阅读说明：本技术 一种电动汽车的控制方法及装置 (Control method and device for electric automobile ) 是由 阿拉坦套力古拉 于 2021-08-03 设计创作，主要内容包括：本说明书实施例公开了一种电动汽车的控制方法及装置,所述方法通过实时获取的车辆纵向加速度、纵向驱动力可以输出实时的目标整车质量、实时的目标道路坡度。向智能驾驶控制器提供实时的整车质量和目标道路坡度确定控制参数,基于控制参数控制车辆在坡道行驶,可以提高车辆纵向控制算法的鲁棒性,进而提高智能驾驶的安全性。(The embodiment of the specification discloses a control method and a control device for an electric automobile, and the method can output real-time target whole automobile mass and real-time target road gradient through real-time acquired longitudinal acceleration and longitudinal driving force of the automobile. Real-time finished vehicle quality and target road gradient determination control parameters are provided for the intelligent driving controller, and the vehicle is controlled to run on a slope based on the control parameters, so that the robustness of a vehicle longitudinal control algorithm can be improved, and the safety of intelligent driving is further improved.)

1. A control method of an electric vehicle, characterized by comprising:

acquiring longitudinal acceleration and longitudinal driving force of a vehicle;

calculating the target overall vehicle mass of the vehicle according to the longitudinal acceleration and the longitudinal driving force;

calculating a first road gradient according to the target vehicle mass and the current road condition;

obtaining a second road gradient according to the longitudinal acceleration and the current vehicle acceleration;

calculating the gradient of a target road according to the current gradient change rate, the gradient of the first road and the gradient of the second road; the gradient change rate is used for representing the change frequency of the gradient in a preset time period;

determining control parameters according to the target whole vehicle mass and the target road gradient;

controlling the vehicle to travel on a grade based on the control parameter.

2. The control method according to claim 1, wherein the calculating a target full vehicle mass of the vehicle from the longitudinal acceleration and the longitudinal driving force includes:

constructing a dynamic model of the longitudinal force of the vehicle according to at least the longitudinal acceleration, the longitudinal driving force and the current air resistance and rolling resistance;

analyzing the air resistance and the rolling resistance in the dynamic model, and then carrying out differential processing to obtain the incidence relation between the vehicle mass of the vehicle and the longitudinal acceleration and the longitudinal driving force;

and filtering the longitudinal acceleration and the longitudinal driving force in the incidence relation to screen high-frequency parts of the longitudinal acceleration and the longitudinal driving force, and eliminating signal noise according to a recursive least square algorithm to obtain the target whole vehicle mass.

3. The control method according to claim 2, characterized in that the dynamics model includes:

wherein m is the total mass of the vehicle, theIs longitudinal acceleration, F is longitudinal driving force, F_airFor air resistance, said F_rollFor rolling resistance, g is gravity acceleration, and theta is road surface gradient。

4. The control method of claim 2, wherein calculating the first road grade based on the target gross vehicle mass and the current road condition comprises:

obtaining a relational expression of longitudinal driving force, a mass speed function and a mass gradient function according to the dynamic model; wherein the mass-to-speed function is used to characterize a mass-to-acceleration functional relationship of the vehicle, and the mass-to-grade function is used to characterize a mass-to-grade functional relationship of the vehicle;

estimating the gradient of the first road by adopting a least square method with a forgetting factor based on the relational expression;

the relational expression includes:

wherein, the theta_dAnd b is the first road gradient, b is a mass gradient function, and f is a road surface rolling resistance coefficient.

5. The control method according to claim 2, wherein said deriving a second road gradient from said longitudinal acceleration and a current vehicle acceleration comprises:

according to a first predetermined formulaCalculating the second road slope, wherein θ_kIs the second road gradient, saidThe acceleration is a longitudinal acceleration, and the a is a vehicle acceleration measured at the current moment.

6. The control method according to claim 4 or 5, wherein the calculating a target road gradient from the current gradient change rate, the first road gradient, and the second road gradient includes:

according to a second predetermined formulaObtaining the target road gradient; wherein τ is a time constant, s is a duration, andis the first road gradient theta_dThrough a low-pass filter processed portion, theIs the second road gradient theta_kThe processed part is passed through high pass filtering.

7. The control method according to claim 6, characterized in that the determination is made according to different road surface gradient change frequenciesAnd the above-mentionedThe weight occupied.

