CN112319240A - Electric vehicle drivability management method, device, apparatus, and storage medium - Google Patents
Electric vehicle drivability management method, device, apparatus, and storage medium Download PDFInfo
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- CN112319240A CN112319240A CN202011297289.XA CN202011297289A CN112319240A CN 112319240 A CN112319240 A CN 112319240A CN 202011297289 A CN202011297289 A CN 202011297289A CN 112319240 A CN112319240 A CN 112319240A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
- B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
- B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
- B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/42—Drive Train control parameters related to electric machines
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- 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
- Y02T10/72—Electric energy management in electromobility
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Abstract
The invention belongs to the technical field of automobiles, and discloses a method, a device, equipment and a storage medium for managing the drivability of an electric vehicle. The invention obtains first evaluation information of the whole vehicle drivability of the target vehicle, judges whether the target vehicle meets the preset drivability standard according to the first evaluation information, and when the target vehicle does not meet the preset drivability standard, carrying out motor control mode self-learning on the target vehicle, acquiring second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, judging whether the target vehicle meets the preset drivability standard according to the second evaluation information, when the target vehicle meets the preset drivability standard, the current motor control mode of the target vehicle is stored, so that the good driving experience of the whole vehicle is kept in the use life cycle of a user.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a method, a device, equipment and a storage medium for managing drivability of an electric vehicle.
Background
In the development process of finishing the drivability of the whole electric vehicle, zero-crossing control of motor torque is emphasized to be finished, and when the direction and the state of a motor are changed, the shaking of the whole vehicle caused by the gap between transmission chain gears is eliminated. Meanwhile, the meshing clearance between the gears cannot be avoided in the design, manufacture and assembly processes. As the driving range of the vehicle increases, the meshing clearance between the transmission lines gradually increases in the use wear, and the wear of each vehicle and the size of the transmission line clearance are different.
Therefore, under the condition that the transmission system is abraded to different degrees, the motor torque zero-crossing control finished when the vehicle leaves a factory cannot ensure that the whole vehicle still has good driving experience. Therefore, the development of the drivability of the pure electric vehicle in the whole life cycle becomes a new subject for improving the productivity of the pure electric vehicle
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for managing the drivability of an electric vehicle, and aims to solve the technical problem that the driving experience of the whole vehicle can only be improved by replacing parts in the prior art.
In order to achieve the above object, the present invention provides a drivability management method for an electric vehicle, the method including the steps of:
acquiring first evaluation information of the overall drivability of a target vehicle;
judging whether the target vehicle meets a preset drivability standard or not according to the first evaluation information;
when the target vehicle does not meet the preset drivability standard, performing motor control mode self-learning on the target vehicle;
acquiring second evaluation information of the drivability of the whole vehicle after the target vehicle self-learns in a motor control mode, and judging whether the target vehicle meets a preset drivability standard or not according to the second evaluation information;
and when the target vehicle meets a preset drivability standard, acquiring and storing a current motor control mode of the target vehicle.
Preferably, after the step of obtaining second evaluation information of the drivability of the target vehicle after the target vehicle self-learns in the motor control mode and judging whether the target vehicle meets a preset drivability standard according to the second evaluation information, the method further includes:
when the target vehicle does not meet a preset drivability standard, judging whether the current drivability of the target vehicle meets the last drivability of the target vehicle;
when the current driving performance of the target vehicle does not meet the last driving performance of the target vehicle, acquiring a motor control mode corresponding to the last driving performance of the target vehicle;
and controlling the motor of the target vehicle according to the motor control mode.
Preferably, after the step of determining whether the current drivability of the target vehicle meets the last drivability of the target vehicle when the target vehicle does not meet the preset drivability criterion, the method further comprises:
and when the current drivability of the target vehicle meets the last drivability of the target vehicle, performing motor control mode self-learning on the target vehicle, returning to the step of obtaining second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, and judging whether the target vehicle meets a preset drivability standard according to the second evaluation information.
