KR102546509B1 - Intelligent speed control system for e-mobility - Google Patents

Abstract

An intelligent speed control system for e-mobility is disclosed. The e-mobility intelligent speed control system collects global positioning system (GPS) data, battery management system (BMS) data, and motor controller data of e-mobility, and collects the GPS data, the BMS data, and the motor controller data. A driving pattern of the e-mobility may be analyzed using controller data, and speed control of the e-mobility may be performed based on the driving pattern.

Description

Intelligent speed control system for e-mobility}

The description below relates to technology for controlling the speed of e-mobility to reduce greenhouse gases.

Among the various gases that make up the atmosphere, the gases that cause the greenhouse effect are called greenhouse gases. Greenhouse gases include carbon dioxide, methane, nitrous oxide, Freon, and ozone. Of course, water vapor plays the biggest role in causing the natural greenhouse effect, but carbon dioxide is the most representative greenhouse gas that causes global warming.

Global warming has progressed at a rapid pace since the latter half of the 20th century, and abnormal weather phenomena such as torrential rains, droughts, and typhoons are rapidly increasing. If the current level of pollution continues, global greenhouse gas emissions are expected to seriously threaten humanity and ecosystems in the not-too-distant future.

Therefore, in order to respond to global warming caused by greenhouse gases, international cooperation is being initiated to reduce the emission of greenhouse gases. Various efforts are being made to reduce greenhouse gas emissions in the form of general-purpose activities in everyday life, such as using public transportation or turning off lights, as well as international industries in various fields such as the transportation field.

As part of these efforts, demand for e-mobility, such as an electric vehicle or an electric two-wheeled vehicle, is increasing as an electric transportation means powered by electricity replacing conventional transportation means using gasoline as a main fuel.

As an example of e-mobility related technology, Korean Patent Registration No. 10-2241508 (registration date April 12, 2021) discloses a battery management technology capable of preventing overdischarge of batteries used in E-mobility. .

It provides an e-mobility intelligent speed control system to reduce the electricity consumption of e-mobility and increase the use time and lifespan of the battery.

An e-mobility intelligent speed control system that can analyze the driving pattern of e-mobility and control the e-mobility speed based on the analysis result is provided.

A computer-implemented e-mobility intelligent speed control system comprising: at least one processor configured to execute computer readable instructions contained in a memory, wherein the at least one processor comprises a global positioning system (GPS) of e-mobility ) process of collecting data, BMS (battery management system) data and motor controller data; analyzing a driving pattern of the e-mobility using the GPS data, the BMS data, and the motor controller data; and an e-mobility intelligent speed control system that processes a process of performing speed control of the e-mobility based on the driving pattern.

According to one aspect, the at least one processor includes the GPS data including GPS speed based on location coordinates from the e-mobility, the BMS data including voltage, current, and temperature of the battery, and a speedometer. The motor controller data including information and current set values for each motor parameter may be collected.

According to another aspect, the at least one processor may classify a user type corresponding to the GPS data, the BMS data, and the motor controller data from among a plurality of user types.

According to another aspect, the at least one processor may classify a user type based on a speed (RPM) and a battery current limit.

According to another aspect, the at least one processor may classify the user type based on an acceleration time, a set output setting point, and a battery current limit.

According to another aspect, the at least one processor determines a maximum speed of resolution indicating a motor rotation revolution per minute (RPM) value and a maximum current value supplied from a battery according to a user type based on the driving pattern. max battery current limit, max phase current, which indicates the maximum current value supplied to the motor, acceleration time, deceleration time, and step-by-step acceleration rate (set At least one motor parameter setting value among output setting points) may be determined.

According to another aspect, the at least one processor may determine a motor parameter setting value by deriving conditions for rapid acceleration and overspeed prevention from the driving pattern.

According to another aspect, in the e-mobility, the maximum speed may be limited by controlling the battery discharge current through a parallel field effect transistor (FET) driving circuit.

