Method, device, equipment, medium and product for monitoring electric vehicle batteries

Abstract

A method for monitoring an electric vehicle battery includes: collecting a corresponding first number of voltage value samples for each battery cell of a plurality of battery cells of an electric vehicle battery; determining a voltage statistical characteristic of each battery cell based on the corresponding first number of voltage value samples of each battery cell; determining an average voltage statistical characteristic of the plurality of battery cells based on the corresponding voltage statistical characteristics of the plurality of battery cells; comparing the voltage statistical characteristic of each battery cell with the average voltage statistical characteristic; and determining a condition of the battery cell based on a result of the comparison.

Description

Technical Field

[0001] The present disclosure relates to the field of electric vehicles, and in particular to a method, apparatus, computer device, readable storage medium, computer program product, and vehicle for monitoring a battery of an electric vehicle. Background Art

[0002] Electric vehicles are vehicles powered by an onboard power source and driven by an electric motor. Compared to traditional vehicles, electric vehicles have a lower environmental impact and hold broad development prospects. As the core power source of electric vehicles, onboard batteries, monitoring their operating conditions, plays a crucial role in ensuring their safety. However, existing technologies for monitoring electric vehicle batteries still have significant room for improvement.

[0003] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0004] It would be advantageous to provide a mechanism that alleviates, mitigates, or even eliminates one or more of the above-mentioned problems.

[0005] According to one aspect of the present disclosure, a method for monitoring an electric vehicle battery is provided, comprising: collecting a corresponding first number of voltage value samples for each battery cell of a plurality of battery cells of a battery of the electric vehicle; determining a voltage statistical characteristic of each battery cell based on the corresponding first number of voltage value samples of each battery cell; determining an average voltage statistical characteristic of the plurality of battery cells based on the corresponding voltage statistical characteristics of the plurality of battery cells; comparing the voltage statistical characteristic of each battery cell with the average voltage statistical characteristic; and determining a condition of the battery cell based on a result of the comparison.

[0006] According to another aspect of the present disclosure, an electric vehicle battery detection device is provided, including: an acquisition module configured to collect a corresponding first number of voltage value samples for each battery cell of a plurality of battery cells of an electric vehicle battery; a first determination module configured to determine the voltage statistical characteristics of each battery cell based on the corresponding first number of voltage value samples of each battery cell; a second determination module configured to determine the average voltage statistical characteristics of the plurality of battery cells based on the corresponding voltage statistical characteristics of the plurality of battery cells; a comparison module configured to compare the voltage statistical characteristics of each battery cell with the average voltage statistical characteristics; and a third determination module configured to determine the condition of the battery cell based on a result of the comparison.

[0007] According to yet another aspect of the present disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor is configured to execute the computer program to implement the steps of the above method.

[0008] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0009] According to yet another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the steps of the above method when executed by a processor.

[0010] According to yet another aspect of the present disclosure, a vehicle is provided, comprising the above-mentioned apparatus or the above-mentioned computer device.

[0011] These and other aspects of the disclosure will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, like reference numerals designate similar, but not necessarily identical, elements.

[0013] Figure 1 is a schematic diagram illustrating an application scenario according to an exemplary embodiment;

[0014] Figure 2 is a flow chart illustrating a method for monitoring a battery of an electric vehicle according to an exemplary embodiment;

[0015] Figure 3 is a diagram showing a method of performing a multi-processor circuit according to an exemplary embodiment. Figure 2 A flowchart of example operations for collecting voltage value samples in the method;

[0016] Figure 4 is a diagram showing a method of performing a multi-processor circuit according to an exemplary embodiment. Figure 2 A flowchart of example operations for determining voltage statistical characteristics in a method;

[0017] Figure 5 is a diagram showing a method for performing a multi-processor circuit according to an exemplary embodiment. Figure 4 Schematic diagram of determining a battery cell condition in a method;

[0018] Figure 6 is a diagram showing a method for performing a multi-processor circuit according to an exemplary embodiment. Figure 4Another schematic diagram of determining a battery cell condition in a method;

[0019] Figure 7 is a block diagram illustrating a structure of an electric vehicle battery monitoring device according to an exemplary embodiment;

[0020] Figure 8 is a block diagram illustrating an exemplary computer device that can be applied to the exemplary embodiments. DETAILED DESCRIPTION

[0021] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0022] The terms used in the description of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. As used herein, the term "plurality" means two or more, and the term "based on" should be interpreted as "based at least in part on". In addition, the terms "and / or" and "at least one of..." cover any one of the listed items and all possible combinations.

