Vehicle power detection method, device, equipment and medium

Abstract

The invention discloses a vehicle power detection method, device and equipment and a medium. The method comprises the steps that when it is determined that a vehicle stably runs straight, vehicle real-time state data at the current moment are collected; acquiring a change parameter integration value and an error covariance at the current moment; according to the change parameter integration value, the error covariance and the vehicle real-time state data at the current moment, the change parameter integration value and the error covariance at the next moment are calculated, the change parameter integration value is an integration value formed by parameter values of all parameters in a change parameter set, and the error covariance is an error covariance; the change parameters include parameters that change is caused by the weight and the volume of the load of the vehicle. According to the embodiment of the invention, the accuracy of the vehicle longitudinal dynamic model can be improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a vehicle power detection method, device, equipment and medium. Background Art

[0002] With the rapid development of science and technology, the longitudinal dynamics model of vehicles has received widespread attention. The longitudinal dynamics model of vehicles can be applied in vehicle dynamics analysis, control system design, vehicle safety and stability, etc.

[0003] Currently, the longitudinal dynamics model of the vehicle is calculated based on fixed parameters such as drag coefficient, rolling resistance coefficient and frontal area.

[0004] However, using fixed parameters to calculate the vehicle longitudinal dynamics model results in the inability to reflect the actual vehicle state. Summary of the invention

[0005] The present invention provides a vehicle power detection method, device, equipment and medium. The embodiments of the present invention can improve the accuracy of the longitudinal dynamics model of the whole vehicle.

[0006] In a first aspect, an embodiment of the present invention provides a vehicle power detection method, the method comprising:

[0007] When it is determined that the vehicle is moving straight and steadily, the real-time status data of the vehicle at the current moment is collected;

[0008] Get the integrated value of the changing parameters and the error covariance at the current moment;

[0009] According to the integrated value of the changing parameters, the error covariance and the real-time status data of the vehicle at the current moment, the integrated value of the changing parameters and the error covariance at the next moment are calculated. The integrated value of the changing parameters is an integrated value formed by the parameter values ​​of each parameter in the changing parameter set, wherein the changing parameters include parameters caused by changes in the weight and volume of the vehicle's load.

[0010] In a second aspect, an embodiment of the present invention further provides a vehicle power detection device, the device comprising:

[0011] A real-time status data acquisition module is used to collect the real-time status data of the vehicle at the current moment when it is determined that the vehicle is driving steadily and straight;

[0012] A change parameter integration and error covariance acquisition module is used to obtain the change parameter integration value and error covariance at the current moment;

[0013] The next moment parameter prediction module is used to calculate the change parameter integration value and error covariance at the next moment based on the change parameter integration value, error covariance and vehicle real-time status data at the current moment. The change parameter integration value is an integration value formed by the parameter values ​​of each parameter in the change parameter set, wherein the change parameter includes the parameters caused by the weight and volume of the vehicle's load.

[0014] In a third aspect, an embodiment of the present invention further provides a vehicle power detection device, the vehicle power detection device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the vehicle power detection method according to any embodiment of the present invention.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle power detection method of any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention can reflect the normal operation status of the vehicle by collecting the real-time status data of the vehicle at the current moment when determining that the vehicle is driving straight and stable, and provide a stable parameter benchmark for subsequent analysis; by obtaining the integrated value of the changing parameters and the error covariance at the current moment, and calculating the integrated value of the changing parameters and the error covariance at the next moment, it is calculated based on the data of the previous moment, and the parameter changes at future moments are predicted, which improves the predictability of the vehicle state and helps to make timely adjustments; the changing parameters include the parameters caused by the weight and volume of the vehicle's load, which can more comprehensively reflect the impact of the load on the vehicle performance and provide a more reliable data basis for subsequent optimization control.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a vehicle power detection method provided according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a vehicle power detection method provided according to an embodiment of the present invention;

[0024] Figure 3 is a force diagram of a vehicle in a vehicle power detection method provided according to an embodiment of the present invention;

[0025] Figure 4 is a structural diagram of a vehicle power detection device provided according to an embodiment of the present invention;

[0026] Figure 5 It is a structural schematic diagram of a vehicle power detection device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] In the technical solution of the embodiment of the present invention, the acquisition, storage and application of the real-time status data of the vehicle, etc., are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0030] Figure 1 A flow chart of a vehicle power detection method provided by an embodiment of the present invention. The embodiment of the present invention is applicable to vehicle power detection, and the method can be executed by a vehicle power detection device, which can be implemented in the form of hardware and / or software.

[0031] See also Figure 1 The vehicle power detection method shown includes:

[0032] S101. When it is determined that the vehicle is traveling straight and steadily, collect the real-time status data of the vehicle at the current moment.

[0033] Among them, the vehicle's smooth straight driving may refer to the vehicle driving along a straight path in a stable state without significant acceleration, deceleration or turning. Selecting the state of the vehicle driving smoothly and straight to collect data ensures that the vehicle's dynamic state is relatively stable and the vehicle is less disturbed, which is conducive to the accurate collection of the vehicle's dynamics-related parameters. When the vehicle is driving smoothly and straight, the longitudinal acceleration of the vehicle is mainly determined by factors such as driving force, wind resistance, rolling resistance and gravity component. The smooth straight driving state can ensure that the force conditions of the vehicle are more realistic. Exemplarily, when the vehicle is driving at a constant speed on a flat road without acceleration, deceleration or turning, the vehicle in this state can be a vehicle driving smoothly and straight, and the dynamic data in this state is relatively stable and predictable.

