Vehicle load determination method and device, equipment and storage medium

Abstract

The invention discloses a vehicle load determination method and device, equipment and a storage medium, and belongs to the technical field of automobiles. The method specifically comprises the following steps: acquiring a current plate spring deformation value and driving state information of a vehicle; wherein the deformation value of the plate spring is collected by a displacement sensor of the plate spring; based on the plate spring deformation value and the driving state information, determining a fatigue coefficient corresponding to the plate spring deformation value; based on a first calibration relation, the fatigue coefficient and the plate spring deformation value, obtaining a first load corresponding to the fatigue coefficient and the plate spring deformation value; the first calibration relationship is a three-dimensional association relationship among the calibrated plate spring deformation value, the fatigue coefficient and the load; and based on the first load, determining a target load by using a preset load estimation strategy. According to the invention, the timeliness of the load estimation result of the vehicle can be optimized.

Description

Technical Field

[0001] This application relates to the technical field of automobiles, specifically to technical fields such as vehicle detection technology, and particularly relates to a method, device, equipment and storage medium for determining the load of a vehicle. Background Art

[0002] In the field of new energy commercial vehicles, the vehicle load is proportional to the recovery force. By associating vehicle load data in aspects such as energy drive and regenerative braking, accurately estimating the load when the vehicle is empty or fully loaded can improve the overall driving smoothness and overall performance of the vehicle.

[0003] Currently, the vehicle load estimation method in the related art mainly estimates the vehicle load value through the acceleration in the X, Y, and Z axes. However, the result of the load estimation in this solution cannot meet the driving or braking requirements under certain specific working conditions of new energy commercial vehicles, resulting in certain driving safety risks. Summary of the Invention

[0004] This application provides a method, device, equipment and storage medium for determining the load of a vehicle, which can solve the problem of poor timeliness and reliability of the result of vehicle load estimation. The technical solution is as follows:

[0005] In a first aspect, a method for determining the load of a vehicle is provided. The method includes:

[0006] Obtain the current leaf spring deformation value and driving state information of the vehicle; wherein, the leaf spring deformation value is collected by a displacement sensor of the leaf spring;

[0007] Based on the leaf spring deformation value and driving state information, determine the fatigue coefficient corresponding to the leaf spring deformation value;

[0008] Based on the first calibration relationship, the fatigue coefficient and the leaf spring deformation value, obtain the first load corresponding to the fatigue coefficient and the leaf spring deformation value; the first calibration relationship is a three-dimensional correlation relationship of the calibrated leaf spring deformation value, fatigue coefficient, and load;

[0009] Based on the first load, use a preset load estimation strategy to determine the target load.

[0010] In a possible implementation manner, the step of obtaining the first load corresponding to the fatigue coefficient and the leaf spring deformation value based on the first calibration relationship, the fatigue coefficient, and the leaf spring deformation value includes:

[0011] Match the leaf spring deformation value and fatigue coefficient in the first calibration relationship with the current leaf spring deformation value and fatigue coefficient;

[0012] Obtain a first load corresponding to the fatigue coefficient and the leaf spring deformation value based on the load corresponding to the result of the matching process.

[0013] In a possible implementation, the calibration process for the first calibration relationship includes:

[0014] Obtain a plurality of fatigue coefficients based on the leaf spring fatigue cycle;

[0015] For each initial load, perform the following operations: detect the leaf spring deformation value corresponding to each fatigue coefficient based on the initial load; perform a correction process on the initial load based on each fatigue coefficient to obtain a corrected load corresponding to each fatigue coefficient;

[0016] Calibrate the first calibration relationship based on a plurality of fatigue coefficients corresponding to each initial load, the leaf spring deformation value corresponding to each fatigue coefficient, and the corrected load corresponding to each fatigue coefficient.

[0017] In a possible implementation, the driving state information includes the vehicle speed. The determining of the target load based on the first load by using a preset load estimation strategy includes:

[0018] Determine whether the vehicle speed is a preset speed threshold;

[0019] In response to the vehicle speed being the preset speed threshold, determine the target load based on the first load.

[0020] In a possible implementation, the determining of the target load based on the first load by using a preset load estimation strategy includes:

[0021] Obtain a first estimation coefficient, a second load, and a second estimation coefficient;

[0022] Perform a weighted calculation process on the first load and the second load based on the first estimation coefficient and the second estimation coefficient;

[0023] Determine the target load based on the result of the weighted calculation process.