Preferably, the step of obtaining the first evaluation information of the overall drivability of the target vehicle includes:
acquiring motor rotating speed information of the target vehicle within a preset vehicle speed range;
determining a rotation speed change rate according to the rotation speed information;
acquiring a preset drivability evaluation standard;
and obtaining first evaluation information of the target vehicle according to the rotating speed change rate and the preset drivability evaluation standard.
Preferably, the step of obtaining the motor speed information of the target vehicle within a preset vehicle speed range includes:
detecting whether a motor of the target vehicle is in a zero-crossing working condition within a preset vehicle speed range;
and when the motor of the target vehicle is in a zero-crossing working condition, acquiring the motor rotating speed information of the target vehicle.
Preferably, the step of obtaining first evaluation information of the target vehicle according to the rotation speed change rate and the preset drivability evaluation criterion includes:
obtaining an evaluation grade corresponding to the rotating speed change rate according to the preset drivability evaluation standard;
and acquiring the first evaluation information according to the preset vehicle speed range and the evaluation level corresponding to the rotating speed change rate.
Preferably, the step of obtaining and storing the current motor control mode of the target vehicle when the target vehicle meets a preset drivability criterion includes:
and when the target vehicle meets a preset drivability standard, acquiring a current motor control mode of the target vehicle, and storing a representative zone bit of the current motor control mode.
In addition, in order to achieve the purpose, the invention also provides a device for managing the drivability of the electric vehicle, which comprises an acquisition module, a judgment module, a motor control mode self-learning module and a storage module;
the acquisition module is used for acquiring first evaluation information of the overall drivability of the target vehicle;
the judging module is used for judging whether the target vehicle meets a preset drivability standard or not according to the first evaluation information;
the motor control mode self-learning module is used for carrying out motor control mode self-learning on the target vehicle when the target vehicle does not meet the preset drivability standard;
the judging module is also used for acquiring second evaluation information of the whole vehicle drivability of the target vehicle after the target vehicle self-learns in a motor control mode, and judging whether the target vehicle meets a preset drivability standard or not according to the second evaluation information;
and the storage module is used for acquiring and storing the current motor control mode of the target vehicle when the target vehicle meets the preset drivability standard.
Further, to achieve the above object, the present invention also proposes an electric vehicle drivability management apparatus comprising: a memory, a processor, and an electric vehicle drivability management program stored on the memory and executable on the processor, the electric vehicle drivability management program configured to implement the steps of the electric vehicle drivability management method as described above.
In addition, to achieve the above object, the present invention further provides a storage medium having an electric vehicle drivability management program stored thereon, which when executed by a processor implements the steps of the electric vehicle drivability management method as described above.
The invention obtains first evaluation information of the whole vehicle drivability of the target vehicle, judges whether the target vehicle meets the preset drivability standard according to the first evaluation information, and when the target vehicle does not meet the preset drivability standard, carrying out motor control mode self-learning on the target vehicle, acquiring second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, judging whether the target vehicle meets the preset drivability standard according to the second evaluation information, when the target vehicle meets the preset drivability standard, the current motor control mode of the target vehicle is acquired and stored, so that the good driving experience of the whole vehicle is kept in the life cycle of the user, and compared with the existing mode that the driving experience of the whole vehicle can be improved only by replacing parts, the mode provided by the invention can be used for keeping the good driving experience of the whole vehicle and reducing the maintenance cost of the vehicle in the life cycle of the user.
Drawings
Fig. 1 is a schematic structural diagram of an electric vehicle drivability management apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a first embodiment of a drivability management method for an electric vehicle according to the present invention;
FIG. 3 is a flowchart illustrating a drivability management method for an electric vehicle according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a third exemplary embodiment of a method for managing drivability of an electric vehicle according to the present invention;
fig. 5 is a block diagram illustrating a driving ability management apparatus for an electric vehicle according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electric vehicle drivability management apparatus in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the electric vehicle drivability management apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the electric vehicle drivability management apparatus and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and an electric vehicle drivability management program.