[0034] Among them, the real-time status data of the vehicle may refer to the numerical value of the current motion state of the vehicle collected by sensors and controllers during the operation of the vehicle. The real-time status data of the vehicle may include engine torque, gear ratio of the transmission, main reduction ratio, actual vehicle speed and longitudinal acceleration. The real-time status data of the vehicle is used to provide accurate and true input values ​​for the longitudinal dynamics model of the vehicle to help estimate parameters that cannot be directly measured. For example, the longitudinal acceleration can be obtained by an acceleration sensor, the engine torque can be obtained by a torque sensor, and the actual vehicle speed can be obtained by a vehicle speedometer, that is, the real-time data obtained in the above manner may refer to the real-time status data of the vehicle.

[0035] In a specific example, when the vehicle is in a steady straight-moving state and meets the following conditions: ABS (Antilock Brake System) communication is normal or the vehicle speed sensor is faulty; the current vehicle speed is greater than the preset speed threshold; the engine controller communication is normal; the acceleration sensor is faulty; the engine torque is greater than the preset torque threshold; and the real-time vehicle status data at the current moment is collected.

[0036] S102: Obtain the integrated value of the change parameter and the error covariance at the current moment.

[0037] Among them, the integrated value of the changing parameter can refer to the estimated value of the integrated value of the changing parameter that cannot be obtained by the sensor. The estimated value of the integrated value can be obtained by integrating and calculating the relevant changing parameters of the longitudinal dynamics of the vehicle. For example, the integrated value of the changing parameter can be an estimated value of the comprehensive value obtained according to the changes in the load weight, volume and shape. There are multiple changing parameters, and the changing parameters can include transmission efficiency, tire radius, vehicle mass, drag coefficient, air density, windward area, vehicle relative speed, ambient wind speed and rolling resistance coefficient. Through the collected data, it is impossible to accurately determine the precise value of a single changing parameter in these changing parameters, and only the estimated value of the relationship of the combination of these changing parameters can be determined, and the estimated value of the relationship of the combination is the integrated value of the changing parameter. Since the value of a single changing parameter in these changing parameters is affected by many factors, for example, the tire is slightly deformed due to load, temperature and speed during driving, resulting in an unfixed actual radius of the tire, the integrated value of the changing parameter that cannot be obtained can be estimated based on the parameter value that can be obtained, and an estimated value approximating the actual parameter can be obtained, and the exact value cannot be obtained. The parameter values ​​that can be obtained may include motor torque, gear ratio of the gearbox, main reduction ratio, actual vehicle speed, longitudinal acceleration, etc. For example, when a truck is fully loaded with goods, the tires of the truck are deformed due to the squeezing of the goods, so that the tire radius changes, and because the items in the cargo box are higher than the height of the cargo box after being fully loaded, the windward area of ​​the truck changes.

[0038] Among them, the error covariance may refer to the uncertainty of the integrated value of the estimated variable parameter. The error covariance is used to express the uncertainty caused by the measurement error in the parameter estimation process. Exemplarily, in the linear Kalman filter theory, the error covariance is used to correct the deviation in the parameter estimation process, and the integrated value of the variable parameter required by the vehicle longitudinal dynamics model can be adaptively adjusted in the subsequent calculation process. In the linear Kalman filter process, the error covariance gradually corrects the parameter estimation through the real-time state data of the vehicle obtained by the sensor and the initial value of the state estimation, so that the error covariance gradually converges, making the parameter estimation more accurate, wherein the initial value of the state estimation may refer to the transmission efficiency, tire radius, vehicle mass, wind resistance coefficient, air density, frontal area, vehicle relative speed, ambient wind speed, and rolling resistance coefficient of the vehicle calculated by the conventional data of the vehicle, for example, the conventional radius of the vehicle may be 0.557 meters. In the early stage of the linear Kalman filter, due to the inaccurate initial value of the state estimation, the error covariance is large, and as the parameter estimation is gradually corrected, the error covariance gradually decreases, and the parameter estimation is more accurate.

[0039] S103. Calculate the integrated value of the changing parameters and the error covariance at the next moment based on the integrated value of the changing parameters, the error covariance and the real-time status data of the vehicle at the current moment. The integrated value of the changing parameters is an integrated value formed by the parameter values ​​of each parameter in the changing parameter set, wherein the changing parameters include parameters caused by changes in the weight and volume of the vehicle's load.

[0040] Among them, the changing parameters include the parameters that change due to the weight and volume of the vehicle's load, which can refer to the change in the changing parameters caused by the change in the weight and volume of the vehicle's load, which in turn causes the change in the integrated value of the changing parameters to change, and there is an error with the initial value of the state estimation. For example, when a truck is fully loaded with goods, when the truck is driving steadily and straight, the wheels of the truck are squeezed by the goods, causing the wheels to deform, so that the tire radius changes, that is, different weights of goods cause different changes in the tire radius. Similarly, the transmission efficiency, vehicle mass, drag coefficient, air density, windward area, vehicle relative speed, ambient wind speed, and rolling resistance coefficient of the truck will also change to varying degrees, and then the integrated values ​​of the above parameters will also change to varying degrees.