[0023] It should be understood that the term "vehicle" or other similar terms used in this document generally include motor vehicles, such as passenger vehicles including cars, sport utility vehicles (SUVs), buses, trucks, various commercial vehicles, ships including various boats and vessels, aircraft, etc.

[0024] With the increasing popularity and application of electric vehicles, ensuring their safety has become a hot research area. Electric vehicles use high-voltage power batteries. Due to the characteristics of batteries, especially high-power batteries with relatively large energy storage, serious faults such as thermal runaway and insulation failures can cause battery fires and endanger personnel safety. Therefore, serious faults affecting the safety of high-voltage power batteries require special monitoring and appropriate early warning measures to prevent them.

[0025] In the analysis of quality issues in daily work with high-voltage power batteries, voltage deviations are often detected after the customer has already received a driveline warning or has been affected by a high-voltage power battery shutdown.

[0026] The present disclosure provides a method for monitoring the battery of an electric vehicle. The principles of the embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0027] Figure 1 FIG. 1 is a schematic diagram showing an application scenario 100 according to an exemplary embodiment. Figure 1 As shown, the application scenario 100 may include a vehicle to be powered 110 , a network 120 , a server 130 , and a database 140 .

[0028] The electric vehicle 110 may include various sensors, such as a voltage sensor, a current sensor, a speed sensor, etc. The sensors installed on the electric vehicle 110 may be used to obtain sensing data such as voltage and current of the high-voltage battery.

[0029] The electric vehicle 110 may further include at least one processor and a memory. In some embodiments, the positioning object 110 may utilize the processor to process the raw sensing data acquired by the sensor to obtain voltage statistics for battery fault analysis and further execute the method provided in this application.

[0030] In other embodiments, positioning object 110 further includes a communication unit, and can transmit the received raw sensing data and / or battery voltage statistics determined based on the raw sensing data to server 130 via network 120, and the method provided by the present application can be executed by server 130. In some examples, server 130 can process the raw sensing data to generate voltage statistics.

[0031] In some implementations, the server 130 may execute the method provided in this application using an application built into the server. In other implementations, the server 130 may execute the method provided in this application by calling an application stored externally on the server.

[0032] The network 120 may be a single network or a combination of at least two different networks. For example, the network 120 may include, but is not limited to, a local area network, a wide area network, a public network, a private network, or a combination of several of them.

[0033] The server 130 can be a single server or a server group, wherein the servers in the group are connected via a wired or wireless network. A server group can be centralized, such as a data center, or distributed. The server 130 can be local or remote.

[0034] The database 140 can generally refer to a device with a storage function. The database 140 is mainly used to store various data used, generated, and output from the operation of the electric vehicle 110 and the server 130. The database 140 can be local or remote. The database 140 can include various memories, such as random access memory (RAM), read-only memory (ROM), etc. The storage devices mentioned above are just some examples, and the storage devices that can be used in the system are not limited to them.

[0035] The database 140 may be connected or communicated with the server 130 or a portion thereof via the network 120 , or directly connected or communicated with the server 130 , or a combination of the two.

[0036] In some embodiments, database 140 may be a standalone device. In other embodiments, database 140 may be integrated into at least one of electric vehicle 110 and server 130. For example, database 140 may be located on electric vehicle 110 or on server 130. For another example, database 140 may be distributed, with a portion located on electric vehicle 110 and another portion located on server 130.

[0037] Figure 2 FIG. 2 is a flow chart illustrating a method 200 for verifying a positioning result of an object according to an exemplary embodiment. Figure 2 , method 200 includes steps 210 to 250.

[0038] In step 210, a corresponding first number of voltage value samples are collected for each of the multiple battery cells of the electric vehicle battery. For example, the high-voltage battery of the electric vehicle may include multiple battery cells, each of which has a voltage during the operation of the electric vehicle. For example, during battery monitoring, the voltage value of each battery cell may be continuously collected until the first number of voltage values ​​of the voltage cell are collected. For example, the voltage value of each battery cell is collected using a voltage sensor installed on the electric vehicle. The first number may be, for example, 10,000 voltage data. Presetting a larger first number value can reduce the error in voltage acquisition, thereby improving the accuracy of subsequent monitoring and analysis.

