CN114371435A - BMS sensor effectiveness evaluation method, system, electronic device and medium - Google Patents
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Abstract
The invention provides a BMS sensor effectiveness evaluation method and system, relates to the technical field of BMS sensors, and aims to realize more comprehensive, objective and accurate effectiveness evaluation on the BMS sensor. The method comprises the following steps: selecting the distribution condition of the abnormal marking values from the fault characteristic data set as a first criterion, and obtaining a first discrimination value based on the first criterion; selecting the distribution condition of continuous identical values from the fault characteristic data set as a second criterion, and obtaining a second criterion value based on the second criterion; selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion, and obtaining a third discrimination value based on the third criterion; and judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a BMS sensor state value, and evaluating the effectiveness of the BMS sensor based on the BMS sensor state value. The BMS sensor effectiveness evaluation system is applied to a BMS sensor effectiveness evaluation method.
Description
Technical Field
The invention relates to the technical field of BMS sensors, in particular to a BMS sensor effectiveness evaluation method, a BMS sensor effectiveness evaluation system, electronic equipment and a medium.
Background
In recent years, with energy shortage and deterioration of ecological environment, development of green clean energy such as wind power and photovoltaic power generation is urgently needed. However, the new energy power generation conditions are limited by natural resources, and the problems of large waveform, poor stability and the like exist. In order to better apply the new energy power generation technology to the power grid, the large-scale energy storage technology is needed to relieve the power demand of peak load and improve the power quality and the power supply reliability. At present, the electrochemical energy storage technology is developed more mature, and a commonly used energy storage battery is a lithium ion battery. However, the lithium ion battery has low thermal stability and is easy to cause safety problems such as battery thermal runaway and the like, so that the guarantee of the high-efficiency and stable operation of the lithium ion battery is of great importance to the development of energy storage power stations. The Battery Management System (BMS) can evaluate the running state of the battery by monitoring various parameters in the actual running process of the battery, thereby carrying out effective balance control and safety protection on the battery and ensuring the safety and stability of the battery.
The accuracy of the BMS in monitoring the state of the battery is mainly dependent on voltage sensors, current sensors, and temperature sensors in the BMS. If the BMS sensor breaks down, the battery abuse phenomena such as overcharge, overdischarge and overcurrent can be caused to the battery, so that thermal runaway of the battery is caused, and safety accidents such as explosion of an energy storage power station can happen in severe cases. Therefore, the effectiveness of the BMS sensor must be evaluated on line in real time, and if the BMS sensor fails, timely safety maintenance measures need to be taken to ensure safe and stable operation of the energy storage power station.
At present, a plurality of methods for diagnosing sensor faults are researched, for example, a sample is trained by combining an evidence theory fusion method and a neural network based on a principal component analysis method, so that the purpose of evaluating the fault state of a sensor is achieved. In addition, wavelet decomposition is carried out on data acquired by the sensor in real time, so that required characteristic vectors are extracted, and fault classification of the sensor is established by combining the characteristic vectors. The above method mainly identifies the failure mode of the sensor, and in practical application, further understanding of the effectiveness and failure degree of the sensor is required.
Disclosure of Invention
In view of the above, the present invention provides a method, a system, an electronic device and a medium for evaluating the validity of a BMS sensor, so as to achieve more comprehensive, objective and accurate validity evaluation of the BMS sensor.
The invention provides a BMS sensor effectiveness evaluation method, which comprises the following steps:
step 1: acquiring real-time monitoring data of a BMS sensor, and acquiring a fault feature data set representing faults of the BMS sensor based on the real-time monitoring data;
step 2: selecting the distribution condition of abnormal marking values from the fault characteristic data set as a first criterion for BMS sensor effectiveness evaluation, and obtaining a first discrimination value based on the first criterion;
and step 3: selecting the distribution condition of continuous same values from the fault characteristic data set as a second criterion for BMS sensor effectiveness evaluation, and obtaining a second judgment value based on the second criterion;
and 4, step 4: selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion for BMS sensor effectiveness evaluation, and obtaining a third judgment value based on the third criterion;
and 5: and judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a BMS sensor state value, and evaluating the effectiveness of the BMS sensor based on the BMS sensor state value.
Preferably, the acquiring a fault feature data set characterizing a fault of the BMS sensor based on the real-time monitoring data includes: and acquiring a fault characteristic data set representing the faults of the BMS sensor from the real-time monitoring data by using a sliding window method.
