CN114684159A - Vehicle mass estimation method and device, electronic equipment and storage medium - Google Patents
Vehicle mass estimation method and device, electronic equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
- B60W40/13—Load or weight
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/107—Longitudinal acceleration
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W2520/00—Input parameters relating to overall vehicle dynamics
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Abstract
The application provides a vehicle mass estimation method, a device, an electronic device and a storage medium, wherein the vehicle comprises a three-coordinate acceleration sensor, and the method comprises the following steps: acquiring an acceleration measurement signal of the acceleration sensor; determining a frequency distribution state of a longitudinal acceleration measurement signal based on the acceleration measurement signal; determining a first actual acceleration of the vehicle based on the frequency distribution state; determining a vehicle mass from the first actual acceleration. Decoupling is carried out on the estimation of the road gradient in the vehicle mass estimation process, estimation errors are eliminated, the accuracy of vehicle mass estimation is improved, the vehicle can more accurately decide gears in a corresponding state, and the vehicle performance is improved.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a vehicle mass estimation method and device, electronic equipment and a storage medium.
Background
On a heavy automobile carrying an AMT gearbox, accurate estimation of the automobile weight is a precondition for deciding a proper gear, and improvement of the automobile performance is facilitated. In the prior art, the vehicle mass is calculated by a method based on a vehicle dynamic model, which has the advantages of low cost, but the estimation precision is not high due to the fact that the road gradient estimation and the vehicle mass estimation are coupled to a certain degree.
Therefore, how to eliminate the estimation error and improve the estimation accuracy is an urgent technical problem to be solved.
Disclosure of Invention
In order to solve the technical problem of how to eliminate estimation errors and improve estimation accuracy set forth in the background art, the present application provides a vehicle mass estimation method, apparatus, electronic device, and storage medium.
According to a first aspect an embodiment of the present application provides a vehicle mass estimation method, the vehicle comprising a three-coordinate acceleration sensor, the method comprising: acquiring an acceleration measurement signal of the acceleration sensor; determining a frequency distribution state of a longitudinal acceleration measurement signal based on the acceleration measurement signal; determining a first actual acceleration of the vehicle based on the frequency distribution state; determining a vehicle mass from the first actual acceleration.
Optionally, the determining the frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal includes: acquiring stress information of the acceleration sensor in the vertical direction; analyzing a vertical frequency value in the stress information; and taking the vertical frequency value as the error frequency value of the longitudinal acceleration measuring signal.
Optionally, the determining the frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal includes: acquiring vehicle speed information; an error frequency value in the longitudinal acceleration measurement signal is determined based on the vehicle speed information.
Optionally, the determining a first actual acceleration of the vehicle based on the frequency distribution state includes: determining a longitudinal acceleration measurement signal filtering threshold based on the error frequency value; and filtering the longitudinal acceleration by adopting the filtering threshold value to obtain the first actual acceleration.
Optionally, the method further includes: acquiring stress information of the acceleration sensor in the vertical direction; acquiring a road gradient value; determining a longitudinal acceleration error value based on the force information and the slope value; a second actual acceleration of the vehicle is determined based on the acceleration measurement signal and the longitudinal acceleration error value.
Optionally, the determining the vehicle mass according to the first actual acceleration includes: acquiring a first actual acceleration; calculating a vehicle mass based on the first actual acceleration and the vehicle longitudinal dynamics.
Optionally, the calculating the vehicle mass based on the first actual acceleration and the vehicle longitudinal dynamics comprises calculating using a recursive least squares method.
According to still another aspect of an embodiment of the present application, there is also provided a vehicle mass estimation device characterized by comprising: the acquisition module is used for acquiring an acceleration measurement signal of the acceleration sensor; the first calculation module is used for determining the frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal; the second calculation module is used for determining first actual acceleration of the vehicle based on the frequency distribution state; and the third calculation module is used for determining the vehicle mass according to the first actual acceleration.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory are configured to communicate with each other via the communication bus, and the memory is configured to store a computer program; a processor for performing the vehicle mass estimation method steps of any of the above embodiments by executing the computer program stored on the memory.
According to yet another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to perform the vehicle mass estimation method steps in any of the above embodiments when executed.
