A method and device for predicting the torque aging of battery box fixing bolts

Abstract

This invention relates to the field of safety technology for new energy electric vehicles, and in particular to a method and device for predicting the torque aging of battery housing fixing bolts. This solution first establishes a torque aging model for the battery housing fixing bolts based on the relationship between the torque of the fixing bolts and the changes in vehicle mileage and vibration aging parameters of the fixing bolts; wherein, the vibration aging parameters are parameters related to the position of the fixing bolts. Then, the vehicle's mileage data is obtained and substituted into the aforementioned torque aging model to predict the torque aging of the fixing bolts. The influencing factors selected in this solution not only closely match the actual operating conditions of the vehicle and the characteristics of the bolts themselves, but also only require measuring the vehicle's mileage data to predict the torque aging of the fixing bolts, making it simple and easy to operate.

Description

Technical Field

[0001] This invention relates to the field of safety technology for new energy electric vehicles, and in particular to a method and device for predicting the torque aging of battery box fixing bolts. Background Technology

[0002] For new energy vehicles, the torque of the battery pack mounting bolts gradually decreases with increasing mileage. As the torque decreases, the bolts become less effective at securing the battery pack, and bolts may even come loose, causing the battery pack to shift and leading to battery power supply line malfunctions, thus posing a safety hazard during vehicle operation. Therefore, it is necessary to study the torque decay pattern of the battery pack mounting bolts and to develop early warning systems for bolt loosening.

[0003] Existing research on the torque decay law of fixing bolts often involves measuring the torque at certain intervals or when the bolt is in a certain state throughout its entire service life, and then statistically analyzing the measurement results. Although this method can describe the torque decay law relatively accurately, the operation process is cumbersome, time-consuming, labor-intensive, and costly, and it cannot provide real-time information on the torque decay of the fixing bolts of the vehicle battery box.

[0004] Therefore, some have proposed using torque-related influencing factors to determine torque changes. Since there are many external factors affecting the torque decay of fixing bolts, such as the material properties of the fastener, the tightening speed and sequence, and the ambient temperature, existing technologies have used these factors to study the torque decay law of fixing bolts. However, these factors have a relatively small impact on the torque decay of fixing bolts, so the predicted torque decay of fixing bolts is not accurate enough. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the torque aging of battery box fixing bolts, in order to solve the problems of cumbersome research process, time-consuming and labor-intensive research, and inaccurate prediction of torque decay of fixing bolts in the existing technology; and to provide a device for predicting the torque aging of battery box fixing bolts, so as to realize the function of the method for predicting the torque aging of battery box fixing bolts.

[0006] To address the aforementioned technical problems, this invention provides a method for predicting the torque aging of battery housing fixing bolts, the specific steps of which are as follows:

[0007] 1) Based on the relationship between the torque of the battery box fixing bolts and the changes in vehicle mileage and vibration aging parameters of the battery box fixing bolts, a torque aging model for the battery box fixing bolts is established; where the vibration aging parameters are parameters related to the position of the battery box fixing bolts.

[0008] 2) Obtain the vehicle's mileage data and input it into the battery box fixing bolt torque aging model to predict the battery box fixing bolt torque aging.

[0009] Beneficial Effects: This invention, by combining actual vehicle operating conditions, studies and analyzes that torque attenuation is mainly related to vehicle mileage and the vibration aging parameters of the battery box mounting bolts. Furthermore, the vibration aging parameters are related to the position of the battery box mounting bolts. Then, a torque aging model for the battery box mounting bolts is established using the relationship between these parameters. The influencing factors selected in this model not only closely match the actual operating conditions of the vehicle and the characteristics of the bolts themselves, but also only require measuring the vehicle's mileage data to predict the torque aging of the battery box mounting bolts, making it simple and easy to operate.

[0010] Furthermore, based on the various bolt torque aging conditions predicted using the battery box fixing bolt torque aging model, different warning levels are set for the vehicle, and corresponding warnings or braking are performed according to the warning level.

[0011] Beneficial effects: Based on the torque aging model of the battery box fixing bolts, this invention predicts the torque aging of various bolts and sets different warning levels for the vehicle. This not only allows the vehicle to be aware of its current status during driving, but also enables different warning prompts or driving braking measures to be taken for different warning levels, thus ensuring driving safety.

[0012] Furthermore, the vibration aging parameters of the battery box fixing bolts are obtained by normalizing the torque values ​​of the fixing bolts at corresponding positions on the battery box of the same type of vehicle at various mileage stages and then taking the average value.