[0039] At step 220, based on the first number of voltage value samples corresponding to each battery cell, a voltage statistical characteristic of each battery cell is determined. For example, the voltage statistical characteristic of a battery cell can be used to analyze whether the battery cell deviates from a normal voltage range. By using the voltage statistical characteristic of the battery cell, voltage deviations can be monitored to identify battery cells with potential faults. This prevents the user from being notified of a battery cell fault until a powertrain warning is received or the high-voltage power battery is shut down.

[0040] At step 230, an average voltage statistical feature of the plurality of battery cells is determined based on the corresponding voltage statistical features of the plurality of battery cells. For example, after determining the voltage statistical feature of each battery cell, an average of the corresponding voltage statistical features of the plurality of battery cells may be calculated to serve as the average voltage statistical feature.

[0041] At step 240 , the voltage statistics of each battery cell are compared with the average voltage statistics.

[0042] In step 250, the condition of the battery cell is determined based on the comparison result. In some exemplary embodiments, the battery cell can be determined to be abnormal in response to the comparison result indicating that the difference between the voltage statistical characteristics of the battery cell and the average voltage statistical characteristics exceeds a first threshold. For example, the first threshold can be set first, and then the difference between the voltage statistical characteristics of each battery cell and the average voltage statistical characteristics is calculated. If the difference is greater than the first threshold, the battery cell is determined to be abnormal. For example, the appropriate first threshold can be determined based on long-term sample collection and analysis in multiple electric vehicles, as well as in combination with the final abnormality of the battery cell.

[0043] In some exemplary embodiments, a battery cell may be determined to be abnormal if the comparison result indicates that the voltage statistical characteristic of the battery cell is greater than three times the average voltage statistical characteristic. For example, in situations where it is difficult to accurately select a first threshold, the condition of the battery cell may be determined based on the relative ratio / multiple of the voltage statistical characteristic of each battery cell to the average voltage statistical characteristic, thereby avoiding misjudgment of the battery cell condition due to selecting an inappropriate first threshold.

[0044] In some exemplary embodiments, the electric vehicle battery comprises a lithium battery.

[0045] In some exemplary embodiments, voltage collection, statistics, and analysis can be performed locally in the electric vehicle. In this example, upon determining that a battery cell is abnormal, a signal containing the battery cell information can be sent to the electric vehicle's control system or a remote server. Upon receiving the signal, the electric vehicle's control system or remote server can alert the customer or invite them to perform maintenance and inspections, and then focus on monitoring the battery cell for a specific period of time.

[0046] In summary, the battery monitoring method 200 disclosed in the present application can monitor the operating status of the battery based on voltage acquisition. Before the user receives a transmission system warning or is affected by the shutdown of the high-voltage power battery, the method 200 can identify the battery cell that is most likely to have problems based on voltage deviation monitoring, thereby warning the user in advance. Since in the high-voltage battery of an electric vehicle including multiple battery cells, the performance problem of a certain battery cell, such as reduced capacity, will affect the performance of the entire high-voltage battery. Therefore, the method 200 can also ensure the operating performance of the entire high-voltage battery by identifying the battery cell that is most likely to have problems and issuing timely warnings.

[0047] Figure 3 is a diagram showing a method of performing a multi-processor circuit according to an exemplary embodiment. Figure 2 Flowchart of example operations for collecting voltage value samples in method 200. Figure 3 , collecting the corresponding first number of voltage value samples (step 210 ) further includes steps 310 to 320 .

[0048] In step 310 , during the operation of the electric vehicle, voltage value collection is performed at preset time intervals, and each voltage value collection obtains a corresponding voltage value sample for each battery cell in the plurality of battery cells.

[0049] In step 320 , when the electric vehicle is not running, voltage value acquisition is not performed.

[0050] In some exemplary embodiments, the voltage value of each battery cell can be collected at a preset interval of 30 seconds, and the collected voltage value is used as a voltage value sample of the battery cell. Since the voltage value is collected during the operation of the electric vehicle, the real-time performance of battery monitoring is enhanced.

