Electric vehicle and control device

Abstract

To provide an electric vehicle that can reproduce an acceleration feeling of a virtual vehicle having acceleration characteristics with an acceleration capability thereof exceeding an acceleration capability of the electric vehicle.SOLUTION: The electric vehicle comprises a processing circuit and a storing device. The storing device stores a database for managing a plurality of vehicle models modeled after a plurality of virtual vehicles which have different acceleration characteristics in response to driving operation by a driver. The processing circuit reads, from the database, a target vehicle model corresponding to a target virtual vehicle selected by the driver and calculates virtual acceleration of the target virtual vehicle by using the target vehicle model. Further the processing circuit calculates adjusted virtual acceleration by multiplying the virtual acceleration by a coefficient of 1 or less according to acceleration characteristics of the target virtual vehicle. The processing circuit controls an electric motor so that acceleration of an electric vehicle is equal to the adjusted virtual acceleration.SELECTED DRAWING: Figure 6

Description

[Technical Field]

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

[0002] An electric motor can be controlled to output a desired motor torque by controlling the applied voltage and magnetic field. Taking advantage of this, technologies have been devised to appropriately control the electric motor of an electric vehicle to reproduce various driving sensations in the electric vehicle. For example, Patent Document 1 discloses a technology for simulating the driving sensation associated with manual gear shifting in a manual transmission vehicle in an electric vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6787507 Summary of the Invention [Problem to be solved by the invention]

[0004] One of the elements that characterize the driving feel of each vehicle is the feeling of vehicle acceleration in response to driving operations. The feeling of vehicle acceleration is an important factor when a driver feels enjoyment in driving a vehicle. In particular, preferences for the feeling of vehicle acceleration vary from driver to driver. Drivers may also want to enjoy a variety of vehicle acceleration feelings depending on their mood.

[0005] Therefore, the inventors of the present disclosure have studied a technology that uses a vehicle model to calculate virtual acceleration when a virtual vehicle is driven, and controls the electric motor so that the calculated virtual acceleration is realized in an electric vehicle. A vehicle model can be provided for each of multiple virtual vehicles. Therefore, this technology makes it possible to simulate the acceleration sensations of multiple virtual vehicles in a single electric vehicle.

[0006] However, the acceleration that an electric vehicle can achieve is naturally limited by the motor torque output characteristics of the electric vehicle's electric motor. Therefore, for a virtual vehicle with acceleration characteristics that exceed the acceleration capacity of the electric vehicle, the electric vehicle may not be able to directly reproduce the acceleration characteristics. As a result, there is a problem that the acceleration feel of such a virtual vehicle cannot be reproduced in the electric vehicle. On the other hand, increasing the acceleration capacity of the electric vehicle to enable the acceleration characteristics of such a virtual vehicle to be realized would unnecessarily increase the cost of the electric vehicle.

[0007] One object of the present disclosure is to provide an electric vehicle that can reproduce the acceleration feel of a virtual vehicle that has acceleration characteristics that exceed the acceleration capability of the electric vehicle. [Means for solving the problem]

[0008] A first aspect of the present disclosure relates to an electric vehicle having an electric motor as a drive source.

[0009] The electric vehicle includes driving operation members used for driving the electric vehicle, a processing circuit, and a storage device. The storage device stores a database that manages multiple vehicle models that model multiple virtual vehicles with different acceleration characteristics in response to driving operations by the driver. The processing circuit retrieves from the database a target vehicle model corresponding to a target virtual vehicle selected by the driver from the multiple virtual vehicles, and calculates a virtual acceleration of the target virtual vehicle in response to operation of the driving operation members using the target vehicle model based on the operation status of the driving operation members and the running status of the electric vehicle. The processing circuit also performs an adjustment process that calculates an adjusted virtual acceleration by multiplying the virtual acceleration by a coefficient of 1 or less that corresponds to the acceleration characteristic of the target virtual vehicle. The processing circuit then controls the electric motor so that the acceleration of the electric vehicle is equal to the adjusted virtual acceleration.

[0010] A second aspect of the present disclosure relates to a control device for an electric vehicle having an electric motor as a drive source, wherein the electric vehicle is equipped with driving operation members used for driving the electric vehicle.

[0011] The control device includes a processing circuit and a storage device. The storage device stores a database that manages multiple vehicle models that model multiple virtual vehicles with different acceleration characteristics in response to driving operations by the driver. The processing circuit retrieves from the database a target vehicle model corresponding to a target virtual vehicle selected by the driver from the multiple virtual vehicles, and calculates a virtual acceleration of the target virtual vehicle in response to operation of the driving operation members using the target vehicle model based on the operation states of the driving operation members and the running state of the electric vehicle. The processing circuit also performs an adjustment process that calculates an adjusted virtual acceleration by multiplying the virtual acceleration by a coefficient of 1 or less that corresponds to the acceleration characteristics of the target virtual vehicle. The processing circuit then controls the electric motor so that the acceleration of the electric vehicle equals the adjusted virtual acceleration. [Effects of the Invention]

[0012] According to the present disclosure, an adjusted virtual acceleration is calculated by multiplying the virtual acceleration of the target virtual vehicle by a coefficient of 1 or less that corresponds to the acceleration characteristics of the target virtual vehicle. Then, the electric motor is controlled so that the acceleration of the electric vehicle is equal to the adjusted virtual acceleration. As a result, even if the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle, acceleration characteristics that can reproduce the acceleration feel of the target virtual vehicle within the range of the acceleration capacity of the electric vehicle can be realized. In this way, according to the present disclosure, it is possible to reproduce the acceleration feel of a virtual vehicle having acceleration characteristics that exceed the acceleration capacity of the electric vehicle. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a diagram showing a configuration of an electric vehicle according to an embodiment; [Figure 2] FIG. 3 is a tree diagram showing an example of a selection input received by an HMI regarding a control mode of an electric vehicle according to the embodiment. [Figure 3] FIG. 2 is a diagram illustrating an example of a functional configuration of a control device that functions as a motor control device. [Figure 4] FIG. 10 is a diagram showing an example of acceleration characteristics of an electric vehicle achieved when the control mode is an on-demand mode. [Figure 5] FIG. 10 is a diagram showing an example of acceleration characteristics of an electric vehicle that are typically realized when the acceleration characteristics of a target virtual vehicle exceed the acceleration capacity of the electric vehicle. [Figure 6] 4 is a diagram illustrating an example of a functional configuration of an on-demand mode driving force calculation unit illustrated in FIG. 3. FIG. [Figure 7] FIG. 2 is a diagram showing an example of acceleration characteristics of an electric vehicle achieved by the motor control device according to the embodiment. [Figure 8] 10 is a flowchart showing a processing flow of processing executed by an on-demand mode driving force calculation unit. [Figure 9] 10 is a flowchart illustrating an example of a process related to setting a coefficient. [Figure 10] FIG. 2 is a diagram illustrating an example of a configuration of a vehicle model. [Figure 11] 10 is a flowchart showing an example of a process related to setting of a coefficient in the first modified example. [Figure 12] FIG. 10 is a diagram illustrating an example of a coefficient setting unit in a second modified example. [Figure 13] FIG. 2 is a diagram illustrating an example of a functional configuration of a control device that functions as a sound control device. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In each drawing, the same or corresponding parts are denoted by the same reference numerals, and the description thereof will be simplified or omitted.

