Electric vehicles

Abstract

This technology helps drivers become accustomed to the standard acceleration characteristics of electric vehicles. [Solution] The electric vehicle is equipped with one or more processors that control the output of the electric motor. When the electric vehicle is in proficiency mode, one or more processors acquire information on a target virtual vehicle selected from among multiple virtual vehicles. At the start of proficiency mode, one or more processors control the output of the electric motor so that the acceleration characteristics of the electric vehicle in response to the driver's driving operations become the simulated acceleration characteristics of the target virtual vehicle, and as the driver's proficiency increases, they control the output of the electric motor so that the acceleration characteristics of the electric vehicle in response to the driver's driving operations become closer to the standard acceleration characteristics.

Description

Technical Field

[0001] The present disclosure relates to an electric vehicle having an electric motor as a drive source.

Background Art

[0002] An electric motor can be controlled to output a desired motor torque by controlling the applied voltage and field excitation. Utilizing this, a technique for reproducing various driving environments in an electric vehicle by appropriately controlling the electric motor of the electric vehicle has been considered. For example, Patent Document 1 discloses an electric vehicle capable of pseudo-reproducing the driving feeling of a clutch pedal-less manual transmission vehicle having a sequential shifter.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Consider the case where a user newly switches to an electric vehicle. At this time, when there is a significant difference in acceleration characteristics between the vehicle the user was previously driving and the electric vehicle. In such a case, it becomes difficult for the user (driver) to immediately adapt to the standard acceleration characteristics of the electric vehicle. As a result, there is a risk that the user (driver) may feel tired or become negligent in safe driving.

[0005] Furthermore, it is conceivable that the aforementioned methods could be used to drive electric vehicles in a way that replicates the acceleration characteristics of various virtual vehicles. On the other hand, electric vehicles can, of course, also be driven with their standard acceleration characteristics. Therefore, it is conceivable that a user (driver) might drive an electric vehicle driven to replicate the acceleration characteristics of a certain virtual vehicle, and then try to drive an electric vehicle driven with its standard acceleration characteristics. In this case, the replicated acceleration characteristics and the standard acceleration characteristics may differ significantly. In such cases, it becomes difficult for the driver to immediately adapt to the standard acceleration characteristics of the electric vehicle.

[0006] This disclosure has been made in view of the above-mentioned issues. One purpose of this disclosure is to provide a technology that helps drivers become accustomed to the standard acceleration characteristics of electric vehicles. [Means for solving the problem]

[0007] One aspect of this disclosure relates to an electric vehicle having an electric motor as a power source. The electric vehicle comprises one or more processors that control the output of the electric motor. When the electric vehicle is in proficiency mode, one or more processors acquire information on a target virtual vehicle selected from a plurality of virtual vehicles. At the start of proficiency mode, one or more processors control the output of the electric motor so that the acceleration characteristics of the electric vehicle in response to the driver's driving input become the simulated acceleration characteristics of the target virtual vehicle, and as the driver's proficiency increases, they control the output of the electric motor so that the acceleration characteristics of the electric vehicle in response to the driver's driving input become closer to the standard acceleration characteristics. [Effects of the Invention]

[0008] According to this disclosure, when an electric vehicle is in proficiency mode, the acceleration characteristics of the electric vehicle in response to the driver's driving input change from the simulated acceleration characteristics of the target virtual vehicle to the standard acceleration characteristics, depending on the driver's level of proficiency. This allows the driver to gradually become accustomed to the standard acceleration characteristics of the electric vehicle in proficiency mode. [Brief explanation of the drawing]

[0009] [Figure 1] This is a diagram showing the configuration of an electric vehicle according to an embodiment. [Figure 2] This figure shows an example of the standard acceleration characteristics of an electric vehicle and the simulated acceleration characteristics of a target virtual vehicle. [Figure 3] This is a tree diagram showing an example of a selection input accepted by the HMI regarding the control mode of an electric vehicle according to the embodiment. [Figure 4] This figure shows an example of the functional configuration of a control device that functions as a drive control device. [Figure 5] This figure shows an example of the functional configuration of the second target drive force calculation unit. [Figure 6] This flowchart shows the processing flow for the proficiency assessment process related to one of the conditions that lower proficiency. [Figure 7] This is a flowchart showing the processing flow for the proficiency mode calculation process. [Figure 8] This figure shows an example of a vehicle model configuration. [Figure 9] This figure shows an example of the functional configuration of a control device that functions as an in-vehicle equipment control device. [Modes for carrying out the invention]

[0010] Embodiments of this disclosure will be described below with reference to the drawings. In each drawing, the same or corresponding parts are denoted by the same reference numerals, and their descriptions are simplified or omitted.

[0011] 1. Configuration of the powertrain of an electric vehicle Figure 1 is a schematic diagram showing the configuration of an electric vehicle 100 according to an embodiment of this disclosure. First, the configuration of the power system of the electric vehicle 100 will be described with reference to Figure 1.

[0012] The electric vehicle 100 includes an electric motor (M) 2 as a driving source for traveling. The electric motor 2 is, for example, a three-phase AC motor. An inverter (INV) 16 is attached to the electric motor 2. The output shaft of the electric motor 2 is connected to a propeller shaft 5 via a reduction gear (not shown). The propeller shaft 5 is connected to a differential gear 6. The differential gear 6 is connected to left and right drive wheels 8 by left and right drive shafts 7. The drive wheels may be front wheels or rear wheels.

[0013] The inverter 16, the electric motor 2, the reduction gear, and the differential gear 6 may be integrally configured as an e-axle. In this case, the electric vehicle 100 does not include the propeller shaft 5, and the e-axle is connected to the drive shaft 7. As another modification, the configuration of the electric vehicle 100 may be four-wheel drive. For example, the electric vehicle 100 may include a transfer connected to the electric motor 2, and the transfer may be configured to distribute the output of the electric motor 2 to the front and rear wheels. Also, for example, it may be configured to include an e-axle for each of the front drive shaft 7 and the rear drive shaft 7.

[0014] The inverter 16 is connected to a battery (BATT) 14. The inverter 16 is, for example, a voltage-type inverter, and controls the motor torque of the electric motor 2 by PWM control. That is, the electric vehicle 100 is a battery electric vehicle (BEV: battery electric vehicle) that travels using the electric energy stored in the battery 14 with the electric motor 2 as a driving source.

[0015] 2 Configuration of the control system of the electric vehicle Subsequently, the configuration of the control system of the electric vehicle 100 will be described with reference to FIG. 1.

[0016] The electric vehicle 100 includes a vehicle speed sensor 30. The vehicle speed sensor 30 outputs a signal indicating the vehicle speed of the electric vehicle 100. At least one of the wheel speed sensors (not shown) provided on each of the left and right front wheels and the left and right rear wheels is used as the vehicle speed sensor 30.