[0013] Beneficial effects: Since the vibration aging of bolts installed at different locations in the vehicle battery box varies, the torque values ​​of bolts installed at various fixing points in the battery box of multiple vehicles of the same model are measured, and the normalized mean of the bolt torque values ​​at corresponding locations in each vehicle is used as the vibration aging parameter of the bolts at the corresponding locations. The calculation process is simple and reliable.

[0014] Furthermore, the vibration aging parameters of the battery box fixing bolts were obtained by grouping vehicles of the same model according to different mileage stages, measuring the torque value of the fixing bolts at each corresponding position of the battery box in each group of vehicles, and then normalizing the measured torque values ​​of each group and taking the average value.

[0015] Beneficial effects: As the vehicle's mileage increases, the vibration aging of the bolts mounted on the battery box becomes more severe. Moreover, the vibration aging of bolts installed at different locations on the vehicle's battery box varies. Therefore, by grouping vehicles of the same model according to different mileage stages, and measuring the torque value of the bolts installed at various fixed points on the battery box of each group, the normalized mean of the bolt torque values ​​at the corresponding positions in that group is used as the vibration aging parameter for the bolts at that position in that mileage stage. When making predictions, the torque aging parameter for the corresponding group is selected based on the vehicle's mileage to predict the torque amount. The calculation process is not only simple and reliable, but also more accurate.

[0016] Furthermore, the torque aging model for the battery box fixing bolts is: Torque = Initial Torque - a * (Vehicle Mileage)^(b * Vibration Aging Parameter), where a and b are fitting parameters.

[0017] Furthermore, a and b are obtained using the least squares method.

[0018] Furthermore, the formula for normalization is:

[0019]

[0020] The present invention also provides a battery box fixing bolt torque aging prediction device, the device including a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to execute the above-described battery box fixing bolt torque aging prediction method by executing the executable instructions. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the construction process of the torque aging model for the battery housing fixing bolts in this embodiment.

[0022] Figure 2 This is a schematic diagram illustrating the torque decay law of the fixing bolt in an embodiment;

[0023] Figure 3 This is a schematic diagram showing the location of the battery box fixing points in an embodiment;

[0024] Figure 4 These are simulation graphs of the optimized actual torque and fitted torque from the embodiment. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical principles and practical applications of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0026] Example 1 of the method for predicting the torque aging of battery housing fixing bolts:

[0027] By studying various factors affecting the torque decay of battery box mounting bolts and combining them with changes in electric vehicle driving parameters, it was found that the torque decay of battery box mounting bolts is mainly related to vehicle mileage and vibration aging parameters of battery box mounting bolts. Therefore, this embodiment establishes a torque aging model for battery box mounting bolts based on the relationship between the torque of battery box mounting bolts and the changes in vehicle mileage and vibration aging parameters of battery box mounting bolts (hereinafter referred to as vibration aging parameters).

[0028] The process of constructing the torque aging model of battery box fixing bolts is as follows: Figure 1 As shown, the details are as follows:

[0029] 1) Constructing a prediction model

[0030] By utilizing the relationship between the torque of the fixing bolts on the battery box and the changes in vehicle mileage and vibration aging parameters of the fixing bolts, the following functional relationship can be constructed.

[0031] Torque = f(vehicle mileage, vibration aging parameters).

[0032] Based on practical engineering experience, the torque aging process of fixing bolts is a process that starts with a relatively constant torque, gradually accelerating towards deterioration. A schematic diagram illustrating the torque decay pattern of fixing bolts is shown below. Figure 2 As shown. Therefore, the torque aging model of the battery box fixing bolts is defined as follows.

[0033] Torque = Initial torque - a * (vehicle mileage)^(b * vibration aging parameters),

[0034] Where a and b are fitting parameters, and both are obtained using the least squares method; the initial torque can be obtained during the first installation.

[0035] 2) Determine vibration aging parameters

[0036] Because the vibration of the fixing bolts on the battery box installed at different locations on the vehicle body varies during the operation of an electric vehicle, and the vibration of the fixing bolts at different locations on the same battery box also varies, the vibration aging parameters of the fixing bolts on the battery box are parameters related to the bolt installation position.

[0037] The vibration aging parameters of the fixing bolts are determined based on their installation location. The specific process is as follows:

[0038] This embodiment selects 15 electric vehicles of the same model and divides them into five groups (A, B, C, D, and E) according to their mileage. Each electric vehicle of this model has six battery packs, each secured by six identical bolts at different fixing points. The fixing points of the battery packs are located as follows: Figure 3 As shown.

[0039] The relationship between vibration aging parameters and bolt installation position is expressed by the following formula.

[0040] Vibration aging parameter = f (position of battery box fixing point).

[0041] Since vibration aging parameters are an important factor affecting bolt torque, the vibration aging parameters are determined by measuring the bolt torque at the fixing point of the battery box.