[0015] 1. Electric vehicle powertrain configuration 1 is a diagram schematically illustrating the configuration of an electric vehicle 100 according to an embodiment of the present disclosure. First, the configuration of the power system of the electric vehicle 100 will be described with reference to FIG.

[0016] The electric vehicle 100 is equipped with an electric motor (M) 2 as a drive source for traveling. The electric motor 2 is, for example, a three-phase AC motor. An output shaft 3 of the electric motor 2 is connected to one end of a propeller shaft 5 via a gear mechanism 4. The other end of the propeller shaft 5 is connected to a drive shaft 7 at the front of the vehicle via a differential gear 6.

[0017] The electric vehicle 100 has driving wheels 8 which are front wheels and driven wheels 12 which are rear wheels. The driving wheels 8 are provided on both ends of the drive shaft 7, respectively.

[0018] The electric vehicle 100 includes a battery (BATT) 14 and an inverter (INV) 16. The battery 14 stores electrical energy for driving the electric motor 2. In other words, the electric vehicle 100 is a battery electric vehicle (BEV) that runs on electrical energy stored in the battery 14. The inverter 16 is, for example, a voltage-type inverter. The inverter 16 controls the motor torque output by the electric motor 2 by PWM control.

[0019] 2. Electric vehicle control system configuration Next, the configuration of the control system of the electric vehicle 100 will be described with reference to FIG.

[0020] The electric vehicle 100 is equipped with a vehicle speed sensor 30 for detecting vehicle speed. At least one of wheel speed sensors (not shown) provided on each of the left and right front wheels 8 and the left and right rear wheels 12 is used as the vehicle speed sensor 30. The vehicle speed is one of the driving conditions of the electric vehicle 100. The electric vehicle 100 may further be equipped with sensors for detecting other driving conditions of the electric vehicle 100, such as yaw rate, attitude, and the surrounding environment.

[0021] The electric vehicle 100 is equipped with an accelerator pedal stroke sensor 32. The accelerator pedal stroke sensor 32 is provided on the accelerator pedal 22 and outputs a signal indicating the operation state of the accelerator pedal 22. The operation state of the accelerator pedal typically includes the accelerator opening degree and the accelerator opening speed. The electric vehicle 100 is also equipped with a brake pedal stroke sensor 34. The brake pedal stroke sensor 34 is provided on the brake pedal 24 and outputs a signal indicating the operation state of the brake pedal 24. The operation state of the brake pedal 24 typically includes the brake opening degree and the brake opening speed.

[0022] The accelerator pedal 22 and the brake pedal 24 are each one of the driving operation members used to drive the electric vehicle 100. In addition, the electric vehicle 100 may be equipped with various other driving operation members, such as a steering wheel for steering.

[0023] The electric vehicle 100 is equipped with a rotational speed sensor 40. The rotational speed sensor 40 is provided in the electric motor 2 and outputs a signal indicating the rotational speed of the electric motor 2.

[0024] 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 a battery 14. The battery management system 10 has a function of estimating the state of charge (SOC) of the battery 14.

[0025] The electric vehicle 100 includes a human-machine interface (HMI) 20 as an interface with the driver and an in-vehicle speaker 21. The HMI 20 presents various information to the driver through visual and audio displays and accepts various inputs from the driver. The HMI 20 includes a display (e.g., a multi-information display or a meter display), switches, a touchpad, a speakerphone, a touchscreen, and the like. For example, the HMI 20 displays various information on a display and accepts inputs from the driver regarding the displayed content by operating a switch. For example, the HMI 20 displays various information on a touchscreen and accepts inputs from the driver regarding the displayed content by touching the touchscreen. The in-vehicle speaker 21 is a sound generator that generates artificial sounds within the vehicle cabin. In particular, the in-vehicle speaker 21 can output a pseudo-engine sound, which will be described later. The in-vehicle speaker 21 may be configured as part of the HMI 20.

[0026] The electric vehicle 100 is equipped with a control device 101. Various sensors and devices to be controlled mounted on the electric vehicle 100 are connected to the control device 101 via an in-vehicle network such as a controller area network (CAN). In addition to the vehicle speed sensor 30, accelerator pedal stroke sensor 32, brake pedal stroke 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.

[0027] The control device 101 generates control signals related to 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 includes at least a processing circuit 102 and a storage device 103.

[0028] The processing circuitry 102 performs various processes. The processing circuitry 102 may be, for example, a general-purpose processor, a special-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an integrated circuit, a conventional circuit, or a combination of one or more of these. A processor including transistors and other circuits is an example of the processing circuitry 102. The processing circuitry 102 may also be referred to as circuitry or processing circuitry. Circuitry is hardware that is programmed to realize or executes the functions described in this disclosure.

[0029] The storage device 103 stores various information necessary for the processing circuit 102 to execute processing. The storage device 103 is configured with a recording medium such as a RAM (Random Access Memory), a ROM (Read Only Memory), an SSD (Solid State Drive), or an HDD (Hard Disk Drive). The storage device 103 stores a computer program 104 executable by the processing circuit 102 and various data 105. The computer program 104 is configured with a plurality of instructions that describe the processing to be executed by the processing circuit 102. The computer program 104 may be recorded on a computer-readable recording medium. The functions of the control device 101 are realized by cooperation between the processing circuit 102, which executes the computer program 104, and the storage device 103.

[0030] The control device 101 according to this embodiment has at least two control modes, a normal mode and an on-demand mode, for controlling the electric vehicle 100. The control of the electric vehicle 100 performed by the control device 101 changes depending on the selected control mode. The control modes of the electric vehicle 100 will be described below.