Preferably, the BMS sensor failure comprises: the method comprises the steps of monitoring data abnormality marking value faults, monitoring data continuous identical value faults and monitoring data variation coefficient faults in real time.
Preferably, when the failure of the BMS sensor is the failure of the abnormal mark value of the real-time monitoring data, the step 2 of obtaining the distribution condition of the abnormal mark value based on the failure characteristic data set is performed as the first criterion T for the validity evaluation of the BMS sensor1(ii) a Based on said first criterion T1Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with the first criterion T1Then the first judgment value P1If the data characteristic of the fault signature data set does not meet the first criterion T, 01Then the first judgment value P1＝1。
Preferably, when the failure of the BMS sensor is a failure of consecutive identical values of the real-time monitoring data, the step 3 of obtaining the distribution condition of consecutive identical values based on the failure characteristic data set as the second criterion T for the BMS sensor effectiveness evaluation is performed2(ii) a Based on said second criterion T2Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with the second criterion T2Then the second judgment value P2If the data characteristic of the fault signature data set does not meet the second criterion T, 02Then the second judgment value P2＝1。
Preferably, when the failure of the BMS sensor is the failure of the coefficient of variation of the real-time monitoring data, the step 4 of obtaining the distribution condition of the coefficient of variation based on the failure characteristic data set is performed as a third criterion T for the BMS sensor effectiveness evaluation3(ii) a Based on said third criterion T3Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with the third criterion T3Then the third judgment value P3If the data characteristic of the fault signature data set does not meet the third criterion T, 03Then the third judgment value P3＝1。
Preferably, the judging the state of the BMS sensor based on the first, second and third judging values to obtain the state value of the BMS sensor, and the evaluating the validity of the BMS sensor based on the state value of the BMS sensor includes:
based on the first discrimination value P1The second judgment value P2And the third discrimination value P3Judging the state of the BMS sensor, and obtaining the state value of the BMS sensor by using a product distribution method:
wherein, Pi(i-1, 2,3) is determined by the criterion Ti(i is 1,2,3) and the corresponding discrimination value is obtained after discrimination; when F is equal to 1, the BMS sensor works normally; when F is 0, the BMS sensor malfunctions.
Compared with the prior art, the BMS sensor effectiveness evaluation method provided by the invention has the following beneficial effects: acquiring real-time monitoring data of the BMS sensor, and acquiring a fault feature data set representing faults of the BMS sensor based on the real-time monitoring data; selecting the distribution condition of the abnormal marking values from the fault characteristic data set as a first criterion for BMS sensor effectiveness evaluation, and obtaining a first discrimination value based on the first criterion; selecting the distribution condition of continuous same values from the fault characteristic data set as a second criterion for BMS sensor effectiveness evaluation, and obtaining a second judgment value based on the second criterion; selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion for BMS sensor effectiveness evaluation, and obtaining a third judgment value based on the third criterion; and judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a BMS sensor state value, and evaluating the effectiveness of the BMS sensor based on the BMS sensor state value. According to the actual operation condition of the BMS sensor, the invention integrates a plurality of judgment data to judge the state of the BMS sensor, and completes the multidimensional comprehensive evaluation of the BMS sensor, so that the validity evaluation process of the BMS sensor based on the multidimensional criterion integration is more comprehensive and objective, and the evaluation result is more accurate.
The present invention also provides a BMS sensor effectiveness evaluating system, the system including:
the acquisition module is used for acquiring real-time monitoring data of the BMS sensor and acquiring a fault characteristic data set representing faults of the BMS sensor based on the real-time monitoring data;
the first judgment value module is used for selecting the distribution condition of the abnormal marking values from the fault characteristic data set as first criterion for BMS sensor effectiveness evaluation and obtaining a first judgment value based on the first criterion;
the second judgment value module is used for selecting the distribution condition of continuous same values from the fault characteristic data set as a second criterion for BMS sensor effectiveness evaluation and obtaining a second judgment value based on the second criterion;
the third judgment value module is used for selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion for BMS sensor effectiveness evaluation and obtaining a third judgment value based on the third criterion;
and the validity evaluation module is used for judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a state value of the BMS sensor and evaluating the validity of the BMS sensor based on the state value of the BMS sensor.
