Intelligent monitoring method, system and equipment for vehicle storage battery and medium

Abstract

The invention provides an intelligent monitoring method, system and device for a vehicle storage battery and a medium, and belongs to the technical field of vehicle storage batteries. The method comprises the steps that storage battery type parameters are written into a non-volatile memory of a vehicle control unit through a vehicle diagnostic tool; the vehicle control unit reads the parameters and sends an initialization message to the intelligent battery sensor through an LIN bus for parameter calibration; periodically requesting the state information of the storage battery and judging the feedback credibility of the sensor; when the feedback is credible, non-standard electricity utilization, low electric quantity and low health degree are directly judged based on the sensor data, and an alarm is given; and when the feedback is not credible, T-BOX time information is received, recently credible SOC, SOH, temperature and time difference are used as BP neural network input, and judgment and alarm are executed after the current battery state is estimated. And the continuity of the key monitoring function is ensured.

Description

Technical Field

[0001] This invention belongs to the field of automotive battery technology, and particularly relates to an intelligent monitoring method, system, device and medium for automotive batteries. Background Technology

[0002] As a core component for vehicle starting, lighting, and powering electronic devices, the real-time monitoring and health management of the battery's status are crucial for ensuring vehicle operational safety and improving operational efficiency. Effective monitoring can prevent vehicle breakdowns caused by sudden battery failures, extend battery life, and reduce maintenance costs.

[0003] In existing technologies, when the intelligent battery sensor (IBS) itself malfunctions, the communication link with the vehicle controller is interrupted, or the data transmission is unreliable, the entire monitoring system will lose an accurate data source and will be unable to effectively judge and warn of improper battery power use, low battery level, and deterioration of battery health. The system's robustness and functional continuity are insufficient.

[0004] Therefore, the present invention provides a method, system, device and medium for intelligent monitoring of vehicle batteries. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for intelligent monitoring of vehicle batteries, which at least solves the problems of interrupted battery status monitoring function and insufficient system reliability caused by intelligent battery sensor (IBS) failure or communication interruption in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for intelligent monitoring of vehicle batteries, the method comprising: Step S1: Write the battery type parameters into the non-volatile memory of the vehicle controller using a vehicle diagnostic tool; the type parameters include the battery nominal capacity, characteristic voltage and battery type. The vehicle controller reads the type parameters and sends an initialization message containing the type parameters to the intelligent battery sensor IBS via the LIN bus to calibrate the parameters of the intelligent battery sensor IBS. Step S2: The vehicle controller periodically requests battery status information and intelligent battery sensor IBS operating status information from the intelligent battery sensor IBS via the LIN bus; the vehicle controller determines whether the intelligent battery sensor IBS feedback is reliable based on the received battery status information and intelligent battery sensor IBS operating status information, and sets an error flag; when the error flag indicates that the intelligent battery sensor IBS feedback is reliable, step S3 is executed; when the error flag indicates that the intelligent battery sensor IBS feedback is unreliable, step S4 is executed. Step S3: The vehicle controller, based on the battery charge SOC and battery health SOH fed back by the intelligent battery sensor IBS, performs judgment on irregular power consumption behavior, battery charge SOC and battery health SOH, and sends alarm messages to the vehicle remote communication terminal T-BOX or prompt information to the instrument panel via the CAN bus according to the judgment results. Step S4: The vehicle controller receives time information sent by the vehicle remote communication terminal T-BOX, and uses the most recent reliable battery SOC, battery health SOH, battery temperature, and iteration time difference as the initial input to the BP neural network algorithm. Based on the BP neural network algorithm, it estimates the current battery SOC and battery health SOH. Based on the estimated battery SOC and battery health SOH, it performs the non-standard power consumption behavior judgment, battery SOC judgment, and battery health SOH judgment, and sends an alarm message to the vehicle remote communication terminal T-BOX or a prompt message to the instrument panel via the CAN bus according to the judgment results.

[0007] Further, in step S2, the vehicle controller determines the reliability of the intelligent battery sensor (IBS) feedback based on the received battery status information and the intelligent battery sensor (IBS) operating status information, specifically including: If the intelligent battery sensor IBS reports a battery fault, or if the intelligent battery sensor IBS itself has an internal fault, or if the vehicle controller determines that the intelligent battery sensor IBS has an internal fault, or if there is a LIN bus transmission fault, then the feedback from the intelligent battery sensor IBS is deemed unreliable; otherwise, it is deemed reliable.