[0031] 3 Electric vehicle control modes As described above, there are at least two control modes for the electric vehicle 100: normal mode and on-demand mode. The normal mode is a control mode in which the electric vehicle 100 is operated as a normal BEV. When the normal mode is selected, the control device 101 controls the electric vehicle 100 so that it operates as a normal BEV. On the other hand, the on-demand mode is a control mode in which the electric vehicle 100 reproduces the acceleration feel of a virtual vehicle (hereinafter referred to as the "target virtual vehicle") selected by the driver from among multiple virtual vehicles. When the on-demand mode is selected, the control device 101 controls the electric vehicle 100 so that the driver can obtain a feeling of acceleration as if they were driving the target virtual vehicle. Details of the various controls of the electric vehicle 100 in both the normal mode and the on-demand mode will be described later.

[0032] In the on-demand mode, the multiple virtual vehicles include various vehicles with different acceleration characteristics in response to the driver's driving operations. Each virtual vehicle may be a simulated real vehicle, or may be a fictitious vehicle. Differences in acceleration characteristics are generally due to differences in the configuration of the powertrain from the drive source to the drive wheels or differences in the powertrain control method. Therefore, the multiple virtual vehicles can be considered to include various vehicles with different powertrain-related configurations or control methods at least in part.

[0033] The control mode is selected by the driver operating the HMI 20. The HMI 20 is configured to receive a control mode selection input from the driver. Furthermore, for the on-demand mode, the HMI 20 is configured to receive a target virtual vehicle selection input from the driver.

[0034] 2 is a tree diagram showing an example of a selection input received by the HMI 20. For example, the HMI 20 receives a selection input from the driver via a display or a touch screen in accordance with the tree shown in FIG. 2 as follows:

[0035] First, the HMI 20 displays a setting menu screen on the display or touch screen in accordance with the driver's operation. The initial screen of the setting menu screen displays the options "Control Mode" and "Target Virtual Vehicle". The option "Control Mode" is an option for accepting a selection input of the control mode from the driver. The option "Target Virtual Vehicle" is an option for accepting a selection input of the target virtual vehicle from the driver.

[0036] When the option "control mode" is selected, the options "normal mode" and "on-demand mode" are then displayed on the settings menu screen. When the option "normal mode" is selected, the HMI 20 determines that the control mode of the electric vehicle 100 is the normal mode. When the option "on-demand mode" is selected, the HMI 20 determines that the control mode of the electric vehicle 100 is the on-demand mode. In this way, the HMI 20 accepts the control mode selection input from the driver.

[0037] On the other hand, when the option "on-demand mode" is selected, the settings menu screen then displays options "CONV" and "HEV." The options "CONV" and "HEV" each indicate a classification of multiple virtual vehicles that can be selected in on-demand mode. CONV is a classification that indicates a conventional internal combustion engine vehicle. HEV is a classification that indicates a hybrid electric vehicle. When the option "CONV" is selected, the settings menu screen then displays options "virtual vehicle A1," "virtual vehicle A2," and "virtual vehicle B1." Virtual vehicle A1, virtual vehicle A2, and virtual vehicle B1 are virtual vehicles classified as CONV among the multiple selectable virtual vehicles. Similarly, when the option "HEV" is selected, the settings menu screen then displays options "virtual vehicle C1" and "virtual vehicle C2." Virtual vehicle C1 and virtual vehicle C2 are virtual vehicles classified as HEV among the multiple selectable virtual vehicles. When one of these options is selected, the HMI 20 determines the corresponding virtual vehicle as the target virtual vehicle. For example, when the option "Virtual Vehicle A2" is selected, the HMI 20 determines the virtual vehicle A2 as the target virtual vehicle. In this way, the HMI 20 receives a selection input of the target virtual vehicle from the driver.

[0038] The classification of the multiple virtual vehicles in the above description is an example, and the options related to the classification may be changed as appropriate. For example, the options related to the classification may further include an option indicating a plug-in hybrid electric vehicle or a fuel cell electric vehicle. Furthermore, for example, the options related to the classification may indicate other classifications, such as a classification related to the type of internal combustion engine installed (e.g., a supercharged inline-four engine, a flat-six engine, a V12 engine). Alternatively, when the option "on-demand mode" is selected, options related to the virtual vehicle may be displayed without displaying options related to the classification.

[0039] The name displayed on the setting menu screen for each option may be set appropriately in consideration of ease of understanding by the driver. For example, for an option related to a virtual vehicle, the displayed name may be a more specific name, such as the model name or product name, that allows the driver to easily imagine the virtual vehicle.

[0040] As described 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.

[0041] The control device 101 according to this embodiment functions as a motor control device that controls the electric motor 2 in accordance with at least the driver's driving operation with respect to the control of the electric vehicle 100. More specifically, the processing circuit 102 executes a computer program 104 for electric motor control stored in a storage device 103, causing the control device 101 to function as a motor control device. The electric motor 2 is the drive source of the electric vehicle 100. Therefore, the motor control device can also be said to be a device that controls the drive of the electric vehicle 100. The control of the electric vehicle 100 by the motor control device will be described below.

[0042] 4 Motor control device 3 is a diagram showing an example of the functional configuration of the motor control device 101a. The motor control device 101a calculates a target driving force for the electric vehicle 100 in response to the driver's driving operation. The motor control device 101a then controls the electric motor 2 via the inverter 16 so as to apply the calculated target driving force to the electric vehicle 100.

[0043] Signals from the HMI 20 and the sensor system 50 are input to the motor control device 101a. The sensor system 50 includes a vehicle speed sensor 30, an accelerator pedal stroke sensor 32, a brake pedal stroke sensor 34, a rotational speed sensor 40, and a battery management system 10. The sensor system 50 may also include other sensors not shown. For example, the sensor system 50 may 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, a sensor for detecting the ambient environment of the electric vehicle 100 (e.g., a camera, radar, LiDAR), etc.

[0044] The signals input from the HMI 20 to the motor control device 101a 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 motor control device 101a include a signal indicating the vehicle speed of the electric vehicle 100, a signal indicating the operation state of the accelerator pedal 22, a signal indicating the operation state of the brake pedal 24, a signal indicating the rotational speed of the electric motor 2, and a signal indicating the state of the battery 14 (e.g., cell voltage, current, temperature, SOC).

[0045] The motor control device 101a includes, as functional blocks, a mode information acquisition unit 110, an on-demand mode driving force calculation unit 120, a normal mode driving force calculation unit 130, a target driving force switching unit 140, and an electric motor control unit 150. These functional blocks are realized by cooperation between a processing circuit 102 that executes a computer program 104 and a storage device 103.