8. Control method according to claim 1, characterized in that the longitudinal acceleration is measured by an inertial measurement unit IMU.

9. The control method according to claim 1, characterized in that the longitudinal driving force is measured by a motor controller MCU.

10. A control device for intelligently driving an electric vehicle, the device comprising:

the acquisition unit is used for acquiring the longitudinal acceleration and the longitudinal driving force of the vehicle;

the extracting unit is used for calculating the target whole vehicle mass of the vehicle according to the longitudinal acceleration and the longitudinal driving force;

the first calculating unit is used for calculating a first road gradient according to the target whole vehicle mass and the current road condition;

the second calculation unit is used for obtaining a second road gradient according to the longitudinal acceleration and the current vehicle acceleration;

the fusion unit is used for calculating the gradient of the target road according to the current gradient change rate, the gradient of the first road and the gradient of the second road; the gradient change rate is used for representing the change frequency of the gradient in a preset time period of the slope where the vehicle is located;

the parameter calculation unit is used for determining control parameters according to the target whole vehicle mass and the target road gradient;

and the control unit is used for controlling the vehicle to run on the slope.

Technical Field

The application relates to the technical field of intelligent driving, in particular to a control method and device of an electric automobile.

Background

At present, with the continuous development of science and technology, the application of intelligent driving technology in electric vehicles is more and more extensive.

In a control system of an intelligent driving electric automobile, the mass of the whole automobile and the gradient of a road are two important parameters in intelligent driving control, and the influence on an intelligent driving control algorithm is great. However, many intelligent driving electric vehicles are not provided with a measuring sensor corresponding to the total vehicle mass and the road gradient, so that the accuracy of a control algorithm is not high, and the safety of intelligent driving is affected.

Therefore, the robustness of acquiring the whole vehicle mass and the road gradient required by the intelligent driving control algorithm in real time needs to be improved, so that the precision of the intelligent driving control algorithm is improved, and the safety of intelligent driving is further improved.

Disclosure of Invention

The embodiment of the specification provides a control method and a control device for an electric automobile, and aims to solve the problem that the intelligent driving safety is poor in the existing control method.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

the embodiment of the specification provides a control method of an electric automobile, which comprises the following steps:

acquiring longitudinal acceleration and longitudinal driving force of a vehicle;

calculating the target overall vehicle mass of the vehicle according to the longitudinal acceleration and the longitudinal driving force;

calculating a first road gradient according to the target vehicle mass and the current road condition;

obtaining a second road gradient according to the longitudinal acceleration and the current vehicle acceleration;

calculating the gradient of a target road according to the current gradient change rate, the gradient of the first road and the gradient of the second road; the gradient change rate is used for representing the change frequency of the gradient in a preset time period;

determining control parameters according to the target whole vehicle mass and the target road gradient;

controlling the vehicle to travel on a grade based on the control parameter.

In accordance with an embodiment of the method, in another aspect, the present invention further provides a control apparatus for an electric vehicle, where the apparatus includes:

the acquisition unit is used for acquiring the longitudinal acceleration and the longitudinal driving force of the vehicle;

the extracting unit is used for calculating the target whole vehicle mass of the vehicle according to the longitudinal acceleration and the longitudinal driving force;

the first calculating unit is used for calculating a first road gradient according to the target whole vehicle mass and the current road condition;

the second calculation unit is used for obtaining a second road gradient according to the longitudinal acceleration and the current vehicle acceleration;

the fusion unit is used for calculating the gradient of the target road according to the current gradient change rate, the gradient of the first road and the gradient of the second road; the gradient change rate is used for representing the change frequency of the gradient in a preset time period of the slope where the vehicle is located;

the parameter calculation unit is used for determining control parameters according to the target whole vehicle mass and the target road gradient;

and the control unit is used for controlling the vehicle to run on the slope.

One embodiment of the present description achieves the following advantageous effects:

real-time target whole vehicle mass and real-time target road gradient can be output through the vehicle longitudinal acceleration and the longitudinal driving force which are acquired in real time. On the basis, the road gradient is estimated by adopting a kinematics and dynamics method, a more accurate road gradient value is obtained according to the change frequency of the gradient, real-time target whole vehicle mass and target road gradient are provided for an intelligent driving controller to obtain control parameters, and the vehicle is controlled to run on a ramp based on the control parameters, so that the robustness of a vehicle longitudinal control algorithm can be improved, and the safety of intelligent driving is further improved.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

Fig. 1 is a flowchart of a control method for intelligently driving an electric vehicle according to an embodiment of the present disclosure;

fig. 2 is a schematic view illustrating stress analysis of a whole vehicle in a control method for intelligently driving an electric vehicle according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of a control device for an intelligent driving electric vehicle according to an embodiment of the present disclosure.