In the electric vehicle drivability management apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the electric vehicle drivability management apparatus of the present invention may be provided in the electric vehicle drivability management apparatus, which calls the electric vehicle drivability management program stored in the memory 1005 through the processor 1001 and executes the electric vehicle drivability management method provided by the embodiment of the present invention.
Based on the above electric vehicle drivability management device, an embodiment of the present invention provides an electric vehicle drivability management method, and referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of the electric vehicle drivability management method according to the present invention.
In this embodiment, the electric vehicle drivability management method includes the steps of:
step S10: acquiring first evaluation information of overall drivability of target vehicle
It should be noted that the execution main body of the embodiment may be a computing service device with network communication and program operation, such as a vehicle controller, a vehicle computer, and the like. The present embodiment and the following embodiments will be described below by taking the vehicle control unit as an example.
It should be understood that the first evaluation information may be information for evaluating whether the drivability of the whole vehicle meets the driving requirement of the user, and in the expert evaluation system, the dynamic performance of the vehicle is divided into three main indexes: drivability + drivability (controllability) + comfort. The drivability is the direction of the vehicle in the X direction, all dynamic changes, the main calculation and evaluation index is the change of the acceleration in the X direction, and the good driving feeling expected by the consumer is the following-up movement of the vehicle; the maneuverability is all dynamic changes of the vehicle in the Y direction, the main calculation and evaluation indexes are the acceleration in the Y direction, the yaw velocity of the vehicle in the Z normal plane and the roll in the X normal direction, and the good maneuverability is the combination of human and vehicle; the riding comfort is all dynamic changes of the vehicle in the Z direction, the main calculation and evaluation indexes are the acceleration in the Z direction and the pitching change of the vehicle in the Y normal plane, and good riding comfort feeling is as flat as the ground.
In specific implementation, the vehicle control unit monitors the fluctuation degree of the motor rotating speed under the motor zero-crossing working condition to obtain motor rotating speed information under a certain vehicle speed, determines the rotating speed change rate of the motor according to the rotating speed information, obtains the influence of the motor rotating speed change rate on the drivability of the vehicle under the current vehicle speed, and obtains the first evaluation information.
Step S20: and judging whether the target vehicle meets a preset drivability standard or not according to the first evaluation information.
It should be noted that the preset drivability criterion may be preset drivability that meets the current driving demand.
In specific implementation, the vehicle control unit compares the acquired first evaluation information with the preset drivability standard, and determines whether the target vehicle meets the preset drivability standard according to the first evaluation information.
Step S30: and when the target vehicle does not meet the preset drivability standard, performing motor control mode self-learning on the target vehicle.
It should be noted that, the motor control mode self-learning may be to replace a motor zero-crossing control mode, so as to improve the problem of influence on the drivability of the whole vehicle in the current motor control mode.
In specific implementation, when the target vehicle does not meet preset drivability standards, the vehicle control unit performs motor control mode self-learning on the target vehicle.
Step S40: and acquiring second evaluation information of the drivability of the whole vehicle after the target vehicle self-learns in a motor control mode, and judging whether the target vehicle meets a preset drivability standard or not according to the second evaluation information.
It should be noted that the second evaluation information may be information for evaluating whether drivability of the entire vehicle after the motor control mode self-learning meets the driving demand of the user.
In specific implementation, the vehicle control unit obtains second evaluation information of vehicle drivability of the target vehicle after the target vehicle self-learns in the motor control mode, and judges whether the target vehicle meets a preset drivability standard after the target vehicle self-learns in the motor control mode according to the second evaluation information.
Step S50: and when the target vehicle meets a preset drivability standard, acquiring and storing a current motor control mode of the target vehicle.
It should be noted that, the obtaining and storing the current motor control mode of the target vehicle may be obtaining the current motor control mode of the target vehicle, and storing the representative flag bit of the current motor control mode, so that the motor control mode meeting the preset drivability standard after the motor control mode self-learning may be directly obtained when the vehicle is powered on next time.