[0017] The electric vehicle 100 includes an accelerator position sensor 32. The accelerator position sensor 32 is provided on the accelerator pedal 22 and outputs a signal indicating the operating state of the accelerator pedal 22. The operating state of the accelerator pedal 22 typically includes the accelerator opening and the accelerator opening speed. Note that the electric vehicle 100 may include a lever-type or dial-type accelerator operating device that is manually operated instead of the accelerator pedal 22. Even in this case, the accelerator position sensor 32 outputs a signal indicating the operating state of these accelerator operating devices.

[0018] The electric vehicle 100 includes a brake position sensor 34. The brake position sensor 34 is provided on the brake pedal 24 and outputs a signal indicating the operating state of the brake pedal 24. The operating state of the brake pedal 24 typically includes the brake opening and the brake opening speed.

[0019] The accelerator pedal 22 and the brake pedal 24 are each one of the driving operation members used for driving the electric vehicle 100. In addition, the electric vehicle 100 may include various driving operation members such as a steering wheel for driving related to steering.

[0020] The electric vehicle 100 includes a rotational speed sensor 40. The rotational speed sensor 40 is provided on the electric motor 2 and outputs a signal indicating the rotational speed of the electric motor 2.

[0021] The electric vehicle 100 is equipped with a battery management system (BMS) 10. The battery management system 10 is a device that monitors the cell voltage, current, temperature, etc., of the battery 14. In particular, the battery management system 10 has a function to estimate the charge state (SOC) of the battery 14.

[0022] The electric vehicle 100 is equipped with a human-machine interface (HMI) 20. The HMI 20 presents various information to the driver through displays and sounds, and also accepts various inputs from the driver. The HMI 20 consists of a display (e.g., multi-information display, meter display, multimedia display), a touchscreen, switches (e.g., steering wheel switches, multimedia switches, door switches), a touchpad, a speakerphone, a microphone, etc. Furthermore, the HMI 20 may also include a user terminal (e.g., a smartphone, tablet) connected to the electric vehicle 100. For example, the HMI 20 displays various information on the display and accepts input from the driver regarding the displayed content through touch operations on the touchscreen.

[0023] The electric vehicle 100 is equipped with a speaker 11. The speaker 11 includes at least an in-vehicle speaker that generates sound inside the cabin of the electric vehicle 100. As another example, the speaker 11 may also include an external speaker that generates sound outside the electric vehicle 100. The electric vehicle 100 may have both an in-vehicle speaker and an external speaker as the speaker 11. The speaker 11 may be configured as part of the HMI 20. The output of the speaker 11 is controlled by a control device 101, which will be described later.

[0024] The electric vehicle 100 is equipped with an instrument cluster 13. The instrument cluster 13 displays various types of information. Examples of instruments 13 include a speedometer, odometer, tachometer, trip meter, battery level indicator, etc. The instrument cluster 13 may also be configured as part of the HMI 22. The display of the instrument cluster 13 is controlled by the control device 101, which will be described later.

[0025] The electric vehicle 100 is equipped with a control device 101. Various sensors and controlled devices mounted on the electric vehicle 100 are connected to the control device 101 via an in-vehicle network such as a control area network (CAN). In addition to the vehicle speed sensor 30, accelerator position sensor 32, brake position sensor 34, and rotational speed sensor 40, various other sensors may be mounted on the electric vehicle 100 and connected to the control device 101 via the in-vehicle network.

[0026] The control device 101 generates control signals for various controls of the electric vehicle 100 based on signals acquired from each sensor. The control device 101 is typically an electronic control unit (ECU). The control device 101 may be a combination of multiple ECUs. The control device 101 comprises one or more processors 102 (hereinafter simply referred to as processor 102) and one or more storage devices 103 (hereinafter simply referred to as storage devices 103).

[0027] The processor 102 performs various processes. The processor 102 consists of, for example, a general-purpose processor, a special-purpose processor, a CPU (central processing unit), a GPU (graphics processing unit), an ASIC (application-specific integrated circuit), an FPGA (field-programmable gate array), an integrated circuit, a conventional circuit, and one or more combinations thereof. The processor 102 can also be called processing circuitry. Processing circuitry is hardware programmed to realize the functions of the control device 101, or hardware that performs the functions of the control device 101.

[0028] The storage device 103 stores various information necessary for the execution of processing by the processor 102. The storage device 103 is composed of recording media such as RAM (random access memory), ROM (read-only memory), SSD (solid state drive), HDD (hard disk drive), etc. The storage device 103 stores a computer program 104 that can be executed by the processor 102 and various data 105. The computer program 104 consists of multiple instruction codes that describe the processing to be executed by the processor 102. The computer program 104 is recorded on a computer-readable recording medium. The functions of the control device 101 are realized through the cooperation of the processor 102, which executes the computer program 104, and the storage device 103.

[0029] The control device 101 according to this embodiment has at least two control modes for controlling the electric vehicle 100: a normal mode and a proficiency mode. Depending on the selected control mode, the control of the electric vehicle 100 performed by the control device 101 changes. The control modes of the electric vehicle 100 will be described below.

[0030] 3. Control modes for electric vehicles As mentioned above, the electric vehicle 100 has at least two control modes: a normal mode and a proficiency mode. The normal mode is a control mode in which the electric vehicle 100 is controlled to operate as a normal BEV. Hereafter, the acceleration characteristics of the electric vehicle 100 in normal mode will be referred to as the "standard acceleration characteristics". The proficiency mode is a control mode designed to help the user (driver) become accustomed to driving in normal mode, such as when the user has just switched to the electric vehicle 100.

[0031] If a user has just switched to a new electric vehicle 100, the acceleration characteristics of the electric vehicle 100 may differ significantly from those of the vehicle the user previously drove. In such cases, it may be difficult for the user (driver) to immediately adapt to driving with standard acceleration characteristics. As a result, the user (driver) may feel tired or become less attentive to safe driving. The proficiency mode addresses this situation by helping the driver become accustomed to driving with standard acceleration characteristics. Specifically, when the control mode is set to proficiency mode, at the start of the mode, the electric vehicle 100 is controlled to drive with the acceleration characteristics (hereinafter referred to as "simulated acceleration characteristics") of a virtual vehicle selected from among several virtual vehicles (hereinafter referred to as "target virtual vehicle"). At this time, the driver can, for example, select a vehicle they previously drove as the target virtual vehicle. This allows the driver to drive the electric vehicle 100 with the simulated acceleration characteristics of the vehicle they previously drove immediately after starting the proficiency mode. In proficiency mode, the driver's proficiency level is assessed, and as proficiency increases, the electric vehicle 100 is controlled to operate with acceleration characteristics that approach the standard acceleration characteristics. In other words, as the driver's proficiency increases, they will operate the electric vehicle 100 with acceleration characteristics that are closer to the standard acceleration characteristics. Ultimately, the driver will operate the electric vehicle 100 with standard acceleration characteristics.