[0041] According to the integrated value of the changing parameters and the error covariance at the current moment, the integrated value of the changing parameters and the error covariance at the next moment are calculated. After continuous iterative calculation, the finally obtained integrated value of the changing parameters and the error covariance tend to converge. The finally obtained integrated value of the changing parameters and the real-time status data of the vehicle are applied to the longitudinal dynamics model of the vehicle.

[0042] The technical solution of the embodiment of the present invention can reflect the normal operation status of the vehicle by collecting the real-time status data of the vehicle at the current moment when determining that the vehicle is driving straight and stable, and provide a stable parameter benchmark for subsequent analysis; by obtaining the integrated value of the changing parameters and the error covariance at the current moment, and calculating the integrated value of the changing parameters and the error covariance at the next moment, it is calculated based on the data of the previous moment, and the parameter changes at future moments are predicted, which improves the predictability of the vehicle state and helps to make timely adjustments; the changing parameters include the parameters caused by the weight and volume of the vehicle's load, which can more comprehensively reflect the impact of the load on the vehicle performance and provide a more reliable data basis for subsequent optimization control.

[0043] Figure 2 The flowchart of a vehicle power detection method provided by an embodiment of the present invention is as follows. Based on the above embodiments, the vehicle power detection operation is optimized and improved in the embodiment of the present invention.

[0044] Optionally, "calculate the integrated value of the changing parameters and the error covariance at the next moment based on the integrated value of the changing parameters, the error covariance and the real-time status data of the vehicle at the current moment" is refined into "calculate the prior estimated covariance at the next moment based on the error covariance at the current moment and the real-time collected parameter values ​​in the real-time status data of the vehicle; the real-time status data of the vehicle includes longitudinal acceleration and real-time collected parameter values, and the longitudinal acceleration is equal to the product of the real-time collected parameter value and the integrated value of the changing parameters; calculate the integrated value of the changing parameters at the next moment based on the integrated value of the changing parameters at the current moment, the real-time status data of the vehicle and the prior estimated covariance at the next moment; calculate the error covariance at the next moment based on the real-time collected parameter values ​​and the prior estimated covariance" to improve the operation of vehicle dynamic detection.

[0045] It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions of other embodiments.

[0046] See also Figure 2 The vehicle power detection method shown includes:

[0047] S201. When it is determined that the vehicle is traveling straight and steadily, collect the real-time status data of the vehicle at the current moment.

[0048] S202: Obtain the integrated value of the change parameter and the error covariance at the current moment.

[0049] S203. Calculate the prior estimated covariance at the next moment based on the error covariance at the current moment and the real-time collected parameter value in the vehicle real-time status data; the vehicle real-time status data includes longitudinal acceleration and real-time collected parameter value, and the longitudinal acceleration is equal to the product of the real-time collected parameter value and the integrated value of the change parameter.

[0050] The prior estimation covariance may refer to a metric for measuring the uncertainty of the state assessment at the next moment. The prior estimation covariance at the next moment is determined based on the error covariance at the current moment, and the prior estimation covariance is used to indicate the error degree of the prediction, thereby determining the accuracy of the estimated value at the next moment.

[0051] Among them, longitudinal acceleration can refer to the acceleration of the vehicle in the direction of travel. Longitudinal acceleration represents the rate of change of the vehicle's speed in this direction. Longitudinal acceleration reflects the power output of the vehicle, and longitudinal acceleration is also the core variable in the vehicle dynamics model.

[0052] The real-time acquisition of parameter values ​​may refer to the current engine torque, gear ratio, main reduction ratio and actual vehicle speed acquired through sensors. The real-time acquisition of values ​​provides the current state information of the vehicle, and the real-time acquisition of values ​​is used as known data for the longitudinal dynamics model of the vehicle. The state estimation and error covariance are adjusted by combining the real-time acquired data with the linear Kalman filter theory.

[0053] S204, calculating the integrated value of the changing parameter at the next moment according to the integrated value of the changing parameter at the current moment, the real-time state data of the vehicle and the prior estimated covariance at the next moment.

[0054] After obtaining the current vehicle real-time state data and the integrated value of the change parameters, the state at the next moment is predicted by calculating the prior estimated covariance of the next moment. Then, combined with the current measurement data, the prior covariance is used to adjust the state estimate and calculate the integrated value of the change parameters at the next moment.

[0055] S205 . Calculate the error covariance at the next moment according to the real-time collected parameter value at the next moment and the priori estimated covariance.

[0056] The real-time parameter values ​​collected at the next moment and the prior estimated covariance are used to calculate and update the error covariance at the next moment. The error covariance reflects the difference between the system prediction value and the actual observation value. By adjusting the error covariance, the accuracy of subsequent state estimation can be better controlled.

[0057] It can be seen that in this embodiment, by gradually updating the prior estimation covariance and the integrated value of the change parameter at the next moment according to the error covariance, the real-time state data of the vehicle and the integrated value of the change parameter at the current moment, it means that in each iterative calculation, the longitudinal dynamics model of the vehicle can be made more accurate according to the most appropriate data and the adjustment of the error. The real-time update process can make the identified parameters closer to the actual operating state of the vehicle, reduce the prediction error and improve the calculation accuracy.

[0058] In some embodiments, obtaining the integrated value of the variation parameter and the error covariance at the current moment includes:

[0059] Obtaining preset integrated values ​​of the changing parameters and the error covariance, and using them as the integrated values ​​of the changing parameters and the error covariance at the initial moment;

[0060] According to the integrated value of the change parameter at the current moment, the real-time state data of the vehicle and the prior estimated covariance at the next moment, the integrated value of the change parameter at the next moment is calculated, including:

[0061] According to the integrated value of the changing parameter at the initial moment, the real-time state data of the vehicle and the prior estimated covariance at the next moment, the integrated value of the changing parameter at the next moment is calculated.