Detailed Description

To make the objects, technical solutions and advantages of one or more embodiments of the present disclosure more apparent, the technical solutions of one or more embodiments of the present disclosure will be described in detail and completely with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present specification, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from the embodiments given herein without making any creative effort fall within the scope of protection of one or more embodiments of the present specification.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

In the prior art, the mass of the whole vehicle and the gradient of a road are two important parameters in intelligent driving and automatic driving control, and the influence on a control algorithm of the intelligent driving is great. There is a need to improve the adaptive ability of the longitudinal speed controller to disturbances to improve the safety of autonomous driving, intelligent driving.

In the embodiment of the invention, the real-time whole vehicle mass and road gradient value can be provided for the intelligent driving controller aiming at the automatic driving control part of the intelligent driving electric vehicle which is not provided with the measuring sensors corresponding to the whole vehicle mass and the road gradient, so that the control parameters can be obtained according to the whole vehicle mass and the road gradient value, the robustness of a vehicle longitudinal control algorithm is improved, and the safety of automatic driving is improved.

By the method of the embodiment of the invention, the intelligent driving controller can estimate the real-time whole vehicle mass and road gradient by inputting the longitudinal acceleration and the longitudinal driving force of the vehicle, so that the robustness of a vehicle longitudinal control algorithm is improved, and the safety of automatic driving is improved.

In order to solve the defects in the prior art, the scheme provides the following embodiments:

fig. 1 is a schematic flow chart of a control method for intelligently driving an electric vehicle in an embodiment of the present specification.

As shown in fig. 1, the embodiment of the invention discloses a control method for intelligently driving an electric vehicle, which is applied to an intelligent driving controller. The method comprises the following steps:

step 100, acquiring longitudinal acceleration and longitudinal driving force of a vehicle;

in the embodiment of the invention, the longitudinal acceleration can be acquired in real time through the inertial measurement unit IMU, and the longitudinal driving force can be provided in real time through the click controller MCU.

The IMU can be used for measuring three-axis attitude angles, angular velocities and accelerations of objects, and can measure longitudinal acceleration of vehicles. The MCU may provide longitudinal driving force of the vehicle.

Step 200, calculating the target whole vehicle mass of the vehicle according to the longitudinal acceleration and the longitudinal driving force;

the calculating the target vehicle mass of the vehicle according to the longitudinal acceleration and the longitudinal driving force comprises the following steps:

constructing a dynamic model of the longitudinal force of the vehicle according to at least the longitudinal acceleration, the longitudinal driving force and the current air resistance and rolling resistance;

analyzing the air resistance and the rolling resistance in the dynamic model, and then carrying out differential processing to obtain the incidence relation between the vehicle mass of the vehicle and the longitudinal acceleration and the longitudinal driving force;

and filtering the longitudinal acceleration and the longitudinal driving force in the incidence relation to screen high-frequency parts of the longitudinal acceleration and the longitudinal driving force, and eliminating signal noise according to a recursive least square algorithm to obtain the target whole vehicle mass.

In the embodiment of the invention, a dynamic model is constructed in advance, and as shown in fig. 2, fig. 2 is a stress analysis diagram of the whole vehicle in the embodiment of the invention, wherein F is a longitudinal driving force, and F is a longitudinal driving force_airAs air resistance, F_rollMg is vehicle gravity and θ is road slope for rolling resistance.

According to the stress analysis diagram, a dynamic model of the vehicle in the longitudinal direction is established:

in the above formula [ 1 ], m is the total mass of the vehicle,is longitudinal acceleration, F is longitudinal driving force, F_airAs air resistance, F_rollG is the acceleration of gravity, and θ is the road surface gradient.

The air resistance and rolling resistance were then resolved:

F_roll＝fmgcosθ 【3】

in the formulas [ 2 ] and [ 3 ], rho is air density, C_dIs the wind resistance coefficient, A is the windward area, v is the longitudinal velocity, and f is the road surface rolling resistance coefficient.