Compared with the prior art, the beneficial effects of the BMS sensor effectiveness evaluation system provided by the invention are the same as those of the BMS sensor effectiveness evaluation method in the technical scheme, and are not repeated herein.
The present invention also provides an electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements any of the steps of the BMS sensor validity assessment method described above.
Compared with the prior art, the beneficial effects of the electronic device provided by the invention are the same as the beneficial effects of the BMS sensor effectiveness evaluation method in the technical scheme, and are not repeated herein.
The present invention also provides a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of a BMS sensor effectiveness evaluation method of any one of the above.
Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the invention are the same as the beneficial effects of the BMS sensor effectiveness evaluation method in the technical scheme, and are not repeated herein.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 illustrates a flowchart of a BMS sensor validity evaluation method according to an embodiment of the present invention;
fig. 2 is a schematic structural view illustrating a BMS sensor effectiveness evaluation system according to an embodiment of the present invention;
fig. 3 shows a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The "plurality" mentioned in the present embodiment means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a is present alone, A and B are present simultaneously, and B is present alone. The terms "exemplary" or "such as" are used herein to mean serving as an example, instance, or illustration, and are intended to present concepts in a concrete fashion, and should not be construed as preferred or advantageous over other embodiments or designs.
Fig. 1 is a flowchart illustrating a BMS sensor effectiveness evaluation method according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S1: acquiring real-time monitoring data of the BMS sensor, and acquiring a fault characteristic data set representing faults of the BMS sensor based on the real-time monitoring data.
It should be noted that, first, real-time monitoring data of the BMS sensor is acquired, and a fault feature data set representing the failure of the BMS sensor is acquired from the real-time monitoring data according to a failure mode of the BMS sensor.
Due to the complex operating conditions of the battery during actual operation, the sensor may gradually fail as the operating time increases, or may fail due to interference from external conditions. The embodiment of the invention mainly discusses the faults of abnormal marking values of real-time monitoring data, the faults of continuous identical values of the real-time monitoring data and the faults of variation coefficients of the real-time monitoring data.
It should be understood that the fault of the abnormal mark value of the real-time monitoring data can be that the collected data is empty and always zero or the voltage value sign is inconsistent with the charge-discharge state of the battery. The failure of the real-time monitoring data coefficient of variation can be caused by relatively large deviation degree of the acquired data due to poor stability of the sensor. It should be understood that the three sensor faults described above do not occur individually, but may occur simultaneously, and that the three sensor faults described above may have similar characteristics in terms of data.
If all the historical data of the sensors in the battery management system BMS are directly used to evaluate the sensors, the evaluation rate and the accuracy of the evaluation may be reduced. Therefore, in the embodiment of the invention, the sensor historical data with the time length of T is extracted from the real-time monitoring data by using a sliding window method and is used as a fault characteristic data set for representing the faults of the BMS sensor, so that the sensor is evaluated in real time.
Step 2: and selecting the distribution condition of the abnormal marking values from the fault characteristic data set as a first criterion for BMS sensor effectiveness evaluation, and obtaining a first discrimination value based on the first criterion.
It should be noted that, when the BMS sensor fails to monitor the abnormal data flag value in real time, an invalid data set such as data loss and blank data may occur during the data acquisition process. Therefore, the distribution condition of the abnormal mark value is selected from the fault characteristic data set as a first criterion T of the BMS sensor effectiveness evaluation1. Using a corresponding first criterion T1And the judgment is carried out. The corresponding criterion is as follows: first criterion T1: the total length of time during which invalid data occurs exceeds a first preset time threshold t 1. Applying a first criterion T1Judging the fault characteristic data set, if the data characteristics of the fault characteristic data set accord with a first criterion T1If the total time length of the invalid data exceeds the first preset time threshold t1, the first judgment value at the moment is P10; if the data characteristic of the fault characteristic data set does not meet the first criterion T1If the first judgment value is P11. The first discrimination value is expressed by a specific number, which is beneficial to the later calling and storing of the discrimination program.
And step 3: and selecting the distribution condition of continuous identical values from the fault characteristic data set as a second criterion for BMS sensor effectiveness evaluation, and obtaining a second judgment value based on the second criterion.
