CN109767520B - Method and device for handling vehicle load - Google Patents
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Abstract
本发明提供一种车辆载重处理方法及装置，涉及电动车辆技术领域。该车辆载重处理方法包括：获取预设时间间隔内的车辆状态参数；对车辆状态参数进行预处理，得到第一有效车辆状态参数；根据所述第一有效车辆状态参数得到车辆在预设时间间隔内的各加速度值；根据车辆在预设时间间隔内的各加速度值，对各加速度值对应的第一有效车辆状态参数进行修正，得到第二有效车辆状态参数；根据该状态参数和预设的第一训练模型，得到第一车辆载重预估值；最终进行数据回归分析得到车辆的真实载重。使用该方法可以实时测量车辆的载重。
The invention provides a vehicle load processing method and device, which relate to the technical field of electric vehicles. The vehicle load processing method includes: obtaining vehicle state parameters within a preset time interval; preprocessing the vehicle state parameters to obtain a first valid vehicle state parameter; obtaining a vehicle at a preset time interval according to the first valid vehicle state parameter According to the acceleration values of the vehicle within the preset time interval, the first valid vehicle state parameters corresponding to the acceleration values are corrected to obtain the second valid vehicle state parameters; according to the state parameters and the preset The first training model is performed to obtain a first estimated value of vehicle load; finally, data regression analysis is performed to obtain the real load of the vehicle. Using this method, the load of the vehicle can be measured in real time.
Description
Technical Field
The invention relates to the technical field of electric vehicles, in particular to a vehicle load processing method and device.
Background
With the rapid development of modern transportation industry, vehicles continuously enter urban distribution chains under the encouragement of national policies, and meanwhile, traffic inspection, overrun control and vehicle use condition assessment are also continuously deep, so that a vehicle load measuring system is more and more widely applied.
In the prior art, the purpose of measuring the load of a vehicle is realized by driving the vehicle to an area specially provided with a vehicle load measuring system, measuring a load signal by a sensor in the area, then processing the load signal by data to obtain the load of the vehicle, and transmitting the load of the vehicle to a cab.
However, the requirement for measuring the load of the vehicle in real time cannot be met by adopting the prior art.
Disclosure of Invention
The present invention is directed to provide a method and a device for processing a vehicle load, which can measure the load of a vehicle in real time.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a vehicle load processing method, including:
acquiring vehicle state parameters within a preset time interval;
preprocessing the vehicle state parameters to obtain first effective vehicle state parameters;
obtaining each acceleration value of the vehicle within the preset time interval according to the first effective vehicle state parameter;
according to each acceleration value of the vehicle within the preset time interval, correcting a first effective vehicle state parameter corresponding to each acceleration value to obtain a second effective vehicle state parameter;
obtaining a first vehicle load predicted value according to the second effective vehicle state parameter and a preset first training model;
confirming whether the first vehicle load estimated value meets a preset condition or not;
and if the first vehicle load estimated value meets the preset condition, performing data regression analysis according to the first vehicle load estimated value to obtain the real load of the vehicle.
In one embodiment, the step of preprocessing the vehicle state parameter to obtain a first valid vehicle state parameter includes:
deleting missing data in the vehicle state parameters, screening abnormal data in the vehicle state parameters, and replacing the abnormal data with an average value of two adjacent data of the abnormal data to obtain the first effective vehicle state parameters.
In one embodiment, the vehicle state parameters include wheel speed, vehicle driveline efficiency, tire radius, motor input voltage, and motor input current.
In one embodiment, the step of obtaining a first estimated vehicle load value according to the second effective vehicle state parameter and a preset first training model includes:
obtaining the driving force output by the wheel according to the motor input voltage, the motor input current, the wheel rotating speed, the tire radius and the transmission system efficiency;
and obtaining a first estimated vehicle load total weight according to the fact that the driving force output by the wheels is equal to the driving force input by the wheels and the driving force required by the wheels.