[0024] In a possible implementation, obtaining the second load includes:

[0025] Send the driving state information to the vehicle networking, so that the vehicle networking determines the second load based on the second calibration relationship and the driving state information and returns the second load; wherein, the second calibration relationship is a three-dimensional correlation relationship of calibrated braking force, acceleration, and load;

[0026] Obtain the second load returned by the vehicle networking.

[0027] Second aspect, a device for determining the load of a vehicle is provided, and the device includes:

[0028] An acquisition unit, configured to acquire the current leaf spring deformation value and driving state information of the vehicle; wherein, the leaf spring deformation value is acquired by using a displacement sensor of the leaf spring;

[0029] A determination unit, configured to determine a fatigue coefficient corresponding to the leaf spring deformation value based on the leaf spring deformation value and the driving state information;

[0030] An obtaining unit, configured to obtain a first load corresponding to the fatigue coefficient and the leaf spring deformation value based on a first calibration relationship, the fatigue coefficient, and the leaf spring deformation value; the first calibration relationship is a three-dimensional correlation relationship among the calibrated leaf spring deformation value, the fatigue coefficient, and the load;

[0031] An estimation unit, configured to determine a target load based on the first load by using a preset load estimation strategy.

[0032] Third aspect, a computer-readable storage medium is provided, and at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method in the above aspect and any possible implementation manner.

[0033] Fourth aspect, an electronic device is provided, including:

[0034] At least one processor; and

[0035] A memory communicatively connected to the at least one processor; wherein,

[0036] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in the above aspect and any possible implementation manner.

[0037] Fifth aspect, a computer program product is provided, including a computer program, and the computer program implements the method in the above aspect and any possible implementation manner when being executed by a processor.

[0038] Sixth aspect, a new energy vehicle is provided, including the above-mentioned electronic device.

[0039] The beneficial effects of the technical solution provided by this application at least include:

[0040] As can be seen from the above technical solutions, the embodiments of the present application can obtain the current leaf spring deformation value and driving state information of the vehicle. The leaf spring deformation value is collected by a displacement sensor of the leaf spring. Furthermore, based on the leaf spring deformation value and driving state information, the fatigue coefficient corresponding to the leaf spring deformation value can be determined. Based on the first calibration relationship, the fatigue coefficient, and the leaf spring deformation value, the first load corresponding to the fatigue coefficient and the leaf spring deformation value can be obtained. The first calibration relationship is a three-dimensional correlation relationship among the calibrated leaf spring deformation value, fatigue coefficient, and load. Based on the first load, using a preset load estimation strategy, the target load can be determined. Since the fatigue coefficient determined based on the driving state information, the real-time detected leaf spring deformation value, and the first calibration relationship can be used to query and obtain the corresponding first load, and then using the load estimation strategy, a more accurate target load can be estimated in a timely manner based on the first load. When the actual vehicle load changes, the estimated target load can be updated in a timely manner, which can improve the accuracy and timeliness of vehicle load estimation, ensure the effectiveness of vehicle energy management, and thus ensure the reliability and safety of vehicle driving.

[0041] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 It is a schematic flowchart of a method for determining vehicle load provided by an embodiment of the present application;

[0044] Figure 2 It is a schematic diagram of the process of a method for determining vehicle load provided by another embodiment of the present application;

[0045] Figure 3 It is a structural block diagram of a device for determining vehicle load provided by another embodiment of the present application;

[0046] Figure 4 It is a block diagram of an electronic device for implementing the method for determining vehicle load in the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The exemplary embodiments of the present application will be described below with reference to the accompanying drawings. Various details of the embodiments of the present application are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, descriptions of well-known functions and structures are omitted in the following description for clarity and conciseness.

[0048] Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts fall within the scope of protection of the present application.

[0049] It should be noted that the terminal devices involved in the embodiments of the present application may include, but are not limited to, intelligent devices such as mobile phones, personal digital assistants (PDAs), wireless handheld devices, and tablet computers. The display devices may include, but are not limited to, devices with display functions such as personal computers and televisions.

[0050] In addition, the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0051] Currently, the vehicle load estimation method in the related art mainly estimates the vehicle load by estimating the accelerations on the X, Y, and Z axes to determine the vehicle load situation.

