CN116227135B - Torque distribution model construction method, torque distribution model construction device, computer equipment and storage medium - Google Patents

Torque distribution model construction method, torque distribution model construction device, computer equipment and storage medium Download PDF

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CN116227135B
CN116227135B CN202211639845.6A CN202211639845A CN116227135B CN 116227135 B CN116227135 B CN 116227135B CN 202211639845 A CN202211639845 A CN 202211639845A CN 116227135 B CN116227135 B CN 116227135B
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motor
torque
vehicle
loss
power
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CN116227135A (en
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杨复钰
王万
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Machinery Industry Planning Research Institute Co ltd
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Machinery Industry Planning Research Institute Co ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
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Abstract

The present application relates to a torque distribution model construction method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: acquiring vehicle data and total required torque in the current vehicle running process; constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data; aiming at the condition that a motor in a non-working state exists in the running process of a vehicle, acquiring idle loss power of the motor in the non-working state, and carrying out loss compensation on a first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle; and constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation. By adopting the method, the waste of power generated during the idle running of the motor can be avoided, and the accuracy of the torque distribution model is improved.

Description

Torque distribution model construction method, torque distribution model construction device, computer equipment and storage medium
Technical Field
The present application relates to the field of vehicles, and in particular, to a torque distribution model construction method, apparatus, computer device, storage medium, and computer program product.
Background
With the development of vehicles, a torque distribution model construction method appears, and the torque distribution model constructed based on the torque distribution model construction method can distribute torque to a motor of the vehicle in the running process of the vehicle, so that the energy utilization rate of the motor is improved, and the dynamic property of the vehicle is further improved.
In the current torque distribution model construction method, total required torque is determined according to the maximum output torque and the maximum efficiency of a motor of a vehicle, a torque distribution model taking the energy utilization rate of the vehicle as an objective function is constructed according to the total required torque and efficiency characteristic data of the motor in an electric state, and the energy utilization rate of the vehicle is highest by adjusting the torque distribution coefficient of the motor, so that the total required torque is distributed according to the corresponding torque distribution coefficient when the energy utilization rate is highest, and the torque of each motor is obtained.
However, in the current torque distribution model construction method, when a torque distribution model having the energy utilization rate of the vehicle as an objective function is constructed, the motor is in an operating state by default, and the non-operating state of the motor is not taken into consideration. Therefore, when the motor is in a non-working state, the accuracy of the torque distribution model constructed according to the torque distribution model construction method is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a torque distribution model construction method, apparatus, computer device, computer readable storage medium, and computer program product.
In a first aspect, the present application provides a torque distribution model construction method. The method comprises the following steps:
acquiring vehicle data and total required torque in the current vehicle running process;
constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data;
aiming at the condition that a motor in a non-working state exists in the running process of the vehicle, acquiring idle loss power of the motor in the non-working state, and carrying out loss compensation on the first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle;
and constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, wherein the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle.
In one embodiment, the acquiring the vehicle data and the total required torque during the current vehicle operation includes:
acquiring vehicle data in the current vehicle running process; the vehicle data comprises driving data, motor rotating speed, motor peak torque and motor peak power;
aiming at target running data in the running data, determining a target language variable according to the corresponding relation between the target running data and the language variable, and determining the running mode of the vehicle according to the mapping relation between the language variable and the running mode;
determining a torque load factor of the vehicle according to the corresponding relation between the opening of an accelerator pedal and the torque load factor in the driving data under the driving mode of the vehicle;
and calculating the torque load coefficient, the motor rotating speed, the motor peak torque and the motor peak power of the vehicle according to a motor output torque algorithm to obtain total required torque.
In one embodiment, the determining, for the target driving data in the driving data, the target language variable according to the corresponding relationship between the target driving data and the language variable includes:
Aiming at target running data in the running data, determining a target domain corresponding to the value of the target running data;
and determining a target language variable corresponding to the target domain according to the corresponding relation between the preset domain and the language variable.
In one embodiment, the motor of the vehicle includes a first motor and a second motor, and the method further includes, for a case that the motor in a non-working state exists in the running process of the vehicle, obtaining an idle loss power of the motor in the non-working state, performing loss compensation on the first loss power equation according to the idle loss power, and before obtaining a second loss power equation corresponding to each motor in the running process of the vehicle:
under the condition that the first motor is in a non-working state, acquiring real-time output torque of the first motor, and calculating the real-time output torque of the first motor, the motor efficiency and the motor rotating speed contained in the vehicle data according to an idling loss power algorithm to obtain first idling loss power;
and under the condition that the second motor is in a non-working state, acquiring the real-time output torque of the second motor, and calculating the real-time output torque of the second motor, the motor efficiency and the motor rotating speed according to the idling loss power algorithm to obtain the second idling loss power.
In one embodiment, for a situation that a motor in a non-working state exists in a running process of the vehicle, obtaining idling loss power of the motor in the non-working state, performing loss compensation on the first loss power equation according to the idling loss power, and obtaining a second loss power equation corresponding to each motor in the running process of the vehicle, where the method includes:
under the condition that the first motor is in a non-working state, acquiring the first idling loss power, and carrying out loss compensation on the first loss power equation according to the first idling loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle;
and under the condition that the second motor is in a non-working state, acquiring the second idling loss power, and carrying out loss compensation on the first loss power equation according to the second idling loss power to acquire a second loss power equation corresponding to each motor in the running process of the vehicle.
In one embodiment, after the torque distribution model with the minimum loss power of each motor as an objective function is constructed according to the first loss power equation and the second loss power equation, the method further includes:
Determining a torque distribution coefficient corresponding to each motor when the power is lost at the minimum according to the torque distribution model;
and obtaining the current total required torque, and distributing the current total required torque according to the torque distribution coefficient to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque.
In one embodiment, after the obtaining the current total required torque and distributing the current total required torque according to the torque distribution coefficient to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotation speed corresponding to the current total required torque, the method further includes:
generating a torque distribution record according to the mapping relation among the current total required torque, the torque distribution coefficient and the motor rotating speed, and storing the torque distribution record; the torque distribution record is used for determining the torque distribution coefficient according to the motor rotating speed and the current total required torque.