[0062] The initial moment may refer to the starting time point. Obtaining the preset integrated value of the changing parameters and the error covariance as the integrated value of the changing parameters and the error covariance at the initial moment may refer to setting the initial state estimation value and the initial error covariance value at the initial moment, wherein the initial state estimation value may be calculated based on the conventional data of the vehicle, the initial error covariance value may refer to the unit matrix, or may be determined based on the final value of the error covariance obtained in advance when it converges, and the final value is used as the initial error covariance value.

[0063] It can be seen that in this embodiment, by obtaining the preset integrated value of the change parameter and the error covariance as the benchmark at the initial moment, a stable starting point can be provided for subsequent parameter updates and identifications, which can reduce the impact of the initial moment on the parameter estimation at subsequent moments and move towards accurate state estimation more quickly.

[0064] In some embodiments, before calculating the integrated value of the change parameter and the error covariance at the next moment according to the integrated value of the change parameter at the current moment, the error covariance and the real-time state data of the vehicle, the method further includes:

[0065] The vehicle acceleration equation, driving equation, wind resistance equation, rolling resistance equation, gravity equation and ramp acceleration equation are combined to construct the longitudinal dynamics equation of the whole vehicle;

[0066] According to the longitudinal dynamics equation of the whole vehicle, the longitudinal acceleration is determined to be equal to the product of the real-time collected parameter value and the integrated value of the changing parameter.

[0067] Among them, the vehicle acceleration equation can be expressed by the following formula:

[0068] F 驱动 -F 风阻 -F 滚阻 -F 重力 =ma

[0069] In the formula, m represents the mass of the vehicle, wherein the mass of the vehicle is a parameter that cannot be obtained by the sensor. Since the braking force generated by the vehicle braking system cannot be accurately obtained and has little effect on the present invention, the braking force generated by the vehicle braking system is ignored.

[0070] The driving equation can be expressed by the following formula:

[0071]

[0072] In the formula, Fdrive represents the driving force of the vehicle, T represents the engine torque, η represents the transmission efficiency, ig represents the gear ratio of the gearbox, i0 represents the main reduction ratio, and r represents the tire radius. Among them, the transmission efficiency and tire radius are parameters that cannot be obtained by sensors.

[0073] The wind resistance equation can be expressed by the following formula:

[0074]

[0075] In the formula, F wind resistance represents the air resistance during vehicle driving, Cd represents the drag coefficient, ρ represents the air density, A represents the windward area, v represents the relative speed of the vehicle, v1 represents the actual speed of the vehicle, and v0 represents the ambient wind speed. Among them, the drag coefficient, air density, windward area, relative speed of the vehicle and ambient wind speed are parameters that cannot be obtained through sensors.

[0076] Among them, the rolling resistance equation can be expressed by the following formula:

[0077] F 滚阻 =mgμcosα=mg(b+kv1)cosα

[0078] Wherein, μ represents the rolling resistance coefficient, which can be further decomposed into b+kv1, k represents the speed-related coefficient, and b represents the constant term. The rolling resistance coefficient is a parameter that cannot be obtained by the sensor.

[0079] Among them, the gravity equation can be expressed by the following formula:

[0080] F 重力 =mgsinα

[0081] The ramp acceleration equation is expressed by the following formula:

[0082] a sen =a+gsinα

[0083] In the formula, a senIt represents the vehicle ramp acceleration measured by the sensor, a represents the vehicle acceleration generated by the vehicle power system, g represents gravity, and α represents the inclination angle of the ramp. When the vehicle is driving on a slope, the slope will additionally apply an acceleration component along the ramp direction.

[0084] The longitudinal dynamics equation of the whole vehicle is constructed by combining the vehicle acceleration equation, driving equation, wind resistance equation, rolling resistance equation, gravity equation and ramp acceleration equation.

[0085] According to the longitudinal dynamics equation of the whole vehicle, determining that the longitudinal acceleration is equal to the product of the real-time acquisition parameter value and the integrated value of the changing parameter can refer to constructing the relationship between the real-time acquisition parameter value obtained by the sensor and the integrated value of the changing parameter.

[0086] In a specific example, the Kalman filter method can be used for parameter identification. First, the system observation equation is listed. The system observation equation can refer to the relationship equation between the real-time acquisition parameter value obtained by the sensor, the integrated value of the changing parameter, and the ramp acceleration. The integrated value of the parameter that the sensor cannot obtain is estimated through the Kalman filter. The system observation equation can be expressed by the following formula:

[0087] Z=HX

[0088] Z=a sen

[0089] H=[Ti g i0 v1 2 v1 1]

[0090] x1

[0091]

[0092] x4

[0093] Wherein, Z represents the observed value, which may refer to the ramp acceleration and can be obtained through the acceleration sensor; H represents the measurement matrix, and the parameters in the measurement matrix can be obtained through the vehicle's sensor; X represents the integration of parameters that cannot be obtained through the sensor, and X may also refer to the integrated value of the variable parameter.

[0094] According to the linear Kalman filter theory, a state estimation method is established to estimate the parameter integration that cannot be obtained by sensors. The steps of this method are as follows:

[0095] First, set the initial state estimation value X0, the initial error covariance value P0 and k=1, where k represents the kth iteration round.