An e-mobility intelligent speed control method performed in a computer device, comprising: collecting GPS data, BMS data, and motor controller data of e-mobility by at least one processor included in the computer device; analyzing, by the at least one processor, a driving pattern of the e-mobility using the GPS data, the BMS data, and the motor controller data; and performing speed control of the e-mobility based on the driving pattern by the at least one processor.

According to one aspect, the analyzing step includes classifying a user type corresponding to the GPS data, the BMS data, and the motor controller data from among a plurality of user types, and the performing step includes the driving pattern. and limiting the maximum speed by controlling the battery discharge current according to the type of user based on the above.

According to the embodiments of the present invention, by analyzing the driving pattern of the e-mobility and controlling the speed of the e-mobility with settings optimized for the driving pattern, unnecessary electricity consumption can be reduced and the use time and lifespan of the battery can be increased. .

According to embodiments of the present invention, by using BMS (battery management system) data, GPS (global positioning system) data, and motor controller data to analyze driving patterns of e-mobility, individual driving characteristics are more accurately determined. Through speed control suitable for individual driving characteristics, it is possible to reduce greenhouse gas emissions by reducing electricity consumption while suppressing aggressive driving naturally.

1 is an exemplary diagram illustrating the energy flow of e-mobility.
2 is a conceptual diagram for explaining an e-mobility electric field configuration according to an embodiment of the present invention.
3 is a block diagram for explaining an example of the internal configuration of an e-mobility intelligent speed control system according to an embodiment of the present invention.
4 is a flowchart illustrating an example of an e-mobility intelligent speed control method according to an embodiment of the present invention.
5 shows an example of BMS data in one embodiment of the present invention.
6 illustrates an example data transmission scenario in one embodiment of the present invention.
7 illustrates an example of a motor controller setting value according to an embodiment of the present invention.
8 illustrates an example of setting motor parameters for each user type according to an embodiment of the present invention.
9 illustrates an example of driving pattern analysis-based speed control according to an embodiment of the present invention.
10 illustrates an example of battery output control according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Embodiments of the present invention relate to a technology for controlling the speed of e-mobility for greenhouse gas reduction.

Embodiments including those specifically disclosed in this specification can reduce electricity consumption and increase battery life and use time through intelligent speed control based on e-mobility driving patterns.

1 and 2 are conceptual diagrams for explaining the electric field configuration of e-mobility in one embodiment of the present invention.

1 and 2, the e-mobility 100 according to the present invention includes a global positioning system (GPS) module (not shown), a motor controller 110, and a vehicle control unit (VCU) 120. ), and a battery management system (BMS) 130.

The e-mobility 100 according to the present invention performs signal communication with the motor controller 110 and displays various status information such as speed, parking, gear, and error alarm, speedometer 140, and motor controller 110 A motor 160 driven under the control of the motor 160 and a hall sensor 161 for detecting a rotational position of the motor 160 may be included.

The motor controller 110 serves to receive electricity and convert it into energy, and can control the speed of the e-mobility 100 using the motor 160 and a reducer.

The VCU 120 is a device for overall control of the e-mobility 100 and may include an inverter that converts DC electricity from a battery into AC and transfers it to the motor 160 .

A battery pack that stores power may include a cell pack including a plurality of battery cells and a connector that outputs the voltage of the cell pack to the outside. The BMS 130 serves to manage electrical connection or disconnection between the cell pack and the connector.

The e-mobility 100 may include communication such as RS-485, CAN, and UART between components such as the motor controller 110, the VCU 120, and the BMS 130.

In particular, the e-mobility 100 according to the present invention implements intelligent speed control under the control of an interworking environment with the server 200 and the server 200 based on communication between the VCU 120 and the server 200.

The e-mobility 100 may transmit data required for driving pattern analysis to the server 200 through the VCU 120, and may control speed based on a policy determined by the server 200 through the driving pattern analysis. .

3 is a block diagram for explaining an example of the internal configuration of an e-mobility intelligent speed control system according to an embodiment of the present invention. For example, the e-mobility intelligent speed control system according to embodiments of the present invention corresponds to the server 200 and may be implemented by the computer device 300 shown in FIG. 3 .