In the embodiment of the application, when the vehicle jolts due to Gaussian white noise generated by unevenness of a road surface in the driving process, when the road has a slope, the Gaussian white noise frequency has a component in the driving direction of the vehicle, and a Gaussian white noise frequency component value exists in a signal frequency value detected by an acceleration sensor. Therefore, an error exists between the acceleration value measured by the sensor and the actual acceleration value, the error in the measured acceleration value is eliminated by eliminating the influence of the Gaussian white noise frequency component on the driving direction, the actual acceleration value is obtained to estimate the vehicle mass, the estimation precision of the estimated vehicle mass is improved, the vehicle can make a more accurate decision on the gear in the corresponding state, and the vehicle performance is improved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a diagram illustrating a hardware environment for a vehicle mass estimation method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a vehicle mass estimation method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart diagram of a vehicle mass estimation method according to another embodiment of the present application;
FIG. 4 is a schematic flow chart of a vehicle mass estimation method according to another embodiment of the present application;
FIG. 5 is a schematic flow chart of a vehicle mass estimation method according to another embodiment of the present application;
FIG. 6 is a block diagram showing a structure of a vehicle mass estimating apparatus according to an embodiment of the present application;
fig. 7 is a block diagram of an alternative electronic device in an embodiment of the present application.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described with reference to the accompanying drawings, in which the same reference numerals indicate the same or structurally similar but functionally identical elements.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
According to an aspect of an embodiment of the present application, there is provided a vehicle mass estimation method. Alternatively, in the present embodiment, the vehicle mass estimation method described above may be applied to a hardware environment constituted by the terminal 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal 102 through a network, which may be used to provide services for the terminal or a client installed on the terminal, may be provided with a database on the server or independent from the server, may be used to provide data storage services for the server 104, and may also be used to handle cloud services, and the network includes but is not limited to: the terminal 102 is not limited to a PC, a mobile phone, a tablet computer, a vehicle-mounted computer, etc. The vehicle mass estimation method according to the embodiment of the present application may be executed by the server 104, the terminal 102, or both the server 104 and the terminal 102. The terminal 102 may execute the vehicle mass estimation method according to the embodiment of the present application by a client installed thereon.
Taking the vehicle mass estimation method of the present embodiment executed by the terminal 102 and/or the server 104 as an example, fig. 2 is a schematic flowchart of an alternative vehicle mass estimation method according to the present embodiment, wherein the vehicle includes a three-coordinate acceleration sensor, for example, referring to fig. 2, the flowchart of the method may include the following steps:
s100, acquiring an acceleration measurement signal of the acceleration sensor.
S200, determining the frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal.
S300, determining a first actual acceleration of the vehicle based on the frequency distribution state.
S400, determining the mass of the vehicle according to the first actual acceleration.
Through the steps S100 to S400, when the vehicle is in a traveling state, the vehicle jolts due to white gaussian noise generated by unevenness of a road surface, and when a slope exists on the road, the frequency of the white gaussian noise has a component in the traveling direction of the vehicle, and a frequency component value of the white gaussian noise exists in the signal frequency value detected by the acceleration sensor. Therefore, an error exists between the acceleration value measured by the sensor and the actual acceleration value, the error in the measured acceleration value is eliminated by eliminating the influence of the Gaussian white noise frequency component on the driving direction, the actual acceleration value is obtained to estimate the vehicle mass, the estimation precision of the estimated vehicle mass is improved, the vehicle can make a more accurate decision on the gear in the corresponding state, and the vehicle performance is improved.
For the technical scheme in step S100, the mass estimation of the vehicle is performed by using a longitudinal acceleration and kinematic formula, the vehicle acceleration sensor measures an acceleration signal, the acceleration sensor is a three-coordinate sensor, and the acquired values may include a longitudinal acceleration value, a lateral acceleration and a vertical acceleration.
Corresponding to the technical solution in step S200, the frequency distribution state includes frequency states of each acceleration signal measured by the acceleration sensor, including a frequency state of acceleration in the longitudinal direction, a frequency state of acceleration in the lateral direction, and a frequency state of acceleration in the horizontal direction.