[0042] This embodiment uses any of the existing methods for measuring bolt torque (e.g., the return method, marking method, tightening method, instantaneous loosening method, etc.) to measure the torque of all fixing bolts on the battery boxes of these 15 electric vehicles. The torque diagram of the fixing bolts on each battery box is shown in Table 1. In the figure, each black rectangle represents a battery box, each small square represents a fixing point, and the value represents the bolt torque measured at the corresponding position (the torque value in the blank position is not measured because it is inconvenient to measure in practice).

[0043] Table 1

[0044]

[0045]

[0046] Then, the measured torque values ​​are normalized using the following formula.

[0047]

[0048] The results after normalizing the measured torque values ​​are shown in Table 2. In the figure, 0 represents the position with the largest fixed torque in a vehicle, and 1 represents the position with the smallest fixed torque in a vehicle.

[0049] Table 2

[0050]

[0051]

[0052]

[0053] The torque values ​​at the same fixed point location for each group of electric vehicles are normalized and then averaged to obtain the vibration aging parameter for that location. The value ranges from 0 to 1, with a closer value to 0 indicating a lower degree of vibration aging and a closer value to 1 indicating a higher degree of vibration aging. Similarly, the normalized values ​​at each corresponding fixed point location for each group of electric vehicles are averaged to obtain the vibration aging parameter for each fixed point location. The vibration aging parameters at all fixed point locations for each group of electric vehicles are then used as the vibration aging parameter set for that model of electric vehicle at various mileage stages.

[0054] 3) Torque prediction based on the torque aging model of the battery box fixing bolts.

[0055] By obtaining the mileage data of the vehicle under test and selecting the vibration aging parameters corresponding to the mileage, and substituting them into the prediction model of the vehicle under test model, the current bolt torque aging condition of the vehicle under test can be predicted.

[0056] The simulation diagram comparing the predicted torque and the actual torque value using this model is shown below. Figure 4 As shown in the figure, the torque value predicted by the model is basically distributed around the actual torque value, which means that the model can accurately predict the torque aging of the battery box fixing bolts.

[0057] 4) Based on the torque decay of the bolts at each fixed point, set different warning levels for the vehicle, and provide corresponding warning prompts or braking according to the warning level.

[0058] To achieve precise control, a tiered warning system is adopted, with at least two warning levels. The current warning level of the vehicle is determined by the magnitude by which the predicted torque exceeds the set torque warning value and the number of bolts exceeding the torque warning value. The more bolts below the torque warning value, the higher the warning level; the larger the difference between the predicted torque value and the torque warning value, the higher the warning level.

[0059] For example, two warning levels can be set: if at most three bolts at the predicted bolt torque at each fixed point have a torque lower than the set torque warning value, and the difference between the torque warning value and the bolt torque value lower than the torque warning value is large, a level one warning is issued, prompting the driver to check the vehicle battery box fixing bolts; if more than six bolts at the predicted bolt torque at each fixed point have a torque lower than the set torque warning value, a level two warning is issued, prompting the driver to take braking measures.

[0060] This embodiment, by setting warning levels and corresponding warning measures, not only enables timely knowledge of the vehicle's current status during driving, but also allows for the implementation of different warning alarms or braking measures for different warning levels, ensuring driving safety.

[0061] The warning level setting strategy in this embodiment is for staff reference only, but is not limited to the setting method in this embodiment. Any changes to the warning level made based on the concept of this embodiment are within the protection scope of this invention.

[0062] This invention, by combining actual vehicle operating conditions, studies and analyzes that torque attenuation is mainly related to vehicle mileage and the vibration aging parameters of the battery box mounting bolts. Furthermore, the vibration aging parameters are related to the position of the battery box mounting bolts. Then, by establishing the relationship between the vibration aging parameters and the location of the mounting point and the bolt torque at that location, the vibration aging parameters are obtained. Finally, a torque aging model for the battery box mounting bolts is established using the least squares method. The influencing factors selected in this model not only closely reflect the actual operating conditions of the vehicle and the characteristics of the bolts themselves, but also only require measuring the vehicle's mileage data to predict the torque aging of the battery box mounting bolts, making it simple and easy to operate.

[0063] Example 2 of the method for predicting the torque aging of battery housing fixing bolts:

[0064] The model construction process in this embodiment is basically the same as that in embodiment 1. The difference is that this embodiment only considers the influence of the installation position of the bolt on its vibration aging condition, and then calculates the vibration aging parameters of the bolt. The calculation process is simple and reliable.