As shown in FIG. 3, a computer device 300 is a component for executing an e-mobility intelligent speed control method according to embodiments of the present invention, and includes a memory 310, a processor 320, and a communication interface 330. ) and an input/output interface 340.

The memory 310 is a computer-readable recording medium and may include a random access memory (RAM), a read only memory (ROM), and a permanent mass storage device such as a disk drive. Here, a non-perishable mass storage device such as a ROM and a disk drive may be included in the computer device 300 as a separate permanent storage device distinct from the memory 310 . Also, an operating system and at least one program code may be stored in the memory 310 . These software components may be loaded into the memory 310 from a recording medium readable by a separate computer from the memory 310 . The separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD/CD-ROM drive, and a memory card. In another embodiment, software components may be loaded into the memory 310 through the communication interface 330 rather than a computer-readable recording medium. For example, software components may be loaded into memory 310 of computer device 300 based on a computer program installed by files received over network 360 .

The processor 320 may be configured to process commands of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to processor 320 by memory 310 or communication interface 330 . For example, processor 320 may be configured to execute received instructions according to program codes stored in a recording device such as memory 310 .

The communication interface 330 may provide a function for the computer device 300 to communicate with other devices through the network 360 . For example, a request, command, data, file, etc. generated according to a program code stored in a recording device such as the memory 310 by the processor 320 of the computer device 300 is transmitted to the network ( 360) to other devices. Conversely, signals, commands, data, files, etc. from other devices may be received by the computer device 300 via the communication interface 330 of the computer device 300 via the network 360 . Signals, commands, data, etc. received through the communication interface 330 may be transmitted to the processor 320 or memory 310, and files may be stored as storage media that the computer device 300 may further include (described above). permanent storage).

The communication method is not limited, and may include not only a communication method utilizing a communication network (eg, a mobile communication network, wired Internet, wireless Internet, and broadcasting network) that the network 360 may include, but also short-distance wired/wireless communication between devices. there is. For example, the network 360 may include a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), and a broadband network (BBN). , one or more arbitrary networks such as the Internet. In addition, the network 360 may include any one or more of network topologies including a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree or a hierarchical network, and the like. Not limited.

The input/output interface 340 may be a means for interface with the input/output device 350 . For example, the input device may include devices such as a microphone, keyboard, camera, or mouse, and the output device may include devices such as a display and a speaker. As another example, the input/output interface 340 may be a means for interface with a device in which functions for input and output are integrated into one, such as a touch screen. The input/output device 350 and the computer device 300 may be configured as one device.

Also, in other embodiments, computer device 300 may include fewer or more elements than those of FIG. 3 . However, there is no need to clearly show most of the prior art components. For example, the computer device 300 may be implemented to include at least a portion of the above-described input/output device 350 or may further include other components such as a transceiver, a camera, various sensors, and a database.

Hereinafter, specific embodiments of e-mobility intelligent speed control technology will be described.

The processor 320 of the computer device 300 may be implemented as a component for performing the following e-mobility intelligent speed control method. Depending on embodiments, components of the processor 320 may be selectively included in or excluded from the processor 320 . Also, components of the processor 320 may be separated or merged to express functions of the processor 320 according to embodiments.

The processor 320 and components of the processor 320 may control the computer device 300 to perform steps included in the e-mobility intelligent speed control method below. For example, the processor 320 and components of the processor 320 may be implemented to execute instructions according to an operating system code and at least one program code included in the memory 310 .

Here, elements of the processor 320 may be representations of different functions performed by the processor 320 according to instructions provided by program codes stored in the computer device 300 .

The processor 320 may read necessary commands from the memory 310 in which commands related to controlling the computer device 300 are loaded. In this case, the read command may include a command for controlling the processor 320 to execute steps to be described later.

Steps included in the e-mobility intelligent speed control method to be described later may be performed in an order different from the shown order, and some of the steps may be omitted or additional processes may be further included.