[0032] Figure 2 shows an example of the standard acceleration characteristics DC of the electric vehicle 100 and the simulated acceleration characteristics VC of the target virtual vehicle. In proficiency mode, the acceleration characteristics of the electric vehicle 100 gradually change from the simulated acceleration characteristics VC to the standard acceleration characteristics DC according to the driver's proficiency level. In this way, proficiency mode helps the driver become accustomed to driving in normal mode.

[0033] The control modes of the electric vehicle 100 may further include an "on-demand mode" that reproduces the driving environment characteristics of the target virtual vehicle in the electric vehicle 100. When the control mode is set to on-demand mode, the electric vehicle 100 is controlled to drive with the simulated acceleration characteristics VC of the target virtual vehicle. In on-demand mode, unlike the learning mode, the acceleration characteristics do not change gradually. In other words, the driver can experience a driving environment as if they were driving the target virtual vehicle.

[0034] Incidentally, there are cases where a driver drives the electric vehicle 100 in on-demand mode for a while and then tries to switch to normal mode. At this time, the standard acceleration characteristics DC and the simulated acceleration characteristics VC of the target virtual vehicle may differ significantly. In such cases, it becomes difficult for the driver to immediately adapt to driving with the standard acceleration characteristics. The proficiency mode can also be used to address such challenges related to on-demand mode. In other words, by switching from on-demand mode to proficiency mode, the acceleration characteristics of the electric vehicle 100 gradually change from the simulated acceleration characteristics VC to the standard acceleration characteristics DC according to the driver's proficiency level. This allows the driver to gradually become accustomed to driving in normal mode.

[0035] Each virtual vehicle is typically a vehicle with acceleration characteristics different from the standard acceleration characteristics DC of electric vehicle 100. Especially for on-demand mode, the virtual vehicles may be various forms of vehicles such as motorcycles or trains. Furthermore, each virtual vehicle may be based on a real vehicle or a vehicle that does not exist in reality. Differences in acceleration characteristics generally stem from differences in the configuration of the powertrain from the drive source to the drive wheels and differences in the powertrain control methods. Therefore, it can be thought that multiple virtual vehicles include various vehicles that differ in at least some elements of the configuration and control methods related to the powertrain.

[0036] The control mode is selected by the driver operating the HMI20. The HMI20 is configured to accept input from the driver to select the control mode. Furthermore, the HMI20 is configured to accept input from the driver to select the target virtual vehicle.

[0037] Figure 3 is a tree diagram showing an example of selection input accepted by the HMI20. For example, the HMI20 accepts selection input from the driver via a display, touchscreen, or user terminal display, following the tree shown in Figure 3 as follows.

[0038] First, the HMI20 displays a settings menu screen on the display, touchscreen, or user terminal according to the driver's input. The initial settings menu screen displays the options "Control Mode" and "Target Virtual Vehicle". The "Control Mode" option is for accepting input from the driver to select the control mode. The "Target Virtual Vehicle" option is for accepting input from the driver to select the target virtual vehicle.

[0039] When the option "Control Mode" is selected, the settings menu screen then displays the options "Normal Mode," "Learning Mode," and "On-Demand Mode." If the option "Normal Mode" is selected, the HMI20 determines that the control mode of the electric vehicle 100 is Normal Mode. If the option "Learning Mode" is selected, the HMI20 determines that the control mode of the electric vehicle 100 is Learning Mode. If the option "On-Demand Mode" is selected, the HMI20 determines that the control mode of the electric vehicle 100 is On-Demand Mode. In this way, the HMI20 accepts the driver's input for selecting a control mode.

[0040] On the other hand, when the option "Target Virtual Vehicle" is selected, the options "CONV" and "HEV" are then displayed on the settings menu screen. The options "CONV" and "HEV" represent classifications of multiple virtual vehicles that can be selected in on-demand mode, respectively. CONV is a classification that represents conventional vehicles equipped with an internal combustion engine. HEV is a classification that represents hybrid electronic vehicles. When the option "CONV" is selected, the options "Virtual Vehicle A1", "Virtual Vehicle A2", and "Virtual Vehicle B1" are then displayed on the settings menu screen. Virtual Vehicle A1, Virtual Vehicle A2, and Virtual Vehicle B1 are virtual vehicles classified as CONV among the multiple virtual vehicles that can be selected. Similarly, when the option "HEV" is selected, the options "Virtual Vehicle C1" and "Virtual Vehicle C2" are then displayed on the settings menu screen. Virtual Vehicle C1 and Virtual Vehicle C2 are virtual vehicles classified as HEV among the multiple virtual vehicles that can be selected. If any of these options is selected, HMI20 determines that the selected virtual vehicle is the target virtual vehicle. For example, if the option "Virtual Vehicle A2" is selected, HMI20 determines that Virtual Vehicle A2 is the target virtual vehicle. In this way, HMI20 accepts the driver's input for the selection of the target virtual vehicle.

[0041] In the above explanation, the classification of multiple virtual vehicles is just an example, and the classification options may be changed as appropriate. For example, the classification options may further include options indicating plug-in hybrid electric vehicles (PHEVs) or fuel cell electric vehicles (FCEVs). Alternatively, the classification options may indicate other classifications, such as classifications related to the type of power source installed (e.g., inline 4 turbocharged engine, flat 6 engine, V12 engine, battery, fuel cell). Furthermore, the classification options may consist of multiple levels. For example, after the option "CONV" is selected, the settings menu screen may display further classification options related to the type of power source. Or, if the option "Target Virtual Vehicle" is selected, the settings menu screen may display a list of virtual vehicle options without displaying the classification options.

[0042] Furthermore, the names displayed on the settings menu screen for each option may be appropriately chosen to facilitate driver understanding. For example, for options related to virtual vehicles, the displayed names may be specific, such as vehicle type or product name, to help the driver visualize the virtual vehicle.

[0043] As explained above, the driver can select a control mode by operating the HMI 20. The control device 101 controls the electric vehicle 100 according to the selected control mode.

[0044] The control device 101 according to this embodiment functions as a drive control device that controls the drive of the electric vehicle 100 by controlling the output of the electric motor 2 in accordance with the driver's driving operations. Specifically, the control device 101 functions as a drive control device when the processor 102 executes a computer program 104 for drive control stored in the storage device 103. The control of the electric vehicle 100 by the drive control device will be described below.