[0008] Furthermore, the conditions under which the vehicle controller determines an internal fault in the intelligent battery sensor (IBS) are as follows: An abnormal change occurred in a certain battery status information received from the intelligent battery sensor (IBS) during multiple consecutive communication cycles. The criteria for determining LIN bus transmission failure are as follows: The vehicle controller failed to send request information for several consecutive cycles or did not receive a response from the intelligent battery sensor (IBS).

[0009] Furthermore, in steps S3 and S4, the judgment of irregular electricity use behavior specifically includes: The vehicle controller sets a threshold for the percentage decrease in battery SOC within one hour based on the nominal capacity of different batteries. If the actual percentage decrease in battery SOC exceeds the threshold, it is determined to be an irregular electricity use behavior.

[0010] Furthermore, in steps S3 and S4, the determination of the battery's state of charge (SOC) specifically includes: If the battery's state of charge (SOC) is below 80%, a low battery alarm will be triggered. The battery health status (SOH) assessment specifically includes: If the battery health status (SOH) is below 85%, a maintenance prompt message will be sent to the instrument.

[0011] Further, in step S4, the input variables of the BP neural network algorithm are:

[0012] in, Indicates input variables, Indicates the iteration time difference. Indicates the battery temperature. Indicates the battery charge level. Indicates the battery's health status; For input variables Normalization is performed:

[0013]

[0014]

[0015] in, This represents the normalized input variable. This indicates that the normalized maximum value is 1. This represents the normalized minimum value of -1. This represents the maximum value of each input variable. This represents the minimum value of each input variable. This represents the maximum iteration time difference. Indicates the maximum battery temperature. Indicates the maximum battery capacity. Indicates the maximum battery capacity; This represents the minimum iteration time difference. This indicates the minimum battery temperature. This indicates the minimum battery capacity. This indicates the minimum battery capacity. The BP neural network algorithm includes an intermediate layer with 5 nodes. The expression for the output of the intermediate layer is:

[0016] in, This indicates the output after processing by the intermediate layer. This represents a 5×4 dimensional intermediate layer weight matrix. This represents the 5×1 dimensional intermediate layer deviation matrix. The hyperbolic tangent sigmoid transfer function is expressed as follows:

[0017] In the formula, Represents the natural constant; The output layer of the BP neural network algorithm uses a linear activation function, and the expression for the output of the output layer is:

[0018] in, This represents the output after processing by the output layer. This represents the 2×5 dimensional output layer weight matrix. This represents the 2×1 dimension output layer bias matrix.

[0019] Furthermore, the BP neural network algorithm further includes: performing inverse normalization on the output after processing by the output layer to obtain a battery state estimate, the expression of which is:

[0020] in, This represents a 2×1 dimensional matrix containing the SOC and SOH estimates. This represents the dot product operation of matrices. A 2×1 constant matrix , This represents the maximum value that includes both the SOC and SOH estimates. It is the minimum value that includes both the SOC estimate and the SOH estimate; The expressions for the SOC estimate and the SOH estimate are:

[0021] in, for The element in the first row and first column of the matrix. for The element in the second row and first column of the matrix. Input variables The element in the first row and first column.

[0022] Secondly, embodiments of this application also provide a system applied to the intelligent monitoring method for vehicle batteries as described in the above aspects, the system comprising: The parameter configuration and initialization module is used to write the battery type parameters into the non-volatile memory of the vehicle controller through vehicle diagnostic tools. The type parameters include the battery nominal capacity, characteristic voltage and battery type. The vehicle controller reads the type parameters and sends an initialization message containing the type parameters to the intelligent battery sensor IBS through the LIN bus to perform parameter calibration on the intelligent battery sensor IBS. The status monitoring and fault diagnosis module is used by the vehicle controller to periodically request battery status information and intelligent battery sensor IBS operating status information from the intelligent battery sensor IBS via the LIN bus; the vehicle controller determines whether the feedback from the intelligent battery sensor IBS is reliable based on the received battery status information and intelligent battery sensor IBS operating status information, and sets an error flag bit; when the error flag bit indicates that the feedback from the intelligent battery sensor IBS is reliable, the first status judgment and alarm module is executed; when the error flag bit indicates that the feedback from the intelligent battery sensor IBS is unreliable, the second status estimation and alarm module is executed. The first state judgment and alarm module is used by the vehicle controller to judge irregular power consumption behavior, battery SOC and battery SOH based on the battery power SOC and battery health SOH fed back by the intelligent battery sensor IBS, and send alarm messages to the vehicle remote communication terminal T-BOX or send prompt information to the instrument panel through the CAN bus according to the judgment results. The second state estimation and alarm module is used by the vehicle controller to receive time information sent by the vehicle remote communication terminal T-BOX, and uses the most recent reliable battery SOC, battery health SOH, battery temperature, and iteration time difference as the initial input of the BP neural network algorithm. Based on the BP neural network algorithm, it estimates the current battery SOC and battery health SOH. Based on the estimated battery SOC and battery health SOH, it performs the judgment of irregular power consumption behavior, battery SOC judgment, and battery health SOH judgment, and sends alarm messages to the vehicle remote communication terminal T-BOX or sends prompt information to the instrument panel via the CAN bus according to the judgment results.

