain a performance statistics chart; and performing index analysis of multiple dimensions on the battery of the target vehicle based on the performance statistics chart.
One business analysis task corresponds to index analysis of one dimension. According to the embodiment of the application, index analysis of various dimensions of the battery is achieved through the Web back end and the Web front end by utilizing a plurality of business analysis tasks. The charging process monomer voltage task is to analyze the change of the voltages of a plurality of monomers in the charging process; the temperature change task of the single body in the discharging process analyzes the temperature change of a plurality of single bodies in the discharging process; the task of charging interval days is to analyze the interval of battery charging or charging habit of a target vehicle; the charge over-current task is to analyze the over-current of a plurality of monomers during the charging process. The overcurrent means that the cell current exceeds a specified value.
In an alternative embodiment, after the offline computing engine schedules each service analysis task to process the data in the target number bin by using the query language to obtain the service data of each service analysis task, the method further includes: scheduling each service analysis task by using a distributed processing engine to process the battery data subjected to the standardized processing in real time so as to extract service data belonging to each service analysis task; and performing index analysis of multiple dimensions on the battery of the target vehicle based on the data processed by the distributed processing engine and the offline computing engine.
In the embodiment of the present application, the processing result of the distributed processing engine is used to assist the processing result of the offline computing engine to perform index analysis of multiple dimensions on the battery, and the embodiment of the present application corresponds to the embodiment of "the processing result of the offline computing engine is used to assist the processing result of the distributed processing engine to perform monitoring and early warning of multiple dimensions on the battery", which are similar, so that redundant description is omitted.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein in detail.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
Fig. 3 is a schematic diagram of a multi-dimensional monitoring and early warning device for a vehicle battery according to an embodiment of the present application.
As shown in fig. 3, the multi-dimensional monitoring and early warning device for a vehicle battery includes:
the receiving module 301 is configured to be a receiving module and is configured to receive battery data of a target vehicle reported by the vehicle cloud platform at a first preset interval;
a processing module 302 configured to sequentially perform data analysis and normalization processing on the battery data;
the engine module 303 is configured to process the battery data after the standardized processing in real time by using the distributed processing engine so as to extract data belonging to each early warning task, and determine whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task;
the early warning module 304 is configured to monitor and early warn the battery of the target vehicle in multiple dimensions based on the data of each early warning task and whether to trigger the early warning result corresponding to each early warning task.
According to the technical scheme provided by the embodiment of the application, the battery data of the target vehicle reported by the vehicle cloud platform at a first preset interval is received; sequentially carrying out data analysis and standardization processing on battery data; processing the battery data subjected to standardized processing in real time by using a distributed processing engine so as to extract data belonging to each early warning task, and determining whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task; based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered, the battery of the target vehicle is monitored and early warned in multiple dimensions. According to the embodiment of the application, the distributed processing engine is utilized to process the battery data in real time, and the battery is monitored and pre-warned in various dimensions by utilizing the plurality of pre-warning tasks, so that the problems that in the prior art, the battery of the new energy automobile is lack of effective monitoring and the battery abnormality pre-warning accuracy is low can be solved by adopting the technical means, the battery of the new energy automobile is effectively monitored, and the battery abnormality pre-warning accuracy is improved.
An early warning task comprising: a battery overcurrent early warning task, a battery overtemperature early warning task, a thermal runaway abnormal early warning task, a dynamic pressure difference abnormal early warning task and a voltage outlier abnormal early warning task; the data of the battery overcurrent early warning task is data about battery current in a charging process, wherein the battery comprises a plurality of single units; the data of the battery over-temperature early warning task is data about the temperatures of a plurality of monomers; the data of the thermal runaway abnormality pre-warning task is data on temperatures and voltages of a plurality of monomers; the data of the dynamic pressure difference abnormality early warning task is data about output voltages of a plurality of monomers in a discharge state; the data of the voltage outlier warning task is data about open circuit voltages of a plurality of monomers.
Optionally, the engine module 303 is further configured to record the number of times that the current exceeds the threshold when the battery current is greater than the first threshold in the data of the battery overcurrent early-warning task, and determine to trigger an alarm corresponding to the battery overcurrent early-warning task when the number of times that the accumulated current exceeds the threshold is greater than the second threshold; recording the times that the temperature exceeds a threshold value when the highest temperature of any monomer in the data of the battery over-temperature early-warning task exceeds a third threshold value, and determining to trigger the alarm of the battery over-temperature early-warning task when the accumulated temperature exceeds the threshold value and the times is larger than a fourth threshold value; and calculating the comprehensive scores of the temperatures and the voltages of the monomers by using an entropy weight method on the temperatures and the voltages of the plurality of monomers in the data of the thermal runaway abnormal early-warning task, and determining whether the early-warning results corresponding to the early-warning tasks corresponding to the thermal runaway abnormal early-warning task are triggered or not when the difference between the comprehensive score of the temperatures or the voltages of at least one monomer and the average value of the comprehensive scores of all the monomers is larger than a fifth threshold value.