[0051] In some exemplary embodiments, step 210 may further include step 330. In step 330, in response to determining that the number of voltage value samples collected for each battery cell is greater than the first number, the first number of voltage value samples that are most recent in time are selected as the first number of voltage value samples for the battery cell. For example, 10,000 voltage value samples are collected at intervals of 30 seconds. When more than 10,000 samples are collected, a first-in-first-out rule may be adopted to discard the earliest collected samples exceeding 10,000, to ensure that the data used for analysis are all the most recent data in time, thereby improving the real-time performance and accuracy of battery condition monitoring.

[0052] Figure 4 is a diagram showing a method of performing a multi-processor circuit according to an exemplary embodiment. Figure 2 Flowchart of example operations for determining voltage statistical characteristics in method 200. Figure 4 , determining the voltage statistical characteristics of each battery cell (step 220 ) further includes steps 410 to 430 .

[0053] At step 410 , for corresponding voltage value samples of a plurality of battery cells obtained during each voltage value collection performed during collection of a corresponding first number of voltage value samples for each battery cell: an average value of the corresponding voltage value samples of the plurality of battery cells is determined.

[0054] In step 420 , the absolute value of the difference between the voltage value sample of each battery cell and the mean value is determined.

[0055] In step 430 , a sum of the absolute values ​​determined during the collection of the corresponding first number of voltage value samples is calculated for each battery cell as a voltage statistical feature of the battery cell.

[0056] For example, the first number can be set to n, and in the i-th acquisition, the voltage value sample of the x-th battery cell acquired is The mean of the voltage value samples determined by step 410 is The absolute value of the difference between the voltage value sample of the x-th battery cell determined in step 420 and the mean value is Then the voltage statistical feature f(x) of the x-th battery cell determined in step 430 is:

[0057]

[0058] For example, when collecting voltage samples for each battery cell at predetermined intervals of 30 seconds, the voltage value of each battery cell can first be collected using, for example, a voltage sensor, and the voltage value can be used as a corresponding voltage sample. The mean of the voltage samples for all battery cells is then calculated. The difference between the voltage sample for each battery cell and the mean is then calculated, and the absolute value of the difference is obtained. Furthermore, when collecting, for example, 10,000 pieces of data, the method described in steps 410 to 420 is performed for each collection to obtain the absolute value of the difference between each battery cell and the mean voltage value collected that time. Finally, for each battery cell, the 10,000 calculated absolute values ​​are summed to obtain a total, which serves as the voltage statistical feature of the battery cell.

[0059] In summary, by calculating the absolute value of the difference between each battery cell's voltage and the average voltage of all battery cells during each acquisition period, we can characterize the difference between each battery cell and the average voltage of all battery cells. By continuously accumulating this difference, battery cells with abnormal voltage differences can be more clearly identified.

[0060] In some exemplary embodiments, determining the voltage statistical characteristics of each battery cell (step 220) may include calculating the mean of a first number of voltage value samples of the battery cell as the voltage statistical characteristics of the battery cell. For example, for example, 10,000 collected voltage value samples, the mean of each battery cell may be calculated. That is, for each battery cell, the mean of the 10,000 voltage value samples is calculated as the voltage statistical characteristics of the battery cell.

[0061] Figure 5 is a diagram showing a method for performing a multi-processor circuit according to an exemplary embodiment. Figure 4 Schematic diagram of determining the battery cell condition in the method. Figure 5 , the battery 500 includes 96 battery cells. Figure 4 The method described above determines the voltage statistical feature 510 of each battery cell. For example, the voltage statistical feature 510 corresponding to battery cells 89 and 96 is obtained by accumulating the absolute values ​​determined during each acquisition of battery cells 89 and 96 when the first number is 5842.

[0062] In some exemplary embodiments, when determining the battery cell status, the mean of the voltage statistical characteristics 510 of battery cells 1 to 96 is first calculated, and then the difference between the voltage statistical characteristics 510 of battery cells 1 to 96 and the mean is compared. When the difference is greater than a first threshold, the battery cell is determined to be abnormal. For example, Figure 5The voltage statistical feature 520 of the battery cell 84 in FIG. 5 has a relatively abnormal peak value, and the difference between the peak value and the mean value of the voltage statistical feature is greater than the first threshold value. Therefore, the battery cell 84 is identified as abnormal.