[0052] However, this solution is limited in that the vehicle must be dynamic and the provided acceleration must reach a certain threshold to estimate the load, which has a greater adverse impact on certain specific working conditions of new energy commercial vehicles. For example, when a commercial vehicle is in an unloaded state or an empty cab goes uphill to pick up goods, after loading, since the vehicle is not moving and the load data is not updated, the load estimation at this time is still the value of the empty vehicle before. If it goes downhill fully loaded at this time, the driver generally uses energy recovery to assist downhill to reduce the pressure on the braking hardware. However, at this time, affected by the unloaded load value, the energy recovery force is small, and the driver will increase the frequency of using the brakes. If the downhill route is long or the slope is large, it will greatly increase the pressure on the braking system or cause it to fail, resulting in serious consequences. Or, when a commercial vehicle goes uphill fully loaded and then goes downhill unloaded, if the estimated vehicle load is not updated, it is easy to activate the Antilock Brake System (ABS), and dangerous situations such as wheel locking and skidding may occur.

[0053] Therefore, there is an urgent need for a method for determining the vehicle load to achieve timely and accurate estimation of the load of new energy commercial vehicles, ensure the effectiveness of vehicle energy recovery management, and thus ensure the reliability and safety of vehicle driving.

[0054] Please refer to Figure 1 , which shows a schematic flowchart of a method for determining the vehicle load provided by an embodiment of the present application. The method for determining the vehicle load may specifically include:

[0055] Step 101, obtain the current leaf spring deformation value and driving state information of the vehicle; wherein, the leaf spring deformation value is collected by a displacement sensor of the leaf spring.

[0056] Step 102, determine the fatigue coefficient corresponding to the leaf spring deformation value based on the leaf spring deformation value and the driving state information.

[0057] Step 103, obtain the first load corresponding to the fatigue coefficient and the leaf spring deformation value based on the first calibration relationship, the fatigue coefficient, and the leaf spring deformation value; the first calibration relationship is a three-dimensional correlation relationship between the calibrated leaf spring deformation value, the fatigue coefficient, and the load.

[0058] Step 104, determine the target load based on the first load by using a preset load estimation strategy.

[0059] So far, the target load can be output through the bus to perform energy management based on the target load. For example, the energy recovery control module can be output to adjust the energy recovery strategy. The target load can also be output to the vehicle networking for the background to manage and control the vehicle.

[0060] It should be noted that the leaf spring of the vehicle can be an elastic element provided in the vehicle suspension. A displacement sensor can be provided on the leaf spring. The displacement sensor can be used to sense the real-time leaf spring deformation value.

[0061] Here, the leaf spring of the vehicle can be replaced by other elastic elements of the vehicle suspension that can be used to sense the change in vehicle load, such as coil springs, torsion bar springs, or air suspensions, etc. A displacement sensor can also be provided on the elastic element to sense the deformation value of the elastic element.

[0062] It should be noted that the first calibration relationship can be a pre-calibrated three-dimensional correlation relationship table or three-dimensional correlation relationship diagram of the leaf spring deformation value, the fatigue coefficient, and the load.

[0063] In this way, based on the fatigue coefficient determined according to the driving state information, the leaf spring deformation value detected in real time, and the first calibration relationship, the corresponding first load can be obtained. Then, by using the load estimation strategy, a more accurate target load can be estimated in a timely manner based on the first load. When the actual vehicle load changes, the estimated target load can be updated in a timely manner, improving the accuracy and timeliness of vehicle load estimation, ensuring the effectiveness of vehicle energy management, and thus ensuring the reliability and safety of vehicle driving.

[0064] Optionally, in a possible implementation manner of this embodiment, the driving state information may include the driving mileage of the vehicle. In step 102, based on the current leaf spring deformation value and the current driving mileage of the vehicle, the fatigue coefficient corresponding to the leaf spring deformation value can be determined.

[0065] In this implementation manner, the fatigue coefficient may be preset according to the fatigue cycle of the leaf spring. The fatigue cycle of the leaf spring may be determined according to the driving mileage of the vehicle. There is a corresponding relationship between the fatigue coefficient and the driving mileage of the vehicle. Based on the current driving mileage of the vehicle, the preset corresponding relationship between the fatigue coefficient and the driving mileage of the vehicle, the current fatigue coefficient of the leaf spring can be determined.