In one embodiment, the confirming whether the first estimated vehicle load amount meets a preset condition includes:
obtaining the number of the first estimated vehicle load values;
confirming whether the number of the first vehicle load estimated values meets a preset upper limit threshold value or not;
correspondingly, if the preset upper limit threshold value is reached, performing data regression analysis according to the first vehicle load estimated value to obtain the real load of the first vehicle.
In one embodiment, after the step of confirming whether the number of the first estimated vehicle load values meets the preset upper threshold, the method further includes:
and if the preset upper limit threshold value is not reached, continuously acquiring the first vehicle load estimated value reaching the preset upper limit threshold value.
In one embodiment, the preset first training model includes any one of a back propagation algorithm training BP, a back neural network propagation LM-BP, and a radial basis function neural network RBF training model.
In one embodiment, after the step of obtaining the true load of the vehicle, the method further comprises:
and sending the real load of the vehicle to a display device or a vehicle-mounted terminal of the vehicle through a data bus.
In a second aspect, an embodiment of the present invention further provides a vehicle load handling apparatus, including:
the acquisition module is used for acquiring vehicle state parameters in a preset time interval;
the preprocessing module is used for preprocessing the vehicle state parameters to obtain first effective vehicle state parameters;
the calculation module is used for obtaining each acceleration value of the vehicle in the preset time interval according to the first effective vehicle state parameter;
the correction module is used for correcting the first effective vehicle state parameters corresponding to the acceleration values according to the acceleration values of the vehicle within the preset time interval to obtain second effective vehicle state parameters;
the training model module is used for obtaining a first vehicle load prediction value according to the second effective vehicle state parameter and a preset first training model;
the confirming module is used for confirming whether the first vehicle load predicted value meets a preset condition or not;
and the analysis module is used for carrying out data regression analysis according to the first vehicle load estimated value to obtain the real load of the vehicle if the preset condition is met.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the vehicle load handling method according to the first aspect.
The invention has the beneficial effects that:
according to the vehicle load processing method and device provided by the embodiment of the invention, each acceleration value of the vehicle in the preset time interval is obtained by obtaining the vehicle state parameter in the preset time interval, the state parameter corresponding to the vehicle is corrected according to each acceleration value to obtain the second effective vehicle state parameter, the first vehicle load predicted value is obtained according to the second effective vehicle state parameter and the preset first training model, and if the first vehicle load predicted value meets the preset condition, the real load of the first vehicle can be obtained, and the method can be used for measuring the load of the vehicle in real time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram illustrating steps of a vehicle load handling method according to an embodiment of the present invention;
FIG. 2 is a schematic step diagram of a first estimated vehicle load value provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a predetermined condition determining step according to an embodiment of the present invention;
FIG. 4 is a schematic block diagram of a vehicle load handling device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Icon: 101-an acquisition module; 102-a pre-processing module; 103-a calculation module; 104-a correction module; 105-a training model module; 106-a confirmation module; 107-an analysis module; 201-a processor; 202-a storage medium; 203-bus.
Detailed Description
To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings may be obtained from the drawings without inventive effort.
First embodiment
Fig. 1 is a schematic step diagram of a vehicle load processing method according to an embodiment of the present invention, and referring to fig. 1, the embodiment of the present invention provides a vehicle load processing method applied to a new energy electric vehicle, where the vehicle load processing method includes:
s101, obtaining vehicle state parameters in a preset time interval.
Specifically, the preset time interval may be half an hour, 20 minutes, 10 minutes, 5 minutes, or 1 minute, and in order to make the acquired vehicle state parameters more accurate, the preset time interval takes a time interval of 1 minute as an example, and the corresponding vehicle state parameters of the preset driving range are acquired through a Controller Area Network (CAN) bus of the vehicle, where the vehicle state parameters include a vehicle speed and a wheel rotation speed.
Before the vehicle state parameters are acquired, the vehicle needs to be powered on, so that the load processing unit of the vehicle is initialized and self-checked.
S102, preprocessing the vehicle state parameters to obtain first effective vehicle state parameters.