[0063] In some other exemplary embodiments, the voltage statistical characteristics 510 of battery cells 1 to 96 may be compared with the voltage statistical characteristics mean value by multiples, and when the voltage statistical characteristics 510 is greater than three times the average voltage statistical characteristics, the battery cell is determined to be abnormal. Figure 5 The voltage statistical feature 520 of the battery cell 84 in FIG. 5 is greater than the average voltage statistical feature. Therefore, the battery cell 84 is identified as abnormal.

[0064] Figure 6 is a diagram showing a method for performing a multi-processor circuit according to an exemplary embodiment. Figure 4 Another schematic diagram of determining the condition of a battery cell in a method of Figure 6 The battery 600 includes 96 battery cells. The voltage statistical feature 610 of each battery cell is based on collecting 20079 voltage values ​​and using Figure 4 Determined by the method described in . Figure 6 The voltage statistical characteristics of each of the battery cells 1 to 96 shown in FIG are neither greater than the first threshold value of the voltage statistical characteristics mean value nor greater than three times the voltage statistical characteristics. Therefore, each of the battery cells 1 to 96 is not identified as abnormal.

[0065] While various operations are depicted in the drawings as following a particular order, this should not be understood as requiring that these operations be performed in the particular order shown or in sequential order, nor should it be understood that all illustrated operations must be performed to achieve desirable results.

[0066] Figure 7 FIG. 7 is a block diagram showing a structure of an apparatus 700 for verifying a positioning result of an object according to an exemplary embodiment. Figure 7 As shown, the apparatus 700 includes a collection module 710 , a first determination module 720 , a second determination module 730 , a comparison module 740 and a third determination module 750 .

[0067] The acquisition module 710 is configured to acquire a first number of voltage value samples for each of the plurality of battery cells of the electric vehicle battery. In some exemplary embodiments, the acquisition module 710 may be further configured to: acquire voltage values ​​at predetermined intervals during operation of the electric vehicle, obtaining a corresponding voltage value sample for each of the plurality of battery cells during each voltage acquisition; and not acquire voltage values ​​when the electric vehicle is not in operation.

[0068] The first determination module 720 is configured to determine a voltage statistical feature of each battery cell based on a first number of voltage value samples corresponding to each battery cell.

[0069] The second determination module 730 is configured to determine an average voltage statistical feature of the plurality of battery cells according to corresponding voltage statistical features of the plurality of battery cells.

[0070] The comparison module 740 is configured to compare the voltage statistical feature of each battery cell with the average voltage statistical feature.

[0071] The third determination module 750 is configured to determine the condition of the battery cell according to the comparison result.

[0072] It should be understood that Figure 7 The modules of the apparatus 700 shown in FIG. 7 can be compared with those in the above referenced Figures 2 to 6 The steps in the method 200 described above correspond to each other. Therefore, the operations, features and advantages described above for the method 200 are also applicable to the apparatus 700 and the modules included therein. For the sake of brevity, some operations, features and advantages are not described here in detail.

[0073] While specific functionality has been discussed above with reference to specific modules, it should be noted that the functionality of the various modules discussed herein may be separated into multiple modules, and / or at least some functionality of multiple modules may be combined into a single module. A specific module as discussed herein performing an action includes the specific module itself performing the action, or alternatively, the specific module calling or otherwise accessing another component or module that performs the action (or performs the action in conjunction with the specific module). Thus, a specific module that performs an action may include the specific module itself that performs the action and / or another module that the specific module calls or otherwise accesses to perform the action.

[0074] It should also be understood that various techniques may be described herein in the general context of software hardware elements or program modules. Figure 7The various modules described can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program code / instructions that are configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuits. For example, in some embodiments, one or more modules in the device 700 can be implemented together in a system on a chip (SoC). The SoC may include an integrated circuit chip (which includes a processor (e.g., a central processing unit (CPU), a microcontroller, a microprocessor, a digital signal processor (DSP)), a memory, one or more communication interfaces, and / or one or more components in other circuits), and may optionally execute the received program code and / or include embedded firmware to perform the functions.

[0075] According to one aspect of the present disclosure, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory. The processor is configured to execute the computer program to implement the steps of any one of the method embodiments described above.

[0076] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any method embodiment described above are implemented.

[0077] According to one aspect of the present disclosure, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the steps of any one of the method embodiments described above are implemented.

[0078] According to one aspect of the present disclosure, a vehicle is provided, comprising the above-mentioned apparatus or computer device.