[0096] Secondly, calculate the prior estimate error covariance The calculation formula is as follows:

[0097]

[0098] In the formula, A represents the unit matrix, A T represents the transpose of the identity matrix, represents the error covariance at the previous moment.

[0099] Again, calculate the Kalman gain Kk, the calculation formula is as follows:

[0100]

[0101] Wherein, H represents the observation matrix, HT represents the transpose of the observation matrix, and R represents the measurement noise covariance matrix. The measurement noise covariance matrix is ​​related to the accelerometer sensor used. When the sensor is measured, the error of the sensor is normally distributed. The acceleration sensor is measured and tested, and the variance of the sensor is calculated, which is used as the measurement noise covariance matrix.

[0102] Then, based on the observed value Zk, the updated state prior estimate Xk is calculated:

[0103] X k =X k-1 +K k (Z k -HX k-1 )

[0104] Finally, calculate the updated error covariance Pk:

[0105]

[0106] After completion, the counter k is incremented, and the calculation of the prior estimation error covariance is started again to implement the iterative calculation of the Kalman filter, and finally the parameter change parameter integration value required by the vehicle longitudinal dynamics model is obtained.

[0107] It can be seen that in this embodiment, by constructing the longitudinal dynamics equation of the whole vehicle, different dynamic factors are incorporated into the longitudinal dynamics model of the whole vehicle, thereby reducing the error in acceleration estimation. This modeling method can more accurately reflect the actual dynamic response of the vehicle; since the influence of external conditions such as vehicle drive, wind resistance, rolling resistance, gravity and ramp acceleration is taken into account, the constructed longitudinal dynamics equation of the whole vehicle can adapt to the dynamic changes of the vehicle in different driving environments and make more comprehensive predictions; but the interaction relationship between different physical quantities is simplified through the joint solution, and the computational complexity brought by processing each equation separately is avoided, thereby improving the computational efficiency and real-time performance.

[0108] In some embodiments, the longitudinal dynamics equation of the vehicle is:

[0109]

[0110] Figure 3 It is a force diagram of a vehicle in a vehicle dynamics detection method provided according to an embodiment of the present invention.

[0111] Among them, by substituting the driving equation, wind resistance equation, rolling resistance equation, gravity equation and ramp acceleration equation into the vehicle acceleration equation, the longitudinal dynamics equation of the whole vehicle can be obtained. Among them, since the ramp angle is generally small, cosα is approximately calculated to be 1.

[0112] It can be seen that in this embodiment, the longitudinal dynamics equation of the whole vehicle takes into account factors such as the vehicle's driving force, wind resistance, rolling resistance and gravity, and accurately calculates the vehicle's longitudinal acceleration. The longitudinal dynamics equation of the whole vehicle thus constructed can provide more reliable basic data for the vehicle's control system, thereby improving the control efficiency of the power system.

[0113] In some embodiments, the real-time status data of the vehicle includes: longitudinal acceleration, torque, transmission gear ratio, main reduction ratio and actual vehicle speed; the set of changing parameters includes: transmission efficiency, wheel radius, drag coefficient, air density, frontal area, vehicle environment relative speed, wind speed and rolling resistance coefficient.

[0114] It can be seen that in this embodiment, by combining multi-dimensional data, the obtained vehicle longitudinal dynamics model can accurately simulate the vehicle's dynamic performance, consider the vehicle's force conditions from multiple angles, and effectively enhance the adaptability of the vehicle longitudinal dynamics model under different speeds, environmental conditions and road conditions.

[0115] In some embodiments, the torque includes engine torque or electric machine torque.

[0116] The embodiment of the present invention is not only applicable to the longitudinal dynamics model of traditional fuel vehicles, but also to the longitudinal dynamics model of new energy vehicles. The engine torque in the embodiment of the present invention can be replaced by the motor torque, and the same technical effect of the present invention can be achieved.

[0117] It can be seen that in this embodiment, by the way that the torque includes engine torque or motor torque, the embodiment of the present invention can be applicable to both traditional internal combustion engine vehicles and electric or hybrid vehicles, so that the method can be widely used in different types of vehicles, thereby improving its market adaptability and flexibility.

[0118] In some embodiments, it also includes:

[0119] When the vehicle brakes or turns, freeze the current state estimation value, error covariance, vehicle real-time state data and integrated value of changing parameters;

[0120] When the vehicle resumes stable straight driving, the state estimation value, error covariance, vehicle real-time state data and integrated value of the changed parameters are restored and updated;

[0121] When the vehicle is stationary and the engine is running, reset the state estimate and error covariance to initial values.

[0122] Freezing can mean temporarily keeping the current state estimation value, error covariance, vehicle real-time state data and change parameter integration value unchanged when the vehicle brakes or turns. When the vehicle brakes or turns, the above data will change significantly, resulting in inaccurate state estimation.

[0123] Among them, the recovery update can refer to reactivating and updating the previously frozen state estimation value, error covariance and change parameter integration value after the vehicle returns to a stable straight driving state. Through the recovery update, the consistency and coherence of the detection data can be ensured, providing support for obtaining an accurate vehicle longitudinal dynamics model.

