A vehicle load self-adaptive estimation method and device and a storage medium

Abstract

The present application relates to a kind of vehicle load adaptive estimation method, device and storage medium, involve load estimation technical field.The present application includes building load estimation model, setting load estimation trigger condition.In the process of vehicle operation, load load estimation model, the initial load of initial load estimation model is given, output load is equal to initial load;If the vehicle state triggers the load estimation trigger condition set in the process of vehicle operation, acceleration data is collected and recorded, average acceleration is obtained by the sliding average processing acceleration data of fixed interval, load mode is identified according to the acceleration fluctuation interval where average acceleration is located, load estimation value is determined according to load mode, and output load is equal to load estimation value.The present application does not depend on the calculation accuracy of mechanical theory model and road slope, can conveniently and quickly adaptively estimate the whole vehicle load of AMT light truck under different load conditions, and can reduce calculation complexity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of load estimation, in particular to a vehicle load adaptive estimation method, device and storage medium. BACKGROUND

[0002] Load is an important parameter affecting the performance of automatic shift light truck. The accuracy of load estimation will directly affect the selection of starting gear and the rationality of shift point calculation. If the starting gear is selected too high, the engine will stall due to insufficient power, resulting in failed start. If the shift point calculation is not reasonable, it will not only cause fuel consumption to rise, but also affect the driving experience due to unreasonable shift timing.

[0003] Currently, the estimation method of automatic shift light truck load is mainly based on Newton's second law to calculate the vehicle dynamics model. This algorithm is based on the classical mechanical theory model and is widely used, but its estimation accuracy is heavily dependent on the calculation accuracy of the AMT system for the road slope. In order to ensure the calculation accuracy of the road slope, currently, the slope sensor is mainly installed to ensure the calculation accuracy of the road slope. Generally, it is installed on the light truck frame with the controller. The heavier the load carried by the light truck, the greater the deformation of the frame, which causes the originally horizontally installed slope sensor to be misidentified as uphill or downhill, ultimately resulting in a large deviation in the estimation of the load by the AMT system, affecting the driving performance. Currently, there is a lack of a method that does not rely on the calculation accuracy of the road slope and can adaptively estimate the load of the automatic shift light truck. SUMMARY

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a vehicle load adaptive estimation method, device and storage medium.

[0005] In a first aspect, the present application provides a vehicle load adaptive estimation method, comprising:

[0006] A load estimation model for estimating load is created in advance, the load estimation model creates the association between different load modes and acceleration fluctuation intervals under a set load estimation trigger condition, and each load mode is correspondingly provided with a load estimation value;

[0007] During vehicle operation, the load estimation model is loaded, the initial load is given by the initialized load estimation model, and the output load is equal to the initial load;

[0008] If the vehicle state triggers the set load estimation trigger condition during vehicle operation, acceleration data is collected and recorded, the acceleration data is processed by fixed interval moving average to obtain average acceleration, the load mode is identified according to the acceleration fluctuation interval where the average acceleration is located, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value.

[0009] Further, the process of creating the load estimation model comprises:

[0010] According to different load conditions of the vehicle, load modes are divided;

[0011] For each load mode, a corresponding load estimation value is set;

[0012] Traverse the set load mode, adjust the actual load of the vehicle according to the load estimation value corresponding to the load mode, and when the actual load of the vehicle reaches the load estimation value, control the vehicle state to meet the load estimation trigger condition, collect the acceleration of the vehicle, and through multiple measurements, obtain the acceleration fluctuation interval corresponding to the traversed load mode;

[0013] Correlate the load mode and the corresponding acceleration fluctuation interval.

[0014] Further, in the process of constructing the load estimation model, after measuring the acceleration in the process of estimating the load, the acceleration is processed according to the same fixed interval sliding average.

[0015] Further, the load conditions include empty load, half load, full load, and overload, and four load modes M1, load mode M2, load mode M3, and load mode M4 are divided according to the empty load, half load, full load, and overload of the vehicle. According to the load conditions of various vehicle types under different load conditions, the corresponding load estimation value is set for each load mode.

[0016] Further, after determining the load mode and obtaining the acceleration fluctuation interval corresponding to each load mode, the combination of any two acceleration fluctuation intervals is obtained, the difference between the two acceleration fluctuation intervals in each combination is calculated and compared, the minimum difference is taken from the difference, and it is ensured that the minimum difference is greater than the preset difference threshold.