[0079] Figure 8 An example configuration of a computer device 800 is shown that may be used to implement the methods described herein.

[0080] Computer device 800 can be any machine configured to perform processing and / or computing, and can be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a smartphone, an in-vehicle computer, or any combination thereof. The aforementioned apparatus for verifying the positioning result of an object can be implemented in whole or in part by computer device 800 or a similar device or system.

[0081] Computer device 800 may include an element that is connected to bus 802 (possibly via one or more interfaces) or communicates with bus 802. For example, computer device 800 may include bus 802, one or more processors 804, one or more input devices 806, and one or more output devices 808. One or more processors 804 may be any type of processor and may include, but are not limited to, one or more general-purpose processors and / or one or more special-purpose processors (e.g., special processing chips). Input device 806 may be any type of device that can input information to computer device 800 and may include, but are not limited to, a mouse, keyboard, touch screen, microphone, and / or remote controller. Output device 808 may be any type of device that can present information and may include, but are not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The computer device 800 may also include or be connected to a non-transitory storage device 810. The non-transitory storage device may be any storage device that is non-transitory and can store data, and may include, but is not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a flexible disk, a hard disk, a magnetic tape, or any other magnetic medium, an optical disk, or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory, and / or any other memory chip or cartridge, and / or any other medium from which a computer can read data, instructions, and / or code. The non-transitory storage device 810 may be detachable from the interface. The non-transitory storage device 810 may contain data / programs (including instructions) / code for implementing the above-described methods and steps. The computer device 800 may also include a communication device 812. The communication device 812 can be any type of device or system that enables communication with external devices and / or with a network, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication device and / or a chipset, such as a Bluetooth™ device, a 1302.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0082] When the computer device 800 is used as an in-vehicle system, the computer device 800 can, for example, receive data indicating the operating status of the vehicle battery, such as voltage and current. The computer device 800 can also be connected to other devices for controlling the driving and operation of the vehicle (such as the engine system, wipers, anti-lock braking system, etc.).

[0083] In addition, the non-transitory storage device 810 may have map information and software elements to enable the processor 804 to perform route guidance processing. In addition, the output device 806 may include a display for displaying a map, a location marker of the vehicle, and an image indicating the driving conditions of the vehicle. The output device 806 may also include a speaker or interface with headphones for audio guidance.

[0084] The bus 802 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. Specifically, for an in-vehicle system, the bus 802 may include a Controller Area Network (CAN) bus or other architectures designed for automotive applications.

[0085] The computer device 800 may also include a working memory 814, which may be any type of working memory that can store programs (including instructions) and / or data useful for the operation of the processor 804, and may include, but is not limited to, random access memory and / or read-only memory devices.

[0086] The software elements (programs) may be located in the working memory 814, including but not limited to an operating system 816, one or more application programs 818, drivers and / or other data and code. Instructions for executing the above methods and steps may be included in one or more application programs 818, and the above-mentioned apparatus 700 for electric vehicle battery testing may be implemented by the processor 804 reading and executing the instructions of one or more application programs 818. The executable code or source code of the instructions of the software elements (programs) may be stored in a non-transitory computer-readable storage medium (such as the above-mentioned storage device 810) and may be stored in the working memory 814 (possibly compiled and / or installed) when executed. The executable code or source code of the instructions of the software elements (programs) may also be downloaded from a remote location.

[0087] It should also be understood that various modifications may be made depending on specific requirements. For example, custom hardware may be used, and / or specific elements may be implemented using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. For example, some or all of the disclosed methods and apparatus may be implemented by programming hardware (e.g., programmable logic circuits including field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) using logic and algorithms according to the present disclosure in assembly language or a hardware programming language (such as VERILOG, VHDL, C++).

[0088] It should also be understood that the aforementioned method can be implemented using a server-client model. For example, the client can receive data input by a user and send the data to the server. The client can also receive data input by the user, perform a portion of the processing in the aforementioned method, and send the processed data to the server. The server can receive the data from the client, execute the aforementioned method or another portion of the aforementioned method, and return the execution results to the client. The client can receive the execution results of the method from the server and present them to the user, for example, via an output device.