[0124] Among them, resetting can refer to restoring the state estimate and error covariance to the initial value when the vehicle is stationary and the engine is in the running state. When the vehicle is stationary, it will cause erroneous estimates of the noise of the surrounding environment and the vehicle itself. By resetting, the influence of the erroneous estimate can be eliminated. Exemplarily, when the vehicle is stationary and the engine is in the running state, it can refer to the scenario where the vehicle stops loading goods. In this scenario, when the vehicle is driving, the transmission efficiency, wheel radius, drag coefficient, air density, frontal area, vehicle environment relative speed, wind speed and rolling resistance coefficient and other parameters will change from the parameters before loading goods, resulting in deviations in the state estimate.

[0125] It can be seen that in this embodiment, when the vehicle brakes or turns, by freezing the state estimate value, error covariance, vehicle real-time state data and change parameter integration value at the current moment, data inconsistency caused by instantaneous state changes can be avoided, and a stable data benchmark can be provided. Freezing the state estimate value at the current moment can ensure that the estimation result at this moment is not disturbed by subsequent dynamic changes, thereby improving the reliability of the monitoring result; when the vehicle resumes smooth straight driving, by restoring and updating the state estimate value, error covariance, vehicle real-time state data and change parameter integration value, when the vehicle resumes smooth straight driving, updating these parameters can ensure that the monitoring system reflects the latest vehicle status; when the vehicle resumes smooth straight driving, updating these parameters can ensure that the monitoring system reflects the latest vehicle status, thereby improving the adaptability to the vehicle's dynamic performance, and by resetting the state estimate value and error covariance to the initial value, a clear initial value can be provided for the next round of state estimation, so that the new measurement can more accurately reflect the actual state of the vehicle.

[0126] In some embodiments, the vehicle is an external cargo vehicle.

[0127] Among them, the vehicle is a vehicle with external cargo, and the vehicle with external cargo may include trucks, vans, box trucks and station wagons. Since the vehicle weight of a fully loaded vehicle and an empty vehicle changes, and the fully loaded vehicle carries cargo, parameters such as the windward area of ​​the fully loaded vehicle change. Therefore, the embodiments of the present invention are also applicable to vehicles with external cargo.

[0128] It can be seen that in this embodiment, a single parameter in the integrated value of the vehicle variation parameter of the external cargo vehicle under different loads changes. Based on the cargo of the vehicle, the embodiment of the present invention can provide an accurate longitudinal dynamic model for the external cargo vehicle.

[0129] In a specific example, the vehicle longitudinal dynamics model obtained by the embodiment of the present invention can be applied to vehicle predictive cruise control, which can significantly improve the driving efficiency and safety of the vehicle. Predictive cruise control can automatically adjust the speed of the vehicle based on traffic conditions, road conditions, and driving environment information to optimize fuel efficiency and reduce emissions, and improve driving safety. Vehicle predictive cruise control relies on various sensor data (such as data obtained by radar, lidar, and cameras) and the vehicle's longitudinal dynamics model to predict and adjust vehicle behavior. The implementation process is as follows:

[0130] The surrounding environment is detected in real time according to the vehicle's sensors, including the distance, speed and acceleration of the vehicle in front, and the actual speed of the vehicle, engine torque and ramp acceleration are obtained; according to an embodiment of the present invention, a longitudinal dynamic model of the vehicle is constructed; the traffic data obtained by the environmental sensor is used to predict the changing trend of the traffic in front, for example, if the speed of the vehicle in front begins to decrease, the speed change trend of the vehicle in front is identified, and the speed of the vehicle in front is obtained; through the longitudinal dynamic model of the vehicle, the difference between the current speed of the vehicle and the speed of the vehicle in front is calculated, and then the throttle and braking force are adjusted to achieve a smooth speed change. For example, when the current speed of the vehicle is greater than the speed of the vehicle in front, the throttle is slightly reduced or the braking force is increased; when the current speed of the vehicle is less than the speed in front, the throttle output is increased; if it is recognized that the vehicle in front brakes suddenly, the vehicle will decelerate in advance to avoid a collision. At this time, the longitudinal dynamic model of the vehicle helps calculate the required deceleration force to ensure personal safety.

[0131] In a specific example, the vehicle longitudinal dynamics model obtained by the embodiment of the present invention can be applied to vehicle posture control, which can significantly improve the stability and controllability of the vehicle during dynamic driving. The vehicle posture control can adjust the vehicle posture (such as pitch angle, yaw angle, roll angle, etc.) in real time. The implementation process is as follows:

[0132] The dynamic state of the vehicle obtained by the three-axis acceleration sensor, the gyroscope and the wheel speed sensor is collected, wherein the three-axis acceleration sensor is used to measure the acceleration of the vehicle in each direction, the gyroscope is used to detect the angular velocity of the vehicle, and the wheel speed sensor is used to provide the rotation speed information of each wheel; according to the embodiment of the present invention, a longitudinal dynamic model of the vehicle is constructed; according to the driving state of the vehicle and the road conditions, the target pitch angle and the target roll angle are set, for example, when turning quickly, it is necessary to ensure that the vehicle maintains a certain target roll angle to enhance grip and stability; the longitudinal dynamic model of the vehicle is used, combined with the sensor data, to calculate the difference between the current posture state and the target posture in real time, and adjust the throttle, brake and steering according to the difference.

[0133] Figure 4 The present invention is a schematic diagram of a vehicle power detection device provided by an embodiment of the present invention. The present invention is applicable to vehicle power detection, the device can execute a vehicle power detection method, and the device can be implemented in the form of hardware and / or software.