In a second aspect, the present application further provides a torque distribution model construction apparatus. The device comprises:
the acquisition module is used for acquiring vehicle data and total required torque in the current vehicle running process;
The first construction module is used for constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data;
the compensation module is used for acquiring idling loss power of the motor in the non-working state according to the situation that the motor in the non-working state exists in the vehicle running process, and carrying out loss compensation on the first loss power equation according to the idling loss power to obtain a second loss power equation corresponding to each motor in the vehicle running process;
the second construction module is used for constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, and the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
Acquiring vehicle data and total required torque in the current vehicle running process;
constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data;
aiming at the condition that a motor in a non-working state exists in the running process of the vehicle, acquiring idle loss power of the motor in the non-working state, and carrying out loss compensation on the first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle;
and constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, wherein the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring vehicle data and total required torque in the current vehicle running process;
constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data;
aiming at the condition that a motor in a non-working state exists in the running process of the vehicle, acquiring idle loss power of the motor in the non-working state, and carrying out loss compensation on the first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle;
and constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, wherein the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
acquiring vehicle data and total required torque in the current vehicle running process;
Constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data;
aiming at the condition that a motor in a non-working state exists in the running process of the vehicle, acquiring idle loss power of the motor in the non-working state, and carrying out loss compensation on the first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle;
and constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, wherein the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle.
The torque distribution model construction method, the torque distribution model construction device, the computer equipment, the storage medium and the computer program product acquire vehicle data and total required torque in the current vehicle running process; constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data; aiming at the condition that a motor in a non-working state exists in the running process of the vehicle, acquiring idle loss power of the motor in the non-working state, and carrying out loss compensation on the first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle; and constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, wherein the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle. By adopting the method, under the condition that the motor in the non-working state exists, the first loss power equation is compensated through the acquired idle loss power, the second loss power equation is obtained, the loss power of the motor under the non-working condition is considered, the torque distribution model is built through the first loss power equation and the second loss power equation, and the accuracy of the torque distribution model is improved.
Drawings
FIG. 1 is a flow diagram of a method of constructing a torque distribution model in one embodiment;
FIG. 2 is a flow chart illustrating steps for acquiring vehicle data and total requested torque in one embodiment;
FIG. 3 is a schematic diagram of accelerator pedal opening versus torque load factor in one embodiment;
FIG. 4 is a schematic diagram of motor characteristics in one embodiment;
FIG. 5 is a flow diagram of the steps for determining linguistic variables in one embodiment;
FIG. 6 is a diagram of the relationship of the subdomains of velocity mean to linguistic variables in one embodiment;
FIG. 7 is a schematic diagram of the relationship of the subdomain of vehicle speed to linguistic variables in one embodiment;
FIG. 8 is a schematic diagram of the relationship of the sub-domains of SOC to linguistic variables in one embodiment;
FIG. 9 is a flow chart illustrating steps for acquiring lost power in one embodiment;
FIG. 10 is a flow chart illustrating the steps of loss compensation in one embodiment;
FIG. 11 is a flow chart illustrating steps for distributing total demand torque in one embodiment;
FIG. 12 is a flowchart illustrating steps for determining a torque distribution coefficient in one embodiment;
FIG. 13 is a schematic diagram of motor torque, total demand torque, and torque split coefficients for one embodiment;
FIG. 14 is a block diagram showing the construction of a model construction device for torque distribution in one embodiment;
fig. 15 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a torque distribution model method is provided, and an application of the method to a vehicle terminal is taken as an example for explanation, and an execution device for executing the torque distribution model method is not limited in this embodiment of the present application, and includes the following steps:
step 102, acquiring vehicle data and total required torque in the current vehicle running process.
In the implementation, in the running process of the vehicle, the vehicle-mounted terminal acquires vehicle data generated by the current vehicle in real time. Then, the in-vehicle terminal determines the running mode of the current vehicle according to the corresponding relation between the running data in the vehicle data and the running mode. And under the current running mode of the vehicle, the vehicle-mounted terminal calculates vehicle data according to a motor output torque algorithm to obtain the total required torque of the vehicle.
And 104, constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data.
The vehicle data includes motor efficiency, torque distribution coefficient and motor torque. The motor efficiency includes the efficiency of each motor.
In implementation, the vehicle-mounted terminal uses a torque distribution coefficient as a variable, and a first loss power equation corresponding to each motor in the running process of the vehicle is constructed according to the efficiency of each motor, the motor rotating speed and the total required torque.
For example, taking two motors included in a vehicle as an example, the two motors are a first motor and a second motor, respectively. The motor efficiency corresponding to the first motor is the first motor efficiency. The motor efficiency corresponding to the second motor is the second motor efficiency. As shown in the following formula set (1), the in-vehicle terminal determines λ as the torque distribution coefficient of the second motor. When λ=0, the representative vehicle is driven by the first motor. When λ=1, the representative vehicle is driven by the second motor; when 0 < lambda < 1, it means that the vehicle is jointly driven by the first motor and the second motor. Vehicle-mounted terminal determining T f For the output torque of the first motor, T r For the output torque of the second motor, T req Is the total required torque. The vehicle-mounted terminal constructs the corresponding relation between the output torque of each motor and the output torque of the first motor and the output torque of the second motor, as shown in a formula group (1).
And then, the vehicle-mounted terminal determines the motor efficiencies of the first motor and the second motor according to the corresponding relation between the motor rotation speed, the output torque and the motor efficiency. The corresponding relation between the motor rotation speed, the output torque and the motor efficiency is shown in the formula (2). η (eta) f For the motor efficiency, eta of the first motor r For the motor efficiency of the second motor, n is the motor speed, f (n, T f ) Is the corresponding relation between the output torque of the first motor and the motor efficiency of the first motor, f (n, T r ) Is the correspondence between the output torque of the second motor and the motor efficiency of the second motor.
The vehicle-mounted terminal takes a torque distribution coefficient as a variable, and constructs a first loss corresponding to each motor in the running process of the vehicle according to the first motor efficiency, the second motor efficiency, the total required torque and the motor rotating speedThe power equation is shown in the following equation (3). P (P) 3 For the loss of power of each motor, T req For total required torque, n is motor speed, lambda is torque distribution coefficient, eta f For a first motor efficiency, eta r For a second motor efficiency.
Step 106, aiming at the condition that the motor in the non-working state exists in the running process of the vehicle, obtaining the idle loss power of the motor in the non-working state, and carrying out loss compensation on the first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle.
In practice, in the event that a motor in a non-operating state is present during the operation of the vehicle, the vehicle-mounted terminal obtains the lost power lost in idling of the motor in the non-operating state. And then, the vehicle-mounted terminal performs loss compensation on the first loss power equation according to the idle loss power of the motor in the non-working state to obtain a second loss power equation corresponding to each motor in the vehicle running process.
And step 108, constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation.