[0023] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the intelligent monitoring method for vehicle batteries as described in the preceding aspects.

[0024] Fourthly, a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the intelligent monitoring method for vehicle batteries as described in the preceding aspects.

[0025] As can be seen from the above technical solutions, the present invention has the following advantages: The intelligent monitoring method for vehicle batteries provided in this application directly judges the status and issues alarms based on the data from the intelligent battery sensor IBS when the feedback is reliable, and switches to the estimated value based on the BP neural network algorithm for status judgment and alarms when the feedback from the intelligent battery sensor IBS is unreliable. This ensures continuous monitoring of the battery status under different operating conditions, overcomes the shortcomings of existing technologies that rely on a single data source and experience monitoring interruption when the sensor fails, and improves the availability and reliability of the system.

[0026] By periodically requesting and judging the reliability of feedback from the intelligent battery sensor (IBS) by the vehicle controller, and setting error flags accordingly, the system can intelligently identify the data source status and adapt to changes in data quality. This avoids misjudgments caused by unreliable data, thereby improving the accuracy of status judgment and the effectiveness of alarms.

[0027] When the feedback from the intelligent battery sensor IBS is detected as unreliable, a BP neural network algorithm is introduced to estimate the current battery state using battery SOC, battery SOH, temperature information, and time difference. This ensures the continuity of battery monitoring function and avoids unexpected interruptions caused by battery problems. Attached Figure Description

[0028] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of the intelligent monitoring method for vehicle batteries.

[0030] Figure 2 This is a flowchart of the BP neural network algorithm for the intelligent monitoring method of vehicle batteries. Detailed Implementation

[0031] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0032] This application provides a method, system, device, and medium for intelligent monitoring of vehicle batteries, addressing the urgent technical problem of achieving seamless monitoring and fault warning of battery status in the event of sensor failure.

[0033] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0034] Figure 1 This is a flowchart illustrating an intelligent monitoring method for vehicle batteries provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, a method for intelligent monitoring of vehicle batteries is provided, the method comprising: Step S1: When the vehicle rolls off the production line, the battery type parameters are written into the non-volatile memory of the vehicle controller using a vehicle diagnostic tool. The type parameters include the battery nominal capacity, characteristic voltage, and battery type. The vehicle controller reads the type parameters and sends an initialization message containing the type parameters to the intelligent battery sensor IBS via the LIN bus to calibrate the parameters of the intelligent battery sensor IBS. It should be noted that the vehicle controller cyclically sends initialization messages containing the aforementioned type parameters to the intelligent battery sensor IBS via the LIN bus until it receives the calibration completion flag returned by the intelligent battery sensor IBS and then stops sending.

[0035] Step S2: The vehicle controller periodically requests battery status information and intelligent battery sensor IBS operating status information from the intelligent battery sensor IBS via the LIN bus; the vehicle controller determines whether the intelligent battery sensor IBS feedback is reliable based on the received battery status information and intelligent battery sensor IBS operating status information, and sets an error flag; when the error flag indicates that the intelligent battery sensor IBS feedback is reliable, step S3 is executed; when the error flag indicates that the intelligent battery sensor IBS feedback is unreliable, step S4 is executed. Step S3: The vehicle controller, based on the battery charge SOC and battery health SOH fed back by the intelligent battery sensor IBS, performs judgment on irregular power consumption behavior, battery charge SOC and battery health SOH, and sends alarm messages to the vehicle remote communication terminal T-BOX or prompt information to the instrument panel via the CAN bus according to the judgment results. Step S4: The vehicle controller receives time information sent by the vehicle remote communication terminal T-BOX, and uses the most recent reliable battery SOC, battery health SOH, battery temperature, and iteration time difference as the initial input to the BP neural network algorithm. Based on the BP neural network algorithm, it estimates the current battery SOC and battery health SOH. Based on the estimated battery SOC and battery health SOH, it performs the non-standard power consumption behavior judgment, battery SOC judgment, and battery health SOH judgment, and sends an alarm message to the vehicle remote communication terminal T-BOX or a prompt message to the instrument panel via the CAN bus according to the judgment results.