The steps included in the e-mobility intelligent speed control method may be performed by the server 200 implemented by the computer device 300, and depending on the embodiment, at least some of the steps are performed by the e-mobility 100. It is also possible to become

4 is a flowchart illustrating an example of an e-mobility intelligent speed control method according to an embodiment of the present invention.

Referring to FIG. 4 , in step S410, the processor 320 may collect data necessary for analyzing a driving pattern of the e-mobility 100 from the e-mobility 100. The VCU 120 of the e-mobility 100 may provide BMS data, GPS data, and motor controller data as data required for driving pattern analysis to the server 200 at regular intervals or upon request. The processor 320 controls the BMS data including voltage, current, and temperature of the battery from the VCU 120 of the e-mobility 100, instrument panel information such as speed (motor pulse count) and odometer, and motor control. It is possible to receive motor controller data including a current set value for , and GPS data including GPS speed based on location coordinates.

5 shows an example of BMS data. Referring to FIG. 5 , the BMS data transmitted to the instrument panel 140 and the VCU 120 may include discharge current, voltage, temperature, remaining capacity (SoC), and the like of the battery. The processor 320 may collect BMS data including voltage, current, and temperature of the battery from the VCU 120 .

6 shows a transmission scenario of BMS data, GPS data, and motor controller data using the VCU 120 . Referring to FIG. 6, the VCU 120 of the e-mobility 100 periodically provides device information M1, device state M2, driving state M3, network information M4, and battery while power is maintained. Information (M5), motor controller information (M6), etc. may be provided. The driving state M3 may correspond to GPS data, the battery information M5 may correspond to BMS data, and the motor controller information M6 may correspond to motor controller data. For example, device information (M1), device status (M2), driving status (M3), network information (M4), battery information (M5), and motor controller information (M6) are provided when power is supplied to the battery. device information (M1), driving state (M3), and battery information (M5) may be provided at the start of driving, and device information (M1), driving state (M3), and network information (M4) may be provided during parking. , battery information M5 may be provided.

7 shows an example of a motor controller setting value. Referring to FIG. 7, parameters that can be set for speed control of the e-mobility 100 (hereinafter, referred to as 'motor parameters') include a maximum speed representing a motor rotation RPM (revolution per minute) value of resolution), maximum battery current limit indicating the maximum current value supplied from the battery, maximum phase current indicating the maximum current value supplied to the motor, acceleration time, deceleration It may include deceleration time, step-by-step acceleration rate (set output setting point), and the like. The processor 320 may also collect current setting values for each motor parameter as motor controller data from the VCU 120 .

Referring back to FIG. 4 , in step S420, the processor 320 may analyze the driving pattern of the e-mobility 100 using the BMS data, GPS data, and motor controller data collected in step S410. there is. The processor 320 may classify user types according to driving characteristics by analyzing driving characteristics of users using the e-mobility 100 based on GPS speed, battery current, and instrument panel speed data. For example, the processor 320 may classify user types based on a correlation between speed (RPM) and battery current limit for each model. As another example, the processor 320 classifies the user type based on the correlation between the acceleration time, the set output setting point, and the rise time of the battery current limit for each same model. can do. For example, the user type may be classified into a normal user, a delivery user, and a student (or tourist). Data categories may be pre-determined for each user type, and user driving characteristics are analyzed by determining to which user type category the BMS data, GPS data, and motor controller data collected from the user's e-mobility 100 belong. can do. Depending on the embodiment, it is also possible to analyze a driving pattern by creating a plurality of user groups through data clustering for all service users and finding a user group having a data pattern similar to that of the target user among them. In addition to this method, it is also possible to use a deep learning model for classifying user types using BMS data, GPS data, and motor controller data as input parameters for driving pattern analysis.