The torque distribution model is used for determining corresponding distribution torque of each motor in the running process of the vehicle. The torque distribution model contains constraints. The driving conditions include that the vehicle is driven on a horizontal road surface with good road conditions in a straight line, the wheel speeds of the front axle and the rear axle of the vehicle are the same, no slip phenomenon exists, and the torques distributed on the left side and the right side between the coaxial axles of the vehicle are the same.
In implementation, under the condition that the vehicle simultaneously meets a plurality of preset driving conditions, the vehicle-mounted terminal constructs a torque distribution model taking the minimum loss power of each motor as an objective function according to a first loss power equation and a second loss power equation.
For example, taking the vehicle as an example with two motors, the two motors are a first motor and a second motor respectivelyAnd two motors. The vehicle-mounted terminal stores a first loss power equation and a second loss power equation. The first lost power equation is shown in equation (3) above. The motor in the non-working state exists in the running process of the vehicle in two conditions, wherein the first condition is that the first motor is in the non-working state, and the corresponding second loss power equation is shown as a formula (5). The second condition is that the second motor is in a non-working state, and the corresponding second loss power equation is formula (4). As shown in the following formula (4), the formula (4) is a loss power equation of the second motor in the non-operating state. P (P) 1 Power is lost for the second motor in the inactive state. T (T) req Is the total required torque. η (eta) f Representing the motor efficiency of the first motor. n represents the motor speed. P (P) drag_r And (n) represents the lost power of the second motor.
As shown in the following equation (5), equation (5) is a lost power equation of the first motor in the non-operating state. P (P) 2 The power is lost when the first motor is in a non-working state. T (T) req Is the total required torque. η (eta) r Representing the motor efficiency of the first motor. n represents the motor speed. P (P) drag_f And (n) represents the lost power of the first motor.
The constraint is shown in formula set (6). T (T) fmax For peak torque of the first motor, T f For the output torque of the first motor, T r For the output torque of the second motor, T rmax Is the peak torque of the second motor. T (T) req Is the total required torque.
When each motor meets constraint conditions and the vehicle simultaneously meets a plurality of driving barsIn the case of the component, the vehicle-mounted terminal constructs a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, as shown in a formula (7). P (P) 1 And the power loss of the second motor in the non-working state. P (P) 2 The power is lost when the first motor is in a non-working state. P (P) 3 The power loss of the first motor and the second motor in the working state is realized.
P loss ＝min{P 1 ,P 2 ,P 3 } (7)
In the torque distribution model construction method, under the condition that the motor in the non-working state exists, the first loss power equation is subjected to error compensation through the obtained idle loss power, the second loss power equation is obtained, the loss power of the motor under the non-working condition is considered, the torque distribution model is constructed through the first loss power equation and the second loss power equation, and the accuracy of the torque distribution model is improved.
In one embodiment, as shown in FIG. 2, the specific process of step 102 includes:
step 202, acquiring vehicle data in the current vehicle running process;
the vehicle data includes driving data, accelerator opening, motor rotation speed, motor peak torque and motor peak power. The unit of the accelerator pedal opening is a percentage (%). The motor speed is in revolutions per minute (r/min). The peak torque of the motor is in units of nm (n·m). The peak motor torque is in kilowatts (kW).
In practice, during operation of the vehicle, the vehicle-mounted terminal acquires vehicle data generated by the current vehicle.
Optionally, the vehicle data is acquired by a plurality of sensors installed on the vehicle, and the embodiment of the application is not limited to the process and the device for acquiring the vehicle data.
Step 204, determining a target language variable according to the corresponding relation between the target running data and the language variable, and determining the running mode of the vehicle according to the mapping relation between the language variable and the running mode aiming at the target running data in the running data.
The running data includes an acceleration average value, an SOC (State-Of-Charge), and a speed. The average acceleration is in meters per square second (m/s) 2 ) SOC is in percent (%), and speed is in kilometers per hour (km/h). The target travel data is each of the travel data. The linguistic variables include three levels of low, medium and high, S being used to represent cases where the linguistic variable is low, M being used to represent cases where the linguistic variable is medium, and B being used to represent cases where the linguistic variable is high. The running modes of the vehicle include an energy saving mode (Eco) and a power mode (Sport).
In implementation, the vehicle-mounted terminal traverses each piece of the travel data to obtain the target travel data. The corresponding relation between the target driving data and the language variable is prestored in the vehicle-mounted terminal. And the vehicle-mounted terminal determines a target language variable corresponding to the target running data according to the corresponding relation between the target running data and the language variable. The vehicle-mounted terminal stores the mapping relation between the language variable and the driving data. And the vehicle-mounted terminal determines a driving mode mapped by the target language variable according to the mapping relation between the language variable and the driving data.
Specifically, the corresponding relation between the acceleration mean value and the language variable is prestored in the vehicle-mounted terminal. And the vehicle-mounted terminal determines an acceleration mean target language variable corresponding to the value of the acceleration mean according to the value of the acceleration mean. The corresponding relation between the SOC and the language variable is stored in the vehicle-mounted terminal in advance. And the vehicle-mounted terminal determines an SOC target language variable corresponding to the value of the SOC according to the value of the SOC. The corresponding relation between the speed and the language variable is prestored in the vehicle-mounted terminal. And the vehicle-mounted terminal determines a speed target language variable corresponding to the speed value according to the speed value. The vehicle-mounted terminal stores the mapping relation between each language variable and the driving mode in advance. And the vehicle-mounted terminal determines the driving modes mapped by the SOC target language variable, the acceleration mean target language variable and the speed target language variable according to the mapping relation between the language variable and the driving modes.
For example, the vehicle-mounted terminal stores in advance a mapping relationship between each linguistic variable and the travel pattern, as shown in table 1. The linguistic variables of each acceleration mean, speed, and SOC map a driving pattern of the vehicle. The acceleration average value target language variable obtained by the vehicle-mounted terminal is S, the SOC target language variable is B, and the speed target language variable is M. And the vehicle-mounted terminal inquires the mapped driving mode in the table 1 according to the SOC target language variable, the acceleration average value target language variable and the speed target language variable to obtain the current driving mode of the vehicle as Eco, namely the energy-saving mode.
TABLE 1
Step 206, determining the torque load factor of the vehicle according to the corresponding relation between the accelerator opening degree and the torque load factor in the driving data in the driving mode of the vehicle.
In the implementation, the vehicle-mounted terminal stores the corresponding relation between the accelerator opening and the torque load coefficient of the vehicle in different running modes in advance. And the vehicle-mounted terminal determines the torque load coefficient corresponding to the running mode of the vehicle according to the corresponding relation between the opening of the accelerator pedal and the torque load coefficient under different running modes.