Optionally, the early warning module 304 is further configured to process the data of each early warning task and whether to trigger the early warning result corresponding to each early warning task by using the Web back end; based on the processing result of the Web back end, performing page rendering by using the Web front end to obtain an intelligent early warning chart; and monitoring and early warning of multiple dimensions are carried out on the battery of the target vehicle based on the intelligent early warning chart.
Optionally, the processing module 302 is further configured to store the battery data reported by the vehicle cloud platform at the first preset interval and subjected to the standardization processing into the target number bin until reaching the second preset interval, and perform the following processing: scheduling each business analysis task through an offline computing engine so as to process the data in the target number bin by using the query language and obtain business data of each business analysis task; and carrying out index analysis of multiple dimensions on the battery of the target vehicle based on the service data of each service analysis task.
A business analysis task comprising: a charging process monomer voltage task, a discharging process monomer temperature change task, a charging interval day task and a charging overcurrent task; service data of a charging process monomer voltage task, comprising: a slow charge state, a slow charge charging gun connection state, a fast charge charging gun connection state, and a monomer voltage; service data of a single temperature change task in a discharging process comprises the following steps: a slow charge state, a slow charge gun connection state, a fast charge gun connection state, and a temperature; service data of the charging interval days task charging interval days, comprising: a slow charge state, a slow charge gun connection state, a fast charge gun connection state, and mileage; service data of the charging overcurrent task, including: slow charge state, slow charge charging gun connection state, fast charge charging gun connection state, current electric quantity and single current.
Optionally, the processing module 302 is further configured to process the service data of each service analysis task by using the Web backend; based on the processing result of the Web back end, performing page rendering by using the Web front end to obtain a performance statistics chart; and performing index analysis of multiple dimensions on the battery of the target vehicle based on the performance statistics chart.
Optionally, the processing module 302 is further configured to store the battery data reported by the vehicle cloud platform at the first preset interval and subjected to the standardization processing into the target number bin; scheduling each early warning task through an offline computing engine so as to process the data in the target number bin by using the query language and obtain the data of each early warning task; based on the data obtained by the distributed processing engine and the off-line computing engine, the battery of the target vehicle is monitored and pre-warned in multiple dimensions.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Fig. 4 is a schematic diagram of an internet of vehicles server 4 provided by an embodiment of the present disclosure. As shown in fig. 4, the internet of vehicles server 4 of this embodiment includes: battery data center 401, memory 402, and computer program 403 stored in memory 402 and executable on battery data center 401. The steps of the various method embodiments described above are implemented by the battery data center 401 when executing the computer program 403. Alternatively, the battery data center 401, when executing the computer program 403, implements the functions of the modules/units in the above-described device embodiments.
The internet of vehicles server 4 may include, but is not limited to, a battery data center 401 and a memory 402. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the internet of vehicles server 4 and does not constitute a limitation of the internet of vehicles server 4, and may include more or fewer components than illustrated, or different components.
The memory 402 may be an internal storage unit of the internet of vehicles server 4, for example, a hard disk or a memory of the internet of vehicles server 4. The memory 402 may also be an external storage device of the internet of vehicles server 4, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the internet of vehicles server 4. Memory 402 may also include both internal storage units and external storage devices of the internet of vehicles server 4. The memory 402 is used to store computer programs and other programs and data required by the internet of vehicles server.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a battery data center, may implement the steps of the method embodiments described above. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. The multi-dimensional monitoring and early warning method for the vehicle battery is characterized by comprising the following steps of:
receiving battery data of a target vehicle reported by a vehicle cloud platform at a first preset interval;
sequentially carrying out data analysis and standardization processing on the battery data;
processing the battery data subjected to the standardized processing in real time by using a distributed processing engine so as to extract data belonging to each early warning task, and determining whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task;
and based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered, monitoring and early warning are carried out on the battery of the target vehicle in multiple dimensions.