In step S430, the processor 320 may set a speed control value according to the driving pattern of the e-mobility 100. The processor 320 may determine a setting value suitable for a driving pattern for each motor parameter of the e-mobility 100 and transmit the speed control setting value to the VCU 120 of the e-mobility 100 . At this time, the VCU 120 may receive a speed control setting value suitable for the driving pattern of the e-mobility 100 from the server 200 and reflect it to the motor controller 110 for speed control. The VCU 120 may perform a speed control function suitable for a driving pattern by controlling the motor controller 110 and the BMS 130 based on the speed control setting value determined by the server 200 .

8 illustrates an example of setting motor parameters for each user type. Referring to FIG. 8 , the processor 320 determines the maximum speed of resolution, maximum battery current limit, maximum phase current, and acceleration time according to the user type based on the driving pattern. At least one motor parameter setting value among an acceleration time, a deceleration time, and a step-by-step acceleration rate (set output setting point) may be determined. The processor 320 may determine setting values by deriving setting conditions for each motor parameter for each user type. For example, the maximum speed may be determined as 900 (85 km/h) for delivery users and 600 (45 km/h) for students. In addition, the processor 320 may determine the setting value of the corresponding motor parameter by deriving the rapid acceleration and overspeed prevention conditions from the driving pattern. For example, the current set value of the acceleration time can be changed from 100 ms to 200 ms according to the driving pattern, and the current set value of 10/50/75 can be changed to 5/40/60 according to the driving pattern. there is.

9 illustrates an example of speed control based on driving pattern analysis. Referring to FIG. 9 , the server 200 may collect GPS data, BMS data, and motor controller data from the VCU 120 of the e-mobility 100 through interworking with the e-mobility 100 . The server 200 analyzes driving patterns such as correlation between battery current and GPS speed of the e-mobility 100, frequency of rapid acceleration, and high-speed driving time by using GPS data, BMS data, and motor controller data, and determines the corresponding pattern. Corresponding user types can be classified. The server 200 may determine a setting value for each motor parameter according to a speed control policy pre-determined for a user type through analysis of a driving pattern, and transmit the determined value to the e-mobility 100. The e-mobility 100 may apply a setting value determined by the server 200 based on a driving pattern instead of a current setting value for each motor parameter.

10 illustrates an example of a battery output control process. Referring to FIG. 10 , the e-mobility 100 may extend a battery life cycle through battery self-discharge current control. By turning on/off a field effect transistor (FET) device according to a required output current, a parallel FET driving circuit for output control can be applied in this embodiment. The e-mobility 100 may control (on/off) the discharge FET gate according to the motor parameter set value through the VCU 120 . Through a parallel FET driving circuit, the maximum speed can be limited to 90 km/h or less in 20 A increments, for example. If the maximum battery current limit is set to 60A, the maximum speed can be set to 90km/h according to the maximum battery current limit by turning on the three discharge FET gates in 20A increments.

Accordingly, the e-mobility 100 may extend the life cycle of the battery by preventing overcurrent through control of the discharge current (ie, maximum speed). Speed limits can be set for each type of user according to driving patterns, for example, 45 km/h for students (or tourists), 65 km/h for general users, and 85 km/h for delivery users.

As such, according to the embodiments of the present invention, by analyzing the driving pattern of the e-mobility and controlling the speed of the e-mobility with settings optimized for the driving pattern, unnecessary electricity consumption can be reduced and the use time and lifespan of the battery can be increased. there is. According to embodiments of the present invention, by using BMS (battery management system) data, GPS (global positioning system) data, and motor controller data to analyze driving patterns of e-mobility, individual driving characteristics are more accurately determined. Through speed control suitable for individual driving characteristics, it is possible to reduce greenhouse gas emissions by reducing electricity consumption while suppressing aggressive driving naturally.

The devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, devices and components described in the embodiments include a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), and a programmable PLU (programmable logic unit). logic unit), microprocessor, or any other device capable of executing and responding to instructions. The processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. You can command the device. The software and/or data may be embodied in any tangible machine, component, physical device, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. there is. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. In this case, the medium may continuously store a program executable by a computer or temporarily store the program for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or combined hardware, but is not limited to a medium directly connected to a certain computer system, and may be distributed on a network. Examples of the medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROM and DVD, magneto-optical media such as floptical disks, and ROM, RAM, flash memory, etc. configured to store program instructions. In addition, examples of other media include recording media or storage media managed by an app store that distributes applications, a site that supplies or distributes various other software, and a server.