Specifically, the vehicle-mounted terminal stores in advance the correspondence relationship between the accelerator opening and the torque load factor of the vehicle in different running modes, as shown in fig. 3. The curve a in fig. 3 is the correspondence between the accelerator opening and the torque load factor in the power mode of the vehicle. The curve B in fig. 3 is the correspondence relationship between the accelerator opening and the torque load factor in the energy-saving mode of the vehicle. In the current running mode of the vehicle, the vehicle-mounted terminal determines a torque load factor of the vehicle corresponding to the accelerator pedal opening in the vehicle data according to the correspondence relation between the accelerator pedal opening and the torque load factor.
And step 208, calculating the torque load coefficient, the motor rotating speed, the motor peak torque and the motor peak power of the vehicle according to the motor output torque algorithm to obtain the total required torque.
In practice, the vehicle-mounted terminal obtains a motor output torque algorithm according to the running mode of the vehicle and the operating characteristics of the motor. And the vehicle-mounted terminal calculates the torque load coefficient, the motor rotating speed, the motor peak torque and the motor peak power of the vehicle according to the motor output torque algorithm to obtain the total required torque.
For example, the vehicle includes two motors, which are a first motor and a second motor, respectively. The relationship among the running mode of the vehicle, the accelerator pedal opening, the total required torque, and the motor torque is shown in the following formula set (8). L is the torque load factor. Mode is a running Mode of the vehicle. S is the opening degree of the accelerator pedal. T (T) fmax (n) is the peak torque of the first motor at the rotational speed n. T (T) rmax (n) is the peak torque of the second motor at the rotational speed n. T (T) out Torque is output for the motor. T (T) max (n) is the vehicle total motor peak torque.
The operating characteristics of the motor are shown in fig. 4, and the operating characteristics of the motor corresponding to the maximum torque that the motor can output at each rotational speed are obtained according to fig. 4. Before the motor rotating speed is smaller than the rated rotating speed, the motor torque is constant, and the power is gradually increased; after the motor speed is greater than the rated speed, the motor power is constant but the motor torque gradually decreases. And the vehicle-mounted terminal obtains a motor output torque algorithm according to the formula group (8) and the working characteristics of the motor, and the algorithm is shown in the following formula group (9). T (T) out Torque is output for the motor. T (T) max Peak motor torque; p (P) max Peak power of the motor; l is a motor torque load coefficient; n is n max The peak rotation speed of the motor; ne is the rated rotation speed of the motor; n is the current rotation speed of the motor.
And the vehicle-mounted terminal calculates the torque load coefficient, the motor rotating speed, the motor peak torque and the motor peak power of the vehicle according to the formula group (9) to obtain the total required torque.
In this embodiment, the running mode of the vehicle is determined according to the running data in the vehicle data, and the torque load factor, the motor rotation speed, the motor peak torque and the motor peak power in the vehicle data are calculated according to the running mode, so that the total required torque is obtained, the total required torques in different running modes are defined, and the accuracy of the total required torque is improved.
In one embodiment, as shown in fig. 5, the specific processing procedure for determining the target language variable according to the corresponding relationship between the target running data and the language variable for the target running data in step 202 includes:
step 502, determining a target domain corresponding to the value of the target traveling data according to the target traveling data in the traveling data.
The driving data comprise an acceleration mean value, an SOC and a speed. The target travel data is each of the travel data.
In implementation, the vehicle-mounted terminal traverses each piece of the travel data to obtain the target travel data. The corresponding relation between the target driving data and the sub-discourse domain is prestored in the vehicle-mounted terminal. Aiming at target driving data in the driving data, the vehicle-mounted terminal determines a sub-domain corresponding to the target driving data, namely a target domain.
Specifically, the vehicle-mounted terminal stores the domain of the average acceleration value, the domain of the SOC and the speed domain of the speed in advance. And then, dividing the domain of the acceleration mean value by the vehicle-mounted terminal to obtain the sub domain of the acceleration mean value. And the vehicle-mounted terminal divides the domain of the SOC to obtain the sub-domain of the SOC. The vehicle-mounted terminal divides the domain of the speed to obtain the sub domain of the speed. Then, the vehicle-mounted terminal determines a sub-domain corresponding to the acceleration mean, namely a target domain of the acceleration mean, according to the value of the acceleration mean aiming at the acceleration mean in the vehicle data. Aiming at the SOC in the vehicle data, the vehicle-mounted terminal determines a sub-domain corresponding to the SOC, namely a target domain of the SOC according to the value of the SOC. Aiming at the speed in the vehicle data, the vehicle-mounted terminal determines a sub-domain corresponding to the speed, namely a target domain of the speed according to the value of the speed.
Step 504, determining a target language variable corresponding to the target domain according to the corresponding relation between the preset domain and the language variable.
Wherein the linguistic variables include three levels of low, medium and high.
In the implementation, the corresponding relation between different values of the discourse domain of the target driving data and the linguistic variable, namely the relation between the sub-discourse domain and the linguistic variable, is stored in the vehicle-mounted terminal in advance. And the vehicle-mounted terminal determines a target language variable corresponding to the target domain according to the relation between the sub domain and the language variable.
Specifically, the relationship between the sub-domain of the average acceleration value and the language variable, the relationship between the sub-domain of the vehicle speed and the language variable, and the relationship between the sub-domain of the SOC and the language variable are stored in the vehicle-mounted terminal in advance. And the vehicle-mounted terminal determines a target language variable of the acceleration mean according to the relationship between the sub-argument domain of the acceleration mean and the language variable. And then, the vehicle-mounted terminal determines a target language variable of the speed according to the relationship between the sub-argument domain of the speed and the language variable. And the vehicle-mounted terminal determines a target language variable of the SOC according to the relationship between the sub-argument domain of the SOC and the language variable.