[0046] The mode information acquisition unit 110 receives a signal from the HMI 20 and acquires information regarding whether the normal mode or the on-demand mode has been selected. The mode information acquisition unit 110 also acquires information regarding the target virtual vehicle selected by the driver. The mode information acquisition unit 110 transmits information regarding the selected control mode to the target driving force switching unit 140. The mode information acquisition unit 110 also transmits information regarding the selected target virtual vehicle to the on-demand mode driving force calculation unit 120.

[0047] The on-demand mode driving force calculation unit 120 acquires information about the target virtual vehicle selected by the driver from the mode information acquisition unit 110. Then, the on-demand mode driving force calculation unit 120 calculates a target driving force for the on-demand mode based on signals from the sensor system 50. In other words, the on-demand mode driving force calculation unit 120 calculates a target driving force for reproducing, in the electric vehicle 100, the acceleration feeling of the target virtual vehicle in response to the driving operation of the driver. Details of the processing executed by the on-demand mode driving force calculation unit 120 will be described later.

[0048] The normal mode driving force calculation unit 130 calculates the target driving force for the normal mode based on signals from the sensor system 50. That is, the normal mode driving force calculation unit 130 calculates the target driving force for operating the electric vehicle 100 as a normal BEV. For example, the normal mode driving force calculation unit 130 calculates the target driving force using a map that uses the accelerator opening of the accelerator pedal 22 and the rotational speed of the electric motor 2 as parameters. Furthermore, the normal mode driving force calculation unit 130 may be configured to calculate the target driving force using the brake opening of the brake pedal 24 or the SOC of the battery 14 as parameters. However, in this embodiment, the processing performed by the normal mode driving force calculation unit 130 is not particularly limited. The processing performed by the normal mode driving force calculation unit 130 may also apply other suitable known techniques.

[0049] The target driving force switching unit 140 switches the target driving force of the electric vehicle 100 used to control the electric motor 2 according to the selected control mode. The target driving force switching unit 140 acquires information on the selected control mode from the mode information acquisition unit 110. When the on-demand mode is selected, the target driving force switching unit 140 transmits the target driving force calculated by the on-demand mode driving force calculation unit 120 to the electric motor control unit 150 as the target driving force of the electric vehicle 100. On the other hand, when the normal mode is selected, the target driving force switching unit 140 transmits the target driving force calculated by the normal mode driving force calculation unit 130 to the electric motor control unit 150 as the target driving force of the electric vehicle 100.

[0050] Note that when the on-demand mode is selected, the normal mode driving force calculation unit 130 may be configured not to execute any processing. Similarly, when the normal mode is selected, the on-demand mode driving force calculation unit 120 may be configured not to execute any processing.

[0051] The target driving force of the electric vehicle 100 is input to the electric motor control unit 150 via the target driving force switching unit 140. That is, when the on-demand mode is selected, the target driving force calculated by the on-demand mode driving force calculation unit 120 is input to the electric motor control unit 150. On the other hand, when the normal mode is selected, the target driving force calculated by the normal mode driving force calculation unit 130 is input to the electric motor control unit 150. The electric motor control unit 150 changes the motor torque output by the electric motor 2 so as to apply the input target driving force to the electric vehicle 100. More specifically, the electric motor control unit 150 generates a control signal for the inverter 16 in accordance with the input target driving force. Then, the electric motor control unit 150 changes the motor torque output by the electric motor 2 via PWM control by the inverter 16.

[0052] In this way, the motor control device 101a controls the electric motor 2 to apply a target driving force corresponding to the control mode to the electric vehicle 100. Therefore, according to the motor control device 101a, when the on-demand mode is selected, the acceleration characteristics of the electric vehicle 100 are acceleration characteristics that simulate the acceleration characteristics of the target virtual vehicle selected by the driver. On the other hand, when the normal mode is selected, the acceleration characteristics of the electric vehicle 100 are the acceleration characteristics of a normal BEV.

[0053] FIG. 4 is a diagram showing an example of the acceleration characteristic VAC of the electric vehicle 100 when the on-demand mode is selected. For comparison, FIG. 4 also shows the acceleration characteristic of a normal BEV (dashed line). When the on-demand mode is selected, the acceleration characteristic VAC of the electric vehicle 100 changes to various patterns depending on the target virtual vehicle as the target virtual vehicle is changed. This is because the target driving force calculated in the on-demand mode changes depending on the target virtual vehicle selected by the driver. As a result, in the on-demand mode, the driver can enjoy the acceleration sensation of various virtual vehicles in the electric vehicle 100.

[0054] The calculation of the target driving force in the on-demand mode, that is, the processing executed by the on-demand mode driving force calculation unit 120, will be described in detail below.

[0055] 4.1 Calculation of target driving force in on-demand mode The on-demand mode driving force calculation unit 120 calculates the target driving force so that the acceleration feeling of the target virtual vehicle in response to the driver's driving operation is reproduced in the electric vehicle 100. In particular, the on-demand mode driving force calculation unit 120 uses a vehicle model of the target virtual vehicle (hereinafter referred to as the "target vehicle model") to calculate (simulate) the virtual acceleration when the target virtual vehicle is driven, and employs a method (hereinafter referred to as the "virtual acceleration-based method") of calculating the target driving force so that the acceleration of the electric vehicle 100 becomes the virtual acceleration.

[0056] However, the acceleration that can be achieved by the electric vehicle 100, i.e., the acceleration capacity of the electric vehicle 100, is naturally limited in relation to the motor torque output characteristics of the electric motor 2. In particular, the acceleration capacity of the electric vehicle 100 is equivalent to the acceleration characteristics of a normal BEV. In other words, the acceleration characteristics that can be achieved by the electric vehicle 100 are within the range of the acceleration characteristics of a normal BEV.

[0057] For this reason, with the virtual acceleration-based method, if the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100, a situation may arise in which the acceleration characteristics of the target virtual vehicle cannot be realized as is in the electric vehicle 100. FIG. 5 is a diagram showing an example of the acceleration characteristics VAC of the electric vehicle 100 that are normally realized when the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100. In the example shown in FIG. 5, the acceleration characteristics VAC of the electric vehicle 100 are limited by the acceleration capacity of the electric vehicle 100 (dotted line), and therefore cannot realize the acceleration characteristics of the target virtual vehicle (dashed line). With such acceleration characteristics VAC of the electric vehicle 100, there is a risk that the acceleration feeling of the target virtual vehicle cannot be reproduced when, for example, the driver depresses the accelerator pedal 22 to accelerate the electric vehicle 100.