[0045] 4. Drive control device Figure 4 shows an example of the functional configuration of the drive control device 101a. The drive control device 101a calculates the target driving force TF of the electric vehicle 100 according to the driver's driving operations. The drive control device 101a then controls the output of the electric motor 2 to achieve the calculated target driving force TF.

[0046] The drive control device 101a receives signals from the HMI 20 and the sensor system 50. The sensor system 50 includes a vehicle speed sensor 30, an accelerator position sensor 32, a brake position sensor 34, a rotational speed sensor 40, and a battery management system 10. The sensor system 50 may further include a steering angle sensor for detecting the steering angle of the steering wheel, a yaw rate sensor for detecting the yaw rate of the electric vehicle 100, an IMU (inertial measurement unit) for detecting the attitude of the electric vehicle 100, and sensors for detecting the surrounding environment of the electric vehicle 100 (e.g., a camera, radar, LiDAR), etc.

[0047] The signals input from the HMI20 to the drive control device 101a include a signal indicating the selected control mode and a signal indicating the selected target virtual vehicle. The signals input from the sensor system 50 to the drive control device 101a include a signal indicating the vehicle speed of the electric vehicle 100, a signal indicating the operating state of the accelerator pedal 22, a signal indicating the operating state of the brake pedal 24, a signal indicating the rotational speed of the electric motor 2, and a signal indicating the charge state (SOC) of the battery 14.

[0048] The drive control device 101a includes, as functional blocks, a mode information acquisition unit 110, a first target drive force calculation unit 120, a second target drive force calculation unit 130, an arbitration unit 140, and an electric motor control unit 150. These functional blocks are realized through the cooperation of a processor 102 that executes a computer program 104 and a storage device 103.

[0049] The mode information acquisition unit 110 receives signals from the HMI 20 and acquires information on whether normal mode, proficiency mode, or on-demand mode is selected. The mode information acquisition unit 110 also acquires information on the target virtual vehicle selected from among multiple virtual vehicles. The mode information acquisition unit 110 transmits the information of the selected control mode to the arbitration unit 140. The mode information acquisition unit 110 also transmits the information of the selected target virtual vehicle to the second target driving force calculation unit 130.

[0050] The first target driving force calculation unit 120 calculates the first target driving force TF1, which is the target driving force for achieving the standard acceleration characteristic DC, based on the signals from the sensor system 50. The first target driving force TF1 can also be said to be the target driving force for driving the electric vehicle 100 in normal mode.

[0051] The first target driving force calculation unit 120 calculates the first target driving force TF1 using the map M10. Map M10 provides the first target driving force TF1 using the operating state of the driving control members and the driving state of the electric vehicle 100 as parameters. For example, map M10 provides the first target driving force TF1 using the accelerator opening of the accelerator pedal 22 and the rotational speed of the electric motor 2 as parameters. Furthermore, map M10 may be configured to provide the first target driving force TF1 using the brake opening of the brake pedal 24 and the state of charge (SOC) of the battery 14 as parameters.

[0052] The first target driving force calculation unit 120 transmits the calculated first target driving force TF1 to the arbitration unit 140. In this embodiment, the processing related to the first target driving force calculation unit 120 may be modified as appropriate. The processing related to the first target driving force calculation unit 120 can employ known and preferred methods used to calculate the target driving force in conventional BEVs.

[0053] The second target driving force calculation unit 130 acquires information about the target virtual vehicle from the mode information acquisition unit 110. Then, based on the signals from the sensor system 50, the second target driving force calculation unit 130 calculates the second target driving force TF2, which is the target driving force for realizing the simulated acceleration characteristics VC of the target virtual vehicle. The second target driving force TF2 can also be said to be the target driving force for driving the electric vehicle 100 in on-demand mode. Details of the processing performed by the second target driving force calculation unit 130 will be described later. The second target driving force calculation unit 130 transmits the calculated second target driving force TF2 to the arbitration unit 140.

[0054] The arbitration unit 140 arbitrates the target driving force TF used to control the electric motor 2 according to the selected control mode. Specifically, while the normal mode is selected, the arbitration unit 140 transmits a first target driving force TF1 as the target driving force TF to the electric motor control unit 150. While the on-demand mode is selected, the arbitration unit 140 transmits a second target driving force TF2 as the target driving force TF to the electric motor control unit 150. On the other hand, while the proficiency mode is selected, the arbitration unit 140 executes a proficiency mode calculation process P10. In the proficiency mode calculation process P10, the arbitration unit 140 executes a proficiency level determination process P11 based on signals from the sensor system 50 to determine the driver's proficiency level. Also in the proficiency mode calculation process P10, the arbitration unit 140 calculates a third target driving force that changes from the first target driving force TF1 to the second target driving force TF2 as the driver's proficiency level increases. Details of the proficiency mode calculation process P10 and proficiency level determination process P11 performed by the arbitration unit 140 will be described later. While the proficiency mode is selected, the arbitration unit 140 transmits the third target driving force as the target driving force TF to the electric motor control unit 150.

[0055] The electric motor control unit 150 controls the electric motor 2 to achieve the target driving force TF transmitted from the arbitration unit 140. More specifically, the electric motor control unit 150 generates a control signal for the inverter 16 according to the target driving force TF. The electric motor control unit 150 then changes the motor torque output by the electric motor 2 via PWM control by the inverter 16.

[0056] In this way, the drive control device 101a according to this embodiment calculates the target driving force TF of the electric vehicle 100 according to the control mode and controls the output of the electric motor 2 to achieve the calculated target driving force TF. In particular, according to the drive control device 101a, the acceleration characteristics of the electric vehicle 100 become the standard acceleration characteristics DC when the normal mode is selected. Also, the acceleration characteristics of the electric vehicle 100 become the simulated acceleration characteristics VC of the target virtual vehicle when the on-demand mode is selected. The simulated acceleration characteristics VC changes to various patterns according to the target virtual vehicle as the target virtual vehicle is changed. As a result, in on-demand mode, the driver can enjoy the acceleration characteristics of various virtual vehicles with the electric vehicle 100. Furthermore, according to the drive control device 101a, the acceleration characteristics of the electric vehicle 100 change from the simulated acceleration characteristics VC to the standard acceleration characteristics DC when the proficiency mode is selected, according to the driver's level of proficiency. As a result, in proficiency mode, the driver can gradually become accustomed to driving in normal mode.

[0057] The drive control device 101a may be configured not to execute processing related to the first target drive force calculation unit 120 while the on-demand mode is selected. Similarly, the drive control device 101a may be configured not to execute processing related to the second target drive force calculation unit 130 while the normal mode is selected. By configuring it in this way, the processing cost of the drive control device 101a in each control mode can be reduced.