It should be noted that, when the real-time monitoring data of the BMS sensor is analyzed, it is found that a plurality of continuous collection points of partial data are always kept unchanged. The acquisition period is in seconds(s), the battery charging and discharging process is considered to be continuously circulated, if the long-time data is kept unchanged, the BMS sensor can be considered to be in fault, and therefore the distribution condition with continuous same values is selected from the fault characteristic data set to serve as a second criterion T for evaluating the effectiveness of the BMS sensor2. In particular, the second criterion T2: the battery voltage data is kept unchanged for a long time in the battery charging and discharging process, and continuously the same data exceeds a second preset time threshold t 2. Applying a second criterion T2Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with a second judgment data T2Then the continuously same data exceeds the second preset time threshold t2, and the second judgment value is P20; if the data characteristics of the fault characteristic data set do not accord with the second judgment data T2If the second judgment value is P2＝1。
And 4, step 4: and selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion for evaluating the effectiveness of the BMS sensor, and obtaining a third judgment value based on the third criterion.
The coefficient of variation is defined as the ratio of the mean square error to the mean of the random variable, and mainly reflects a relative deviation degree of the random variable. Wherein, the coefficient of variation expression of the sample is:
Table of its observed valuesThe expression is as follows:
wherein v is the observed value of the coefficient of variation, n is the number of samples, (X)1,X2,…,Xn) Is an observed value of the sample and is,
is mean and S is mean square error.
As can be seen from the definition of the coefficient of variation, the larger the coefficient of variation is, the larger the data deviation degree is, that is, the sensor stability is lower and the effectiveness is reduced, so that the distribution condition of the coefficient of variation is obtained from the fault characteristic data set and used as the third criterion T for evaluating the effectiveness of the BMS sensor3. In particular, the third criterion T3: the rate of change of the coefficient of variation of the fault signature data set exceeds a third predetermined time threshold t 3. Using a third criterion T3Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with a third criterion T3If the variation coefficient exceeds the third predetermined time threshold t3, the third determination value is P30; if the data characteristics of the fault characteristic data set do not meet the third criterion T3If the third decision value is P3＝1。
And 5: and judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a BMS sensor state value, and evaluating the effectiveness of the BMS sensor based on the BMS sensor state value.
It should be noted that the three criteria correspond to three discrimination values respectively, and considering that the fault types corresponding to the three criteria are independent from each other, the embodiment of the present invention obtains the state value of the sensor by using a product distribution method.
Specifically, the state value expression of the sensor is:
pi is {0,1}, where Pi (i is 1,2,3) is a corresponding discrimination value after discrimination by criterion Ti (i is 1,2, 3). When F is equal to 1, the BMS sensor works normally;when F is 0, the BMS sensor malfunctions.
Compared with the prior art, the BMS sensor effectiveness evaluation method provided by the embodiment of the invention has the following beneficial effects: according to the embodiment of the invention, the fault characteristic data set is extracted according to the fault characteristics of the BMS sensor in the operation process through the data monitored by the BMS sensor in real time, wherein the characteristic data set used for evaluation by the sensor is derived from the data of the sliding window. And taking the distribution condition of the abnormal marking values, the distribution condition of continuous identical values and the distribution condition of the variation coefficients in the fault characteristic data set as the criterion of the BMS sensor effectiveness, and obtaining corresponding discrimination values. And calculating to obtain the state value of the BMS sensor by using a product distribution method, and realizing the evaluation of the validity of the BMS sensor. In the evaluation process of the embodiment of the invention, the states of the BMS sensors are judged by fusing a plurality of criteria according to the actual operation condition of the BMS sensors, and the multidimensional comprehensive evaluation of the sensors is completed, so that the validity evaluation process of the BMS sensors based on the multidimensional criteria fusion is more comprehensive and objective, and the evaluation result is more accurate.
Fig. 2 is a schematic structural view illustrating a BMS sensor effectiveness evaluating system according to an embodiment of the present invention, and as shown in fig. 2, the system includes
The acquisition module 1 is used for acquiring real-time monitoring data of the BMS sensor and acquiring a fault characteristic data set representing faults of the BMS sensor based on the real-time monitoring data;
the first discriminant value module 2 is used for selecting the distribution condition of the abnormal mark values from the fault feature data set as a first criterion for BMS sensor effectiveness evaluation, and acquiring a first discriminant value based on the first criterion;
the second judgment value module 3 is used for selecting the distribution condition of continuous same values from the fault characteristic data set as a second criterion of BMS sensor effectiveness evaluation and obtaining a second judgment value based on the second criterion;
the third judgment value module 4 is used for selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion for BMS sensor effectiveness evaluation and obtaining a third judgment value based on the third criterion;
and the validity evaluation module 5 is used for judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a state value of the BMS sensor and evaluating the validity of the BMS sensor based on the state value of the BMS sensor.