[0058] In the example shown in FIG. 5, one of the elements that is not sufficiently realized in the acceleration characteristic VAC is the shape of the acceleration fluctuation in the section that exceeds the acceleration capacity of the electric vehicle 100. For example, during the section SC, the acceleration in the acceleration characteristic of the target virtual vehicle is a shape in which the acceleration fluctuates greatly, whereas the acceleration in the acceleration characteristic VAC is constant at the maximum acceleration that can be achieved by the electric vehicle 100. A second element that is not sufficiently realized in the acceleration characteristic VAC is the magnitude of the acceleration in the section that exceeds the acceleration capacity of the electric vehicle 100. For example, during the section SC, there is a difference between the acceleration in the acceleration characteristic of the target virtual vehicle and the acceleration in the acceleration characteristic VAC.

[0059] Of these two factors, the difference in the shape of the acceleration fluctuation has a significant effect on the acceleration sensation given to the driver. On the other hand, if the difference in the shape of the acceleration fluctuation is small, even if there is a difference in the magnitude of the acceleration, the impact on the acceleration sensation given to the driver is small.

[0060] Based on the above viewpoint, the on-demand mode driving force calculation unit 120 according to this embodiment is configured to realize the acceleration characteristics VAC of the electric vehicle 100 that can reproduce the acceleration feel of the target virtual vehicle, even when the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100. In other words, the on-demand mode driving force calculation unit 120 is configured to realize the acceleration characteristics VAC of the electric vehicle 100 that have the shape of the acceleration fluctuation of the acceleration characteristics of the target virtual vehicle, within the range of the acceleration capacity of the electric vehicle 100. More specifically, in the virtual acceleration-based method, the on-demand mode driving force calculation unit 120 is further configured to execute a process of multiplying the virtual acceleration by a coefficient of 1 or less that corresponds to the acceleration characteristics of the target virtual vehicle. This process adjusts the virtual acceleration so that it does not exceed the acceleration capacity of the electric vehicle 100. Furthermore, because this process involves multiplication of a coefficient, the shape of the acceleration fluctuation is maintained. The on-demand mode driving force calculation unit 120 then calculates a target driving force that results in the virtual acceleration resulting from the adjustment of the acceleration of the electric vehicle 100.

[0061] 6 is a diagram showing an example of the functional configuration of the on-demand mode driving force calculation unit 120. The on-demand mode driving force calculation unit 120 includes, as functional blocks, a virtual acceleration calculation unit 121, an adjustment unit 122, and a target driving force calculation unit 123. The on-demand mode driving force calculation unit 120 is also configured to be able to access the vehicle model database D10.

[0062] The vehicle model database D10 is a database that manages multiple vehicle models 200 that are models of multiple virtual vehicles. The vehicle model database D10 may be realized as data 105 stored in the storage device 103. New vehicle models 200 may be downloaded to the vehicle model database D10 as needed. In the example shown in FIG. 6, 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 behavior of a virtual vehicle in response to a driver's driving operation, using the operation states of the driving control members and the running state of the electric vehicle 100 as input. Each vehicle model 200 is configured to simulate at least the driving force applied to the virtual vehicle in response to a driving operation, particularly the operation of the accelerator pedal 22, and the acceleration and deceleration behavior of the virtual vehicle resulting from the application of that driving force. The simulation results of the acceleration and deceleration behavior of the virtual vehicle in each vehicle model 200 include the virtual acceleration of the virtual vehicle. That is, each vehicle model 200 is configured to be able to calculate the virtual acceleration of the virtual vehicle in response to the driving operation of the driver.

[0063] Typically, each vehicle model 200 is composed of a control model that simulates a control system related to the powertrain of the virtual vehicle, and a plant model that simulates the acceleration and deceleration behavior of the virtual vehicle in response to control signals from the control model. In this case, the plant model includes a model of the powertrain that operates based on control signals from the control model, and a model for simulating the behavior of the virtual vehicle due to the action of the virtual driving force output by the powertrain model. An example of the configuration of vehicle model 200 will be described later.

[0064] Each vehicle model 200 also has parameters 201 related to the operation of the virtual vehicle in the simulation. Examples of the parameters 201 include vehicle weight, tire diameter, each gear ratio, maximum engine torque, engine torque responsiveness, and gear shift timing. The contents of the parameters 201 may differ for each vehicle model 200. The vehicle model 200 expresses a model of one virtual vehicle by combining it with the setting values ​​of its parameters 201. For example, each virtual vehicle corresponds to a combination of the vehicle model 200 and the setting values ​​of the 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 systems are the same type and each virtual vehicle can be expressed by changing the setting values ​​of the parameters 201. [Table 1]

[0065] The virtual acceleration calculation unit 121 acquires information about the target virtual vehicle from the mode information acquisition unit 110. The virtual acceleration calculation unit 121 references 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. The example shown in FIG. 6 shows a case where the virtual acceleration calculation unit 121 reads out vehicle model 200-B. Furthermore, the virtual acceleration calculation unit 121 sets the parameter 201 of the read out vehicle model 200 in accordance with the target virtual vehicle. For example, when the target virtual vehicle is "virtual vehicle B1" in the above table, the virtual acceleration calculation unit 121 sets the parameter 201-B of the vehicle model 200-B to the setting value B1.

[0066] The virtual acceleration calculation unit 121 uses the read target vehicle model to calculate the virtual acceleration VG of the target virtual vehicle in response to the operation of the driving operation members of the electric vehicle 100. More specifically, the virtual acceleration calculation unit 121 receives signals from the sensor system 50 and acquires information on the operation states of the driving operation members and information on the running state of the electric vehicle 100 to be input to the target vehicle model. For example, the virtual acceleration calculation unit 121 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 acceleration calculation unit 121 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 acceleration calculation unit 121 inputs the acquired information into the target vehicle model. The virtual acceleration calculation unit 121 then calculates the virtual acceleration VG of the target virtual vehicle by simulating the acceleration and deceleration behavior of the target virtual vehicle using the target vehicle model. The virtual acceleration VG calculated by the virtual acceleration calculation unit 121 is transmitted to the adjustment unit 122 .

[0067] The adjustment unit 122 executes a process (adjustment process) of calculating an adjusted virtual acceleration AVG by multiplying the virtual acceleration VG by a coefficient a that is equal to or less than 1 according to the acceleration characteristics of the target virtual vehicle. The adjustment unit 122 includes a coefficient setting unit 122a and a multiplication unit 122b. The coefficient setting unit 122a sets the coefficient a according to the acceleration characteristics of the target virtual vehicle. The multiplication unit 122b multiplies the virtual acceleration VG by the coefficient a set by the coefficient setting unit 122a. The multiplication result by the multiplication unit 122b becomes the adjusted virtual acceleration AVG. In other words, AVG = a · VG. The adjusted virtual acceleration AVG calculated by the adjustment unit 122 is transmitted to the target driving force calculation unit 123.