[0058] The following describes in detail the processes performed by the second target driving force calculation unit 130 and the proficiency mode calculation process P10 performed by the arbitration unit 140.

[0059] 4.1 Second Target Driving Force Calculation Unit Figure 5 shows an example of the functional configuration of the second target driving force calculation unit 130. The second target driving force calculation unit 130 calculates the second target driving force TF2. The second target driving force calculation unit 130 includes a virtual driving environment calculation unit 131 and a target driving force conversion unit 132 as functional blocks. The second target driving force calculation unit 130 is also configured to access the vehicle model database D10.

[0060] The vehicle model database D10 is a database that manages multiple vehicle models 200, each modeling multiple virtual vehicles. The vehicle model database D10 may be stored in the storage device 103 as data 105. Each vehicle model 200 managed by the vehicle model database D10 may be updated as needed. New vehicle models 200 may also be downloaded to the vehicle model database D10 as needed. In the example shown in Figure 5, the vehicle model database D10 manages three vehicle models 200-A, 200-B, and 200-C. Each vehicle model 200 is a model that simulates the driving environment of a virtual vehicle in response to a driver's driving operation, taking the operating state of the driving control components and the driving state of the electric vehicle 100 as input. In particular, each vehicle model 200 is configured to simulate the acceleration characteristics of the virtual vehicle. That is, each vehicle model 200 is configured to simulate at least the driving force applied to the virtual vehicle in response to the driver's driving operation, and the acceleration and deceleration operation of the virtual vehicle due to the action of that driving force. The simulation results of the acceleration and deceleration of the virtual vehicle using each vehicle model 200 include the virtual acceleration VA of the virtual vehicle.

[0061] Typically, each vehicle model 200 includes a control model that simulates the control system associated with the powertrain of the virtual vehicle, and a plant model that simulates the acceleration and deceleration of the virtual vehicle in response to control signals from the control model. In this case, the plant model includes a powertrain model that operates based on control signals from the control model, and a model for simulating the operation of the virtual vehicle due to the action of the virtual driving force of the powertrain model. An example of the configuration of the vehicle model 200 will be described later.

[0062] Each vehicle model 200 also has parameters 201 related to the operation of the virtual vehicle in the simulation. Examples of parameters 201 include weight, wheel diameter, gear ratio, maximum torque of the drive source, drive torque response, shift schedule, etc. The content of parameters 201 may differ for each vehicle model 200. A vehicle model 200 represents a model of a single virtual vehicle through a combination of its parameters 201 settings. For example, each virtual vehicle represents a model of a single virtual vehicle through a combination of vehicle model 200 and the settings of parameters 201, as shown in the table below. As shown in the table below, the same vehicle model 200 may correspond to different virtual vehicles. This is the case when the powertrain system types are the same and each virtual vehicle can be represented by changing the settings of parameters 201, for example. [Table 1]

[0063] The virtual driving environment calculation unit 131 acquires information about the target virtual vehicle from the mode information acquisition unit 110. The virtual driving environment calculation unit 131 refers to the vehicle model database D10 from the acquired information and reads out the vehicle model 200 (target vehicle model) corresponding to the target virtual vehicle. Furthermore, the virtual driving environment calculation unit 131 sets the parameters 201 of the read-out vehicle model 200 according to the target virtual vehicle. For example, when the target virtual vehicle is "Virtual Vehicle B1" in the table above, the virtual driving environment calculation unit 131 refers to the vehicle model database D10 and reads out vehicle model 200-B. Then, the virtual driving environment calculation unit 131 sets the parameter 201-B of vehicle model 200-B to the set value B1.

[0064] The virtual driving environment calculation unit 131 uses the read-out target vehicle model to simulate the virtual driving environment of the target virtual vehicle in response to the driver's driving operations. More specifically, the virtual driving environment calculation unit 131 receives signals from the sensor system 50 and acquires information on the operating state of the driving control members and information on the driving state of the electric vehicle 100 to be input to the target vehicle model. For example, the virtual driving environment calculation unit 131 acquires the accelerator opening degree of the accelerator pedal 22 and the vehicle speed of the electric vehicle 100. Depending on the configuration of the target vehicle model, the virtual driving environment calculation unit 131 may also acquire information such as the accelerator opening speed of the accelerator pedal 22, the brake opening degree and brake opening speed of the brake pedal 24, the steering angle of the steering wheel, and the yaw rate of the electric vehicle 100. The virtual driving environment calculation unit 131 then inputs the acquired information into the target vehicle model to simulate the virtual driving environment of the target virtual vehicle. In particular, the virtual driving environment calculation unit 131 calculates the virtual acceleration VA of the target virtual vehicle in response to the driver's driving operations by simulating the virtual driving environment of the target virtual vehicle. The virtual driving environment calculation unit 131 transmits the calculated virtual acceleration VA to the target driving force conversion unit 132.

[0065] When the target driving force conversion unit 132 obtains the virtual acceleration VA, it converts the acceleration of the electric vehicle 100 into a driving force to obtain the virtual acceleration VA, and sets that driving force as the second target driving force TF2. For example, the target driving force conversion unit 132 converts the virtual acceleration VA to the second target driving force TF2 using a simplified inverse model of the electric vehicle 100, as shown in the following equation. In the following equation, m is the vehicle weight of the electric vehicle 100, and F load This is the actual driving resistance acting on the electric vehicle 100. The second target driving force calculation unit 130 outputs the second target driving force TF2 calculated by the target driving force conversion unit 132.

number

[0066] As explained above, the second target driving force calculation unit 130 calculates the virtual acceleration VA of the target virtual vehicle in response to the driver's driving operations by simulating the driving environment of the target virtual vehicle using the target vehicle model. Then, the second target driving force calculation unit 130 calculates the second target driving force TF2 so that the acceleration of the electric vehicle 100 becomes the calculated virtual acceleration VA.

[0067] 4.2 Learning Mode Calculation Process The following describes the proficiency mode calculation process P10 performed by the mediation unit 140. As mentioned above, in the proficiency mode calculation process P10, the mediation unit 140 performs the proficiency level determination process P11 to determine the driver's proficiency level. First, let's explain the determination of the driver's proficiency level by the proficiency level determination process P11.

[0068] The driver's proficiency level is judged, for example, on a scale from 0% to 100%. In this case, the proficiency level at the start of the proficiency mode may be 0%. One perspective for judging the driver's proficiency level is the driving time or distance traveled since the start of the proficiency mode. It is assumed that the longer the driving time or distance traveled for a vehicle, the more accustomed the driver becomes to driving that vehicle. Therefore, in the proficiency judgment process P11, the mediation unit 140 increases the proficiency level in proportion to the driving time or distance traveled since the start of the proficiency mode. For example, the mediation unit 140 gradually increases the proficiency level by the amount of the proportionality constant each time the driving time or distance traveled since the start of the proficiency mode increases by a set value. That is, when the proficiency level is P and the proportionality constant is Δx, the mediation unit 140 sets P = P + Δx each time the driving time or distance traveled increases by a set value. The set value is set, for example, to a standard value that takes time for a driver to become accustomed to driving. The set values ​​and proportionality constants may be experimentally determined to suit the environment in which this embodiment is applied.