In specific implementation, when the target vehicle meets a preset drivability standard, the vehicle control unit acquires a current motor control mode of the target vehicle and stores a representative flag bit of the current motor control mode.
The embodiment determines whether the target vehicle meets the preset drivability standard according to the first evaluation information by acquiring the first evaluation information of the overall drivability of the target vehicle, and when the target vehicle does not meet the preset drivability standard, carrying out motor control mode self-learning on the target vehicle, acquiring second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, judging whether the target vehicle meets the preset drivability standard according to the second evaluation information, when the target vehicle meets the preset drivability standard, the current motor control mode of the target vehicle is acquired and stored, so that the good driving experience of the whole vehicle is kept in the user life cycle.
Referring to fig. 3, fig. 3 is a flowchart illustrating a drivability management method for an electric vehicle according to a second embodiment of the present invention.
Based on the first embodiment, in the present embodiment, after the step S40, the method further includes:
step S501: and when the target vehicle does not meet the preset drivability standard, judging whether the current drivability of the target vehicle meets the last drivability of the target vehicle.
It should be noted that the previous drivability may be drivability when the motor control manner self-learning is not performed, and in a specific implementation, since the motor control manner self-learning may be performed multiple times, the previous drivability may also be drivability after the motor control manner self-learning is performed last time, which is not limited in this embodiment.
In specific implementation, the vehicle control unit determines whether the current drivability of the target vehicle meets the last drivability of the target vehicle when the target vehicle does not meet a preset drivability standard.
Step S502: and when the current drivability of the target vehicle does not meet the last drivability of the target vehicle, acquiring a motor control mode corresponding to the last drivability of the target vehicle, and controlling a motor of the target vehicle according to the motor control mode.
It should be understood that, when the current drivability of the target vehicle does not meet the previous drivability of the target vehicle, it is stated that the drivability of the entire vehicle after the motor control manner self-learning is reversed, and a method of recovering the previous motor control manner is adopted in this embodiment to ensure that the drivability of the entire vehicle does not reverse.
In specific implementation, when the current drivability of the target vehicle does not satisfy the last drivability of the target vehicle, the vehicle control unit acquires a motor control mode corresponding to the last drivability of the target vehicle, and controls a motor of the target vehicle according to the motor control mode. When the current drivability of the target vehicle does not satisfy the previous drivability of the target vehicle, the motor of the target vehicle may be self-learned again, and the control manner of the motor may be replaced again.
Step S503: and when the current drivability of the target vehicle meets the last drivability of the target vehicle, performing motor control mode self-learning on the target vehicle, returning to the step of obtaining second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, and judging whether the target vehicle meets a preset drivability standard according to the second evaluation information.
It should be understood that when the current drivability of the target vehicle meets the last drivability of the target vehicle, it is explained that the drivability of the entire vehicle is improved after the motor control manner self-learning is performed, but the preset drivability standard is not yet met, so the motor of the target vehicle is self-learned again, so that the drivability of the entire vehicle meets the preset drivability standard.
In specific implementation, when the current drivability of the target vehicle meets the last drivability of the target vehicle, the vehicle control unit performs motor control mode self-learning on the target vehicle, the step corresponds to step S30, and correspondingly returns to step S40 to obtain second evaluation information of the vehicle drivability of the target vehicle after the motor control mode self-learning, and determines whether the target vehicle meets a preset drivability standard according to the second evaluation information.
The present embodiment determines whether the current driveability of the target vehicle meets the last driveability of the target vehicle when the target vehicle does not meet a preset driveability criterion, when the current drivability of the target vehicle does not satisfy the previous drivability of the target vehicle, acquiring a motor control manner corresponding to the previous drivability of the target vehicle, controlling a motor of the target vehicle according to the motor control manner, and when the current drivability of the target vehicle satisfies the last drivability of the target vehicle, carrying out motor control mode self-learning on the target vehicle, returning to the step of obtaining second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, and judging whether the target vehicle meets a preset drivability standard or not according to the second evaluation information. According to the method and the device, when the target vehicle does not meet the preset drivability standard, whether the current drivability of the target vehicle meets the last drivability of the target vehicle is judged, and the problem that the drivability of the whole vehicle after the motor control mode self-learning is not obviously improved or even reduced is solved.