[0066] Exemplarily, it can be preset that when the driving mileage is within 10,000 kilometers, the fatigue coefficient can be 1; when the driving mileage is between 10,000 kilometers and 20,000 kilometers, the fatigue coefficient can be 0.95; when the driving mileage is between 20,000 kilometers and 30,000 kilometers, the fatigue coefficient can be 0.9, and so on, to preset multiple fatigue coefficients for the full fatigue cycle of the leaf spring.

[0067] It can be understood that as the vehicle usage time goes by, the fatigue coefficient of the leaf spring gradually decreases, and under the same load, the deformation amount of the leaf spring increases.

[0068] Optionally, in a possible implementation manner of this embodiment, in step 103, specifically, the leaf spring deformation value and fatigue coefficient in the first calibration relationship can be matched with the current leaf spring deformation value and fatigue coefficient, and then based on the load corresponding to the result of the matching process, the first load corresponding to the fatigue coefficient and the leaf spring deformation value can be obtained.

[0069] In this implementation manner, the result of the matching process may be the leaf spring deformation value and fatigue coefficient in the first calibration relationship that match the current leaf spring deformation value and fatigue coefficient.

[0070] In a specific implementation process of this implementation manner, the load corresponding to the leaf spring deformation value and fatigue coefficient in the first calibration relationship that match the current leaf spring deformation value and fatigue coefficient is used as the first load corresponding to the current leaf spring deformation value.

[0071] In this way, based on the current leaf spring deformation value and fatigue coefficient, the corresponding load can be queried from the first calibration relationship, and the first load can be quickly and effectively determined, so that the target load of the vehicle can be obtained more accurately and effectively subsequently.

[0072] It should be noted that the specific implementation process provided in this implementation manner can be combined with various specific implementation processes provided in the foregoing implementation manner to implement the method for determining the vehicle load in this embodiment. For a detailed description, reference can be made to the relevant content in the foregoing implementation manner, which will not be elaborated here.

[0073] Optionally, in a possible implementation manner of this embodiment, before step 101, the first calibration relationship can be pre-calibrated. Specifically, first, based on the leaf spring fatigue cycle, multiple fatigue coefficients can be obtained. Secondly, for each initial load, perform detecting the leaf spring deformation value corresponding to each fatigue coefficient based on the initial load; based on each fatigue coefficient, perform a correction process on the initial load to obtain the corrected load corresponding to each fatigue coefficient. Thirdly, based on the multiple fatigue coefficients corresponding to each initial load, the leaf spring deformation value corresponding to each fatigue coefficient, and the corrected load corresponding to each fatigue coefficient, the first calibration relationship is calibrated.

[0074] In this implementation manner, the initial load of the vehicle can be determined according to the vehicle's own weight and the weight of the goods carried. For example, the load of the vehicle can be the sum of the vehicle's own weight and the weight of the goods carried.

[0075] Here, the number of initial loads can be multiple. The initial loads can include but are not limited to no-load load, full-load load, half-load load, etc.

[0076] In a specific implementation process of this implementation manner, for the initial load being the full-load load, first, based on the full-load load, use a displacement sensor to detect the leaf spring deformation value corresponding to each fatigue coefficient. Secondly, based on each fatigue coefficient, perform a correction process on the full-load load to obtain the corrected load corresponding to each fatigue coefficient. Thirdly, based on the multiple fatigue coefficients corresponding to the full-load load, the leaf spring deformation value corresponding to each fatigue coefficient, and the corrected load corresponding to each fatigue coefficient, the first calibration relationship corresponding to the full-load load is calibrated.

[0077] In another specific implementation process of this implementation manner, the initial load can be multiplied by the fatigue coefficient to obtain the corrected load. The corrected load can be the product of the initial load and the fatigue coefficient.

[0078] In this implementation, here, the method of calibrating the first calibration relationship corresponding to each initial load can be executed respectively based on each initial load, and then the first calibration relationships corresponding to each initial load can be combined to obtain the final first calibration relationship.

[0079] It can be understood that the first calibration relationship can include the three-dimensional correlation relationship of the fatigue coefficient, the leaf spring deformation value, and the corrected load. That is, the load in the first calibration relationship is the load corrected by the fatigue coefficient.

[0080] In this way, the load corresponding to the detected leaf spring deformation value can be corrected by the fatigue coefficient to obtain a more real and accurate load, which improves the accuracy of the first calibration relationship, and thus can further improve the accuracy of the first load obtained in the actual load estimation process.

[0081] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the foregoing implementation to implement the method for determining the vehicle load in this embodiment. For a detailed description, reference can be made to the relevant content in the foregoing implementation, which will not be elaborated here.