[0069] On the other hand, the driving time and distance required for a driver to become accustomed to driving may vary depending on the individual driver's characteristics and the characteristics of the roads they drive on. Therefore, when increasing proficiency in proportion to driving time or distance, there is a possibility that proficiency may increase before the driver is sufficiently accustomed to driving. For this reason, in the proficiency judgment process P11, the mediation unit 140 may further monitor the driver's driving operations. The mediation unit 140 may then decrease the proficiency when the driving operations meet conditions indicating that the driver is not accustomed to driving (hereinafter referred to as the "insufficient proficiency condition"). That is, when the driving operations meet the insufficient proficiency condition, the mediation unit 140 sets P = P - Δy, where Δy represents the amount of decrease in proficiency.

[0070] One of the conditions indicating insufficient proficiency concerns the driver's accelerator operation when starting. When a driver is not sufficiently accustomed to driving, it is common to observe driving operations where the accelerator pedal 22 is not pressed smoothly when starting. For example, if the vehicle the driver previously drove had a slow response to the accelerator opening when starting, the driver may press the accelerator pedal 22 harder than necessary. Furthermore, after releasing the accelerator pedal 22, the driver may then lightly press the accelerator pedal 22 again to gradually increase the vehicle speed.

[0071] Figure 6 is a flowchart showing the processing flow of the proficiency assessment process P11 for the aforementioned condition of insufficient proficiency regarding the driver's accelerator operation during starting.

[0072] In step S110, the arbitration unit 140 determines whether the electric vehicle 100 is starting. For example, the arbitration unit 140 determines that the electric vehicle 100 is starting if the previous vehicle speed was 0 and the current accelerator opening is greater than 0. If the electric vehicle 100 is not starting (step S110; No), the process ends without reducing the proficiency level. If the electric vehicle 100 is starting (step S110; Yes), the process proceeds to step S120.

[0073] In step S120, the mediation unit 140 determines whether the accelerator opening became 0 within the first hour. That is, it determines whether the driver released the accelerator pedal 22. The first hour may be experimentally determined as preferable. If the accelerator opening is not 0 within the first hour (step S120; No), the process ends without reducing the proficiency level. If the accelerator opening becomes 0 within the first hour (step S120; Yes), the process proceeds to step S130.

[0074] In step S130, the mediation unit 140 determines whether the accelerator opening has become greater than 0 within two hours of the accelerator opening becoming 0. That is, it determines whether the driver has pressed the accelerator pedal 22 again. The second time may be experimentally determined as preferable. If the accelerator opening has not become greater than 0 within two hours (step S130; No), the process ends without reducing the proficiency level. If the accelerator opening has become greater than 0 within two hours (step S130; Yes), the mediation unit 140 reduces the proficiency level (step S140).

[0075] Thus, in the proficiency judgment process P11, the mediation unit 140 may be configured to reduce the proficiency level when the driving operation satisfies the proficiency deficiency condition. The proficiency deficiency condition may include multiple conditions. For example, the proficiency deficiency condition may include conditions related to the driver's accelerator work during acceleration or deceleration. The mediation unit 140 may be configured to execute processing for each proficiency deficiency condition. In particular, the amount of proficiency reduction Δy may be set to a different value for each proficiency deficiency condition. Furthermore, when the driving operation satisfies the proficiency deficiency condition, the mediation unit 140 may reduce the proportionality coefficient Δx used to increase the proficiency level. The mediation unit 140 may also increase the proportionality coefficient Δx each time a predetermined period of time has elapsed during which the proficiency deficiency condition is not met.

[0076] Furthermore, in the proficiency determination process P11, the mediation unit 140 may be configured to gradually increase or decrease the proficiency level in response to input operations from the driver. For example, the HMI 20 may include a switch for increasing or decreasing the proficiency level.

[0077] As explained above, the mediation unit 140 executes the proficiency level determination process P11 in the proficiency mode calculation process P10 to determine the driver's proficiency level. Next, the entirety of the proficiency mode calculation process P10 will be described.

[0078] Figure 7 is a flowchart showing the processing flow of the proficiency mode calculation process P10 executed by the mediation unit 140. The processing flow shown in Figure 7 is executed repeatedly at predetermined processing cycles.

[0079] In step S210, the arbitration unit 140 acquires various information. For example, the arbitration unit 140 acquires vehicle speed, accelerator pedal position, driving time, mileage, etc.

[0080] Next, in step S220, the mediation unit 140 executes the proficiency judgment process P11 to determine the driver's proficiency level. The contents of the proficiency judgment process P11 are as described above.

[0081] Next, in step S230, the mediation unit 140 determines whether the driver's proficiency in the proficiency mode has been completed. The mediation unit 140 determines that the driver's proficiency has been completed when the driving time or distance traveled exceeds a predetermined value after the proficiency level has reached its maximum (for example, 100%). The predetermined value may be experimentally determined to be suitable. Even if the proficiency level reaches its maximum, there is a possibility that the proficiency level may decrease again afterward. Therefore, by performing the process in this manner, it is possible to reliably determine whether the driver's proficiency has been completed.

[0082] If the driver has completed their training (step S230; Yes), the mediation unit 140 exits the training mode and returns to normal mode (step S240), and outputs the first target driving force TF1 as the target driving force TF (step S250). If the driver has not completed their training (step S230; No), the process proceeds to step S260.

[0083] In step S260, the arbitration unit 140 calculates a third target driving force that changes from a first target driving force TF1 to a second target driving force TF2 as the driver's proficiency increases. For example, the arbitration unit 140 calculates the third target driving force TF3 using the following formula. In the following formula, k P k is a coefficient that increases monotonically with increasing proficiency, ranging from 0 to 1. For example, k P k increases linearly from 0 to 1 in proportion to the level of proficiency. P It may be configured to increase non-linearly. After step S260, the process proceeds to step S270.

number

[0084] In step S270, the arbitration unit 140 outputs a third target driving force TF3 as the target driving force TF. After that, the current process is terminated.

[0085] As explained above, the mediation unit 140 executes the proficiency mode calculation process P10. This allows the acceleration characteristics of the electric vehicle 100 to be changed from simulated acceleration characteristics VC to standard acceleration characteristics DC according to the driver's proficiency level when the control mode is the proficiency mode. As a result, in the proficiency mode, the driver can gradually become accustomed to driving in normal mode.