Referring to fig. 4, fig. 4 is a flowchart illustrating a drivability management method for an electric vehicle according to a third embodiment of the present invention.
Based on the foregoing embodiments, in this embodiment, the step S10 includes:
step S101: and acquiring the motor rotating speed information of the target vehicle within a preset vehicle speed range.
It should be noted that the preset vehicle speed range may be a self-defined vehicle speed range, and is used to test the influence degree of different rotation speeds on the drivability in the vehicle speed range.
It should be understood that the obtaining of the motor rotation speed information of the target vehicle within the preset vehicle speed range further includes detecting whether the motor of the target vehicle is in a zero-crossing condition within the preset vehicle speed range, and obtaining the motor rotation speed information of the target vehicle when the motor of the target vehicle is in the zero-crossing condition. The collected motor rotating speed information needs to meet the condition that the number of times of the motor zero-crossing working condition is larger than the preset threshold number, and the influence of the whole vehicle zero-crossing working condition occurring on a pothole road surface and the like outside the expectation and the influence of the whole vehicle driveability diagnosis are avoided.
In a specific implementation, the vehicle control unit acquires motor rotation speed information of a target vehicle within a preset vehicle speed range, where the preset vehicle speed range may be defined as a high-speed state (above 80KPH), a medium-speed state (50KPH-80KPH), and a low-speed state (below 50 KPH), the rotation speed information of the motor in the three states is respectively acquired, and may also be other self-defined vehicle speed ranges or other preset certain vehicle speeds in a specific implementation, where this embodiment is not limited, for example, the preset vehicle speed is two vehicle speed ranges of 10KPH-40KPH and 40KPH-80KPH, and the motor rotation speed information within the preset vehicle speed range is acquired.
Step S102: and determining the change rate of the rotating speed according to the rotating speed information.
It should be understood that the rotational speed information includes the motor rotational speed from which the rate of change of the motor rotational speed can be calculated.
In specific implementation, the vehicle control unit determines the change rate of the rotating speed of the motor according to the rotating speed information.
Step S103: and acquiring a preset drivability evaluation standard.
The preset drivability evaluation criterion may be a high-low evaluation criterion of a degree of influence of the change rate of the motor rotation speed on the drivability problem of the whole vehicle within a preset vehicle speed range.
It should be understood that, under different vehicle speeds, even if the motor rotation speed change rate is the same, the driving experience brought by the vehicle is different, so the embodiment adopts two-dimensional weighted evaluation of the influence degree of the motor rotation speed change rate on the driver and the passenger under different vehicle speeds to jointly evaluate the driving performance of the whole vehicle.
In a specific embodiment, for example, when the preset vehicle speed is 60KPH or less and the change rate of the motor rotation speed of the target vehicle is 40 and 80, respectively, the evaluation score influencing the drivability of the whole vehicle is divided into 2 and 4, and when the preset vehicle speed is 60KPH or more and the change rate of the motor rotation speed of the target vehicle is 40 and 80, the evaluation score influencing the drivability of the whole vehicle is divided into 3 and 6, and in this case, the weight of the test result is set to 0.6 when the vehicle speed is 60KPH or less, and the weight of the test result when the corresponding vehicle speed is 60KPH or more is 0.4, and the calculated score influencing the drivability of the vehicle is 7.2, considering that the time during which the user travels at a vehicle speed of 60KPH or less is more than the predetermined time. The division of the weight, the criterion for evaluating the drivability, the preset vehicle speed, and the like may be adaptively adjusted according to the actual situation, which is not limited herein.
Step S104: and obtaining first evaluation information of the target vehicle according to the rotating speed change rate and the preset drivability evaluation standard.