[0036] This invention directly determines the alarm when the feedback from the intelligent battery sensor IBS is reliable, and switches to battery state estimation based on BP neural network when it is unreliable, thus realizing continuous and reliable monitoring under different operating conditions.

[0037] It should be noted that in step S2, the vehicle controller determines the reliability of the intelligent battery sensor (IBS) feedback based on the received battery status information and the IBS operating status information. Specifically, this includes: If the intelligent battery sensor IBS reports a battery fault, or if the intelligent battery sensor IBS itself has an internal fault, or if the vehicle controller determines that the intelligent battery sensor IBS has an internal fault, or if there is a LIN bus transmission fault, then the feedback from the intelligent battery sensor IBS is deemed unreliable; otherwise, it is deemed reliable.

[0038] In an exemplary embodiment, the vehicle controller determines an internal fault in the intelligent battery sensor (IBS) as follows: An abnormal change occurred in a certain battery status information received from the intelligent battery sensor (IBS) during multiple consecutive communication cycles. The criteria for determining LIN bus transmission failure are as follows: The vehicle controller failed to send request information for several consecutive cycles or did not receive a response from the intelligent battery sensor (IBS).

[0039] According to another embodiment of the present invention, in steps S3 and S4, the judgment of irregular electricity use behavior specifically includes: The vehicle controller sets a threshold for the percentage decrease in battery SOC within one hour based on the nominal capacity of different batteries. If the actual percentage decrease in battery SOC exceeds the threshold, it is determined to be an irregular electricity use behavior.

[0040] According to an embodiment of this application, in steps S3 and S4, the determination of the battery's state of charge (SOC) specifically includes: If the battery's state of charge (SOC) is below 80%, a low battery alarm will be triggered. The battery health status (SOH) assessment specifically includes: If the battery health status (SOH) is below 85%, a maintenance prompt message will be sent to the instrument.

[0041] In one embodiment, in step S4, the input variables of the BP neural network algorithm are:

[0042] in, Indicates input variables, For 4×1 dimension input variables, Indicates the iteration time difference. Indicates the battery temperature. Indicates the battery charge level. Indicates the battery's health status; To improve the accuracy of the BP neural network algorithm, the input variables are normalized to the interval [-1, 1] to address the problem of excessively large dimensional differences. For the input variables... Normalization is performed:

[0043]

[0044]

[0045] in, This represents the normalized input variable. This indicates that the normalized maximum value is 1. This represents the normalized minimum value of -1, specifically a 4×1 constant matrix. , This represents the maximum value of each input variable. This represents the minimum value of each input variable. This represents the maximum iteration time difference. Indicates the maximum battery temperature. Indicates the maximum battery capacity. Indicates the maximum battery capacity; This represents the minimum iteration time difference. This indicates the minimum battery temperature. This indicates the minimum battery capacity. This indicates the minimum battery capacity. The BP neural network algorithm includes an intermediate layer with 5 nodes. Based on the empirical formula for automotive neural networks: number of intermediate layer nodes = number of input layer nodes + number of output layer nodes + empirical constant, with a 4-dimensional input layer and a 2-dimensional output layer (only SOC and SOH need to be predicted), and an empirical constant of -1, the number of nodes is determined to be 5. Simultaneously, 5 nodes ensure that the vehicle controller completes the entire model within one task cycle (10ms), meeting real-time requirements. Compared to models with 3 / 5 / 7 / 9 nodes, the validation set with 5 nodes exhibits the lowest mean squared error (≤0.003), with no overfitting. The intermediate layer is selected as follows: The function outputs a range of [-1, 1] that aligns with the input normalization interval, enhancing nonlinear fitting capabilities and preventing gradient vanishing. The expression for the output of the intermediate layer is:

[0046] in, This indicates the output after processing by the intermediate layer. This represents a 5×4 dimensional intermediate layer weight matrix. Initialized using Xavier, randomly generated at... The interval is calculated with the denominator being the input dimension plus the intermediate layer dimension, ensuring the stability of the initial gradient. This represents the 5×1 dimensional intermediate layer deviation matrix. This indicates a constant matrix initialized to 0.1 to avoid training stagnation caused by initial output values ​​approaching 0; The hyperbolic tangent sigmoid transfer function is expressed as follows:

[0047] In the formula, Represents the natural constant; The output layer of the BP neural network algorithm uses a linear activation function to directly output continuous values ​​of SOC and SOH, simplifying the onboard calculation process; the expression for the output of the output layer is:

[0048] in, This represents the output after processing by the output layer. This represents the 2×5 dimensional output layer weight matrix. Also using Xavier initialization, randomly generated at... The interval is calculated with the denominator being the intermediate layer dimension plus the output dimension, ensuring the stability of the initial gradient. This represents a 2×1 dimensional output layer bias matrix. This represents a constant matrix initialized to 0.1, to avoid training stagnation caused by initial output values ​​approaching 0.

[0049] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, to fully illustrate the specific implementation process in this embodiment, another intelligent monitoring method for vehicle batteries is provided. The BP neural network algorithm further includes: performing inverse normalization processing on the output after output layer processing to restore the iterative variables to their original numerical range, thereby obtaining a battery state estimate, the expression of which is:

[0050] in, This represents a 2×1 dimensional matrix containing the SOC and SOH estimates. To represent the dot product operation of matrices, in order to maintain dimensionality consistency, denormalization is performed. A 2×1 constant matrix , This represents the maximum value that includes both the SOC and SOH estimates. It is the minimum value that includes both the SOC estimate and the SOH estimate; The inverse normalized output of the SOC estimate is corrected to obtain the naturally decayed SOC estimate. The expressions for the SOC estimate and the SOH estimate are as follows:

[0051] in, for The element in the first row and first column of the matrix. for The element in the second row and first column of the matrix. Input variables The element in the first row and first column, i.e. .

[0052] To determine , , and The final parameters are based on three target battery types (flooded battery, AGM battery, and enhanced flooded battery). For each type, three real vehicle batteries at different aging stages (SOH: 100%, 80%, and 60%) were selected. Data was collected within a temperature range of -40℃ to 60℃ and a discharge current range of 5A to 30A (covering typical operating conditions such as idling, driving, and extreme power consumption in commercial vehicles). The total sample size is 100,000 sets, divided into a training set (70,000 sets), a validation set (20,000 sets), and a test set (10,000 sets) in a 7:2:1 ratio. Each set contains... (1 min ~ 60 min) Input items for (-40℃~60℃), SOC (0%~100%), and SOH (50%~100%), and their corresponding values. and Authentic labels.

[0053] The training set is also normalized to [-1, 1], and the test set and validation set use the same parameters; The loss function uses mean squared error:

[0054] Where N is the number of samples; the optimization algorithm uses momentum gradient descent with a momentum factor of 0.9, an initial learning rate of 0.01, and a decay of 10% every 100 iterations; the termination condition is 5000 iterations or 50 consecutive validation sets. Decrease ≤ After completing the above training, determine... , , and The final parameters.

[0055] After obtaining the SOC and SOH estimates, the logic for judging non-standard power consumption alarms, low battery alarms, and low battery health reminders remains the same as described above. Simultaneously, regardless of the battery condition, the vehicle controller feeds back the battery status to the onboard remote communication terminal T-BOX every 200ms, including misalignment flag information, to ensure the backend can identify the source of the received battery status information and determine whether it comes from feedback from the intelligent battery sensor IBS or is based on the vehicle controller's own estimation.

[0056] The vehicle controller periodically feeds back battery status information to the vehicle remote communication terminal T-BOX via the CAN bus. The feedback information includes the error flag bit, which is used to identify whether the status information comes from the intelligent battery sensor IBS or the estimated value of the vehicle controller.