[0086] 4.3 Example of Vehicle Model Configuration The following describes an example of the configuration of a vehicle model 200 managed by the vehicle model database D10. Figure 8 shows an example of the configuration of a vehicle model 200. The vehicle model 200 includes a control model 210 and a plant model 220. The control model 210 simulates a control system related to the powertrain of a virtual vehicle. The plant model 220 simulates the acceleration and deceleration of the virtual vehicle in response to control signals from the control model 210. The plant model 220 includes a powertrain model that operates based on control signals from the control model 210, and a model for simulating the operation of the virtual vehicle due to the action of the virtual driving force of the powertrain model. The control model 210 can also be said to simulate a control system that calculates the requested output for the powertrain of the virtual vehicle. The plant model 220 can also be said to simulate physical constraints on the requested output of the powertrain.

[0087] The specifications of the control model 210 and the plant model 220 may differ depending on the type of powertrain system. For example, the control system, transmission, and drivetrain configuration differ between a CONV and an HEV. Therefore, the CONV vehicle model 200 and the HEV vehicle model 200 will have different specifications for both the control model 210 and the plant model 220. The example shown in Figure 8 specifically illustrates the case where the virtual vehicle is an automatic transmission (AT) vehicle equipped with an internal combustion engine.

[0088] The control model 210 includes a target virtual driving force calculation unit 211 and a request output calculation unit 212. The target virtual driving force calculation unit 211 calculates the virtual driving force (target virtual driving force) to be requested from the virtual vehicle's powertrain based on the accelerator opening and vehicle speed. For example, the target virtual driving force calculation unit 211 performs the calculation using a map that assigns a target virtual driving force to a combination of accelerator opening and vehicle speed. The request output calculation unit 212 calculates the request output to the powertrain so as to satisfy the calculated target virtual driving force. The calculated request output includes the target engine torque of the internal combustion engine and the target gear of the transmission. The control model 210 transmits the calculated request output to the plant model 220.

[0089] The plant model 220 includes an internal combustion engine model 221, a transmission model 222, a drivetrain model 223, and a vehicle / environment model 224. The internal combustion engine model 221, the transmission model 222, and the drivetrain model 223 are models of the powertrain from the power source to the drive wheels. The vehicle / environment model 224 is a model for simulating the operation of a virtual vehicle due to the action of the virtual driving force of the powertrain model.

[0090] The internal combustion engine model 221 is a model of the internal combustion engine of a virtual vehicle. The internal combustion engine model 221 simulates, for example, the operation of the internal combustion engine in response to a target engine torque input. The internal combustion engine model 221 outputs a virtual engine rotational speed VNe and a virtual engine torque VTe. Parameters 201 that can be changed in the internal combustion engine model 221 depending on the target virtual vehicle include, for example, the maximum engine torque and engine torque responsiveness.

[0091] The transmission model 222 is a model of the transmission of a virtual vehicle. The transmission model 222 simulates, for example, the operation of the transmission in response to the input of a target gear position. The transmission model 222 outputs a virtual transmission output torque from the gear ratio determined by the virtual engine torque VTe output by the internal combustion engine model 221 and the virtual gear position. The transmission model 222 includes a stepped transmission model that simulates a stepped transmission and a continuously variable transmission model that simulates a continuously variable transmission. Either the stepped transmission model or the continuously variable transmission model is selected depending on the target virtual vehicle. Parameters 201 that can be changed in the transmission model 222 depending on the target virtual vehicle include, for example, the gear ratio and the shift schedule. In the case of the stepped transmission model, the gear ratio refers to the gear ratio of each gear position.

[0092] The drivetrain model 223 is a model of the drivetrain of a virtual vehicle. For example, the drivetrain model 223 models the mechanical structure from the transmission to the drive wheels. The drivetrain model 223 calculates the drive wheel torque using the virtual transmission output torque output by the transmission model 222 and a predetermined reduction ratio, and outputs the virtual driving force of the virtual vehicle. Parameters 201 that can be changed in the drivetrain model 223 depending on the target virtual vehicle include, for example, the reduction ratio and the maximum allowable torque of the propeller shaft.

[0093] The vehicle / environment model 224 is a model that represents the mechanical characteristics of a virtual vehicle and the driving environment of the virtual vehicle. The vehicle / environment model 224 calculates the driving resistance acting on the virtual vehicle from the driving environment of the virtual vehicle. Then, the vehicle / environment model 224 simulates the acceleration and deceleration of the virtual vehicle from the virtual driving force output from the drivetrain model 223, the calculated driving resistance, and the mechanical characteristics of the virtual vehicle. The vehicle / environment model 224 outputs a virtual acceleration VA from the acceleration and deceleration of the virtual vehicle. Parameters 201 that can be changed in the vehicle / environment model 224 depending on the target virtual vehicle are, for example, weight, wheel diameter, and CD value.

[0094] As explained above, the vehicle model 200 can be configured. The vehicle model 200 shown in Figure 8 is just one example. The vehicle model 200 can also be configured in more detail depending on the event to be emphasized. For example, consider the case where you want to emphasize the shock or response associated with the gear and clutch engagement of the transmission during kickdown. In this case, the transmission model 222 may be configured to reproduce in detail the gear mechanism of the transmission, such as the planetary ravinio, the inertia of each component, and the changes in the transmission path when the clutch is engaged and disengaged. On the other hand, if you want to reduce the computational load on the vehicle model 200, the transmission model 222 may be simply configured to reproduce only the gear ratio.

[0095] 5. In-vehicle equipment control system The control device 101 according to this embodiment functions as an in-vehicle equipment control device that controls the speaker 11 and the instrument 13. More specifically, the processor 102 functions as an in-vehicle equipment control device by executing a computer program 104 for in-vehicle equipment control stored in the storage device 103. In particular, when the electric vehicle 100 is in on-demand mode, the in-vehicle equipment control device controls the speaker 11 and the instrument 13 according to the driving environment of the target virtual vehicle. The control of the electric vehicle 100 by the in-vehicle equipment control device when the electric vehicle 100 is in on-demand mode will be described below.

[0096] Figure 9 shows an example of the functional configuration of the in-vehicle equipment control device 101b. When the electric vehicle 100 is in on-demand mode, the in-vehicle equipment control device 101b controls the speaker 11 and instrument 13 according to the driving environment of the target virtual vehicle.