For example, the vehicle-mounted terminal has stored therein the average value of acceleration in the domain of [ -6,6], the domain of SOC of [0,100] and the domain of speed of [0,160]. And the vehicle-mounted terminal divides the domain of the acceleration mean value to obtain the sub domain of the acceleration mean value. The subdomain of each acceleration mean corresponds to a linguistic variable. As shown in fig. 6, the intersection of the two lines in fig. 6 represents the boundary of the subdomain of the acceleration mean. The vehicle-mounted terminal divides the domains of the speeds to obtain sub-domains of the speeds, and each sub-domain of the speed corresponds to a language variable, as shown in fig. 7. The intersection of the two lines in fig. 7 represents the boundary of the subdomain of speed. The vehicle terminal divides the domains of the SOC to obtain sub-domains of the SOC, and each sub-domain of the SOC corresponds to a language variable, as shown in FIG. 8. The intersection of the two lines in fig. 8 represents the boundary of the sub-domain of SOC. The average value of the acceleration speed, SOC (system on chip) and speed of the vehicle-mounted terminal are 5, 50 and 70 in the running data acquired by the vehicle-mounted terminal. The vehicle-mounted terminal determines that the target language variable with the acceleration mean value of 5 is M according to the sub-argument of the acceleration mean value and the corresponding language variable in fig. 6. The vehicle-mounted terminal determines that the target language variable with the speed average value of 70 is M according to the sub-argument of the speed and the corresponding language variable in fig. 7. The vehicle-mounted terminal determines that the target language variable of the SOC 70 is M according to the sub-argument of the SOC and the corresponding language variable in FIG. 8.
In this embodiment, the domain corresponding to the target driving data is determined by the target driving data, and the target language variable is determined according to the corresponding relationship between the domain and the language variable, so as to determine the driving mode according to the target language variable.
In one embodiment, the motors of the vehicle include a first motor and a second motor, and the real-time output power of the first motor and the second motor is acquired before the first loss power equation is executed, and the vehicle data and the real-time output power are calculated to obtain the idle loss power of the first motor and the idle loss power of the second motor. Prior to step 106, the specific processing procedure of the torque distribution model construction method further includes:
step 902, under the condition that the first motor is in a non-working state, acquiring real-time output torque of the first motor, and calculating the real-time output torque of the first motor, the motor efficiency and the motor rotating speed contained in the vehicle data according to an idling loss power algorithm to obtain first idling loss power.
Wherein the motor efficiency includes a motor efficiency of the first motor and a motor efficiency of the second motor.
In implementation, the vehicle-mounted terminal obtains the output power of the first motor under the condition that the first motor is in a non-working state. And then, the vehicle-mounted terminal calculates the output power of the first motor and the rotating speed of the motor according to a mechanical power algorithm to obtain the mechanical power of the first motor. And then, the vehicle-mounted terminal calculates the mechanical power of the first motor and the motor efficiency of the first motor according to an idle loss power algorithm to obtain the idle loss power of the first motor.
Specifically, the vehicle-mounted terminal obtains the output power of the first motor under the condition that the first motor is in a non-working state. Then, the in-vehicle terminal calculates the output power of the first motor and the electric power in the vehicle data according to the output power algorithm shown in the following formula (10)And calculating the rotation speed of the motor to obtain the mechanical power of the first motor. T as shown in formula (10) fs For the real-time output torque of the first motor, P mecf The mechanical power of the first motor and n is the motor rotation speed.
Then, the vehicle-mounted terminal calculates the mechanical power of the first motor and the motor efficiency of the first motor according to an idling loss power algorithm as shown in a formula (11) to obtain the idling loss power of the first motor. P is shown in the following formula (11) drag_f (n) is the lost power of the first motor, P mecf For the mechanical power of the first motor, eta f Is the motor efficiency of the first motor.
Optionally, the vehicle-mounted terminal calculates the output power of the first motor, the motor speed in the vehicle data and the motor efficiency of the first motor according to an idle loss power algorithm shown in the following formula (12), so as to obtain the idle loss power of the first motor. P is as shown in formula (12) drag_f(n) Lost power for first motor, eta f Is the motor efficiency of the first motor. T (T) fs The torque is output by the first motor in real time, and n is the motor rotating speed.
And step 904, under the condition that the second motor is in a non-working state, acquiring the real-time output torque of the second motor, and calculating the real-time output torque of the second motor, the motor efficiency and the motor rotating speed according to an idling loss power algorithm to acquire the second idling loss power.
Wherein the motor efficiency includes a motor efficiency of the first motor and a motor efficiency of the second motor.
In implementation, the vehicle-mounted terminal obtains the output power of the second motor under the condition that the second motor is in a non-working state. And then, the vehicle-mounted terminal calculates the output power of the second motor and the rotating speed of the motor according to a mechanical power algorithm to obtain the mechanical power of the second motor. And then, the vehicle-mounted terminal calculates the mechanical power of the second motor and the motor efficiency of the second motor according to an idle loss power algorithm to obtain the idle loss power of the second motor.
Specifically, the vehicle-mounted terminal obtains the output power of the second motor under the condition that the second motor is in a non-working state. Then, the vehicle-mounted terminal calculates the output power of the second motor and the motor rotation speed in the vehicle data according to an output power algorithm shown in the following formula (13) to obtain the mechanical power of the second motor. T is as shown in formula (13) rs For real-time output torque of the second motor, P mecr The mechanical power of the second motor and n is the motor rotation speed.
And then the vehicle-mounted terminal calculates the mechanical power of the second motor and the motor efficiency of the second motor according to an idle loss power algorithm shown in the following formula (14) to obtain the idle loss power of the second motor. P is as shown in formula (14) drag_r (n) is the lost power of the second motor, P mecr For the mechanical power of the second motor, eta r Is the motor efficiency of the second motor.
Optionally, the vehicle-mounted terminal calculates the output power of the second motor, the motor speed in the vehicle data and the motor efficiency of the second motor according to an idle loss power algorithm shown in the following formula (15), so as to obtain the idle loss power of the second motor. P is as shown in formula (15) drag_r (n) is the lost power of the second motor, eta r Motor effect for the second motorThe rate. T (T) rs The torque is output by the second motor in real time, and n is the motor rotating speed.
In this embodiment, the output torque, the motor efficiency and the motor rotation speed of the motor in the non-working state are calculated by the idle loss power algorithm, so as to obtain the idle loss power of the motor in the non-working state, and facilitate the subsequent compensation of the first loss power equation according to the idle loss power of the motor in the non-working state.
In one embodiment, the specific process of step 106 includes:
step 1002, obtaining a first idle loss power under the condition that the first motor is in a non-working state, and performing loss compensation on a first loss power equation according to the first idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle.
In implementation, under the condition that the vehicle runs, if the first motor is in a non-working state, the vehicle-mounted terminal acquires first idling loss power corresponding to the first motor. And then, the vehicle-mounted terminal increases the first idle loss power to a first idle loss power equation according to the first idle loss power, and a second loss power equation corresponding to each motor in the running process of the vehicle is obtained.
For example, the first lost power loss equation is shown in equation (3) above. If the first motor is in a non-working state, the vehicle-mounted terminal acquires first idling loss power corresponding to the first motor, namely P drag_f (n). The vehicle-mounted terminal increases the first idling loss power to the formula (3) to obtain a second loss power equation, namely the formula (5). The meaning of each parameter in formula (3) is fully described in step 104, and the meaning of each parameter in formula (5) is fully described in step 108, which is not described in detail herein.