[0057] This invention also provides an intelligent monitoring system for vehicle batteries, the system comprising: The parameter configuration and initialization module is used to write the battery type parameters into the non-volatile memory of the vehicle controller through vehicle diagnostic tools. The type parameters include the battery nominal capacity, characteristic voltage and battery type. The vehicle controller reads the type parameters and sends an initialization message containing the type parameters to the intelligent battery sensor IBS through the LIN bus to perform parameter calibration on the intelligent battery sensor IBS. The status monitoring and fault diagnosis module is used by the vehicle controller to periodically request battery status information and intelligent battery sensor IBS operating status information from the intelligent battery sensor IBS via the LIN bus; the vehicle controller determines whether the feedback from the intelligent battery sensor IBS is reliable based on the received battery status information and intelligent battery sensor IBS operating status information, and sets an error flag bit; when the error flag bit indicates that the feedback from the intelligent battery sensor IBS is reliable, the first status judgment and alarm module is executed; when the error flag bit indicates that the feedback from the intelligent battery sensor IBS is unreliable, the second status estimation and alarm module is executed. The first state judgment and alarm module is used by the vehicle controller to judge irregular power consumption behavior, battery SOC and battery SOH based on the battery power SOC and battery health SOH fed back by the intelligent battery sensor IBS, and send alarm messages to the vehicle remote communication terminal T-BOX or send prompt information to the instrument panel through the CAN bus according to the judgment results. The second state estimation and alarm module is used by the vehicle controller to receive time information sent by the vehicle remote communication terminal T-BOX, and uses the most recent reliable battery SOC, battery health SOH, battery temperature, and iteration time difference as the initial input of the BP neural network algorithm. Based on the BP neural network algorithm, it estimates the current battery SOC and battery health SOH. Based on the estimated battery SOC and battery health SOH, it performs the judgment of irregular power consumption behavior, battery SOC judgment, and battery health SOH judgment, and sends alarm messages to the vehicle remote communication terminal T-BOX or sends prompt information to the instrument panel via the CAN bus according to the judgment results.

[0058] The intelligent monitoring method for vehicle batteries provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0059] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0060] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0061] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0062] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0063] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0064] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0065] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0066] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0067] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0068] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0069] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0070] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0071] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0072] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0074] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0075] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0076] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.

[0077] The aforementioned electronic device implements the intelligent monitoring method for vehicle batteries of this application, which involves writing battery type parameters into the non-volatile memory of the vehicle controller using vehicle diagnostic tools; the vehicle controller reading the parameters and sending initialization messages to the intelligent battery sensor via the LIN bus for parameter calibration; periodically requesting battery status information and judging the reliability of sensor feedback; when the feedback is reliable, directly judging and alarming based on sensor data for irregular power consumption, low power, and low health; when the feedback is unreliable, receiving T-BOX time information, using the most recent reliable SOC, SOH, temperature, and time difference as input to the BP neural network, estimating the current battery status, and then executing the judgment and alarm. This achieves seamless monitoring of battery status under sensor failure conditions, effectively improving the reliability of a single sensor system through neural network prediction algorithms, extending battery life, and preventing vehicle breakdowns.

[0078] The storage medium provided in this application stores a program product capable of implementing an intelligent monitoring method for vehicle batteries.

[0079] Intelligent monitoring methods for vehicle batteries include: Step S1: Write the battery type parameters into the non-volatile memory of the vehicle controller using a vehicle diagnostic tool; the type parameters include the battery nominal capacity, characteristic voltage and battery type. The vehicle controller reads the type parameters and sends an initialization message containing the type parameters to the intelligent battery sensor IBS via the LIN bus to calibrate the parameters of the intelligent battery sensor IBS. Step S2: The vehicle controller periodically requests battery status information and intelligent battery sensor IBS operating status information from the intelligent battery sensor IBS via the LIN bus; the vehicle controller determines whether the intelligent battery sensor IBS feedback is reliable based on the received battery status information and intelligent battery sensor IBS operating status information, and sets an error flag; when the error flag indicates that the intelligent battery sensor IBS feedback is reliable, step S3 is executed; when the error flag indicates that the intelligent battery sensor IBS feedback is unreliable, step S4 is executed. Step S3: The vehicle controller, based on the battery charge SOC and battery health SOH fed back by the intelligent battery sensor IBS, performs judgment on irregular power consumption behavior, battery charge SOC and battery health SOH, and sends alarm messages to the vehicle remote communication terminal T-BOX or prompt information to the instrument panel via the CAN bus according to the judgment results. Step S4: The vehicle controller receives time information sent by the vehicle remote communication terminal T-BOX, and uses the most recent reliable battery SOC, battery health SOH, battery temperature, and iteration time difference as the initial input to the BP neural network algorithm. Based on the BP neural network algorithm, it estimates the current battery SOC and battery health SOH. Based on the estimated battery SOC and battery health SOH, it performs the non-standard power consumption behavior judgment, battery SOC judgment, and battery health SOH judgment, and sends an alarm message to the vehicle remote communication terminal T-BOX or a prompt message to the instrument panel via the CAN bus according to the judgment results.