[0089] It should also be understood that the components of computer device 800 can be distributed across a network. For example, some processing can be performed by one processor while other processing can be performed by another processor remote from the one processor. Other components of computing system 800 can also be similarly distributed. Thus, computing system 800 can be interpreted as a distributed computing system that performs processing at multiple locations.

[0090] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present disclosure is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples can be omitted or replaced by their equivalents. In addition, the steps can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that as technology evolves, many of the elements described here can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A method for monitoring an electric vehicle battery, the method comprising: For each battery cell of a plurality of battery cells of the electric vehicle battery, collecting a corresponding first number of voltage value samples; determining a voltage statistical feature of each battery cell based on the corresponding first number of voltage value samples of each battery cell; determining an average voltage statistical feature of the plurality of battery cells according to corresponding voltage statistical features of the plurality of battery cells; comparing the voltage statistical characteristics of each battery cell with the average voltage statistical characteristics; as well as Determine the condition of the battery cell based on the comparison result, The collecting of the corresponding first number of voltage value samples includes: During the operation of the electric vehicle, voltage value acquisition is performed at preset time intervals, wherein each voltage value acquisition obtains a corresponding voltage value sample for each battery cell of the plurality of battery cells; and When the electric vehicle is not in operation, the voltage value acquisition is not performed. The step of determining the voltage statistical characteristics of each battery cell includes: For the corresponding voltage value samples of the plurality of battery cells obtained in each voltage value acquisition performed during the acquisition of the corresponding first number of voltage value samples for each battery cell: determining a mean of the corresponding voltage value samples of the plurality of battery cells; Determining the absolute value of the difference between the voltage value sample of each battery cell and the mean value; and A sum of the absolute values ​​determined during the collection of the corresponding first number of voltage value samples is calculated for each battery cell as a voltage statistical feature of the battery cell.

2. The method according to claim 1, wherein The collecting of the corresponding first number of voltage value samples further comprises: In response to determining that the number of the collected voltage value samples of each battery cell is greater than the first number, the first number of voltage value samples that are latest in time are selected as the first number of voltage value samples of the battery cell.

3. The method according to claim 1, wherein The average voltage statistical feature is a mean value of the corresponding voltage statistical features of the plurality of battery cells.

4. The method according to claim 3, wherein: Determining the condition of the battery cell includes: In response to the comparison result indicating that the difference between the voltage statistical feature of the battery cell and the average voltage statistical feature exceeds a first threshold, the battery cell is determined to be abnormal.

5. The method according to claim 3, wherein: Determining the condition of the battery cell includes: In response to the comparison result indicating that the voltage statistical characteristic of the battery cell is greater than three times the average voltage statistical characteristic, the battery cell is determined to be abnormal.

6. The method of claim 1, wherein: The electric vehicle battery includes a lithium battery.

7. An electric vehicle battery detection device comprising: a collection module configured to collect a corresponding first number of voltage value samples for each battery cell of a plurality of battery cells of the electric vehicle battery; A first determining module is configured to determine a voltage statistical feature of each battery cell based on the corresponding first number of voltage value samples of each battery cell; a second determining module configured to determine an average voltage statistical feature of the plurality of battery cells based on corresponding voltage statistical features of the plurality of battery cells; a comparison module configured to compare the voltage statistical feature of each battery cell with the average voltage statistical feature; as well as The third determining module is configured to determine the condition of the battery cell according to the comparison result, Wherein, the acquisition module is further configured to: During the operation of the electric vehicle, voltage value acquisition is performed at preset time intervals, wherein each voltage value acquisition obtains a corresponding voltage value sample for each battery cell of the plurality of battery cells; and When the electric vehicle is not in operation, the voltage value acquisition is not performed. The step of determining the voltage statistical characteristics of each battery cell includes: For the corresponding voltage value samples of the plurality of battery cells obtained in each voltage value acquisition performed during the acquisition of the corresponding first number of voltage value samples for each battery cell: determining a mean of the corresponding voltage value samples of the plurality of battery cells; Determining the absolute value of the difference between the voltage value sample of each battery cell and the mean value; and A sum of the absolute values ​​determined during the collection of the corresponding first number of voltage value samples is calculated for each battery cell as a voltage statistical feature of the battery cell.

8. A computer device comprising: a memory, a processor, and a computer program stored on said memory, The processor is configured to execute the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

11. A vehicle comprising the apparatus according to claim 7 or the computer device according to claim 8.

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