Specifically, the acquired vehicle state parameters are preprocessed, and the preprocessing is to delete the vehicle state parameters which do not meet the conditions to obtain first effective vehicle state parameters which meet the conditions, wherein the first effective vehicle state parameters comprise effective vehicle speed and effective wheel rotating speed. For example, the continuous vehicle speed in the first effective vehicle state parameter is 10m/s, 12m/s, 100m/s, 15m/s, 18m/s, 20m/s, and the vehicle speed may not be obviously out of the normal range at the continuous time interval according to the vehicle speed rationality judgment, so that the obvious out-of-normal range value is replaced by the average value of two adjacent data of the obvious out-of-normal range value, that is, (12+15)/2 is 13.5m/s instead of 100m/s, and the effective vehicle speed is 10m/s, 12m/s, 13.5m/s, 15m/s, 18m/s, 20 m/s.
It should be noted that the vehicle speed is only an example, and the measured vehicle speed may not be an integer during a specific operation, and two bits after the decimal point may be reserved to make the vehicle speed more accurate.
S103, obtaining each acceleration value of the vehicle within a preset time interval according to the first effective vehicle state parameter.
Specifically, the acceleration at each preset time interval is obtained according to the effective vehicle speed, the effective wheel rotation speed and the preset time interval obtained in step S102.
For example, the values of the accelerations obtained from the effective vehicle speed, the effective wheel speed and the preset time interval are respectively 10m/s2、10m/s2、11m/s2、10m/s2、···、10m/s2。
S104, according to each acceleration value of the vehicle within a preset time interval, correcting the first effective vehicle state parameter corresponding to each acceleration value to obtain a second effective vehicle state parameter.
In particular, the second effective vehicle state parameter includes a second effective vehicle speed and a second effective wheel speed.
Furthermore, the load capacity of the vehicle can be accurately calculated in the process of stable running of the vehicle, the acceleration with different data of the acceleration and other acceleration is selected according to each acceleration value of the vehicle within a preset time interval, the effective vehicle speed and the effective wheel rotating speed in the first effective vehicle state parameter corresponding to the acceleration are corrected, and finally the value of the acceleration corresponding to the second effective vehicle speed and the second effective wheel rotating speed after correction is equal to other acceleration values. For example, in step S103, the calculated acceleration values are 10m/S, respectively2、10m/s2、11m/s2、10m/s2、···、10m/s2For acceleration value of 11m/s2Correcting the corresponding effective vehicle speed and the effective wheel speed to finally make the acceleration obtained by the corrected second effective vehicle speed and the second effective wheel speed be 10m/s2。
And S105, obtaining a first vehicle load estimated value according to the second effective vehicle state parameter and a preset first training model.
Specifically, the second effective vehicle state parameter is obtained according to S104, where the second effective vehicle state parameter includes a second effective vehicle speed, a second effective wheel speed, a rotational inertia of a transmission system, a wheel radius, a motor input voltage, a motor input current, and a tire radius, and the first vehicle load prediction value is obtained according to the second effective vehicle speed, the second effective wheel speed, the rotational inertia of the transmission system, the wheel radius, the motor input voltage, the motor input current, the tire radius, and a preset first training model.
And S106, confirming whether the first vehicle load estimated value meets the preset condition.
Specifically, it is determined whether the driving distance corresponding to the data amount of the first vehicle load estimated value meets a preset distance, for example, the preset distance is 3km, and the distance corresponding to the data amount of the first vehicle load estimated value is greater than or equal to 3km, then the distance corresponding to the data amount of the first vehicle load estimated value meets the preset distance, and the distance corresponding to the data amount of the first vehicle load estimated value is less than 3km, then the distance corresponding to the data amount of the first vehicle load estimated value does not meet the preset distance.
And S107, if the preset conditions are met, performing data regression analysis according to the estimated value of the load of the first vehicle to obtain the real load of the first vehicle.
Specifically, if the preset condition is met, the real load of the first vehicle is obtained by analyzing according to the estimated value of the load of the first vehicle.