[0068] The coefficient setting unit 122a sets the coefficient a based on whether the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100. For this reason, the coefficient setting unit 122a manages the acceleration characteristics of each of the multiple virtual vehicles that can be selected in on-demand mode. The coefficient setting unit 122a acquires information about the target virtual vehicle from the mode information acquisition unit 110 and refers to the acceleration characteristics of the target virtual vehicle. The coefficient setting unit 122a also manages the acceleration characteristics of the electric vehicle 100 as a normal BEV (the acceleration capacity of the electric vehicle 100).

[0069] When the acceleration characteristics of the target virtual vehicle do not exceed the acceleration capacity of the electric vehicle 100, the coefficient setting unit 122a sets the coefficient a to 1. That is, in the multiplication unit 122b, the virtual acceleration VG becomes the adjusted virtual acceleration AVG as is.

[0070] On the other hand, when the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100, the coefficient setting unit 122a sets the coefficient a to a value such that the adjusted virtual acceleration AVG falls within the range of the acceleration capacity of the electric vehicle 100. As a more specific example, the coefficient setting unit 122a can set the coefficient a to a value obtained by dividing the maximum acceleration that can be achieved by the electric vehicle 100 (hereinafter referred to as the "maximum possible acceleration") by the maximum acceleration in the acceleration characteristics of the target virtual vehicle (hereinafter referred to as the "virtual maximum acceleration"). In other words, the coefficient setting unit 122a sets the coefficient a to be equal to the maximum possible acceleration / virtual maximum acceleration. FIG. 7 shows an example of the acceleration characteristics VAC of the electric vehicle 100 that are realized when the coefficient a is equal to the maximum possible acceleration / virtual maximum acceleration. As shown in FIG. 7, by multiplying the coefficient a set in this manner, it is possible to realize the acceleration characteristics VAC of the electric vehicle 100 that have the acceleration fluctuation shape of the acceleration characteristics of the target virtual vehicle. Note that the coefficient setting unit 122a may be configured to set the coefficient a to a value smaller than the maximum possible acceleration / virtual maximum acceleration.

[0071] Referring again to Figure 6, the target driving force calculation unit 123 acquires the adjusted virtual acceleration AVG from the adjustment unit 122 and calculates a target driving force for adjusting the acceleration of the electric vehicle 100 to the adjusted virtual acceleration AVG. For example, the target driving force calculation unit 123 calculates the adjusted virtual acceleration AVG as the target driving force F using a simple inverse model of the electric vehicle 100, as shown in the following equation: veh In the following formula, m is the weight of 100 electric vehicles, F load is the actual running resistance applied to the electric vehicle 100. The on-demand mode driving force calculation unit 120 outputs the target driving force calculated by the target driving force calculation unit 123.

number

[0072] In this way, the functional configuration of the on-demand mode driving force calculation unit 120 according to this embodiment can be provided. Fig. 8 is a flowchart showing the processing flow of the processing executed by the on-demand mode driving force calculation unit 120 based on the above-described functional configuration. The processing flow shown in Fig. 8 is repeatedly executed at a predetermined processing cycle.

[0073] In step S110, the on-demand mode driving force calculation unit 120 acquires various information. For example, the on-demand mode driving force calculation unit 120 acquires information about the target virtual vehicle from the mode information acquisition unit 110. The on-demand mode driving force calculation unit 120 also acquires information about the operation status of the driving operation members and information about the running status of the electric vehicle 100 from the sensor system 50.

[0074] Next, in step S120, the on-demand mode driving force calculation unit 120 refers to the vehicle model database D10 and reads out the vehicle model 200 (target vehicle model) corresponding to the target virtual vehicle.

[0075] Next, in step S130, on-demand mode driving force calculation unit 120 calculates the virtual acceleration VG of the target virtual vehicle in response to the operation of the driving operation members using the target vehicle model.

[0076] Next, in step S140, the on-demand mode driving force calculation unit 120 sets a coefficient a that is equal to or less than 1 for the adjustment process in accordance with the acceleration characteristics of the target virtual vehicle. FIG. 9 is a flowchart showing an example of the process related to step S140. The processes shown in FIG. 9 are executed by the coefficient setting unit 122a of the adjustment unit 122. In the example shown in FIG. 9, in step S140, the coefficient setting unit 122a first determines whether the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100 (step S141). If the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100 (step S141; Yes), the coefficient setting unit 122a sets the coefficient a to the maximum possible acceleration / virtual maximum acceleration (step S142). On the other hand, if the acceleration characteristics of the target virtual vehicle do not exceed the acceleration capacity of the electric vehicle 100 (step S141; No), the coefficient setting unit 122a sets the coefficient a to 1 (step S143).

[0077] Referring again to Figure 8, after step S140, next in step S150 (adjustment process), the on-demand mode driving force calculation unit 120 multiplies the virtual acceleration VG by a coefficient a to calculate an adjusted virtual acceleration AVG.

[0078] Next, in step S160, the on-demand mode driving force calculation unit 120 calculates a target driving force for adjusting the acceleration of the electric vehicle 100 to the adjusted virtual acceleration AVG, after which the current processing ends.

[0079] As described above, the on-demand mode driving force calculation unit 120 according to this embodiment calculates the adjusted virtual acceleration AVG by multiplying the virtual acceleration VG by a coefficient equal to or less than 1 according to the acceleration characteristics of the target virtual vehicle. Then, a target driving force is calculated to set the acceleration of the electric vehicle 100 to the adjusted virtual acceleration AVG. As a result, as shown in FIG. 7 , even when the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100, the acceleration characteristics VAC of the electric vehicle 100 can be realized, which can reproduce the acceleration feel of the target virtual vehicle within the acceleration capacity of the electric vehicle 100. In this way, according to this embodiment, the acceleration feel of the virtual vehicle that exceeds the acceleration capacity of the electric vehicle 100 can be reproduced in the electric vehicle 100. As a result, the driver can enjoy the acceleration feel of the virtual vehicle that exceeds the acceleration capacity of the electric vehicle 100. Furthermore, the variety of virtual vehicles that can reproduce the acceleration feel in on-demand mode can be increased, thereby improving user satisfaction.