[0082] Optionally, in a possible implementation of this embodiment, the driving state information may include the vehicle speed. In step 104, it can be determined whether the vehicle speed is a preset speed threshold, and then in response to the vehicle speed being the preset speed threshold, the target load can be determined based on the first load.

[0083] In this implementation, the preset speed threshold can be 0 km / h. At this time, the vehicle can be in a stationary state.

[0084] In a specific implementation process of this implementation, when the vehicle is in a stationary state, the first load can be used as the target load.

[0085] In this way, by judging the vehicle speed, the state of the vehicle can be determined. When the vehicle is stationary, the first load can be directly output as the target load, which can further improve the processing efficiency of load estimation.

[0086] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the foregoing implementation to implement the method for determining the vehicle load in this embodiment. For a detailed description, reference can be made to the relevant content in the foregoing implementation, which will not be elaborated here.

[0087] Optionally, in a possible implementation of this embodiment, in step 104, a first estimation coefficient, a second load, and a second estimation coefficient may also be obtained. Then, based on the first estimation coefficient and the second estimation coefficient, weighted calculation processing may be performed on the first load and the second load, and based on the result of the weighted calculation processing, the target load may be determined.

[0088] In this implementation, the first estimation coefficient may be the weighting coefficient of the first load. The second estimation coefficient may be the weighting coefficient of the second load.

[0089] Preferably, the first estimation coefficient may be 0.8, and the second estimation coefficient may be 0.2.

[0090] In a specific implementation process of this implementation, the product of the first load and the first estimation coefficient is added to the product of the second load and the second estimation coefficient to calculate the target load.

[0091] In this way, by performing weighted calculation processing on the load based on the first load and the first estimation coefficient, as well as the second load and the second estimation coefficient, the stability of the calculated target load can be improved.

[0092] In another specific implementation process of this implementation, in response to the vehicle speed not being equal to the preset speed threshold, weighted calculation processing may be performed on the load based on the first load and the first estimation coefficient, as well as the second load and the second estimation coefficient to determine the target load.

[0093] Here, it can be understood that the second load may be the load of the vehicle estimated by the vehicle networking based on the driving state information of the vehicle and the second calibration relationship in the motion state.

[0094] In yet another specific implementation process of this implementation, the driving state information is sent to the vehicle networking so that the vehicle networking determines the second load based on the second calibration relationship and the driving state information and returns the second load. Then, the second load returned by the vehicle networking can be obtained. Wherein, the second calibration relationship is a three-dimensional correlation relationship of calibrated braking force, acceleration, and load.

[0095] In this implementation, the second calibration relationship may be a three-dimensional correlation relationship of pre-calibrated braking force, acceleration, and load. The second calibration relationship may also be a three-dimensional correlation relationship of pre-calibrated driving force, acceleration, and load.

[0096] In this implementation, the driving state information may further include the vehicle's high-precision map, road spectrum, driving force, braking force, vehicle speed, acceleration, etc.

[0097] Preferably, the driving state information of the vehicle can be sent to the vehicle networking through the TBOX.

[0098] In this way, the second load estimated under the dynamic state of the vehicle can be obtained by acquiring the second load determined by the vehicle networking based on the driving state information of the vehicle and the second calibration relationship. By combining the static first load and the dynamic second load, a more accurate and effective target load can be determined.

[0099] It should be noted that the specific implementation process provided in this implementation manner can be combined with the various specific implementation processes provided in the foregoing implementation manner to implement the method for determining the vehicle load in this embodiment. For a detailed description, reference can be made to the relevant content in the foregoing implementation manner, which will not be elaborated here.

[0100] To better understand the method of the embodiment of the present application, the method of the embodiment of the present application will be described below in conjunction with the accompanying drawings and specific application scenarios.

[0101] Figure 2 is a schematic diagram of the flow of the method for determining the vehicle load provided by another embodiment of the present application, as Figure 2 shown. The method for determining the vehicle load in this embodiment can be applied to the vehicle's vehicle control unit, and specifically can include:

[0102] Step 201, acquire the current leaf spring deformation value and driving state information of the vehicle.

[0103] In this embodiment, the leaf spring deformation value is collected by using a displacement sensor of the leaf spring.

[0104] Preferably, the consistency of the material, structure, and process of the leaf spring meets the requirements of the mass production state. In this way, the consistency of the perception of the leaf spring deformation value can be ensured.