Compared with the prior art, the beneficial effects of the BMS sensor effectiveness evaluation system provided by the invention are the same as those of the BMS sensor effectiveness evaluation method in the technical scheme, and are not repeated herein.
In addition, an embodiment of the present invention further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, respectively, and when the computer program is executed by the processor, the processes of the embodiment of the BMS sensor validity evaluation method are implemented, and the same technical effect can be achieved, and therefore, details are not repeated herein to avoid repetition.
Specifically, referring to fig. 3, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of one BMS sensor effectiveness evaluation method embodiment described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus, and memory controller, a peripheral bus, an Accelerated Graphics Port (AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include: an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA), a Peripheral Component Interconnect (PCI) bus.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described networks may be an ad hoc network (ad hoc network), an intranet (intranet), an extranet (extranet), a Virtual Private Network (VPN), a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), a Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), the Internet (Internet), a Public Switched Telephone Network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless fidelity (Wi-Fi) network, and combinations of two or more of the above. For example, the cellular telephone network and the wireless network may be a global system for Mobile Communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a long term evolution-advanced (LTE-a) system, a Universal Mobile Telecommunications (UMTS) system, an enhanced Mobile Broadband (eMBB) system, a mass Machine Type Communication (mtc) system, an ultra reliable Low Latency Communication (urrllc) system, or the like.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: Read-Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), or Flash Memory.
The volatile memory includes: random Access Memory (RAM), which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory (Static RAM, SRAM), Dynamic random access memory (Dynamic RAM, DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data rate Synchronous Dynamic random access memory (Double Data RateSDRAM, DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various system programs such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media Player (Media Player), Browser (Browser), for implementing various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the processes of the above-mentioned BMS sensor validity evaluation method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the details are not repeated here.
The computer-readable storage medium includes: permanent and non-permanent, removable and non-removable media may be tangible devices that retain and store instructions for use by an instruction execution apparatus. The computer-readable storage medium includes: electronic memory devices, magnetic memory devices, optical memory devices, electromagnetic memory devices, semiconductor memory devices, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), non-volatile random access memory (NVRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic tape cartridge storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanically encoded devices (e.g., punched cards or raised structures in a groove having instructions recorded thereon), or any other non-transmission medium useful for storing information that may be accessed by a computing device. As defined in embodiments of the present invention, the computer-readable storage medium does not include transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses traveling through a fiber optic cable), or electrical signals transmitted through a wire.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, electronic device and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the embodiment of the invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be substantially or partially contributed by the prior art, or all or part of the technical solutions may be embodied in a software product stored in a storage medium and including instructions for causing a computer device (including a personal computer, a server, a data center, or other network devices) to execute all or part of the steps of the methods of the embodiments of the present invention. And the storage medium includes various media that can store the program code as listed in the foregoing.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and the present invention shall be covered by the claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A BMS sensor effectiveness evaluation method is characterized by comprising the following steps:
step 1: acquiring real-time monitoring data of a BMS sensor, and acquiring a fault feature data set representing faults of the BMS sensor based on the real-time monitoring data;
step 2: selecting the distribution condition of abnormal marking values from the fault characteristic data set as a first criterion for BMS sensor effectiveness evaluation, and obtaining a first discrimination value based on the first criterion;
and step 3: selecting the distribution condition of continuous same values from the fault characteristic data set as a second criterion for BMS sensor effectiveness evaluation, and obtaining a second judgment value based on the second criterion;
and 4, step 4: selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion for BMS sensor effectiveness evaluation, and obtaining a third judgment value based on the third criterion;
and 5: and judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a BMS sensor state value, and evaluating the effectiveness of the BMS sensor based on the BMS sensor state value.
2. The BMS sensor validity assessment method of claim 1, wherein the obtaining of a fault signature data set characterizing BMS sensor faults based on the real-time monitoring data comprises:
and acquiring a fault characteristic data set representing the faults of the BMS sensor from the real-time monitoring data by using a sliding window method.
3. The BMS sensor validity evaluation method according to any one of claims 1 to 2,
the BMS sensor failure comprises: the method comprises the steps of monitoring data abnormality marking value faults, monitoring data continuous identical value faults and monitoring data variation coefficient faults in real time.