[0017] Further, the calculation method of the difference is as follows:

[0018]

[0019] Wherein, a1, a2 are the average acceleration of the two sets of acceleration fluctuation intervals, σ1, σ2 are the standard deviation of the acceleration of the two sets of acceleration fluctuation intervals, and n1, n2 are the number of measurements of the two sets of acceleration fluctuation intervals.

[0020] Further, the process of sliding average processing acceleration comprises:

[0021] Initialize the fixed interval size K;

[0022] Obtain the acceleration data sequence;

[0023] The acceleration data sequence is scanned by a fixed interval size sliding window, an acceleration data segment is obtained, the obtained acceleration data segments are averaged, and the average values of the obtained acceleration data segments are stored.

[0024] Further, the load estimation trigger condition needs to preset the following parameters, including: engine transmissible torque range condition, preset gear, vehicle acceleration threshold, vehicle speed range condition, accelerator pedal opening range.

[0025] The load estimation trigger condition includes:

[0026] A, the engine transmissible torque is in the preset engine transmissible torque range condition,

[0027] B, the transmission gear reaches the preset gear,

[0028] C, the real-time vehicle acceleration is greater than the preset vehicle acceleration threshold,

[0029] D, the actual vehicle speed is in the preset vehicle speed range condition,

[0030] E, the opening of the accelerator pedal is in the preset accelerator pedal opening range,

[0031] F, the whole vehicle is in the non-power interruption period,

[0032] G, the A-F conditions are met simultaneously for not less than the preset time.

[0033] In a second aspect, the present application provides a vehicle load adaptive estimation device, comprising: at least one processing unit, the processing unit is connected with a storage unit and a collection unit through a bus unit, the collection unit collects parameters of the whole vehicle state, the storage unit stores a computer program, and the computer program is executed by the processing unit to realize the vehicle load adaptive estimation method.

[0034] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the vehicle load adaptive estimation method.

[0035] The above technical solution provided by the embodiments of the present application has the following advantages compared with the prior art:

[0036] The application comprises building a load estimation model and setting a load estimation trigger condition. During vehicle operation, the load estimation model is loaded, the initialized load estimation model gives an initial load, and the output load is equal to the initial load. If the vehicle state triggers the set load estimation trigger condition during vehicle operation, acceleration data is collected and recorded, the acceleration data is processed by fixed interval moving average to obtain average acceleration, the load mode is identified according to the acceleration fluctuation interval where the average acceleration is located, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value. The application does not depend on the mechanical theory model and the calculation accuracy of the road slope, can conveniently and quickly adaptively estimate the vehicle load of the AMT light truck under different load conditions, can reduce the calculation complexity, is suitable for the load estimation of the controller with weak calculation ability, and can effectively guide the gear shifting of the automatic transmission.

[0037] After the load mode is determined and the acceleration fluctuation intervals corresponding to the load modes are obtained, combinations of any two acceleration fluctuation intervals are obtained, the difference between the two acceleration fluctuation intervals in each combination is calculated and compared, the minimum difference is taken from the difference, and it is ensured that the minimum difference is greater than a preset difference threshold. By controlling that the minimum difference is greater than the preset difference threshold, the load estimation model can identify the load mode covered by itself through acceleration, and it is ensured that the load estimation can be effectively performed. BRIEF DESCRIPTION OF DRAWINGS

[0038] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0040] Figure 1 A flowchart of a vehicle load adaptive estimation method provided by the present application;

[0041] Figure 2 A flowchart of creating a load estimation model for estimating load provided by the present application;

[0042] Figure 3 A flowchart of verifying that the load estimation model can identify the load mode provided by the present application;

[0043] Figure 4 A schematic diagram of a vehicle load adaptive estimation device provided by the present application. DETAILED DESCRIPTION

[0044] The technical solutions and advantages of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0045] It should be noted that, in this document, the terms "comprising", "containing" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of another identical element in the process, method, article or device comprising the element.

[0046] Embodiment 1

[0047] The present application provides a vehicle load adaptive estimation method, which does not depend on the classical mechanical theory model and the calculation accuracy of road slope, and can conveniently and quickly adaptively estimate the whole vehicle load of an AMT light truck under different load conditions, and can reduce the model calculation complexity. Referring to Figure 1 As shown in the figure, the steps of the vehicle load adaptive estimation method provided by the embodiments of the present application include:

[0048] A load estimation model for estimating load is created in advance, the load estimation model creates the correlation between different load modes and acceleration fluctuation intervals under a set load estimation trigger condition, and each load mode is correspondingly provided with a load estimation value.