According to the vehicle load processing method provided by the embodiment, each acceleration value of a vehicle in a preset time interval is obtained by obtaining the vehicle state parameter in the preset time interval, the state parameter corresponding to the vehicle is corrected according to each acceleration value to obtain the second effective vehicle state parameter, the first vehicle load predicted value is obtained according to the second effective vehicle state parameter and the preset first training model, if the first vehicle load predicted value meets the preset condition, the real load of the first vehicle can be obtained, and the method can be used for measuring the load of the vehicle in real time by performing data regression analysis on the second effective vehicle state parameter and the preset first training model.
In one embodiment, the step of preprocessing the vehicle state parameter to obtain a first valid vehicle state parameter includes:
deleting missing data in the vehicle state parameters, screening abnormal data in the vehicle state parameters, and replacing the abnormal data with an average value of two adjacent data of the abnormal data to obtain the first effective vehicle state parameters.
Specifically, according to the requirement of the reasonability of the vehicle state parameters, the occurrence of missing data and abnormal data in the vehicle state parameters is unreasonable, so that the missing data in the vehicle state parameters is deleted, and the abnormal data is replaced by the average value of two adjacent data of the abnormal data in the abnormal data processing method, so that the first effective vehicle state parameters are obtained.
For example, if the vehicle speed parameters are 10m/s, 12m/s, 0m/s, 15m/s, 18m/s, and 20m/s, then 0m/s of the vehicle speed parameters is deleted to obtain the first effective vehicle speeds of 10m/s, 12m/s, 15m/s, 18m/s, and 20 m/s.
In one embodiment, the vehicle state parameters include wheel speed, vehicle driveline efficiency, tire radius, motor input voltage, and motor input current.
Specifically, vehicle state parameters such as wheel rotation speed, vehicle transmission system efficiency, tire radius, motor input voltage, and motor input current are acquired through a Controller Area Network (CAN), and the acquired parameters are acquired according to preset mileage.
It should be noted that the CAN is a serial communication network that effectively supports distributed control or real-time control, and further needs to obtain parameters such as rotational inertia of a transmission system, a wheel radius, an equivalent coefficient of rotational inertia of the transmission system, a rolling resistance coefficient, an air resistance coefficient, a vehicle frontal area, air density, and a vehicle speed.
In one implementation, fig. 2 is a schematic diagram of a step of a first estimated vehicle load value according to an embodiment of the present invention, please refer to fig. 2, where the step of obtaining the first estimated vehicle load value according to a second effective vehicle state parameter and a preset first training model includes:
and S201, obtaining the driving force output by the wheel according to the motor input voltage, the motor input current, the wheel rotating speed, the tire radius and the transmission system efficiency.
Specifically, the input voltage of the motor is U, and the input current of the motor is IThe wheel speed is n, the tire radius is R and the driveline efficiency is η according to the formula
Obtaining and outputting driving force F1。
S202, according to the fact that the driving force output by the wheels is equal to the driving force input by the wheels and the driving force required by the wheels, the first estimated vehicle load weight is obtained.
In particular, according to the formula
Wherein, FaIndicates the required driving force theta of the wheeliRepresenting the moment of inertia, r, of the drive traindIndicating wheel radius, F2Indicating driving force, delta, input to the wheeliRepresenting the equivalent coefficient f of the rotational inertia of the transmission systemRRepresents a rolling resistance coefficient, CDRepresents the coefficient of air resistance, A represents the frontal area of the vehicle, ρaRepresenting air density, u representing vehicle speed, and according to Fa＝F1＝F2Obtaining a first estimated vehicle weight mv+mC。
In an implementation manner, fig. 3 is a schematic diagram of a step of determining a preset condition according to an embodiment of the present invention, please refer to fig. 3, where the determining whether the first estimated vehicle load value meets the preset condition includes:
s301, obtaining the number of the first vehicle load estimated values.
Specifically, according to the first vehicle load estimated value obtained in S202, the number of the first vehicle load estimated values is obtained, and the corresponding relationship between the first vehicle load estimated value and the traveled distance is obtained, for example, 10 first vehicle load estimated values are calculated.
S302, whether the number of the first vehicle load estimated values meets a preset upper limit threshold value or not is confirmed.