Corresponding to the technical solution in step S300, white gaussian noise is generated due to uneven road during the driving process of the vehicle, and the vehicle jolts and points to or deviates from the center of the earth. The Gaussian white noise can cause an acceleration value detected by the acceleration device in the vertical direction to generate errors, the collected longitudinal acceleration and longitudinal dynamics formula of the automobile are adopted when the automobile runs on a flat road and the vehicle mass is estimated, and the Gaussian white noise in the vertical direction is perpendicular to the vehicle running direction and has no influence on the estimated value. However, when the gradient exists, the vehicle is not perpendicular to the driving direction of the vehicle due to the jolt of the vehicle pointing to or departing from the center of the earth, so that white gaussian noise has a component in the driving direction of the vehicle, the estimation of the gradient and the estimation of the vehicle mass are coupled, a certain error is caused between the measured value and the actual value of the sensor device in the longitudinal direction, the detection result is inaccurate, and the estimated vehicle mass is inaccurate. Through the decoupling of the slope estimation and the quality estimation, errors are eliminated, the accuracy and the precision of the slope estimation are improved, the vehicle can adjust proper gears in corresponding states, and the performance of the vehicle is improved.
For the technical solution in step S400, after the actual acceleration is obtained, because the measurement data of the vehicle acceleration sensor is updated in real time, the measurement data of a plurality of times are counted in consideration of the deviation of the measurement value, the quality of the vehicle is obtained comprehensively, and the estimation accuracy is improved.
As an embodiment, the road gradient may cause white gaussian noise to generate an error on the input amount in the quality estimation process, and in order to eliminate the influence of the white gaussian noise in the quality estimation process, obtain a more accurate longitudinal acceleration signal measured by the apparatus, and eliminate the error, it is necessary to analyze the frequency distribution state. For example, referring to fig. 3, the step of determining the frequency distribution state of the longitudinal acceleration signal may include:
s201, stress information of the acceleration sensor in the vertical direction is obtained.
And S202, analyzing the vertical frequency value in the stress information.
And S203, taking the vertical frequency value as an error frequency value of the longitudinal acceleration measurement signal.
White gaussian noise is the jolt of a vehicle caused by the unevenness of the ground when the vehicle is running on the road, and the jolt always points to or departs from the center of the ground. When the vehicle-volume running road has a slope, the sensor in the vertical direction is only subjected to the component force of gravity and is in a balanced state, and the speed acceleration value of the vehicle in the longitudinal direction cannot be influenced. As the bumping direction of the vehicle is always vertical or deviates from the center of the earth due to the Gaussian white noise, an angle is formed between the bumping direction and the vertical direction of the vehicle on the ramp, frequency signals are provided in the vertical direction and the longitudinal direction, and the gravity component force in the vertical direction is in a balanced state by measuring the stress information in the vertical direction of the sensor, so that the measured frequency signal in the vertical direction is consistent with the frequency signal of the Gaussian white noise and is also the Gaussian white noise frequency signal in the longitudinal direction. The frequency information signal acquired by the acceleration sensor includes a white gaussian noise frequency signal.
The method is used for inputting an estimation formula of the vehicle mass, and in order to improve the accuracy of vehicle estimation, the frequency value of the actually measured signal is obtained by analyzing the frequency of the longitudinal acceleration signal of the sensor. The vertical acceleration influence factor and the longitudinal acceleration influence factor of the sensor are the same, the Gaussian white noise has the same frequency signals at the vertical acceleration and the vertical acceleration, the frequency of the Gaussian white noise at the vertical acceleration signal can be obtained by analyzing the frequency of the Gaussian white noise at the vertical acceleration signal, and the actual longitudinal acceleration signal can be obtained by eliminating the error value.
As an exemplary embodiment, the frequency is calculated by dividing the vehicle speed by the time, so the vehicle speed affects the frequency calculation, and the vehicle jolts differently due to the different vehicle speeds during the running process. Under the same road surface condition, the faster the vehicle speed, the higher the frequency of jolting, so the vehicle speed influences the frequency of Gaussian white noise. Illustratively, determining the frequency distribution status is shown in fig. 4, and includes the steps of:
and S211, acquiring vehicle speed information.