[0049] As Figure 2 shown, the process of creating the load estimation model is as follows:

[0050] According to different load conditions of the vehicle, load modes are divided.

[0051] In an example, four load modes M1, load mode M2, load mode M3 and load mode M4 are divided according to the vehicle empty load, half load, full load and overload.

[0052] Corresponding load estimation values are set for each load mode.

[0053] In the above example, the load mode M1, load mode M2, load mode M3, load mode M4 are respectively set to 3 tons, 4 tons, 6 tons, 9 tons of load estimation value.

[0054] Set the load estimation trigger condition.

[0055] Setting the load estimation trigger condition requires pre-setting the following parameters, including: engine transmissible torque range condition, pre-set gear, vehicle acceleration threshold, vehicle speed range condition, throttle pedal opening range. Specifically, an exemplary engine transmissible torque range condition is [150Nm, 250Nm], an exemplary pre-set gear is 7th gear, an exemplary vehicle acceleration threshold is -0.5m / s 2 , an exemplary vehicle speed range condition is [40kph, 70kph], and an exemplary throttle pedal opening range is [40%, 90%].

[0056] In the implementation process, the load estimation trigger condition includes:

[0057] A. The engine transmissible torque is within the pre-set engine transmissible torque range condition.

[0058] B. The transmission gear reaches the pre-set gear.

[0059] C. The real-time vehicle acceleration is greater than the pre-set vehicle acceleration threshold.

[0060] D. The actual vehicle speed is within the pre-set vehicle speed range condition.

[0061] E. The opening of the throttle pedal is within the pre-set throttle pedal opening range.

[0062] F. The whole vehicle is in the non-power interruption period.

[0063] G. A-F conditions are met for no less than a pre-set time. An exemplary pre-set time length is 1s.

[0064] Traverse the set load mode, adjust the actual load of the vehicle according to the load estimation value corresponding to the load mode, and control the vehicle state to meet the load estimation trigger condition after the actual load of the vehicle reaches the load estimation value. The acceleration of the vehicle is collected, and through multiple measurements, the acceleration values collected in a certain period of time are processed by fixed interval moving average during the measurement of the acceleration. The process of fixed interval moving average processing includes: initializing the fixed interval size K; obtaining the acceleration data sequence; scanning the acceleration data sequence through the fixed interval size sliding window to obtain the acceleration data segment, and averaging the obtained acceleration data segment.

[0065] Obtain the acceleration fluctuation interval associated with the traversed load mode.

[0066] Figure 2 In this system, the number of load patterns is n. The load patterns that are traversed are counted by x. Whether the load patterns have been traversed is determined by whether x is greater than or equal to n.

[0067] The above example only presents one load condition. Since the load conditions given in the example include no-load, half-load, full-load, and overload, the load differences are significant, and the acceleration fluctuation ranges corresponding to each load mode differ markedly. In practical applications, the load differences between different load conditions can be adjusted to create different load conditions, and arbitrary combinations of load modes can be obtained based on these different load conditions. To ensure that different load modes can be distinguished, the acceleration fluctuation ranges corresponding to each load mode need to differ significantly. Therefore, as... Figure 3 As shown, after determining the load mode and obtaining the acceleration fluctuation range corresponding to each load mode, obtain any combination of two acceleration fluctuation ranges, calculate and compare the difference between the two acceleration fluctuation ranges in each combination, and take the minimum difference from the difference values, ensuring that the minimum difference value is greater than the preset difference value threshold. If there is a case where the difference value is less than the difference value threshold, then readjust the load condition and load mode, and recreate the load estimation model.

[0068] The method for calculating the distinguishability is as follows:

[0069]

[0070] in, σ1 and σ2 are the mean acceleration values ​​of the two acceleration fluctuation intervals, respectively; σ1 and σ2 are the standard deviations of the acceleration values ​​of the two acceleration fluctuation intervals, respectively; and n1 and n2 are the number of counts of the two acceleration fluctuation intervals, respectively.

[0071] Configure the created load estimation model to the AMT controller, set a default load mode for the load estimation model, and load the load estimation model during vehicle operation. The initialized load estimation model provides the initial load corresponding to the default load mode, and the output load is configured to be equal to the initial load. For example, if load mode M3 is set as the default load mode, the initial load is 6 tons.