4. The BMS sensor validity evaluation method according to claim 3,
when the BMS sensor fault is the fault of the abnormal marking value of the real-time monitoring data, the step 2 of obtaining the distribution condition of the abnormal marking value based on the fault characteristic data set as a first criterion T of the BMS sensor effectiveness evaluation is executed1；
Based on said first criterion T1Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with the first criterion T1Then the first judgment value P1If the data characteristic of the fault signature data set does not meet the first criterion T, 01Then the first judgment value P1＝1。
5. The BMS sensor validity evaluation method according to claim 3,
when the BMS sensor fault is the fault of continuous identical values of the real-time monitoring data, the step 3 of obtaining the distribution condition of the continuous identical values based on the fault characteristic data set as a second criterion T of the BMS sensor effectiveness evaluation is executed2；
Based on said second criterion T2Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with the second criterion T2Then the second judgment value P20, if the number of fault signature data setsBy features not meeting said second criterion T2Then the second judgment value P2＝1。
6. The BMS sensor validity evaluation method according to claim 3,
when the BMS sensor fault is the real-time monitoring data variation coefficient fault, the step 4 of obtaining the distribution condition of the variation coefficient based on the fault characteristic data set as a third criterion T of the BMS sensor effectiveness evaluation is executed3；
Based on said third criterion T3Judging the fault characteristic data set, and if the data characteristics of the fault characteristic data set accord with the third criterion T3Then the third judgment value P3If the data characteristic of the fault signature data set does not meet the third criterion T, 03Then the third judgment value P3＝1。
7. The BMS sensor validity assessment method according to claim 1, wherein the determining the BMS sensor status based on the first determination value, the second determination value and the third determination value to obtain a BMS sensor status value, and the evaluating the BMS sensor validity based on the BMS sensor status value comprises:
based on the first discrimination value P1The second judgment value P2And the third discrimination value P3Judging the state of the BMS sensor, and obtaining the state value of the BMS sensor by using a product distribution method:
wherein, Pi(i-1, 2,3) is determined by the criterion Ti(i is 1,2,3) and the corresponding discrimination value is obtained after discrimination; when F is equal to 1, the BMS sensor works normally; when F is 0, the BMS sensor malfunctions.
8. A BMS sensor validity assessment system, comprising:
the acquisition module is used for acquiring real-time monitoring data of the BMS sensor and acquiring a fault characteristic data set representing faults of the BMS sensor based on the real-time monitoring data;
the first judgment value module is used for selecting the distribution condition of the abnormal marking values from the fault characteristic data set as first criterion for BMS sensor effectiveness evaluation and obtaining a first judgment value based on the first criterion;
the second judgment value module is used for selecting the distribution condition of continuous same values from the fault characteristic data set as a second criterion for BMS sensor effectiveness evaluation and obtaining a second judgment value based on the second criterion;
the third judgment value module is used for selecting the distribution condition of the variation coefficient from the fault characteristic data set as a third criterion for BMS sensor effectiveness evaluation and obtaining a third judgment value based on the third criterion;
and the validity evaluation module is used for judging the state of the BMS sensor based on the first judging value, the second judging value and the third judging value to obtain a state value of the BMS sensor and evaluating the validity of the BMS sensor based on the state value of the BMS sensor.
9. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program, when executed by the processor, implements the steps of a method for BMS sensor validity assessment according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of a BMS sensor effectiveness assessment method according to any one of claims 1 to 7.
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| CN116916374A (en) * | 2023-09-13 | 2023-10-20 | 羿动新能源科技有限公司 | Wireless BMS channel quality evaluation method and system for power battery |
| CN116916374B (en) * | 2023-09-13 | 2024-01-26 | 羿动新能源科技有限公司 | Wireless BMS channel quality evaluation method and system for power battery |
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Inventor after: Yuan Guoqiang Inventor after: Liang Huishi Inventor after: Zhao Jiashen Inventor after: Zhou Kui Inventor after: Gong Xiaoxu Inventor after: Shi Zinan Inventor after: Lin Jun Inventor after: Hu Dongchen Inventor before: Liang Huishi Inventor before: Zhao Jiashen Inventor before: Zhou Kui Inventor before: Gong Xiaoxu Inventor before: Shi Zinan Inventor before: Lin Jun Inventor before: Hu Dongchen |