Step 1004, obtaining second idle loss power under the condition that the second motor is in a non-working state, and performing loss compensation on the first loss power equation according to the second idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle.
In implementation, under the condition that the vehicle runs, if the second motor is in a non-working state, the vehicle-mounted terminal acquires second idling loss power corresponding to the second motor. And then, the vehicle-mounted terminal increases the second idle loss power to the first idle loss power equation according to the second idle loss power, and a second loss power equation corresponding to each motor in the running process of the vehicle is obtained.
For example, the first lost power loss equation is shown in equation (3). If the second motor is in a non-working state, the vehicle-mounted terminal acquires second idling loss power corresponding to the second motor, namely P drag_r (n). And the vehicle-mounted terminal increases the second idling loss power to the formula (3) to obtain a second loss power equation, namely the formula (4). The meaning of each parameter in the formula (3) is fully described in step 104, and the meaning of each parameter in the formula (4) is fully described in step 108, which is not described in detail herein.
In the embodiment, the error compensation is performed on the first lost power equation through the obtained idle lost power, so as to obtain the second lost power equation, consider the lost power of the motor under the non-working condition, and avoid the waste of power generated when the motor idles.
In one embodiment, during the running process of the vehicle, after a torque distribution model is built, current vehicle data is obtained, a torque distribution coefficient corresponding to each current motor when power is lost at minimum is determined according to the torque distribution model, and current total required torque is distributed according to the torque distribution coefficient to obtain distributed torque corresponding to each motor. Following step 108, the specific process of the torque distribution model construction method further includes:
step 1102, determining a torque distribution coefficient corresponding to each motor when power is lost at minimum according to the torque distribution model.
In implementation, the vehicle-mounted terminal adjusts a torque distribution coefficient of a first loss power equation in the torque distribution model, and determines a first loss power corresponding to the first loss power equation. And under the condition that the motor in the non-working state exists, the vehicle-mounted terminal determines a second loss power corresponding to the second loss power equation. Then, the in-vehicle terminal determines a torque distribution coefficient corresponding to the minimum lost power of the first lost power and the second lost power.
Step 1104, obtaining the current total required torque, and distributing the current total required torque according to the torque distribution coefficient to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotation speed corresponding to the current total required torque.
In practice, the vehicle terminal obtains the total required torque of the current vehicle. And the vehicle-mounted terminal carries out numerical operation on the torque distribution coefficient and the total required torque of the vehicle to obtain distribution torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque.
For example, the vehicle includes two motors, which are a first motor and a second motor, respectively. The torque distribution coefficient is a torque distribution coefficient of the second motor. And the vehicle-mounted terminal acquires the current total required torque. Then, the in-vehicle terminal takes the product of the current total required torque and the torque distribution coefficient as the distribution torque of the second motor. And the vehicle-mounted terminal performs difference operation on the 1 and the torque distribution coefficient to obtain the torque distribution coefficient of the first motor. The vehicle-mounted terminal takes the product of the current total required torque and the torque distribution coefficient of the first motor as the distribution torque of the first motor.
Optionally, the vehicle-mounted terminal acquires the road adhesion coefficient in real time. The vehicle-mounted terminal judges whether the tangential acting force of the torque of each motor distributed by the torque distribution coefficient on the ground is smaller than the adhesion force corresponding to the road adhesion coefficient. And if the tangential acting force of the torque of each motor distributed by the torque distribution coefficient on the ground is smaller than or equal to the adhesion force corresponding to the road adhesion coefficient, the vehicle-mounted terminal distributes the current total required torque according to the torque distribution coefficient, so as to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque. And if the tangential acting force of the torque of each motor distributed according to the torque distribution coefficient on the ground is larger than the adhesion force corresponding to the road adhesion coefficient, the vehicle-mounted terminal distributes the current total required torque according to the road adhesion coefficient, so as to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque.
In the embodiment, the torque distribution coefficient is determined according to the torque distribution model, the accuracy of the torque distribution coefficient is improved, the total required torque of the current vehicle is distributed according to the torque distribution coefficient, the distributed torque corresponding to each motor in the running process of the vehicle is obtained, and the efficiency of the distributed torque is improved.
In one embodiment, after the distribution of the current total required torque is completed, the vehicle-mounted terminal generates a torque distribution record according to the mapping relation among the current total required torque, the torque distribution coefficient and the motor rotating speed, so that the torque distribution coefficient can be conveniently determined for distribution according to the total required torque and the motor rotating speed. Following step 1104, the specific process of the torque distribution model construction method further includes:
and generating a torque distribution record according to the mapping relation among the current total required torque, the torque distribution coefficient and the motor rotating speed, and storing the torque distribution record.
The torque distribution record is used for determining a torque distribution coefficient according to the rotating speed of the motor and the current total required torque.
In implementation, the vehicle-mounted terminal generates a torque distribution record according to the mapping relation among the current total required torque, the current motor rotating speed and the compensation torque distribution, and stores the torque distribution record in a preset storage position.
For example, the vehicle includes two motors, which are a first motor and a second motor, respectively. As shown in fig. 12, in the case where both the first motor and the second motor are in the operating state, the vehicle-mounted terminal adjusts the torque distribution coefficient according to a first loss power equation of the torque distribution model, and determines the minimum loss power corresponding to the first loss power equation, that is, the first loss power P 3 . Then, the vehicle-mounted terminal obtains the lost power P of the first motor in a non-working state according to the second lost power equation 2 And the lost power P of the second motor in the non-working state 1 . Vehicle-mounted terminal determines P 1 、P 2 、P 3 Is a minimum loss of power. If P 1 For minimum loss of power, the in-vehicle terminal determines that the value of the torque distribution coefficient is 0. If P 2 For minimum loss of power, the in-vehicle terminal determines that the value of the torque distribution coefficient is 1. If P is 3 Minimum loss power, and vehicle-mounted terminal determines P 3 The corresponding torque distribution coefficient is a torque distribution coefficient. Then, the in-vehicle terminal generates a torque distribution record from the map of the current total required torque, the current motor rotation speed, and the compensation torque distribution, as shown in fig. 13. The vehicle-mounted terminal stores the stored data to a preset storage position.