[0072] Configure the load estimation trigger conditions to the AMT controller. The load estimation trigger conditions include:

[0073] A. The engine can transmit torque in the range of [150Nm, 250Nm];

[0074] B. The transmission gear has reached the preset gear of 7.

[0075] C. Vehicle acceleration greater than -0.5 m / s² 2 ;

[0076] D, actual vehicle speed range [40kph, 70kph];

[0077] E, throttle pedal opening range is [40%, 90%];

[0078] F, the vehicle is in non-power interruption period;

[0079] G, A-F conditions meet at least for a preset time 1s.

[0080] In the process of vehicle operation, the vehicle state is monitored, if the vehicle state triggers the set load estimation trigger condition in the process of vehicle operation, the acceleration data is collected and recorded, the average acceleration is obtained by the sliding average processing of the acceleration data in the fixed interval, the load mode is identified according to the acceleration fluctuation interval where the average acceleration is located, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value. Wherein, in the process of constructing the load estimation model, after measuring the acceleration in the process of estimating the load, the acceleration is processed according to the same sliding average in the fixed interval.

[0081] According to the acceleration fluctuation interval where the average acceleration is located, the load mode is identified, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value.

[0082] Embodiment 2

[0083] As shown in Figure 4 The embodiment of the application provides a vehicle load adaptive estimation device, which comprises at least one processing unit, the processing unit is connected with a storage unit and a collection unit through a bus unit, the collection unit collects the parameters of the vehicle state, the storage unit stores a computer program, and the computer program is executed by the processing unit to realize the vehicle load adaptive estimation method, which comprises:

[0084] A load estimation model for estimating load is created in advance, the load estimation model creates the correlation between different load modes and acceleration fluctuation intervals under the set load estimation trigger condition, and each load mode is correspondingly provided with a load estimation value;

[0085] In the process of vehicle operation, the load estimation model is loaded, and the initial load is given by the initialized load estimation model, and the output load is equal to the initial load;

[0086] If the vehicle state triggers the set load estimation trigger condition in the process of vehicle operation, the acceleration data is collected and recorded, the average acceleration is obtained by the sliding average processing of the acceleration data in the fixed interval, the load mode is identified according to the acceleration fluctuation interval where the average acceleration is located, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value.

[0087] Of course, the storage unit in the vehicle load adaptive estimation device provided by the embodiments of the present application stores a computer program which is not limited to the method operations described above, but can also perform the related operations in the vehicle load adaptive estimation method provided by any of the embodiments of the present application.

[0088] Embodiment 3

[0089] The embodiments of the present application provide a computer readable storage medium storing a computer program, which, when executed by a processor, implements the vehicle load adaptive estimation method, comprising:

[0090] A load estimation model for estimating load is created in advance, the load estimation model creates the association between different load modes and acceleration fluctuation intervals under a set load estimation trigger condition, and each load mode is correspondingly provided with a load estimation value;

[0091] During vehicle operation, the load estimation model is loaded, the initialized load estimation model gives an initial load, and the output load is equal to the initial load.

[0092] If the vehicle state triggers the set load estimation trigger condition during vehicle operation, acceleration data is collected and recorded, the acceleration data is processed by fixed interval moving average to obtain average acceleration, the load mode is identified according to the acceleration fluctuation interval where the average acceleration is located, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value.

[0093] The computer readable storage medium provided by the embodiments of the present application stores a computer program which is not limited to the method operations described above, but can also perform the related operations in the vehicle load adaptive estimation method provided by any of the embodiments of the present application.

[0094] The present application includes building a load estimation model and setting a load estimation trigger condition. During vehicle operation, the load estimation model is loaded, the initialized load estimation model gives an initial load, and the output load is equal to the initial load. If the vehicle state triggers the set load estimation trigger condition during vehicle operation, acceleration data is collected and recorded, the acceleration data is processed by fixed interval moving average to obtain average acceleration, the load mode is identified according to the acceleration fluctuation interval where the average acceleration is located, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value. The present application does not depend on the mechanical theory model and the calculation accuracy of the road slope, and can conveniently and quickly adaptively estimate the whole vehicle load of the AMT light truck under different load conditions, can reduce the calculation complexity, is suitable for the load estimation of the controller with weak calculation ability, and can effectively guide the gear shifting of the automatic transmission.