[0134] See also Figure 4 The vehicle power detection device shown includes: a real-time state data acquisition module 401, a change parameter integration and error covariance acquisition module 402 and a next moment parameter prediction module 403, wherein:

[0135] A real-time status data acquisition module is used to collect the real-time status data of the vehicle at the current moment when it is determined that the vehicle is driving steadily and straight;

[0136] A variation parameter integration and error covariance acquisition module, used to obtain the variation parameter integration value and error covariance at the current moment;

[0137] The next moment parameter prediction module is used to calculate the change parameter integration value and error covariance at the next moment based on the change parameter integration value, error covariance and vehicle real-time status data at the current moment, wherein the change parameter integration value is an integration value formed by the parameter values ​​of each parameter in the change parameter set, wherein the change parameter includes the parameters caused by the weight and volume of the vehicle's load.

[0138] The technical solution of the embodiment of the present invention can reflect the normal operation status of the vehicle by collecting the real-time status data of the vehicle at the current moment when determining that the vehicle is driving straight and stable, and provide a stable parameter benchmark for subsequent analysis; by obtaining the integrated value of the changing parameters and the error covariance at the current moment, and calculating the integrated value of the changing parameters and the error covariance at the next moment, it is calculated based on the data of the previous moment, and the parameter changes at future moments are predicted, which improves the predictability of the vehicle state and helps to make timely adjustments; the changing parameters include the parameters caused by the weight and volume of the vehicle's load, which can more comprehensively reflect the impact of the load on the vehicle performance and provide a more reliable data basis for subsequent optimization control.

[0139] In some embodiments, in terms of calculating the integrated value of the change parameter and the error covariance at the next moment according to the integrated value of the change parameter at the current moment, the error covariance and the real-time state data of the vehicle, the next moment parameter prediction module 403 is specifically used to:

[0140] Calculating the prior estimation covariance at the next moment according to the error covariance at the current moment and the real-time acquisition parameter value in the real-time state data of the vehicle; the real-time state data of the vehicle includes the longitudinal acceleration and the real-time acquisition parameter value, and the longitudinal acceleration is equal to the product of the real-time acquisition parameter value and the integrated value of the change parameter;

[0141] Calculating the integrated value of the change parameter at the next moment according to the integrated value of the change parameter at the current moment, the real-time state data of the vehicle and the a priori estimated covariance at the next moment;

[0142] The error covariance at the next moment is calculated according to the real-time acquisition parameter value at the next moment and the priori estimated covariance.

[0143] In some embodiments, in terms of obtaining the change parameter integration value and the error covariance at the current moment, the change parameter integration and error covariance acquisition module 402 is specifically used to:

[0144] Obtaining preset integrated values ​​of the changing parameters and the error covariance, and using them as the integrated values ​​of the changing parameters and the error covariance at the initial moment;

[0145] The step of calculating the integrated value of the change parameter at the next moment according to the integrated value of the change parameter at the current moment, the real-time state data of the vehicle and the a priori estimated covariance at the next moment comprises:

[0146] The integrated value of the changing parameter at the next moment is calculated according to the integrated value of the changing parameter at the initial moment, the real-time state data of the vehicle and the a priori estimated covariance at the next moment.

[0147] In some embodiments, before calculating the integrated value of the change parameter and the error covariance at the next moment according to the integrated value of the change parameter at the current moment, the error covariance and the real-time state data of the vehicle, the next moment parameter prediction module 403 is specifically used to:

[0148] The vehicle acceleration equation, driving equation, wind resistance equation, rolling resistance equation, gravity equation and ramp acceleration equation are combined to construct the longitudinal dynamics equation of the whole vehicle;

[0149] According to the longitudinal dynamics equation of the whole vehicle, it is determined that the longitudinal acceleration is equal to the product of the real-time collected parameter value and the integrated value of the variable parameter.

[0150] In some embodiments, in terms of the vehicle longitudinal dynamics equation, the next moment parameter prediction module 403 is specifically used to:

[0151]

[0152] In some embodiments, in terms of the real-time status data of the vehicle, the next moment parameter prediction module 403 is specifically used to:

[0153] Longitudinal acceleration, torque, gear ratio, final reduction ratio and actual vehicle speed; the set of variable parameters includes: transmission efficiency, wheel radius, drag coefficient, air density, frontal area, vehicle environment relative speed, wind speed and rolling resistance coefficient.

[0154] In some embodiments, the torque includes engine torque or electric machine torque.

[0155] In some embodiments, when the vehicle brakes or turns, the current state estimation value, error covariance, the vehicle real-time state data and the integrated value of the change parameter are frozen;

[0156] When the vehicle resumes stable straight driving, resuming and updating the state estimation value, the error covariance, the real-time state data of the vehicle and the integrated value of the change parameter;

[0157] When the vehicle is stationary and the engine is in a running state, the state estimation value and the error covariance are reset to initial values.

[0158] In some embodiments, the vehicle is an external cargo vehicle.

[0159] The vehicle power detection device provided in the embodiment of the present invention can execute the vehicle power detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the vehicle power detection method.

[0160] Figure 5 It is a structural schematic diagram of a vehicle power detection device provided according to an embodiment of the present invention.