[0097] The in-vehicle equipment control device 101b receives signals from the HMI 20 and the sensor system 50. The signals input from the HMI 20 to the in-vehicle equipment control device 101b include a signal indicating the control mode selected by the driver and a signal indicating the target virtual vehicle selected by the driver. The signals input from the sensor system 50 to the in-vehicle equipment control device 101b include a signal indicating the vehicle speed of the electric vehicle 100, a signal indicating the operating state of the accelerator pedal 22, a signal indicating the operating state of the brake pedal 24, a signal indicating the rotational speed of the electric motor 2, and a signal indicating the charge state (SOC) of the battery 14.

[0098] The in-vehicle equipment control device 101b includes, as functional blocks, a mode information acquisition unit 110, a virtual driving environment calculation unit 131, a virtual sound generation unit 170, a speaker control unit 180, and an instrument control unit 190. These functional blocks are realized through the cooperation of a processor 102 that executes a computer program 104 and a storage device 103. The mode information acquisition unit 110 may be the same as the one described in Figure 4. The virtual driving environment calculation unit 131 may be the same as the one described in Figure 5.

[0099] The virtual sound generation unit 170 generates virtual sounds that should be heard by the driver in the target virtual vehicle in response to the driver's driving operations. The virtual sound is, for example, the engine sound (simulated engine sound) generated by the internal combustion engine of the target virtual vehicle when the target virtual vehicle is a vehicle equipped with an internal combustion engine (engine vehicle). Alternatively, the virtual sound may be the sound of the drivetrain of the target virtual vehicle. The virtual sound generation unit 170 obtains the sound source of the virtual sound related to the target virtual vehicle by referring to the storage device 103. The storage device 103 may store the sound source of the virtual sound related to each virtual vehicle. The virtual sound generation unit 170 also obtains the information necessary to generate the virtual sound from the virtual driving environment calculation unit 131. For example, when the virtual sound is a simulated engine sound, the virtual sound generation unit 170 obtains the virtual engine rotational speed VNe and the virtual engine torque VTe from the virtual driving environment calculation unit 131. The virtual sound generation unit 170 then generates the virtual sound based on the sound source and the information obtained from the virtual driving environment calculation unit 131.

[0100] The virtual sound generation unit 170 executes a process 171 to calculate the sound pressure of the virtual sound and a process 172 to calculate the frequency of the virtual sound. For example, when the virtual sound is a simulated engine sound, in process 171, the sound pressure of the simulated engine sound is calculated from the virtual engine torque VTe using a sound pressure map. The sound pressure map is typically created so that the sound pressure increases as the virtual engine torque VTe increases. In process 172, the frequency of the virtual sound is calculated from the virtual engine rotational speed VNe using a frequency map. The frequency map is typically created so that the frequency increases as the virtual engine rotational speed VNe increases. The virtual sound generation unit 170 transmits the generated virtual sound data to the speaker control unit 180.

[0101] The speaker control unit 180 controls the output of the speaker 11 based on the sound data transmitted from the virtual sound generation unit 170. As a result, a virtual sound is output from the speaker 11.

[0102] The instrument control unit 190 controls the instrument 13 to display information that should be displayed to the driver in the target virtual vehicle in response to the driver's driving operations (hereinafter referred to as "virtual display information"). The virtual display information is, for example, information such as the virtual engine speed VNe and virtual gear stage of the target virtual vehicle when the target virtual vehicle is an engine-powered vehicle. The instrument control unit 190 obtains information related to the virtual display information from the virtual driving environment calculation unit 131. For example, when the target virtual vehicle is an engine-powered vehicle, the instrument control unit 190 obtains the virtual engine speed VNe and virtual gear stage from the virtual driving environment calculation unit 131. Then, the instrument control unit 190 controls the display of the instrument 13 based on the obtained information. As a result, the virtual display information is displayed on the instrument 13.

[0103] Thus, according to the in-vehicle equipment control device 101b, when the electric vehicle 100 is in on-demand mode, a virtual sound is output from the speaker 11 and virtual display information is shown on the instrument panel 13. This further enhances the driver's sense of realism, making them feel as if they are driving the virtual vehicle.

[0104] 6. Others The technical features of this embodiment are not limited to BEVs, but are broadly applicable to any electric vehicle having an electric motor as a drive source. For example, the technical features of this embodiment are applicable to HEVs and PHEVs that have a mode of running solely on the driving force of the electric motor. They are also applicable to FCEVs that supply electric energy generated by a fuel cell to the electric motor. [Explanation of Symbols]

[0105] 2 Electric motor 14 batteries 22 Accelerator pedal 24 Brake pedal 100 Electric Vehicles 101 Control device 102 processors 103 Storage device 110 Mode Information Acquisition Unit 200 vehicle models DC standard acceleration characteristics VC simulated acceleration characteristics VA (Virtual Acceleration) TF1 First Target Driving Force TF2 Second Target Driving Force TF3 Third Target Driving Force

Claims

1. An electric vehicle having an electric motor as its driving source, The system includes one or more processors that control the output of the electric motor, When the electric vehicle is in proficiency mode, the one or more processors Obtain information on the target virtual vehicle selected from among multiple virtual vehicles. At the start of the proficiency mode, the output of the electric motor is controlled so that the acceleration characteristics of the electric vehicle in response to the driver's driving operations become the simulated acceleration characteristics of the target virtual vehicle. As the driver's proficiency increases, the output of the electric motor is controlled so that the acceleration characteristics of the electric vehicle in response to the driver's driving operations become closer to the standard acceleration characteristics. It is configured in such a way Electric vehicle.

2. An electric vehicle according to claim 1, Operating components used for operation, One or more storage devices for managing multiple vehicle models that model the multiple virtual vehicles, Furthermore, When the electric vehicle is in the learning mode, the one or more processors The target vehicle model corresponding to the target virtual vehicle is obtained from the one or more storage devices, The first target driving force that achieves the standard acceleration characteristics is calculated, Based on the operating state of the driving control member and the driving state of the electric vehicle, a second target driving force is calculated using the target vehicle model to achieve the simulated acceleration characteristics. As the level of proficiency increases, the third target driving force is calculated, which changes from the first target driving force to the second target driving force. The output of the electric motor is controlled to provide the third target driving force to the electric vehicle. It is configured in such a way Electric vehicle.

3. An electric vehicle according to claim 1, The one or more processors described above are: The proficiency mode ends when the driving time or distance traveled exceeds a predetermined value after the proficiency level has reached its maximum. It is configured in such a way Electric vehicle.

4. An electric vehicle according to claim 1, When the electric vehicle is in the learning mode, the one or more processors The level of proficiency is increased in proportion to the driving time or distance traveled from the time the proficiency mode is started. It is configured in such a way Electric vehicle.

5. An electric vehicle according to claim 4, When the electric vehicle is in the learning mode, the one or more processors When the driver's driving actions meet the conditions indicating that the driver is not accustomed to driving, the proficiency level is reduced. It is configured in such a way Electric vehicle.

Images