It should be noted that the first evaluation information may be information for evaluating whether the drivability of the target vehicle meets the preset evaluation criterion, where the first evaluation information may be a score of the drivability evaluation calculated in step S103, or may be visually obtained according to the feeling of the driver and the passenger. The embodiment is not limited herein.
It is to be understood that the manner of obtaining the first evaluation information of the target vehicle based on the rotation speed change rate and the preset drivability evaluation criterion is also applicable to obtaining the second evaluation information. The first evaluation information and the second evaluation information are both information reflecting the current drivability of the vehicle, and when the first evaluation information is the score of the drivability evaluation calculated in step S103, the preset drivability evaluation criterion may be a preset score that meets the needs of the driver and the passenger.
In specific implementation, the vehicle control unit obtains first evaluation information of the target vehicle according to the rotating speed change rate and the preset drivability evaluation standard.
In the embodiment, the motor rotating speed information of the target vehicle in the preset vehicle speed range is acquired, the rotating speed change rate is determined according to the rotating speed information, the preset drivability evaluation standard is acquired, and the first evaluation information of the target vehicle is acquired according to the rotating speed change rate and the preset drivability evaluation standard.
Referring to fig. 5, fig. 5 is a block diagram illustrating a driving ability management apparatus for an electric vehicle according to a first embodiment of the present invention.
As shown in fig. 5, the device for managing drivability of an electric vehicle according to an embodiment of the present invention includes an obtaining module, a determining module, a motor control mode self-learning module, and a storage module;
the acquisition module 10 is used for acquiring first evaluation information of the overall drivability of the target vehicle;
the judging module 20 is configured to judge whether the target vehicle meets a preset drivability standard according to the first evaluation information;
the motor control mode self-learning module 30 is used for performing motor control mode self-learning on the target vehicle when the target vehicle does not meet a preset drivability standard;
the judging module 20 is further configured to obtain second evaluation information of the drivability of the whole vehicle after the target vehicle self-learns in the motor control mode, and judge whether the target vehicle meets a preset drivability standard according to the second evaluation information;
the storage module 50 is configured to acquire and store a current motor control mode of the target vehicle when the target vehicle meets a preset drivability standard.
In the embodiment, by acquiring first evaluation information of the overall drivability of the target vehicle, judging whether the target vehicle meets the preset drivability standard according to the first evaluation information, when the target vehicle does not meet the preset drivability standard, carrying out motor control mode self-learning on the target vehicle, acquiring second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, judging whether the target vehicle meets the preset drivability standard according to the second evaluation information, when the target vehicle meets the preset drivability standard, the current motor control mode of the target vehicle is acquired and stored, so that the good driving experience of the whole vehicle is kept in the user life cycle.
Other embodiments or specific implementation manners of the electric vehicle drivability management device of the present invention may refer to the above method embodiments, and are not described herein again.
Furthermore, an embodiment of the present invention further provides a storage medium having an electric vehicle drivability management program stored thereon, where the electric vehicle drivability management program, when executed by a processor, implements the steps of the electric vehicle drivability management method as described above.
In addition, an embodiment of the present invention further provides an electric vehicle drivability management apparatus, including: the electric vehicle drivability management method comprises a memory, a processor and an electric vehicle drivability management program stored on the memory and operable on the processor, wherein the electric vehicle drivability management program when executed by the processor implements the steps of the electric vehicle drivability management method described above.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An electric vehicle drivability management method characterized by comprising the steps of:
acquiring first evaluation information of the overall drivability of a target vehicle;
judging whether the target vehicle meets a preset drivability standard or not according to the first evaluation information;
when the target vehicle does not meet the preset drivability standard, performing motor control mode self-learning on the target vehicle;
acquiring second evaluation information of the drivability of the whole vehicle after the target vehicle self-learns in a motor control mode, and judging whether the target vehicle meets a preset drivability standard or not according to the second evaluation information;
and when the target vehicle meets a preset drivability standard, acquiring and storing a current motor control mode of the target vehicle.