In the embodiment, the torque distribution record is obtained by storing the mapping relation among the total required torque, the torque distribution coefficient and the motor rotating speed, and the torque distribution coefficient can be rapidly determined through the torque distribution record, so that the speed of acquiring the torque distribution coefficient is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a torque distribution model construction device for realizing the torque distribution model construction method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitations in the embodiments of the torque distribution model building device or devices provided below may be referred to above for the limitations of the torque distribution model building method, which are not repeated here.
In one embodiment, as shown in fig. 14, there is provided a torque distribution model construction apparatus 1400 comprising: an acquisition module 1401, a first construction module 1402, a compensation module 1403, and a second construction module 1404, wherein:
an acquisition module 1401 is configured to acquire vehicle data and total required torque during a current vehicle operation.
The first construction module 1402 is configured to construct a first loss power equation corresponding to each motor during operation of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor speed, and the total required torque included in the vehicle data.
The compensation module 1403 is configured to obtain, for a case where a motor in a non-operating state exists during a vehicle running process, an idle loss power of the motor in the non-operating state, and perform loss compensation on the first loss power equation according to the idle loss power, to obtain a second loss power equation corresponding to each motor during the vehicle running process.
The second construction module 1404 is configured to construct a torque distribution model with minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, where the torque distribution model is used to determine a corresponding distribution torque of each motor during the running process of the vehicle.
In an exemplary embodiment, the acquisition module 1401 includes:
the acquisition sub-module is used for acquiring vehicle data in the current vehicle running process; the vehicle data includes travel data, motor speed, motor peak torque, and motor peak power.
The first determining submodule is used for determining a target language variable according to the corresponding relation between the target running data and the language variable aiming at the target running data in the running data, and determining the running mode of the vehicle according to the mapping relation between the language variable and the running mode.
And the second determining submodule is used for determining the torque load coefficient of the vehicle according to the corresponding relation between the accelerator pedal opening and the torque load coefficient in the driving data under the driving mode of the vehicle.
And the first operation submodule is used for calculating the torque load coefficient, the motor rotating speed, the motor peak torque and the motor peak power of the vehicle according to the motor output torque algorithm to obtain the total required torque.
In an exemplary embodiment, the first determination submodule includes:
and the third determination submodule is used for determining a target domain corresponding to the value of the target running data aiming at the target running data in the running data.
And the fourth determining submodule is used for determining a target language variable corresponding to the target domain according to the corresponding relation between the preset domain and the language variable.
In an exemplary embodiment, the electric machines of the vehicle include a first electric machine and a second electric machine, and the torque distribution model construction apparatus further includes, before the compensation module 1403 performs the operation:
and the second operation submodule is used for acquiring the real-time output torque of the first motor under the condition that the first motor is in a non-working state, and calculating the real-time output torque of the first motor, the motor efficiency and the motor rotating speed contained in the vehicle data according to the idling loss power algorithm to acquire the first idling loss power.
And the third operation submodule is used for acquiring the real-time output torque of the second motor under the condition that the second motor is in a non-working state, and calculating the real-time output torque of the second motor, the motor efficiency and the motor rotating speed according to an idling loss power algorithm to acquire the second idling loss power.
In an exemplary embodiment, the compensation module 1403 includes:
the first compensation sub-module is used for acquiring first idling loss power under the condition that the first motor is in a non-working state, carrying out loss compensation on a first loss power equation according to the first idling loss power, and obtaining a second loss power equation corresponding to each motor in the running process of the vehicle.
The second compensation sub-module is used for obtaining second idling loss power under the condition that the second motor is in a non-working state, carrying out loss compensation on the first loss power equation according to the second idling loss power, and obtaining a second loss power equation corresponding to each motor in the running process of the vehicle.
In an exemplary embodiment, after the second build module 1404 performs the operations, the torque distribution model building apparatus further includes:
and a fifth determining submodule for determining a corresponding torque distribution coefficient of each motor when the power is lost minimum according to the torque distribution model.
And the distribution sub-module is used for obtaining the current total required torque, distributing the current total required torque according to the torque distribution coefficient, and obtaining the distribution torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque.
In an exemplary embodiment, after the allocation submodule performs the operation, the torque allocation model building device further includes:
the storage sub-module generates a torque distribution record according to the mapping relation among the current total required torque, the torque distribution coefficient and the motor rotating speed, and stores the torque distribution record; the torque distribution record is used for determining a torque distribution coefficient according to the motor rotation speed and the current total required torque.
The respective modules in the torque distribution model construction apparatus described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 15. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a torque distribution model building method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 15 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application is applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims (10)

1. A method of torque distribution model construction, the method comprising:
acquiring vehicle data and total required torque in the current vehicle running process; constructing a first loss power equation corresponding to each motor in the running process of the vehicle based on the motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data; the motor of the vehicle includes a first motor and a second motor; the first lost power equation is Wherein P is 3 For the loss of power of the motor, T req For the total required torque, n is the motor speed, lambda is the torque distribution coefficient, eta f For a first motor efficiency, eta r For a second motor efficiency;
aiming at the condition that a motor in a non-working state exists in the running process of the vehicle, acquiring idle loss power of the motor in the non-working state, and carrying out loss compensation on the first loss power equation according to the idle loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle; if the first motor is in a non-working state, the second loss power equation is Wherein T is req For the total required torque, η r Representing a motor efficiency of the first motor; n represents the motor rotation speed, P 2 For the loss of power, P, of the first motor in a non-working state drag_f (n) represents the firstLost power of the motor; if the second motor is in a non-working state, the second lost power equation is +.>Wherein T is req For the total required torque, η f Represents the motor efficiency of the first motor, n represents the motor speed, P drag_r (n) represents the lost power of the second motor, P 1 Lost power for the second motor in a non-operating state;
according to the first loss power equation and the second loss power equation, constructing a torque distribution model taking the minimum loss power of each motor as an objective function, wherein the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle; the torque distribution model is P loss ＝min{P 1 ，P 2 ，P 3 }, wherein P 1 For the loss of power, P, of the second motor in the non-working state 2 For the loss of power, P, of the first motor in a non-working state 3 To avoid the loss of power during the idle running of the motor, P loss Is the minimum lost power;
the acquiring the vehicle data and the total required torque in the current vehicle running process comprises the following steps:
acquiring vehicle data in the current vehicle running process; the vehicle data comprises driving data, motor rotating speed, motor peak torque and motor peak power;
aiming at target running data in the running data, determining a target language variable according to the corresponding relation between the target running data and the language variable, and determining the running mode of the vehicle according to the mapping relation between the language variable and the running mode;
Determining a torque load factor of the vehicle according to the corresponding relation between the opening of an accelerator pedal and the torque load factor in the driving data under the driving mode of the vehicle;
and calculating the torque load coefficient, the motor rotating speed, the motor peak torque and the motor peak power of the vehicle according to a motor output torque algorithm to obtain total required torque.