Specifically, the preset upper limit threshold value is obtained by the first vehicle reaching the set travel distance and through the corresponding relation between the mileage obtained through the training of the big data platform and the vehicle load data, for example, after the total travel distance is 3km, the requirement of monitoring and measuring can be met by predicting the cargo load precision.
And S303, correspondingly, if the preset upper limit threshold value is reached, performing data regression analysis according to the estimated value of the load of the first vehicle to obtain the real load of the first vehicle.
Specifically, if the number of the first vehicle load estimated values and the corresponding travel distance reach the preset upper limit threshold value according to the S302, the first vehicle load estimated values are analyzed to obtain the actual load of the first vehicle.
In one embodiment, after the step of determining whether the number of the first estimated vehicle load values meets the preset upper threshold, the method further comprises:
if the preset upper limit threshold value is not reached, the first vehicle load estimated value of the number reaching the preset upper limit threshold value is continuously obtained.
Specifically, if the number of the first vehicle load predicted values does not reach the preset upper limit threshold, vehicle state parameters in a preset time interval are continuously acquired, and the first vehicle load predicted values are calculated according to the vehicle state parameters until whether the first vehicle load predicted values meet the preset upper limit threshold or not.
In one embodiment, the preset first training model includes any one of a back propagation algorithm training BP, a back neural network propagation LM-BP, and a radial basis function neural network RBF training model.
Specifically, the preset first training model includes multiple model training modes such as Back Propagation (BP), inverse neural network Propagation (LM-BP), Radial Basis Function (RBF), and the like, and is not limited to the above model training modes.
In one embodiment, after the step of obtaining the actual load of the vehicle, the method further comprises:
and sending the real load of the vehicle to a display device or a vehicle-mounted terminal of the vehicle through a data bus.
Specifically, the real load of the vehicle is sent to a display device or a vehicle-mounted terminal of the vehicle through the CAN, and the step enables a user to observe the real load of the vehicle in real time through the display device or the vehicle-mounted terminal.
Second embodiment
Fig. 4 is a schematic block diagram of a vehicle load processing device according to an embodiment of the present invention, and referring to fig. 4, the embodiment of the present invention further provides a vehicle load processing device, including:
the acquiring module 101 is used for acquiring vehicle state parameters within a preset time interval;
the preprocessing module 102 is configured to preprocess the vehicle state parameter to obtain a first effective vehicle state parameter;
the calculation module 103 is configured to obtain each acceleration value of the vehicle within the preset time interval according to the first effective vehicle state parameter;
a correction module 104, configured to correct, according to each acceleration value of the vehicle within the preset time interval, a first effective vehicle state parameter corresponding to each acceleration value to obtain a second effective vehicle state parameter;
a training model module 105, configured to obtain a first vehicle load predicted value according to the second effective vehicle state parameter and a preset first training model;
a confirming module 106, configured to confirm whether the first vehicle load predicted value meets a preset condition;
and the analysis module 107 is configured to perform data regression analysis according to the first vehicle load estimated value to obtain the real load of the vehicle if the preset condition is met.
The above-mentioned apparatus is used for executing the method provided by the foregoing embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
Third embodiment
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 5, an embodiment of the present invention further provides an electronic device, including: a processor 201, a storage medium 202 and a bus 203, wherein the storage medium 202 stores machine-readable instructions executable by the processor 201, when the electronic device is operated, the processor 201 communicates with the storage medium 202 through the bus 203, and the processor 201 executes the machine-readable instructions to perform the steps of the vehicle load processing method according to the first embodiment.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another device or system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
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| US11453404B2 (en) * | 2019-09-05 | 2022-09-27 | Baidu Usa Llc | Gear based vehicle load inference system |
| CN112461346A (en) * | 2020-11-13 | 2021-03-09 | 北京旷视机器人技术有限公司 | Robot load detection method and device, robot and readable storage medium |
| CN114494581A (en) * | 2021-12-29 | 2022-05-13 | 深圳市优必选科技股份有限公司 | Vehicle driving parameter simulation method and device, electronic equipment and storage medium |
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