[0080] 4.1.2 Example of vehicle model configuration Here, an example of the configuration of a vehicle model 200 managed by the vehicle model database D10 will be described. FIG. 10 is a diagram showing an example of the configuration of the 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 operations of the virtual vehicle in response to control signals from the control model 210. The plant model 220 includes a model of the powertrain that operates based on the control signals from the control model 210, and a model for simulating the operation of the virtual vehicle due to the action of a virtual driving force output by the powertrain model. The control model 210 can also be said to simulate a control system that calculates the required output of the powertrain of the virtual vehicle. The plant model 220 can also be said to simulate physical constraints on the required output of the powertrain.

[0081] The specifications of the control model 210 and the plant model 220 differ depending on the type of powertrain system. For example, the configurations of the control system, transmission, and drivetrain differ between a CONV and an HEV. Therefore, the CONV vehicle model 200 and the HEV vehicle model 200 have different specifications for both the control model 210 and the plant model 220. The example shown in FIG. 10 particularly illustrates a case where the virtual vehicle is an automatic transmission vehicle (AT vehicle) equipped with an internal combustion engine.

[0082] The control model 210 includes a target virtual driving force calculation unit 211 and a required output calculation unit 212. The target virtual driving force calculation unit 211 calculates a virtual driving force (target virtual driving force) required from the output of the powertrain of the virtual vehicle based on the accelerator opening and vehicle speed. For example, the target virtual driving force calculation unit 211 performs calculations using a map that assigns a target virtual driving force to a combination of accelerator opening and vehicle speed. The required output calculation unit 212 calculates a required output for the powertrain so as to satisfy the calculated target virtual driving force. The calculated required output includes a target engine torque of the internal combustion engine and a target gear position of the transmission. The control model 210 transmits the calculated required output to the plant model 220.

[0083] The plant model 220 includes an internal combustion engine model 221, a transmission model 222, a drivetrain model 223, and a vehicle and 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 drive source to the drive wheels. The vehicle and environment model 224 is a model for simulating the operation of a virtual vehicle due to the action of a virtual driving force output by the powertrain model.

[0084] The internal combustion engine model 221 is a model of the internal combustion engine of the virtual vehicle. The internal combustion engine model 221 simulates the operation of the internal combustion engine in response to an input of a target engine torque, for example. The internal combustion engine model 221 outputs a virtual engine speed and a virtual engine torque. In the internal combustion engine model 221, parameters 201 that can be changed depending on the target virtual vehicle include, for example, the maximum engine torque and the engine torque responsiveness.

[0085] The transmission model 222 is a model of the transmission of the virtual vehicle. The transmission model 222 simulates the operation of the transmission in response to, for example, the input of a target gear. The transmission model 222 outputs a virtual transmission output torque from the virtual engine torque output by the internal combustion engine model 221 and a gear ratio determined by the virtual gear. 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. In the transmission model 222, parameters 201 that can be changed depending on the target virtual vehicle include, for example, each gear ratio, gear shift timing, etc. In the case of a stepped transmission model, the gear ratio means the gear ratio of each gear.

[0086] The drivetrain model 223 is a model of the drivetrain of the 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 drivewheel torque using the virtual transmission output torque output by the transmission model 222 and a predetermined reduction ratio, and outputs the virtual drive force of the virtual vehicle. In the drivetrain model 223, parameters 201 that can be changed depending on the target virtual vehicle include, for example, the reduction ratio, the maximum allowable torque of the propeller shaft, etc.

[0087] The vehicle and environment model 224 is a model that represents the dynamic characteristics of the virtual vehicle and the driving environment of the virtual vehicle. The vehicle and environment model 224 calculates the running resistance acting on the virtual vehicle from the driving environment of the virtual vehicle. The vehicle and environment model 224 then simulates the acceleration and deceleration of the virtual vehicle from the virtual driving force output from the drivetrain model 223, the calculated running resistance, and the dynamic characteristics of the virtual vehicle. The vehicle and environment model 224 outputs virtual acceleration from the acceleration and deceleration of the virtual vehicle. Parameters 201 that can be changed in the vehicle and environment model 224 depending on the target virtual vehicle include, for example, vehicle weight, tire diameter, CD value, etc.

[0088] As described above, vehicle model 200 can be configured. Vehicle model 200 shown in FIG. 4 is an example. Vehicle model 200 can also be configured in more detail depending on the event to be emphasized. For example, consider a case where it is desired to emphasize the shock or response associated with the shifting of the transmission gear and clutch during kickdown. In this case, transmission model 222 may be configured to precisely reproduce the gear mechanism of the transmission, such as a planetary gearbox or a ravigneaux gearbox, the inertia of each component, and changes in the transmission path when the clutch is engaged and disengaged. On the other hand, if it is desired to reduce the computational load on vehicle model 200, transmission model 222 may be simply configured to reproduce only the gear ratio.

[0089] 4.1.3 Modifications regarding coefficient setting by the coefficient setting unit The above description shows an example of the setting of the coefficient a by the coefficient setting unit 122a in the adjustment process (see FIG. 9). In this example, the acceleration characteristic VAC of the electric vehicle 100 is realized, which has the acceleration fluctuation shape of the acceleration characteristic of the target virtual vehicle across all vehicle speed ranges (see FIG. 7). Furthermore, since the acceleration characteristic indicates the acceleration when the accelerator pedal depression is at its maximum, this example takes into consideration the maximum accelerator pedal depression as well. On the other hand, depending on the magnitude of the accelerator pedal depression and the vehicle speed range in the acceleration scene requested by the driver, there are cases where the virtual acceleration VG of the target virtual vehicle can be realized directly by the electric vehicle 100. By setting the coefficient a taking this case into further consideration, it is possible to more accurately reproduce the acceleration feel of the target virtual vehicle. Therefore, in this embodiment, the coefficient setting unit 122a may set the coefficient a using a first or second modified example described below.

[0090] In the first modified example, the coefficient a is set to 1 regardless of the acceleration characteristics of the target virtual vehicle as long as the virtual acceleration VG calculated by the virtual acceleration calculation unit 121 is a realizable acceleration. Setting of the coefficient a by the coefficient setting unit 122a according to the first modified example will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an example of processing (processing related to step S140 shown in Fig. 8) executed by the coefficient setting unit 122a when the first modified example is adopted.

[0091] In step S241, the coefficient setting unit 122a determines whether the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100. If the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100 (step S241; Yes), the processing proceeds to step S242. If the acceleration characteristics of the target virtual vehicle do not exceed the acceleration capacity of the electric vehicle 100 (step S241; No), the coefficient setting unit 122a sets the coefficient a to 1 (step S243).