[0095] The application determines the load modes, obtains the acceleration fluctuation intervals corresponding to each load mode, obtains the combination of any two acceleration fluctuation intervals, calculates and compares the distinctness between the two acceleration fluctuation intervals in each combination, takes the minimum distinctness from the distinctness, and ensures that the minimum distinctness is greater than the preset distinctness threshold. By controlling the minimum distinctness to be greater than the preset distinctness threshold, the load estimation model can identify the load mode covered by itself through acceleration, and ensure that the load estimation can be effectively performed.

[0096] In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the described electrical circuit is only a logical function division. There can be another division for actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings between the units can be indirect couplings or couplings through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0097] The above descriptions are only specific embodiments of the present application, making those skilled in the art understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of adaptive estimation of vehicle load, characterized in that, The application comprises: A load estimation model for estimating load is created in advance, the load estimation model creates a correlation between different load modes and acceleration fluctuation intervals under a set load estimation trigger condition, and each load mode is provided with a corresponding load estimation value; the process of creating the load estimation model comprises: dividing load modes according to different load conditions of a vehicle; setting corresponding load estimation values for each load mode; traversing the set load modes, adjusting the actual load of the vehicle according to the load estimation value corresponding to the load mode, controlling the vehicle state to meet the load estimation trigger condition after the actual load of the vehicle reaches the load estimation value, collecting the acceleration of the vehicle, obtaining the acceleration fluctuation interval corresponding to the traversed load mode through multiple measurements; correlating the load mode and the corresponding acceleration fluctuation interval; after determining the load mode and obtaining the acceleration fluctuation interval corresponding to each load mode, obtaining the combination of any two acceleration fluctuation intervals, calculating and comparing the difference between the two acceleration fluctuation intervals in each combination, taking the minimum difference from the difference, ensuring that the minimum difference is greater than a preset difference threshold, if there is a difference less than the difference threshold, readjusting the load condition and the load mode, and re-creating the load estimation model; the calculation method of the difference is as follows: ； wherein, respectively the acceleration mean value of the acceleration fluctuation interval of the two groups, respectively the acceleration standard deviation of the acceleration fluctuation interval of the two groups, respectively the number of counts of the acceleration fluctuation interval of the two groups, During the operation of the vehicle, the load estimation model is loaded, the initial load is given by the initialized load estimation model, and the output load is equal to the initial load; If the vehicle state triggers the set load estimation trigger condition during the operation of the vehicle, the acceleration data is collected and recorded, the average acceleration is obtained by processing the acceleration data through fixed interval moving average, the load mode is identified according to the acceleration fluctuation interval where the average acceleration is located, the load estimation value is determined according to the load mode, and the output load is equal to the load estimation value.

2. The vehicle load self-adaptive estimation method according to claim 1, characterized in that, During the construction of the load estimation model, after measuring the acceleration, the acceleration is processed through the same fixed interval moving average.

3. The method of claim 1, wherein The load conditions include empty load, half load, full load and overload, and four load modes M1, M2, M3 and M4 are divided according to the empty load, half load, full load and overload of the vehicle, and the corresponding load estimation values are set for each load mode according to the load conditions of various vehicle types under different load conditions.

4. The method of claim 1, wherein The process of processing the acceleration through moving average comprises: Initializing the fixed interval size; Obtaining the acceleration data sequence; Scanning the acceleration data sequence through the fixed interval size sliding window to obtain acceleration data segments, averaging the obtained acceleration data segments, and saving the average values of the obtained acceleration data segments.

5. The method of claim 1, wherein The load estimation trigger condition needs to be set in advance, including: engine transmissible torque range condition, preset gear, vehicle acceleration threshold, vehicle speed range condition, accelerator pedal opening range; The load estimation trigger condition comprises: A, the engine transmissible torque is within the preset engine transmissible torque range condition, B, the transmission gear reaches the preset gear, C, the real-time vehicle acceleration is greater than the preset vehicle acceleration threshold, D. the actual vehicle speed is in a preset vehicle speed range, E. the opening of the accelerator pedal is in a preset accelerator pedal opening range, F. the vehicle is in a non-power interruption period, G. the conditions A-F are met simultaneously for no less than a preset time.

6. A vehicle load self-adapting estimation device, characterized by, The application further provides a vehicle load adaptive estimation method and a computer program product. The computer program is executed by the processor to implement the vehicle load adaptive estimation method according to any one of claims 1-5.

7. A computer-readable storage medium storing a computer program, characterized in that, ​

Images