[0161] like Figure 5 As shown, the vehicle power detection device 500 includes at least one processor 501, and a memory connected to the at least one processor 501, such as a read-only memory (ROM) 502, a random access memory (RAM) 503, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 501 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 502 or the computer program loaded from the storage unit 508 to the random access memory (RAM) 503. In RAM503, various programs and data required for the operation of the vehicle power detection device 500 can also be stored. The processor 501, ROM502 and RAM503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0162] Multiple components in the vehicle power detection device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the vehicle power detection device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0163] The processor 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 501 executes the various methods and processes described above, such as a vehicle power detection method.

[0164] In some embodiments, the vehicle power detection method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the vehicle power detection device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the processor 501, one or more steps of the vehicle power detection method described above may be performed. Alternatively, in other embodiments, the processor 501 may be configured to execute the vehicle power detection method in any other appropriate manner (e.g., by means of firmware).

[0165] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0166] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0167] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0168] To provide interaction with a user, the systems and techniques described herein may be implemented on an operational detection device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which a user can provide input to the vehicle dynamics detection device. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0169] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0170] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0171] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0172] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle power detection method, characterized in that: Methods include: When it is determined that the vehicle is moving straight and steadily, the real-time status data of the vehicle at the current moment is collected; Obtaining the integrated value of the change parameter and the error covariance at the current moment; According to the integrated value of the changing parameters at the current moment, the error covariance and the real-time status data of the vehicle, the integrated value of the changing parameters and the error covariance at the next moment are calculated, wherein the integrated value of the changing parameters is an integrated value formed by the parameter values ​​of each parameter in the changing parameter set, wherein the changing parameters include parameters caused by changes in the weight and volume of the vehicle's load.

2. The method according to claim 1, characterized in that The step of calculating the integrated value of the change parameter and the error covariance at the next moment according to the integrated value of the change parameter at the current moment, the error covariance and the real-time state data of the vehicle comprises: Calculating the prior estimation covariance at the next moment according to the error covariance at the current moment and the real-time acquisition parameter value in the real-time state data of the vehicle; the real-time state data of the vehicle includes the longitudinal acceleration and the real-time acquisition parameter value, and the longitudinal acceleration is equal to the product of the real-time acquisition parameter value and the integrated value of the change parameter; Calculating the integrated value of the change parameter at the next moment according to the integrated value of the change parameter at the current moment, the real-time state data of the vehicle and the a priori estimated covariance at the next moment; The error covariance at the next moment is calculated according to the real-time acquisition parameter value at the next moment and the priori estimated covariance.

3. The method according to claim 2, characterized in that The obtaining of the integrated value of the variation parameter and the error covariance at the current moment includes: Obtaining preset integrated values ​​of the changing parameters and the error covariance, and using them as the integrated values ​​of the changing parameters and the error covariance at the initial moment; The step of calculating the integrated value of the change parameter at the next moment according to the integrated value of the change parameter at the current moment, the real-time state data of the vehicle and the a priori estimated covariance at the next moment comprises: The integrated value of the changing parameter at the next moment is calculated according to the integrated value of the changing parameter at the initial moment, the real-time state data of the vehicle and the a priori estimated covariance at the next moment.

4. The method according to claim 2, characterized in that: Before calculating the integrated value of the change parameter and the error covariance at the next moment according to the integrated value of the change parameter at the current moment, the error covariance and the real-time state data of the vehicle, the method further includes: The vehicle acceleration equation, driving equation, wind resistance equation, rolling resistance equation, gravity equation and ramp acceleration equation are combined to construct the longitudinal dynamics equation of the whole vehicle; According to the longitudinal dynamics equation of the whole vehicle, it is determined that the longitudinal acceleration is equal to the product of the real-time collected parameter value and the integrated value of the variable parameter.

5. The method according to claim 4, characterized in that The longitudinal dynamic equation of the whole vehicle is:

6. The method according to claim 5, characterized in that The real-time status data of the vehicle include: longitudinal acceleration, torque, gear ratio of the transmission, main reduction ratio and actual speed of the vehicle; the set of variable parameters include: transmission efficiency, wheel radius, drag coefficient, air density, frontal area, vehicle environment relative speed, wind speed and rolling resistance coefficient.

7. The method according to claim 6, characterized in that The torque includes engine torque or motor torque.

8. The method according to claim 1, characterized in that Also includes: When the vehicle brakes or turns, freezing the current state estimation value, error covariance, the vehicle real-time state data and the integrated value of the change parameter; When the vehicle resumes stable straight driving, resuming and updating the state estimation value, the error covariance, the real-time state data of the vehicle and the integrated value of the change parameter; When the vehicle is stationary and the engine is in a running state, the state estimation value and the error covariance are reset to initial values.

9. The method according to claim 1, characterized in that: The vehicle is an external cargo vehicle.

10. A vehicle power detection device, characterized in that: The device comprises: A real-time status data acquisition module is used to collect the real-time status data of the vehicle at the current moment when it is determined that the vehicle is driving steadily and straight; A variation parameter integration and error covariance acquisition module, used to obtain the variation parameter integration value and error covariance at the current moment; The next moment parameter prediction module is used to calculate the change parameter integration value and error covariance at the next moment based on the change parameter integration value, error covariance and vehicle real-time status data at the current moment, wherein the change parameter integration value is an integration value formed by the parameter values ​​of each parameter in the change parameter set, wherein the change parameter includes the parameters caused by the weight and volume of the vehicle's load.

11. A vehicle power detection device, characterized in that: The vehicle power detection device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle power detection method according to any one of claims 1 to 7.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle power detection method according to any one of claims 1 to 7 when executed.

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