2. The electric vehicle drivability management method according to claim 1, wherein after the step of obtaining second evaluation information of the drivability of the target vehicle after the motor control means self-learning and determining whether the target vehicle meets a preset drivability criterion according to the second evaluation information, the method further comprises:
when the target vehicle does not meet a preset drivability standard, judging whether the current drivability of the target vehicle meets the last drivability of the target vehicle;
when the current driving performance of the target vehicle does not meet the last driving performance of the target vehicle, acquiring a motor control mode corresponding to the last driving performance of the target vehicle;
and controlling the motor of the target vehicle according to the motor control mode.
3. The electric vehicle drivability management method of claim 2, further comprising, after the step of determining whether the current drivability of the target vehicle satisfies the last drivability of the target vehicle when the target vehicle does not satisfy the preset drivability criteria:
and when the current drivability of the target vehicle meets the last drivability of the target vehicle, performing motor control mode self-learning on the target vehicle, returning to the step of obtaining second evaluation information of the whole vehicle drivability of the target vehicle after the motor control mode self-learning, and judging whether the target vehicle meets a preset drivability standard according to the second evaluation information.
4. The electric vehicle drivability management method according to claim 1, wherein the step of acquiring the first evaluation information of the overall drivability of the target vehicle includes:
acquiring motor rotating speed information of the target vehicle within a preset vehicle speed range;
determining a rotation speed change rate according to the rotation speed information;
acquiring a preset drivability evaluation standard;
and obtaining first evaluation information of the target vehicle according to the rotating speed change rate and the preset drivability evaluation standard.
5. The electric vehicle drivability management method according to claim 4, wherein the step of acquiring the motor rotation speed information of the target vehicle within the preset vehicle speed range includes:
detecting whether a motor of the target vehicle is in a zero-crossing working condition within a preset vehicle speed range;
and when the motor of the target vehicle is in a zero-crossing working condition, acquiring the motor rotating speed information of the target vehicle.
6. The electric vehicle drivability management method of claim 5, wherein the step of obtaining the first evaluation information of the target vehicle based on the rotation speed change rate and the preset drivability evaluation criterion includes:
obtaining an evaluation grade corresponding to the rotating speed change rate according to the preset drivability evaluation standard;
and acquiring the first evaluation information according to the preset vehicle speed range and the evaluation level corresponding to the rotating speed change rate.
7. The electric vehicle drivability management method of any one of claims 1 to 6, wherein the step of obtaining and storing the current motor control mode of the target vehicle when the target vehicle meets a preset drivability criterion comprises:
and when the target vehicle meets a preset drivability standard, acquiring a current motor control mode of the target vehicle, and storing a representative zone bit of the current motor control mode.
8. The electric vehicle drivability management device is characterized by comprising an acquisition module, a judgment module, a motor control mode self-learning module and a storage module;
the acquisition module is used for acquiring first evaluation information of the overall drivability of the target vehicle;
the judging module is used for judging whether the target vehicle meets a preset drivability standard or not according to the first evaluation information;
the motor control mode self-learning module is used for carrying out motor control mode self-learning on the target vehicle when the target vehicle does not meet the preset drivability standard;
the judging module is also used for acquiring second evaluation information of the whole vehicle drivability of the target vehicle after the target vehicle self-learns in a motor control mode, and judging whether the target vehicle meets a preset drivability standard or not according to the second evaluation information;
and the storage module is used for acquiring and storing the current motor control mode of the target vehicle when the target vehicle meets the preset drivability standard.
9. An electric vehicle drivability management apparatus characterized by comprising: a memory, a processor, and an electric vehicle drivability management program stored on the memory and executable on the processor, the electric vehicle drivability management program configured to implement the steps of the electric vehicle drivability management method of any one of claims 1 to 7.
10. A storage medium having stored thereon an electric vehicle drivability management program which, when executed by a processor, implements the steps of the electric vehicle drivability management method according to any one of claims 1 to 7.
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