2. The method of claim 1, wherein determining whether to trigger an early warning corresponding to an early warning task based on data of the respective early warning task comprises:
the early warning task comprises the following steps: the battery comprises a plurality of monomers;
when the battery current is larger than a first threshold value in the data of the battery overcurrent early-warning task, recording the times that the current exceeds the threshold value, and when the accumulated times that the current exceeds the threshold value are larger than a second threshold value, determining to trigger an alarm corresponding to the battery overcurrent early-warning task;
recording the times that the temperature exceeds a threshold value when the highest temperature of any monomer in the data of the battery over-temperature early-warning task exceeds a third threshold value, and determining to trigger the alarm of the battery over-temperature early-warning task when the accumulated temperature exceeds the threshold value and the times is larger than a fourth threshold value;
calculating the comprehensive scores of the temperatures and the voltages of the monomers in the data of the thermal runaway abnormal early warning task by using an entropy weight method, and determining to trigger the early warning corresponding to the thermal runaway abnormal early warning task when the difference between the comprehensive score of the temperatures or the voltages of at least one monomer and the average value of the comprehensive scores of all the monomers is larger than a fifth threshold value;
calculating the difference of maximum voltages among a plurality of monomers according to the data of the dynamic pressure difference abnormality early warning task, and determining to trigger the early warning of different grades corresponding to the dynamic pressure difference abnormality early warning task according to the interval and duration of the difference of the maximum voltages;
and calculating standard deviation of output voltages of a plurality of monomers according to the data of the voltage outlier abnormality early warning task, and determining to trigger early warning corresponding to the voltage outlier abnormality early warning task when the standard deviation is larger than a fourth threshold.
3. The method of claim 1, wherein monitoring and pre-warning the battery of the target vehicle in multiple dimensions based on the data of each pre-warning task and whether to trigger the pre-warning result corresponding to each pre-warning task, comprising:
processing the data of each early warning task and whether triggering the early warning result corresponding to each early warning task by utilizing the Web back end;
based on the processing result of the Web back end, performing page rendering by using the Web front end to obtain an intelligent early warning chart;
and monitoring and early warning the battery of the target vehicle in multiple dimensions based on the intelligent early warning chart.
4. The method according to claim 1, wherein the method further comprises:
the battery data which are reported by the vehicle cloud platform at the first preset interval and subjected to the standardization processing are stored in a target number bin;
scheduling each early warning task through an offline computing engine so as to process the data in the target number bin by using a query language and obtain the data of each early warning task;
and based on the data processed by the distributed processing engine and the offline computing engine, monitoring and early warning of multiple dimensions are carried out on the battery of the target vehicle.
5. The method according to claim 1, wherein the method further comprises:
and storing the battery data which is reported by the vehicle cloud platform at the first preset interval and subjected to the standardization processing into a target number bin until reaching a second preset interval, and performing the following processing:
scheduling each business analysis task through an offline computing engine so as to process the data in the target number bin by using a query language and obtain business data of each business analysis task;
performing index analysis of multiple dimensions on the battery of the target vehicle based on service data of each service analysis task;
wherein the second preset interval may be divided into a plurality of the first preset intervals.
6. The method of claim 5, wherein performing a multi-dimensional index analysis on the battery of the target vehicle based on the business data of each business analysis task comprises:
processing the service data of each service analysis task by utilizing the Web back end;
based on the processing result of the Web back end, performing page rendering by using the Web front end to obtain a performance statistics chart;
and performing index analysis of multiple dimensions on the battery of the target vehicle based on the performance statistical chart.
7. The method of claim 5, wherein after scheduling each business analysis task by an offline computing engine to process the data in the target bin using a query language to obtain business data for each business analysis task, the method further comprises:
scheduling each service analysis task by using a distributed processing engine to process the battery data subjected to the standardized processing in real time so as to extract service data belonging to each service analysis task;
and performing index analysis of multiple dimensions on the battery of the target vehicle based on the data processed by the distributed processing engine and the offline computing engine.
8. The utility model provides a vehicle battery multidimension degree control early warning device which characterized in that includes:
the receiving module is configured to receive battery data of a target vehicle reported by the vehicle cloud platform at a first preset interval;
the processing module is configured to sequentially perform data analysis and standardization processing on the battery data;
the engine module is configured to process the battery data subjected to the standardized processing in real time by utilizing the distributed processing engine so as to extract data belonging to each early warning task, and determine whether to trigger the early warning corresponding to the early warning task based on the data of each early warning task;
and the early warning module is configured to monitor and early warn the battery of the target vehicle in multiple dimensions based on the data of each early warning task and whether the early warning result corresponding to each early warning task is triggered.
9. An internet of vehicles server comprising a memory, a battery data center and a computer program stored in the memory and operable on the battery data center, the battery data center implementing the vehicle battery multi-dimensional monitoring and pre-warning method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the multi-channel data transmission-based vehicle software upgrade method according to any one of claims 1 to 7.
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| CN117526529A (en) * | 2024-01-05 | 2024-02-06 | 上海泰矽微电子有限公司 | Charging control method and device, electronic equipment and medium |
| CN117526529B (en) * | 2024-01-05 | 2024-03-08 | 上海泰矽微电子有限公司 | Charging control method and device, electronic equipment and medium |
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