Then, substituting equations [ 2 ] and [ 3 ] into equation [ 1 ], equation 4 can be obtained:

the differential processing of the acceleration of the above equation will result in the following equation:

in the embodiment of the invention, the driving torque change rate of the driving motor is generally higher, and when the driving force change rate is higher, the change rate of the driving acceleration is far greater than that of the speed. It is considered that the differential of the speed is a small amount compared with the differential of the running acceleration, and therefore:

in the embodiment of the present invention, when estimating the gradient, it is assumed that the gradient change is generally small, and is not directly related to the vehicle running acceleration, and the change of the gradient is random, so that the differential of the gradient to the acceleration is:

thus, when the driving force change frequency is high, it is possible to obtain:

that is, at a high rate of change of the driving force, the influence of the rolling resistance, the wind resistance, and the road surface gradient on the vehicle mass estimation can be ignored. The real-time whole vehicle mass of the vehicle in the running process can be obtained by adopting an equation (8), which is approximately equal to the differentiation of the driving force information on the acceleration information.

Further, the differential of the longitudinal driving force with respect to the running acceleration can be approximated to the ratio of the longitudinal driving force to the high-frequency information of the running acceleration, and therefore the high-frequency information can be obtained by applying the high-pass filter to both of them, and the differential can be obtained.

In the embodiment of the invention, the influence of the low-frequency parts of rolling resistance, wind resistance and road surface gradient is filtered by high-pass filtering to obtain the high-frequency part of the running accelerationAnd a high frequency part F of the driving force^*. And the following formula is obtained:

the effect of signal noise on m in equation 9 is then removed by recursive least squares.

Therefore, according to the recursive least square method, let x be F^* Then, the equation will be obtained:

wherein the content of the first and second substances,the mass of the whole vehicle is estimated. Then, a least square method is adopted for solving.

In a linear system, the solving process is equivalent to finding the parametersMake a functionA minimum value is obtained. Wherein t is the current sampling time. Thus, it can be solved by the following formula:

when the formula (11) is given to take a minimum value,

from the equation [ 12 ], as t increases,will be increasingly calculated. Since the estimation operation of the vehicle mass is carried out in real time, the mass is estimated by using a Recursive Least Square (RLS) method in the embodiment of the invention,i.e. the estimate of the last sampling instant is corrected by the measurement of the current sampling instant. The RLS algorithm is shown by the following equation:

L(t)＝P(t-1)δ(t)(1+δ(t)P(t-1)δ(t))^-1 【14】

P(t)＝(1-L(t)δ(t))P(t-1) 【15】

the whole vehicle mass at each moment can be estimated through an equation (13), a least square gain L is calculated through an equation (14), and an error covariance P is updated through an equation (15).

Based on the above, the target vehicle mass can be estimated in real time according to the longitudinal acceleration and the longitudinal driving force.

And step 300, calculating a first road gradient according to the target whole vehicle mass and the current road condition.

And step 400, obtaining a second road gradient according to the longitudinal acceleration and the current vehicle acceleration.

500, calculating a target road gradient according to the current gradient change rate, the first road gradient and the second road gradient; the gradient change rate is used for representing the change frequency of the gradient in a preset time period.

And 600, determining control parameters according to the target whole vehicle mass and the target road gradient.

And 700, controlling the vehicle to run on the slope based on the control parameters.

In the embodiment of the invention, the first road gradient is estimated by adopting a dynamic method, the second road gradient is estimated by adopting a kinematic method, and then the target road gradient suitable for controlling the longitudinal speed of the intelligent driving automobile is obtained by a preset rule based on the gradient change rate.

Wherein, in the estimation process of the first road gradient, a formula (4) is used for calculation. And a least square algorithm with a forgetting factor is adopted to estimate the result.

Let x be F, x is,substituting mg (sin θ + fcos θ) into equation [ 4 ], yields equation [ 16 ], as shown below:

x＝μ+b 【16】

where x is the longitudinal driving force, μ is the mass velocity function, and b is the mass gradient function.

In the embodiment of the invention, a least square method with a forgetting factor is adopted to estimate b. In a linear system, it is equivalent to find the parametersObtaining a minimum value:

wherein λ is a forgetting factor. The larger the lambda is, the higher the identification precision is, but the convergence speed is reduced; the smaller λ is, the lower the recognition accuracy is, and the lower the recognition accuracy is, but the convergence speed is increased. So λ needs to be taken after comprehensive consideration.