[0105] Preferably, a displacement sensor is provided at the leaf spring, and the displacement sensor is used to sense and monitor the leaf spring deformation value, that is, the change in fatigue strength.

[0106] Step 202, based on the leaf spring deformation value and the driving state information, determine the fatigue coefficient corresponding to the leaf spring deformation value.

[0107] In this embodiment, the driving state information may include the driving mileage of the vehicle. Based on the current driving mileage of the vehicle and the pre-set association relationship between the driving mileage and the fatigue coefficient, the corresponding fatigue coefficient can be determined.

[0108] Exemplarily, it can be pre-set that when the driving mileage is within 10,000 kilometers, the fatigue coefficient can be 1. When the driving mileage is between 10,000 kilometers and 20,000 kilometers, the fatigue coefficient can be 0.95. When the driving mileage is between 20,000 kilometers and 30,000 kilometers, the fatigue coefficient can be 0.9, and so on.

[0109] Step 203: Obtain the first load corresponding to the fatigue coefficient and the leaf spring deformation value based on the first calibration relationship, the fatigue coefficient, and the leaf spring deformation value.

[0110] In this embodiment, the first calibration relationship can be a map of the correlation relationship among the fatigue coefficient - leaf spring deformation value - load, calibrated based on the full fatigue cycle of the leaf spring and multiple initial loads.

[0111] Here, when the vehicle load changes, the leaf spring deformation will surely change accordingly. By detecting the leaf spring deformation value and the fatigue coefficient corresponding to the driving mileage, the corresponding load, that is, the first load, can be queried from the first calibration relationship.

[0112] For example, when the fatigue coefficient is 0.8 and the leaf spring deformation value is 50 mm, the corresponding first load can be queried as 15 tons.

[0113] It can be understood that this load data is not limited to whether the vehicle is in motion loading, and the current first load can be sent to the CAN bus for the vehicle controller to reference conveniently.

[0114] Step 204: Send the driving state information to the vehicle networking, so that the vehicle networking determines the second load based on the second calibration relationship and the driving state information, and returns the second load.

[0115] In this embodiment, the driving state information can be sent to the vehicle networking through the TBOX.

[0116] In this embodiment, the driving state information can include the driving force / braking force, acceleration, speed, road spectrum, and high-precision map of the vehicle.

[0117] Here, the second calibration relationship can be a three-dimensional correlation relationship map of driving force / braking force - acceleration - load calibrated in advance. The corresponding load, that is, the second load, can be queried from the second calibration relationship through the current driving force / braking force and acceleration of the vehicle.

[0118] Step 205: Receive the second load returned by the vehicle networking.

[0119] It can be understood that here, the vehicle can transmit the received second load to the CAN bus for convenient later reference.

[0120] Step 206: In response to the vehicle speed in the driving state information being equal to the preset speed threshold, determine the target load based on the first load.

[0121] Step 207: In response to the vehicle speed in the driving state information not being equal to the preset speed threshold, perform weighted calculation processing on the first load and the second load to determine the target load.

[0122] In this embodiment, a first estimation coefficient and a second estimation coefficient can also be obtained. The first estimation coefficient can be a weighting coefficient of the first load. The second estimation coefficient can be a weighting coefficient of the second load.

[0123] Exemplarily, the first estimation coefficient can be 0.8 and the second estimation coefficient can be 0.2.

[0124] Preferably, the product of the first load and the first estimation coefficient is added to the product of the second load and the second estimation coefficient to calculate the target load.

[0125] In this embodiment, the preset speed threshold can be 0 km / h.

[0126] Here, when the vehicle speed is equal to the preset speed threshold, it can indicate that the vehicle is in a stationary state. When the vehicle speed is not equal to the preset speed threshold, it can indicate that the vehicle is in a moving state.

[0127] Here, the target load can be uploaded to the CAN bus and the vehicle network for use in vehicle energy management and background management.

[0128] By adopting the solution in this embodiment, when the vehicle load changes significantly, the vehicle's own weight data can be quickly estimated, overcoming the problem of too small or too large recovery force when the commercial vehicle is loaded with goods full at the top and empty at the bottom or full at the bottom and empty at the top, thus ensuring the reliability and safety of vehicle driving.

[0129] Moreover, the vehicle load data can be quickly and effectively estimated, which is convenient for the vehicle energy management and the vehicle network platform to reference. Furthermore, while improving the vehicle driving smoothness, the safety is also taken into account.