As described above, although the embodiments have been described with limited examples and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (10)

In the computer-implemented e-mobility intelligent speed control system,
at least one processor configured to execute computer readable instructions contained in memory;
including,
The at least one processor,
Collecting global positioning system (GPS) data, battery management system (BMS) data, and motor controller data of e-mobility;
analyzing a driving pattern of the e-mobility using the GPS data, the BMS data, and the motor controller data; and
The process of performing speed control of the e-mobility based on the driving pattern
processing,
The at least one processor,
Classification of user types based on acceleration time, set output setting point, and battery current limit
Characterized by an e-mobility intelligent speed control system. According to claim 1,
The at least one processor,
The GPS data including the GPS speed based on location coordinates from the e-mobility, the BMS data including the voltage, current, and temperature of the battery, and the speedometer information and the current setting value for each motor parameter. Collecting motor controller data
Characterized by an e-mobility intelligent speed control system. According to claim 1,
The at least one processor,
Classifying a user type corresponding to the GPS data, the BMS data, and the motor controller data among a plurality of user types
Characterized by an e-mobility intelligent speed control system. According to claim 1,
The at least one processor,
Classification of user types based on speed (RPM) and battery current limit
Characterized by an e-mobility intelligent speed control system. delete In the computer-implemented e-mobility intelligent speed control system,
at least one processor configured to execute computer readable instructions contained in memory;
including,
The at least one processor,
Collecting global positioning system (GPS) data, battery management system (BMS) data, and motor controller data of e-mobility;
analyzing a driving pattern of the e-mobility using the GPS data, the BMS data, and the motor controller data; and
The process of performing speed control of the e-mobility based on the driving pattern
processing,
The at least one processor,
According to the user type based on the driving pattern, the maximum speed of resolution indicating the value of revolution per minute (RPM) of the motor rotation, the maximum battery current limit indicating the maximum current value supplied from the battery, and the motor At least one motor parameter setting value of the maximum phase current representing the maximum current value supplied to the to decide
Characterized by an e-mobility intelligent speed control system. According to claim 1,
The at least one processor,
Deriving rapid acceleration and overspeed prevention conditions from the driving pattern to determine motor parameter setting values
Characterized by an e-mobility intelligent speed control system. According to claim 1,
In the e-mobility, limiting the maximum speed by controlling the battery discharge current through a parallel field effect transistor (FET) driving circuit
Characterized by an e-mobility intelligent speed control system. In the e-mobility intelligent speed control method performed on a computer device,
Collecting GPS data, BMS data, and motor controller data of e-mobility by at least one processor included in the computer device;
analyzing, by the at least one processor, a driving pattern of the e-mobility using the GPS data, the BMS data, and the motor controller data; and
Performing, by the at least one processor, speed control of the e-mobility based on the driving pattern
including,
The analysis step is
Classifying user types based on acceleration time, set output setting point, and battery current limit
E-mobility intelligent speed control method comprising a. In the e-mobility intelligent speed control method performed on a computer device,
Collecting GPS data, BMS data, and motor controller data of e-mobility by at least one processor included in the computer device;
analyzing, by the at least one processor, a driving pattern of the e-mobility using the GPS data, the BMS data, and the motor controller data; and
Performing, by the at least one processor, speed control of the e-mobility based on the driving pattern
including,
The step of performing the speed control of the e-mobility,
According to the user type based on the driving pattern, the maximum speed of resolution indicating the value of revolution per minute (RPM) of the motor rotation, the maximum battery current limit indicating the maximum current value supplied from the battery, and the motor At least one motor parameter setting value of the maximum phase current representing the maximum current value supplied to the step to decide
E-mobility intelligent speed control method comprising a.

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