[0065] This embodiment can select multiple electric vehicles of the same model with the same mileage, or multiple electric vehicles of the same model with different mileages. If multiple electric vehicles of the same model with different mileages are selected, the selected vehicles are treated as a whole. By measuring the torque values ​​of the fixing bolts at corresponding positions of the battery box of all vehicles, and then normalizing the measured torque values ​​and taking the average value, the vibration aging parameters of the bolts at each fixing point of the battery box of that electric vehicle model are obtained, thus obtaining the torque aging model of the battery box fixing bolts.

[0066] Once the torque aging model of the battery box fixing bolts is established, the torque decay of the bolts fixing the battery box of the electric vehicle model can be obtained simply by acquiring the vehicle's mileage and substituting it into the model.

[0067] Example of a battery housing fixing bolt torque aging prediction device:

[0068] The battery casing fixing bolt torque aging prediction device of the present invention includes a processor and a memory. The processor executes a computer program stored in the memory to enable the present invention to implement the method of the above-described method embodiments. That is, the method in the above method embodiments should be understood as a process based on the above-described battery casing fixing bolt torque aging prediction method that can be implemented by computer program instructions. These computer programs can be provided to issue instructions to the processor, causing the processor to execute these instructions to generate the functions specified for implementing the above-described method flow.

[0069] In this embodiment, the processor refers to a processing device such as a microprocessor (MCU) or a programmable logic device (FPGA); the memory refers to a physical device used to store information, which typically involves digitizing the information and then storing it using media that utilizes electrical, magnetic, or optical methods. Examples include: various types of memory that store information using electrical energy, such as RAM and ROM; various types of memory that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memory, bubble memory, and USB flash drives; and various types of memory that store information using optical methods, such as CDs or DVDs. Of course, there are other types of memory, such as quantum memories and graphene memories.

[0070] The device consisting of the aforementioned memory, processor, and computer program is implemented in a computer by the processor executing the corresponding program instructions. The processor can run various operating systems, such as Windows, Linux, Android, and iOS.

[0071] As an alternative implementation, the device may also include a display for showing the classification results for staff reference.

[0072] The processor in this embodiment can be a Linux server. The processor executes corresponding program instructions as Python scripts. The Python scripts are executed daily at set times to write the classification results into the database and display them on the energy management system interface.

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Many changes and modifications can be made without departing from the scope of the invention. Therefore, the above detailed description is intended to be illustrative rather than restrictive, and it should be understood that the above claims (including all equivalents) are intended to define the spirit and scope of the invention. These embodiments should be understood as illustrative only and not as limiting the scope of protection of the invention. After reading the description of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent changes and modifications also fall within the scope defined by the claims of this invention.

Claims

1. A battery case fixing bolt torque aging prediction method characterized by, The specific steps are: 1) According to the change relationship between the battery box fixing bolt torque and the vehicle driving mileage and the vibration aging parameter of the battery box fixing bolt, an aging model of the battery box fixing bolt torque is established; wherein the vibration aging parameter is a parameter related to the position of the battery box fixing bolt; 2) Obtain the driving mileage data of the vehicle and substitute it into the battery box fixing bolt torque aging model to predict the aging condition of the battery box fixing bolt torque.

2. The battery case fixation bolt torque aging prediction method according to claim 1, characterized by, According to the various bolt torque aging conditions predicted by the battery box fixing bolt torque aging model, different warning levels are set for the vehicle, and corresponding warnings or braking are performed according to the warning level.

3. The battery case fixation bolt torque aging prediction method according to claim 1, characterized by, The vibration aging parameter of the battery box fixing bolt is obtained by normalizing and averaging the measured fixing bolt torque values of the same type of vehicle at each corresponding position of the battery box at each driving mileage stage.

4. The battery case fixation bolt torque aging prediction method according to claim 1, characterized by, The vibration aging parameter of the battery box fixing bolt is obtained by normalizing and averaging the measured fixing bolt torque values of the same type of vehicle at each corresponding position of the battery box at each driving mileage stage.

5. The battery case fixation bolt torque aging prediction method according to claim 3 or 4, characterized by, The battery box fixing bolt torque aging model is: torque = initial torque - a * (vehicle driving mileage) ^ (b * vibration aging parameter), a and b are fitting parameters.

6. The battery case fixation bolt torque aging prediction method according to claim 5, characterized by, The a and b are obtained by least squares method.

7. The battery case fixation bolt torque aging prediction method according to claim 3 or 4, characterized by, The formula of the normalization processing is:

8. A battery case fixing bolt torque aging prediction device characterized by comprising: The device includes a processor and a memory, wherein the memory is used to store executable instructions of the processor; the processor is configured to execute the executable instructions to perform the battery box fixing bolt torque aging prediction method of any one of claims 1-7.

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