2. The method according to claim 1, wherein the determining, for the target traveling data in the traveling data, a target linguistic variable according to a correspondence relationship between the target traveling data and the linguistic variable, includes:
aiming at target running data in the running data, determining a target domain corresponding to the value of the target running data;
and determining a target language variable corresponding to the target domain according to the corresponding relation between the preset domain and the language variable.
3. The method according to claim 1, wherein the motor of the vehicle includes a first motor and a second motor, and the method further includes, for a case where a motor in a non-operating state exists in the vehicle running process, obtaining an idle loss power of the motor in the non-operating state, performing loss compensation on the first loss power equation according to the idle loss power, and before obtaining a second loss power equation corresponding to each motor in the vehicle running process:
Under the condition that the first motor is in a non-working state, acquiring real-time output torque of the first motor, and calculating the real-time output torque of the first motor, the motor efficiency and the motor rotating speed contained in the vehicle data according to an idling loss power algorithm to obtain first idling loss power;
and under the condition that the second motor is in a non-working state, acquiring the real-time output torque of the second motor, and calculating the real-time output torque of the second motor, the motor efficiency and the motor rotating speed according to the idling loss power algorithm to obtain the second idling loss power.
4. The method according to claim 3, wherein, for the case that the motor in the non-working state exists in the vehicle running process, obtaining the idle loss power of the motor in the non-working state, performing loss compensation on the first loss power equation according to the idle loss power, and obtaining a second loss power equation corresponding to each motor in the vehicle running process, including:
under the condition that the first motor is in a non-working state, acquiring the first idling loss power, and carrying out loss compensation on the first loss power equation according to the first idling loss power to obtain a second loss power equation corresponding to each motor in the running process of the vehicle;
And under the condition that the second motor is in a non-working state, acquiring the second idling loss power, and carrying out loss compensation on the first loss power equation according to the second idling loss power to acquire a second loss power equation corresponding to each motor in the running process of the vehicle.
5. The method of claim 1, wherein after constructing a torque distribution model based on the first and second equations of lost power and with minimum lost power of each motor as an objective function, the method further comprises:
determining a torque distribution coefficient corresponding to each motor when the power is lost at the minimum according to the torque distribution model;
and obtaining the current total required torque, and distributing the current total required torque according to the torque distribution coefficient to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque.
6. The method according to claim 5, wherein the obtaining the current total required torque, and the distributing the current total required torque according to the torque distribution coefficient, to obtain the distributed torque corresponding to each motor during the running of the vehicle at the motor speed corresponding to the current total required torque, includes:
Obtaining a road surface adhesion coefficient and a current total required torque, and judging whether tangential acting force of torque of each motor distributed by the torque distribution coefficient on the ground is smaller than adhesion corresponding to the road surface adhesion coefficient;
if the tangential acting force of the torque of each motor distributed by the torque distribution coefficient on the ground is smaller than or equal to the adhesion force corresponding to the road adhesion coefficient, distributing the current total required torque according to the torque distribution coefficient to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque;
and if the tangential acting force of the torque of each motor distributed by the torque distribution coefficient on the ground is larger than the adhesion force corresponding to the road adhesion coefficient, distributing the current total required torque according to the road adhesion coefficient to obtain the distributed torque corresponding to each motor in the running process of the vehicle at the motor rotating speed corresponding to the current total required torque.
7. The method according to claim 5, wherein after the obtaining the current total demand torque and distributing the current total demand torque according to the torque distribution coefficient to obtain the distributed torque corresponding to each motor during the running of the vehicle at the motor speed corresponding to the current total demand torque, the method further comprises:
Generating a torque distribution record according to the mapping relation among the current total required torque, the torque distribution coefficient and the motor rotating speed, and storing the torque distribution record; the torque distribution record is used for determining the torque distribution coefficient according to the motor rotating speed and the current total required torque.
8. A torque distribution model construction apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring vehicle data and total required torque in the current vehicle running process;
a first construction module forThe motor efficiency, the torque distribution coefficient, the motor rotating speed and the total required torque contained in the vehicle data are used for constructing a first loss power equation corresponding to each motor in the running process of the vehicle; the motor of the vehicle includes a first motor and a second motor; the first lost power equation isWherein P is 3 For the loss of power of the motor, T req For the total required torque, n is the motor speed, lambda is the torque distribution coefficient, eta f For a first motor efficiency, eta r For a second motor efficiency;
the compensation module is used for acquiring idling loss power of the motor in the non-working state according to the situation that the motor in the non-working state exists in the vehicle running process, and carrying out loss compensation on the first loss power equation according to the idling loss power to obtain a second loss power equation corresponding to each motor in the vehicle running process; if the first motor is in a non-working state, the second loss power equation is Wherein T is req For the total required torque, η r Representing a motor efficiency of the first motor; n represents the motor rotation speed, P 2 For the loss of power, P, of the first motor in a non-working state drag_f (n) represents lost power lost to idle of the first motor; if the second motor is in a non-working state, the second lost power equation is +.>Wherein T is req For the total required torque, η f Represents the motor efficiency of the first motor, n represents the motor speed, P drag_r (n) represents the lost power of the second motor, P 1 Lost power for the second motor in a non-operating state;
the second construction module is used for constructing a torque distribution model taking the minimum loss power of each motor as an objective function according to the first loss power equation and the second loss power equation, and the torque distribution model is used for determining the corresponding distribution torque of each motor in the running process of the vehicle; the torque distribution model is P loss ＝min{P 1 ，P 2 ，P 3 }, wherein P 1 For the loss of power, P, of the second motor in the non-working state 2 For the loss of power, P, of the first motor in a non-working state 3 To avoid the loss of power during the idle running of the motor, P loss Is the minimum lost power;
the acquisition module is specifically used for acquiring vehicle data in the current vehicle running process; the vehicle data comprises driving data, motor rotating speed, motor peak torque and motor peak power; aiming at target running data in the running data, determining a target language variable according to the corresponding relation between the target running data and the language variable, and determining the running mode of the vehicle according to the mapping relation between the language variable and the running mode; determining a torque load factor of the vehicle according to the corresponding relation between the opening of an accelerator pedal and the torque load factor in the driving data under the driving mode of the vehicle; and calculating the torque load coefficient, the motor rotating speed, the motor peak torque and the motor peak power of the vehicle according to a motor output torque algorithm to obtain total required torque.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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