Benzhuizi (Chinese character) tabletWhen the minimum value is obtained, the following are obtained:

by the RLS estimation method, we can get:

the b values at different times can be estimated by the equation [ 19 ]. Based on a dynamic method, the first road gradient theta of the road surface can be obtained_d：

Wherein, the theta_dAnd b is the first road gradient, b is a mass gradient function, and f is a road surface rolling resistance coefficient.

According to kinematic theory, the measured value of the acceleration is related to the road gradient and to the acceleration of the vehicle itself:

so that the road gradient estimated value theta based on kinematics can be obtained_k:

Wherein, the theta_kAnd a is the second road grade, where a is the currently measured vehicle acceleration.

Therefore, the first road gradient estimated based on the dynamics method and the second road gradient estimated based on the kinematics method can be obtained in real time.

And then fusing the first road gradient and the second road gradient.

Slope value theta estimated by a kinetic method_dThe high-frequency noise is greatly influenced, low-pass filtering processing is adopted for the high-frequency noise, the high-frequency noise part is filtered, and the low-frequency part is reserved. Slope value theta estimated by kinematics_kThe static deviation of the IMU belongs to low-frequency noise, so high-pass filtering is used for filtering the low-frequency noiseAnd a high frequency part is reserved. And finally, comprehensively processing the filtered result to obtain the road gradient estimated value theta.

The fusing the first road gradient and the second road gradient to obtain a target road gradient comprises:

according to a second predetermined formulaObtaining the target road gradient; wherein τ is a time constant, s is a duration, andis the first road gradient theta_dThrough a low-pass filter processed portion, theIs the second road gradient theta_kThe processed part is passed through high pass filtering.

When the change frequency of the road surface gradient is higher, the weight occupied by the first road gradient is high, and the transient gradient value can be accurately and quickly estimated; when the change frequency of the road surface gradient is low, the weight occupied by the second road gradient is higher, and the steady-state gradient value can be accurately estimated. Therefore, in the embodiment of the invention, the determination is made according to different road surface gradient change frequenciesAnd the above-mentionedThe weight occupied.

In the embodiment of the invention, the difference of the weights can be calibrated in advance according to the change frequency of the gradient in a preset time period, so that the accuracy of the obtained target road gradient is improved. The predetermined time period may be calibrated according to actual needs, and is defined herein.

Controlling the vehicle to travel on a grade based on the control parameter. The target whole vehicle mass and the target road gradient are provided for a longitudinal vehicle speed control system of the intelligent driving electric vehicle, so that the control of the intelligent driving electric vehicle is realized, the robustness of the vehicle control system is improved, and the safety of automatic driving is further improved.

In an embodiment of the invention, the control parameter may represent a parameter required to control the vehicle to travel on a slope, for example: desired torque of the engine, accelerator pedal opening, desired longitudinal acceleration, desired longitudinal deceleration, and the like.

Therefore, in the embodiment of the invention, the real-time target whole vehicle mass and the real-time target road gradient can be output through the vehicle longitudinal acceleration and the longitudinal driving force which are acquired in real time. The relation between the driving force and the whole vehicle mass, the whole vehicle longitudinal acceleration, the air resistance and the rolling resistance can be obtained by analyzing the longitudinal dynamics of the vehicle, the whole vehicle mass and the gradient are decoupled by utilizing the characteristic of high accuracy of the motor driving force, the high-frequency parts of the longitudinal acceleration signal and the driving force signal are screened, and the whole vehicle mass estimation value is obtained by using a recursive least square method. On the basis, the road gradient is estimated by adopting a kinematics method and a dynamics method, and finally, an accurate road gradient value is obtained through the estimation values of the two methods based on the gradient change rate.

Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method. Fig. 3 is a schematic structural diagram of a control device of an intelligent driving electric vehicle corresponding to fig. 1 provided in an embodiment of this specification. As shown in fig. 3, the apparatus is applied to an intelligent driving controller, and may include:

an acquisition unit 301 for acquiring a longitudinal acceleration and a longitudinal driving force of the vehicle;

an extracting unit 302, configured to calculate a target overall vehicle mass of the vehicle according to the longitudinal acceleration and the longitudinal driving force;

a first calculating unit 303, configured to calculate a first road gradient according to the target overall vehicle mass and the current road condition;

a second calculation unit 304, configured to obtain a second road gradient according to the longitudinal acceleration and the current vehicle acceleration;

the fusion unit 305 is configured to calculate a target road gradient according to a current gradient change rate, the first road gradient, and the second road gradient; the gradient change rate is used for representing the change frequency of the gradient in a preset time period of the slope where the vehicle is located;

the parameter calculation unit 306 is used for determining a control parameter according to the target overall vehicle mass and the target road gradient;

a control unit 307 for controlling the vehicle to travel on a slope.

The extraction unit 302 includes:

the model establishing subunit is used for establishing a dynamic model of the longitudinal force of the vehicle at least according to the longitudinal acceleration, the longitudinal driving force, the air resistance and the rolling resistance which are acquired in real time;

the analysis subunit is configured to analyze the air resistance and the rolling resistance in the dynamic model and then perform differential processing to obtain a correlation between the vehicle mass of the vehicle and the longitudinal acceleration and the longitudinal driving force;

and the operation subunit is used for filtering the longitudinal acceleration and the longitudinal driving force in the association relationship to screen the high-frequency parts of the longitudinal acceleration and the longitudinal driving force and eliminating signal noise according to a recursive least square algorithm to obtain the target finished automobile mass.

The kinetic model includes:

wherein m is the total mass of the vehicle, theIs longitudinal acceleration, F is longitudinal driving force, F_airFor air resistance, said F_rollThe g is gravity acceleration, and the theta is road surface gradient.

The first computing unit is specifically configured to:

obtaining a relational expression of the longitudinal driving force, a mass speed function and a mass gradient function according to the incidence relation; wherein the mass-to-speed function is used to characterize mass-to-acceleration relationship of the vehicle, and the mass-to-grade function is used to characterize mass-to-grade relationship of the vehicle;

estimating the gradient of the first road by adopting a least square method with a forgetting factor based on the relational expression;

the relational expression includes:

wherein, the theta_dAnd b is the first road gradient, b is a mass gradient function, and f is a road surface rolling resistance coefficient.

The second computing unit is specifically configured to:

according to a first predetermined formulaCalculating the second road slope, wherein θ_kIs the second road gradient, saidThe acceleration is a longitudinal acceleration, and the a is a vehicle acceleration measured at the current moment.

The fusing the first road gradient and the second road gradient to obtain a target road gradient comprises:

according to a second predetermined formulaObtaining the target road gradient; wherein τ is a time constant, s is a duration, andis the first road gradient theta_dThrough a low-pass filter processed portion, theIs the second road gradient theta_kThe processed part is passed through high pass filtering.

Determining the road surface gradient change frequency according to different road surface gradient change frequenciesAnd the above-mentionedThe weight occupied.

The longitudinal acceleration is measured by an inertial measurement unit IMU.

The longitudinal driving force is measured by a motor controller MCU.

It can be understood that, in the embodiment of the present invention, for the execution process of each unit in the control device of the electric vehicle, reference may be made to each step in the control method of the electric vehicle in the foregoing embodiment, which is not described herein again.

In the embodiment of the invention, the longitudinal acceleration and the longitudinal driving force of the vehicle acquired in real time by the acquisition unit can be output by the extraction unit and the fusion unit to obtain the real-time target whole vehicle mass and the real-time target road gradient. The relation between the driving force and the mass of the whole vehicle, the longitudinal acceleration of the whole vehicle, the air resistance and the rolling resistance can be obtained by analyzing the longitudinal dynamics of the vehicle through the extraction unit, the mass and the gradient of the whole vehicle are decoupled by utilizing the characteristic of high accuracy of the driving force of the motor, the high-frequency parts of a longitudinal acceleration signal and a driving force signal are screened, and the estimated value of the mass of the whole vehicle is obtained by using a recursive least square method. On the basis, the first calculating unit and the second calculating unit adopt kinematics and dynamics methods to estimate the road gradient to the road gradient, and finally, the estimated values of the two methods are fused in a fusion unit to obtain an accurate road gradient value based on the gradient change rate. The robustness of a vehicle longitudinal control algorithm is improved, and the safety of intelligently driving the electric automobile is improved.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Since the apparatus embodiments are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital character system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate a dedicated integrated circuit chip. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information which can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.