[0080] By integrating parameter configuration, dual-channel status judgment (direct reading and BP neural network prediction) and multi-level alarm mechanism into the vehicle controller, seamless monitoring and fault warning of battery status are achieved in the event of sensor failure, ensuring the continuity of key monitoring functions.

[0081] In some possible implementations, the intelligent monitoring method for vehicle batteries disclosed herein can be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0082] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0083] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0084] Any changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.

Claims

1. A method for intelligent monitoring of a vehicle battery, characterized in that, The method comprises: Step S1: writing a type parameter of the storage battery into a non-volatile memory of the vehicle controller through a vehicle diagnostic tool; the type parameter comprises a nominal capacity, a characteristic voltage and a battery type of the storage battery; the vehicle controller reads the type parameter and sends an initialization message containing the type parameter to the intelligent battery sensor IBS through a LIN bus to calibrate parameters of the intelligent battery sensor IBS; Step S2: the vehicle controller periodically requests the intelligent battery sensor IBS for storage battery state information and intelligent battery sensor IBS working state information through the LIN bus; the vehicle controller judges whether the feedback of the intelligent battery sensor IBS is credible based on the received storage battery state information and intelligent battery sensor IBS working state information, and sets an error flag; when the error flag indicates that the feedback of the intelligent battery sensor IBS is credible, step S3 is executed; when the error flag indicates that the feedback of the intelligent battery sensor IBS is not credible, step S4 is executed; Step S3: the vehicle controller performs abnormal power consumption behavior judgment, storage battery capacity SOC judgment and storage battery health SOH judgment based on the storage battery capacity SOC and the storage battery health SOH fed back by the intelligent battery sensor IBS, and sends an alarm message to a vehicle remote communication terminal T-BOX or sends prompt information to an instrument according to the judgment result through a CAN bus; Step S4: the vehicle controller receives time information sent by the vehicle remote communication terminal T-BOX, takes the last credible storage battery capacity SOC, the last credible storage battery health SOH, the last credible storage battery temperature and an iteration time difference as initial inputs of a BP neural network algorithm, estimates the current storage battery capacity SOC and the current storage battery health SOH based on the BP neural network algorithm, performs the abnormal power consumption behavior judgment, the storage battery capacity SOC judgment and the storage battery health SOH judgment based on the estimated storage battery capacity SOC and the estimated storage battery health SOH, and sends an alarm message to the vehicle remote communication terminal T-BOX or sends prompt information to the instrument according to the judgment result through the CAN bus.

2. The intelligent monitoring method for automotive battery as claimed in claim 1 wherein, In step S2, the vehicle controller judges whether the feedback of the intelligent battery sensor IBS is credible based on the received storage battery state information and intelligent battery sensor IBS working state information, specifically comprising: If the intelligent battery sensor IBS feeds back a storage battery fault or an internal fault of the intelligent battery sensor IBS, or the vehicle controller judges an internal fault of the intelligent battery sensor IBS, or there is a LIN bus transmission fault, it is determined that the feedback of the intelligent battery sensor IBS is not credible, otherwise it is determined to be credible.

3. The intelligent monitoring method for automotive battery as claimed in claim 2 wherein, The condition for the vehicle controller to judge the internal fault of the intelligent battery sensor IBS is that: Abnormal jumps occur in some storage battery state information fed back by the intelligent battery sensor IBS in continuous multiple communication periods; The judgment condition for the LIN bus transmission fault is that: The vehicle controller fails to send request information continuously for multiple periods or does not receive a response from the intelligent battery sensor IBS.

4. The intelligent monitoring method for automotive batteries as claimed in claim 1, wherein, In step S3 and step S4, the non-standard power consumption behavior judgment specifically includes: The vehicle controller sets a percentage threshold of the SOC of the battery in one hour according to the nominal capacity of the battery, and if the actual percentage of the SOC of the battery is higher than the threshold, it is determined that the non-standard power consumption behavior.

5. The intelligent monitoring method for automotive batteries as claimed in claim 1 wherein, In step S3 and step S4, the SOC of the battery is determined specifically as follows: If the SOC of the battery is lower than 80%, a low battery alarm is given; The SOH of the battery is determined specifically as follows: If the SOH of the battery is lower than 85%, a maintenance prompt message is sent to the instrument.