[0130] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0131] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] Figure 3 The structural block diagram of a vehicle load determination device provided by an embodiment of the present application is shown, as Figure 3As shown in the figure. The vehicle load determination device 300 of this embodiment may include an acquisition unit 301, a determination unit 302, an obtaining unit 303, and an estimation unit 304. Among them, the acquisition unit 301 is used to acquire the current leaf spring deformation value and driving state information of the vehicle; among them, the leaf spring deformation value is collected by a displacement sensor of the leaf spring;

[0133] The determination unit 302 is used to determine the fatigue coefficient corresponding to the leaf spring deformation value based on the leaf spring deformation value and the driving state information;

[0134] The obtaining unit 303 is used to obtain the first load corresponding to the fatigue coefficient and the leaf spring deformation value based on the first calibration relationship, the fatigue coefficient, and the leaf spring deformation value; the first calibration relationship is a three-dimensional correlation relationship of the calibrated leaf spring deformation value, fatigue coefficient, and load;

[0135] The estimation unit 304 is used to determine the target load based on the first load by using a preset load estimation strategy.

[0136] Optionally, in a possible implementation manner of this embodiment, the obtaining unit 303 is used to perform a matching process on the leaf spring deformation value and the fatigue coefficient in the first calibration relationship with the current leaf spring deformation value and fatigue coefficient; based on the load corresponding to the result of the matching process, obtain the first load corresponding to the fatigue coefficient and the leaf spring deformation value.

[0137] Optionally, in a possible implementation manner of this embodiment, the obtaining unit 303 is used to obtain multiple fatigue coefficients based on the leaf spring fatigue cycle; for each initial load, perform detection of the leaf spring deformation value corresponding to each fatigue coefficient based on the initial load; based on each fatigue coefficient, perform a correction process on the initial load to obtain the corrected load corresponding to each fatigue coefficient; based on the multiple fatigue coefficients corresponding to each initial load, the leaf spring deformation value corresponding to each fatigue coefficient, and the corrected load corresponding to each fatigue coefficient, calibrate to obtain the first calibration relationship.

[0138] Optionally, in a possible implementation manner of this embodiment, the driving state information includes the vehicle speed, and the estimation unit 304 is used to determine whether the vehicle speed is a preset speed threshold; in response to the vehicle speed being the preset speed threshold, determine the target load based on the first load.

[0139] Optionally, in a possible implementation manner of this embodiment, the estimation unit 304 is used to obtain a first estimation coefficient, a second load, and a second estimation coefficient; perform a weighted calculation process on the first load and the second load based on the first estimation coefficient and the second estimation coefficient; based on the result of the weighted calculation process, determine the target load.

[0140] Optionally, in a possible implementation manner of this embodiment, the estimation unit 304 is configured to send the driving state information to the vehicle networking, so that the vehicle networking determines the second load based on the second calibration relationship and the driving state information, and returns the second load; where the second calibration relationship is a three-dimensional correlation relationship of calibrated braking force, acceleration, and load; and obtain the second load returned by the vehicle networking.

[0141] In this embodiment, the acquisition unit can acquire the current leaf spring deformation value and driving state information of the vehicle. Furthermore, the determination unit can determine the fatigue coefficient corresponding to the leaf spring deformation value based on the leaf spring deformation value and the driving state information, and the obtaining unit can obtain the first load corresponding to the fatigue coefficient and the leaf spring deformation value based on the first calibration relationship, the fatigue coefficient, and the leaf spring deformation value. The first calibration relationship is a three-dimensional correlation relationship of calibrated leaf spring deformation value, fatigue coefficient, and load, so that the estimation unit can determine the target load based on the first load by using a preset load estimation strategy. Since the fatigue coefficient determined based on the driving state information, the real-time detected leaf spring deformation value, and the first calibration relationship can be used to obtain the corresponding first load, and then the load estimation strategy can be used to estimate a more accurate target load in a timely manner based on the first load, the estimated target load can be updated in a timely manner when the actual vehicle load changes, which can improve the accuracy and timeliness of vehicle load estimation, ensure the effectiveness of vehicle energy management, and thus ensure the reliability and safety of vehicle driving.

[0142] In the technical solution of this application, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved, such as the user's image and attribute data, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0143] According to the embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0144] According to the embodiments of this application, further, a new energy vehicle including the provided electronic device is also provided. For example, the new energy vehicle can be a new energy household vehicle, a new energy commercial vehicle, a new energy logistics vehicle, a new energy large vehicle, etc.