S212, determining an error frequency value in the longitudinal acceleration measuring signal based on the vehicle speed information.
The error caused by the frequency value of the road condition influence is also included in the running of the vehicle, and the error of the detected vehicle quantity can also comprise the detection of road information, and the road information can comprise the road bump condition, namely the distance between a convex part and a convex part of the road surface, the interval between the convex part and a concave part, and the distance between the concave part and the concave part. The detected device can be obtained by ultrasonic, image, laser and historical driving information. The distance between the projections, the spacing between the projections and the recesses, and the distance between the recesses. And obtaining a frequency value of the error based on the obtained road information, so as to analyze the acceleration frequency signal obtained by the sensor.
Alternative embodiment, estimating a vehicleThe input quantity of the mass only adopts a measured longitudinal acceleration signal, the measured acceleration frequency state comprises low-average high frequency and medium frequency, an actual acceleration frequency signal is needed, and an actual value can be obtained by filtering out signals except the actual acceleration frequency. And setting a filtering threshold value of the measured longitudinal acceleration signal based on the estimated error frequency value, leaving the required actual longitudinal acceleration signal, and filtering out influence errors to obtain the actual acceleration. For example, the method adopts first-order low-pass filtering to process the result, removes the influence of Gaussian white noise, and has the following transfer function:
where Ts represents time. Increasing the cut-off bandwidth (omega) of the filter appropriatelyb1/T) is beneficial to quickening the response of the input signal and improving the stability of the phase margin increase, so that the higher the vehicle speed, the higher the frequency of white noise, the higher the vehicle speed, the smaller the value of T can be adjusted according to the vehicle speed.
As an exemplary embodiment, the error caused by white Gaussian noise in the running process of the vehicle can be eliminated by filtering the frequency, and besides, the stress analysis can be carried out to accurately calculate an actual value and eliminate the influence of the error. The acceleration sensor detection principle is that the acceleration value is calculated by analyzing the pressure of the piezoelectric crystal inside the sensor, so that the actual acceleration value can be obtained by calculating the actual stress and eliminating the pressure of Gaussian white noise. Referring to fig. 5, the step of obtaining the actual acceleration value by calculating the force cancellation error includes:
s500, stress information of the acceleration sensor in the vertical direction is obtained.
S600, obtaining a road slope value.
S700, determining a longitudinal acceleration error value based on the stress information and the gradient value.
S800, determining a second actual acceleration of the vehicle based on the acceleration measurement signal and the longitudinal acceleration error value.
For steps S500 to S800, in the longitudinal direction by calculationAnd (4) upward actual stress is carried out, so that the error is eliminated, and the actual acceleration is obtained. As the Gaussian white noise is bumped when the vehicle points to or deviates from the direction of the geocentric, the stress of the sensor changes in the bumping process of the vehicle, and the error stress of the sensor caused by the Gaussian white noise in the longitudinal direction can not be obtained by directly calculating. When the vehicle runs on the slope, the road slope can change the angle between the vertical direction of the vehicle and the direction of the center of the earth, the amplitude generated by Gaussian white noise can have components in the vertical direction and the longitudinal direction respectively, and the relationship between the longitudinal error value and the vertical error value is related according to the magnitude of the slope value. The vertical stress condition is obtained through vertical direction stress analysis, the piezoelectric crystal of the sensor on the vertical direction only detects the component force of the gravity on the vertical direction, the component force of the gravity cannot change, the vertical error value can change, and therefore the sensor can detect the changed error value. And based on a mechanical formula, inputting a vertical error value and calculating an output longitudinal error value. For example,
wherein F1 is the longitudinal error value, F2 is the vertical error value, and theta is the road grade value. The longitudinal force detected by the accelerator comprises an inner error value, the actual longitudinal force is obtained after the error value is eliminated, and then the second actual acceleration can be obtained according to the actual longitudinal force.