6. The intelligent monitoring method for automotive batteries as claimed in claim 1 wherein, In step S4, the input variables of the BP neural network algorithm are: wherein, represents an input variable, represents an iteration time difference, represents a battery temperature, represents a battery charge, represents a battery health; normalizing the input variables performing normalization wherein, represents a normalized input variable, represents a normalized maximum value 1, represents a normalized minimum value -1, represents a maximum value of each input variable, represents a minimum value of each input variable, represents an iteration time difference maximum value, represents a battery temperature maximum value, represents a battery charge maximum value, represents a battery capacity maximum value; represents an iteration time difference minimum value, represents a battery temperature minimum value, represents a battery charge minimum value, represents a battery capacity minimum value; The BP neural network algorithm includes an intermediate layer, the number of nodes of the intermediate layer is 5, and the expression of the output of the intermediate layer is: wherein, represents an output after an intermediate layer processing, represents a 5 x 4 dimensional intermediate layer weight matrix, represents a 5 x 1 dimensional intermediate layer bias matrix, represents a hyperbolic tangent sigmoid transfer function, whose expression is: wherein represents a natural constant; The output layer of the BP neural network algorithm adopts a linear activation function, and the expression of the output of the output layer is: wherein, represents an output after output layer processing, represents a 2 x 5 dimensional output layer weight matrix, represents a 2 x 1 dimensional output layer bias matrix.

7. The intelligent monitoring method for automotive batteries as claimed in claim 6 wherein, The BP neural network algorithm further includes: performing inverse normalization processing on the output processed by the output layer to obtain the estimated value of the state of the battery, and the expression is: wherein represents a 2x1 dimensional matrix containing the SOC estimate and the SOH estimate, represents a dot product operation of matrices, is a constant matrix of 2x1 , is a maximum of the SOC estimate and the SOH estimate, is a minimum of the SOC estimate and the SOH estimate; The expressions of the SOC estimated value and the SOH estimated value are: wherein is the element of the first row and first column of the matrix, is the element of the second row and first column of the matrix, is an input variable the element of the first row and first column.

8. A system for use in the intelligent monitoring method of a vehicle storage battery as claimed in any one of claims 1 to 7, characterized in that, The system includes: A parameter configuration and initialization module is configured to write the type parameters of the battery into the non-volatile memory of the vehicle controller through a vehicle diagnostic tool; the type parameters include the nominal capacity of the battery, the characteristic voltage, and the type of the battery; the vehicle controller reads the type parameters and sends an initialization message containing the type parameters to the intelligent battery sensor IBS through a LIN bus to calibrate the parameters of the intelligent battery sensor IBS; A state monitoring and fault judgment module is configured to periodically request the state information of the battery and the working state information of the intelligent battery sensor IBS from the intelligent battery sensor IBS through the LIN bus by the vehicle controller; the vehicle controller determines whether the feedback of the intelligent battery sensor IBS is reliable based on the received state information of the battery and the working state information of the intelligent battery sensor IBS, and sets an error flag; when the error flag indicates that the feedback of the intelligent battery sensor IBS is reliable, the first state judgment and alarm module is executed; when the error flag indicates that the feedback of the intelligent battery sensor IBS is not reliable, the second state estimation and alarm module is executed; The first state judgment and alarm module is configured to determine the non-standard power consumption behavior, the SOC of the battery, and the SOH of the battery based on the feedback of the intelligent battery sensor IBS, and send an alarm message to the vehicle remote communication terminal T-BOX through the CAN bus or send a prompt message to the instrument according to the determination result. The second state estimation and alarm module is configured to receive time information sent by a vehicle remote communication terminal T-BOX, take the last reliable battery power SOC, battery health SOH, battery temperature and iteration time difference as initial inputs of a BP neural network algorithm, estimate the current battery power SOC and the battery health SOH based on the BP neural network algorithm, perform the non-standard power consumption behavior judgment, the battery power SOC judgment and the battery health SOH judgment based on the estimated battery power SOC and the battery health SOH, and send an alarm message to the vehicle remote communication terminal T-BOX or send prompt information to an instrument through a CAN bus according to a judgment result.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the intelligent monitoring method for a vehicle battery according to any one of claims 1-7 when executing the program.

10. A storage medium having stored thereon a computer program, characterized in that The computer program implements the steps of the intelligent monitoring method for a vehicle battery according to any one of claims 1-7 when executed by the processor.

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