[0145] Figure 4FIG. shows a schematic block diagram of an exemplary electronic device 400 that can be used to implement embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present application described and / or claimed herein.

[0146] As Figure 4 shown, the electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0147] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0148] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the method for determining the vehicle load. For example, in some embodiments, the method for determining the vehicle load can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method for determining the vehicle load described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the method for determining the vehicle load in any other suitable manner (e.g., by means of firmware).

[0149] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0150] The program code for implementing the methods of this application can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0151] In the context of this application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0152] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0153] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0154] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0155] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved, and no limitations are imposed herein.

[0156] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for determining vehicle load, characterized in that: The method comprises: Acquire the current leaf spring deformation value and driving state information of the vehicle; wherein the leaf spring deformation value is collected by using a displacement sensor of the leaf spring; Determining a fatigue coefficient corresponding to the leaf spring deformation value based on the leaf spring deformation value and driving state information; Based on the first calibration relationship, the fatigue coefficient and the leaf spring deformation value, a first load corresponding to the fatigue coefficient and the leaf spring deformation value is obtained; the first calibration relationship is a three-dimensional correlation relationship among the calibrated leaf spring deformation value, the fatigue coefficient, and the load; Based on the first load, a target load is determined using a preset load estimation strategy.

2. The method according to claim 1, characterized in that The obtaining, based on the first calibration relationship, the fatigue coefficient and the leaf spring deformation value, a first load corresponding to the fatigue coefficient and the leaf spring deformation value comprises: Matching the leaf spring deformation value and fatigue coefficient in the first calibration relationship with the current leaf spring deformation value and fatigue coefficient; Based on the load corresponding to the result of the matching process, a first load corresponding to the fatigue coefficient and the leaf spring deformation value is obtained.

3. The method according to claim 1, characterized in that Calibration processing is performed on the first calibration relationship, including: Based on the leaf spring fatigue cycle, multiple fatigue coefficients are obtained; For each initial load, detecting the leaf spring deformation value corresponding to each fatigue coefficient based on the initial load; and performing correction processing on the initial load based on each fatigue coefficient to obtain a corrected load corresponding to each fatigue coefficient; The first calibration relationship is obtained by calibration based on a plurality of fatigue coefficients corresponding to each initial load, a leaf spring deformation value corresponding to each fatigue coefficient, and a corrected load corresponding to each fatigue coefficient.

4. The method according to claim 1, characterized in that: The driving state information includes a vehicle speed, and the determining of a target load based on the first load using a preset load estimation strategy includes: determining whether the vehicle speed is a preset speed threshold; In response to the vehicle speed being a preset speed threshold, a target load is determined based on the first load.

5. The method according to claim 1, characterized in that The determining the target load based on the first load by using a preset load estimation strategy includes: Obtaining a first estimation coefficient, a second load, and a second estimation coefficient; Based on the first estimation coefficient and the second estimation coefficient, performing weighted calculation processing on the first load and the second load; Based on the result of the weighted calculation process, the target load is determined.

6. The method according to claim 5, characterized in that The step of obtaining the second load includes: Sending the driving state information to the Internet of Vehicles, so that the Internet of Vehicles determines the second load based on the second calibration relationship and the driving state information, and returns the second load; wherein the second calibration relationship is a three-dimensional correlation relationship of the calibrated braking force, acceleration, and load; Obtain the second load returned by the Internet of Vehicles.

7. A device for determining vehicle load, characterized in that: The device comprises: An acquisition unit, used to acquire the current leaf spring deformation value and driving state information of the vehicle; wherein the leaf spring deformation value is acquired by using a displacement sensor of the leaf spring; a determination unit, configured to determine a fatigue coefficient corresponding to the leaf spring deformation value based on the leaf spring deformation value and driving state information; An obtaining unit, configured to obtain a first load corresponding to the fatigue coefficient and the leaf spring deformation value based on a first calibration relationship, the fatigue coefficient and the leaf spring deformation value; the first calibration relationship is a three-dimensional correlation relationship among the calibrated leaf spring deformation value, the fatigue coefficient and the load; An estimation unit is used to determine a target load based on the first load using a preset load estimation strategy.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A new energy vehicle, characterized in that: Comprising an electronic device according to claim 8.

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