As an alternative embodiment, the manner of eliminating the error of the estimated vehicle mass can include filtering the value of the elimination error frequency to obtain a first actual acceleration and analyzing and calculating the actual longitudinal force of the sensor by the stress to obtain a second actual acceleration. For example, the first actual acceleration or the second actual acceleration may be obtained separately; first actual acceleration can be obtained through filtering, and then stress analysis is carried out on the first actual acceleration to obtain second actual acceleration; the stress analysis can be firstly carried out to obtain a second actual acceleration, and then the second actual acceleration is processed through filtering to obtain a first actual acceleration; after the first actual acceleration and the second actual acceleration are obtained through filtering and stress analysis respectively, the first actual acceleration and the second actual acceleration are combined to obtain the final actual acceleration.
As an exemplary implementationFor example, the mass of the vehicle amount may be calculated in various ways, such as direct acquisition by a sensor, calculation acquisition based on an acceleration value and a velocity value detected during traveling. For example, the mass of the vehicle is calculated by combining a sensor acquisition principle formula and a vehicle longitudinal dynamics formula. The formula of the sensor acquisition principle is as follows:
in the formula of alphasenxFor longitudinal acceleration, alpha, detected by the sensorsenzVertical acceleration detected by a sensor, g is gravity acceleration, theta is road gradient value,
The ratio of the vehicle speed to the time, and δ are white gaussian noises generated by the unevenness of the road when the vehicle is running at high speed. The formula of longitudinal dynamics of the vehicle weight is as follows:
in the formula, FtIs the driving force of the vehicle, FwIs the air resistance, f is the rolling resistance coefficient, g is the gravitational acceleration, theta is the road gradient value,
Is the ratio of the vehicle speed to time, and m is the vehicle mass. The obtained vehicle mass is:
as an exemplary embodiment, a method of estimating vehicle mass employs a longitudinal dynamics formula and a sensor acquisition principle. The data collected in the vehicle is updated in real time, so that the calculation result is updated in a recursion mode on the basis of the last estimation in order to save memory and calculation resources. And processing the vehicle weight calculation result by adopting a recursive least square algorithm. And (3) constructing an observation model according to a longitudinal dynamics formula and a sensor acquisition principle formula: z is a radical ofk＝Hkx+vkWherein z isk＝Ft-Fw、Hk＝asenx+asenzf、vkTo observe noise, FtIs the driving force of the vehicle, FwIs air resistance, f is rolling resistance coefficient, g is gravity acceleration, alphasenxFor longitudinal acceleration, alpha, detected by the sensorsenzIs the vertical acceleration detected by the sensor. Namely, the purpose of estimating the vehicle weight can be achieved by reducing the sum of squares of the deviation between the observed value and the estimated value:
it should be noted that for simplicity of description, the above-mentioned embodiments of the method are described as a series of acts, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, an optical disk) and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the methods according to the embodiments of the present application.
According to another aspect of the embodiments of the present application, there is also provided a vehicle mass estimation device for implementing the vehicle mass estimation described above. Fig. 6 is a schematic diagram of an alternative vehicle mass estimation device according to an embodiment of the present application, which may include, as shown in fig. 6:
an obtaining module 602, configured to obtain an acceleration measurement signal of an acceleration sensor;
a first calculation module 604 for determining a frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal;
a second calculation module 606 for determining a first actual acceleration of the vehicle based on the frequency distribution state;
a third calculation module 608 for determining a vehicle mass based on said first actual acceleration.
It should be noted that the modules described above are the same as examples and application scenarios realized by corresponding steps, but are not limited to what is disclosed in the foregoing embodiments. It should be noted that the modules described above as a part of the apparatus may be operated in a hardware environment as shown in fig. 1, and may be implemented by software, or may be implemented by hardware, where the hardware environment includes a network environment. According to still another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the vehicle mass estimation method described above, which may be a server, a terminal, or a combination thereof.
Fig. 7 is a block diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 7, including a processor 702, a communication interface 704, a memory 706 and a communication bus 708, where the processor 702, the communication interface 704 and the memory 706 communicate with each other via the communication bus 708, where,
a memory 706 for storing computer programs;
the processor 702, when executing the computer program stored in the memory 706, performs the following steps:
acquiring an acceleration measurement signal of an acceleration sensor;
determining a longitudinal acceleration measurement signal based on the acceleration measurement signal;
determining a first actual acceleration of the vehicle based on the frequency distribution state;
determining a vehicle mass from the first actual acceleration.
The electronic device for writing can be a vehicle-mounted computer.
Alternatively, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The memory may include RAM, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
As an example, as shown in fig. 7, the memory 702 may include, but is not limited to, the obtaining module 602, the identifying module 604, and the prompting module 606 of the vehicle mass estimating apparatus. In addition, other module units in the vehicle mass estimation device may also be included, but are not limited to, and are not described in detail in this example.
The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), an NP (Network Processor), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
It can be understood by those skilled in the art that the structure shown in fig. 7 is only an illustration, and the device implementing the vehicle quality estimation method may be a terminal device, and the terminal device may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 7 is a diagram illustrating a structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 7, or have a different configuration than shown in FIG. 7.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
According to still another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in the present embodiment, the above-described storage medium may be used for program codes for executing the vehicle mass estimation method.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
acquiring an acceleration measurement signal of an acceleration sensor;
determining a longitudinal acceleration measurement signal based on the acceleration measurement signal;
determining a first actual acceleration of the vehicle based on the frequency distribution state;
determining a vehicle mass from the first actual acceleration.
Optionally, for a specific example in this embodiment, reference may be made to the example described in the foregoing embodiment, and details of this are not described again in this embodiment.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another 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, units or modules, and may be in an electrical 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, and may also be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in the embodiment. In addition, functional units in the embodiments of the present application 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, and can also be realized in a form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A vehicle mass estimation method, the vehicle including a three-coordinate acceleration sensor, the method comprising:
acquiring an acceleration measurement signal of the acceleration sensor;
determining a frequency distribution state of a longitudinal acceleration measurement signal based on the acceleration measurement signal;
determining a first actual acceleration of the vehicle based on the frequency distribution state;
determining a vehicle mass from the first actual acceleration.
2. The vehicle mass estimation method according to claim 1, characterized in that the determining the frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal includes:
acquiring stress information of the acceleration sensor in the vertical direction;
analyzing a vertical frequency value in the stress information;
and taking the vertical frequency value as the error frequency value of the longitudinal acceleration measuring signal.
3. The vehicle mass estimation method according to claim 1, characterized in that the determining the frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal includes:
acquiring vehicle speed information;
an error frequency value in the longitudinal acceleration measurement signal is determined based on the vehicle speed information.
4. A vehicle mass estimation method as defined in claim 2 or 3, wherein said determining a first actual acceleration of the vehicle based on said frequency distribution state includes:
determining a longitudinal acceleration measurement signal filtering threshold based on the error frequency value;
and filtering the longitudinal acceleration by adopting the filtering threshold value to obtain the first actual acceleration.
5. The vehicle mass estimation method of claim 1, characterized in that the method further comprises:
acquiring stress information of the acceleration sensor in the vertical direction;
acquiring a road gradient value;
determining a longitudinal acceleration error value based on the force information and the slope value;
a second actual acceleration of the vehicle is determined based on the acceleration measurement signal and the longitudinal acceleration error value.
6. The vehicle mass estimation method of claim 1, wherein said determining a vehicle mass from said first actual acceleration comprises:
acquiring a first actual acceleration;
calculating a vehicle mass based on the first actual acceleration and the vehicle longitudinal dynamics.
7. The vehicle mass estimation method of claim 6, wherein calculating the vehicle mass based on the first actual acceleration and the vehicle longitudinal dynamics includes calculating using a recursive least squares method.
8. A vehicle mass estimation device characterized by comprising:
the acquisition module is used for acquiring an acceleration measurement signal of the acceleration sensor;
the first calculation module is used for determining the frequency distribution state of the longitudinal acceleration measurement signal based on the acceleration measurement signal;
the second calculation module is used for determining first actual acceleration of the vehicle based on the frequency distribution state;
and the third calculation module is used for determining the vehicle mass according to the first actual acceleration.
9. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein said processor, said communication interface and said memory communicate with each other via said communication bus,
the memory for storing a computer program;
the processor for performing the vehicle mass estimation method steps of any one of claims 1 to 7 by executing the computer program stored on the memory.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the vehicle mass estimation method steps of any one of claims 1 to 7 when executed.
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