[0092] In step S242, the coefficient setting unit 122a determines whether the virtual acceleration V G calculated by the virtual acceleration calculation unit 121 is greater than the acceleration that the electric vehicle 100 can achieve. In other words, it determines whether the electric vehicle 100 can achieve the virtual acceleration V G. If the electric vehicle 100 cannot achieve the virtual acceleration V G (step S242; Yes), the coefficient setting unit 122a sets the coefficient a to the maximum possible acceleration / virtual maximum acceleration (step S244). Then, the process proceeds to step S246. On the other hand, if the electric vehicle 100 can achieve the virtual acceleration V G (step S242; No), the coefficient setting unit 122a sets the coefficient a to 1 (step S245). Then, the process proceeds to step S246.

[0093] In step S246, the coefficient setting unit 122a executes abrupt change mitigation processing. The abrupt change mitigation processing mitigates abrupt changes in the value of the coefficient a when the set value of the coefficient a is switched. For example, when the set value of the coefficient a is switched from 1 to the maximum possible acceleration / virtual maximum acceleration, the value of the coefficient a is gradually changed from 1 to the maximum possible acceleration / virtual maximum acceleration. The abrupt change mitigation processing can suppress abrupt changes in the adjusted virtual acceleration AVG and the target driving force.

[0094] As described above, according to the first modification, the coefficient a is set to 1 while the electric vehicle 100 can achieve the virtual acceleration VG. In other words, the adjustment unit 122 sets the virtual acceleration VG as the adjusted virtual acceleration AVG as is. This makes it possible to reproduce the acceleration characteristics of the target virtual vehicle as is in the region where the virtual acceleration VG is possible. In other words, the driver can experience both the shape of the acceleration fluctuations of the target virtual vehicle and the magnitude of the acceleration.

[0095] Next, a second modified example of the setting of the coefficient a by the coefficient setting unit 122a will be described. In the second modified example, when the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100, the value of the coefficient a is changed based on the accelerator opening of the accelerator pedal 22 and the vehicle speed of the electric vehicle 100.

[0096] When the accelerator pedal is depressed to a small degree and the vehicle is accelerating slowly, the virtual acceleration VG rarely exceeds the acceleration that can be achieved by the electric vehicle 100. On the other hand, when the accelerator pedal is depressed to a large degree and the vehicle is accelerating strongly, the virtual acceleration VG is expected to exceed the acceleration that can be achieved by the electric vehicle 100. Therefore, the coefficient setting unit 122a according to the second modified example is configured to set the value of the coefficient a based on the accelerator pedal depression in accordance with the following principles (1) to (3). (1) When the accelerator opening is smaller than the first threshold value, it is determined that gentle acceleration is to be performed, and the coefficient a is set to 1. (2) When the accelerator opening is equal to or greater than the second threshold value, it is determined that strong acceleration is required, and the value of the coefficient a is set so that the virtual acceleration VG is within the range of the acceleration capability of the electric vehicle 100. (3) When the accelerator opening is equal to or greater than the first threshold value and smaller than the second threshold value, it is further determined whether strong acceleration is expected based on the driving situation of the electric vehicle 100. If strong acceleration is expected (for example, during start-up acceleration or kickdown), the value of the coefficient a is set so that the virtual acceleration VG is within the range of the acceleration capability of the electric vehicle 100. Otherwise, the coefficient a is set to 1.

[0097] Furthermore, the vehicle speed of the electric vehicle 100 is related to the magnitude of the difference between the acceleration characteristics of the target virtual vehicle and the acceleration capacity of the electric vehicle 100 when the acceleration characteristics exceed the acceleration capacity of the electric vehicle 100. For example, in the example shown in FIG. 7 , the difference between the acceleration characteristics of the target virtual vehicle and the acceleration capacity of the electric vehicle 100 is large in the low vehicle speed range and the high vehicle speed range. On the other hand, the difference between the acceleration characteristics of the target virtual vehicle and the acceleration capacity of the electric vehicle 100 is small in the medium vehicle speed range. Therefore, in the above policy, the coefficient setting unit 122a according to the second modified example can be further configured to set the value of the coefficient a according to the vehicle speed range of the electric vehicle 100. For example, when the vehicle speed of the electric vehicle 100 is in the low vehicle speed range or the high vehicle speed range, the coefficient setting unit 122a sets the value of the coefficient a to the maximum possible acceleration / virtual maximum acceleration. On the other hand, when the vehicle speed of the electric vehicle 100 is in the medium vehicle speed range, the coefficient setting unit 122a sets the value of the coefficient a to a value greater than the maximum possible acceleration / virtual maximum acceleration.

[0098] FIG. 12 is a diagram illustrating an example of a coefficient setting unit 122a according to a second modified example. In the example illustrated in FIG. 12, compared to the case illustrated in FIG. 6, the coefficient setting unit 122a further receives the accelerator depression of the accelerator pedal 22, the vehicle speed of the electric vehicle 100, and scene determination information. The scene determination information is related to the above-described principle (3) and is information for determining the driving scene of the electric vehicle 100. The scene determination information is acquired by estimating the driving environment around the electric vehicle 100 using a camera, LiDAR, radar, a steering angle sensor, map information, GPS, and the like. For example, when the coefficient setting unit 122a acquires information such as "there are no vehicles ahead," "the road is straight," "the road is wide," and "the traffic light ahead is green" from the scene determination information, it determines that the driving scene is one in which strong acceleration is expected when the accelerator depression is equal to or greater than a first threshold value.

[0099] As described above, according to the second modified example, when the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle 100, the value of the coefficient a changes based on the accelerator pedal depression amount of the accelerator pedal 22 and the vehicle speed of the electric vehicle 100. This makes it possible to reproduce the acceleration feel of the target virtual vehicle in a wider range of scenes, taking into account acceleration scenes required by the driver.

[0100] 5 Sound control device The control device 101 according to this embodiment may function as a sound control device that controls the in-vehicle speaker 21 to output a pseudo engine sound corresponding to a target virtual vehicle in relation to the control of the electric vehicle 100. More specifically, the processing circuit 102 executes a computer program 104 for controlling the in-vehicle speaker stored in the storage device 103, causing the control device 101 to function as a sound control device. The control of the electric vehicle 100 by the sound control device will be described below.

[0101] 13 is a diagram showing the functional configuration of the sound control device 101b. The sound control device 101b generates from the in-vehicle speaker 21 a pseudo engine sound that simulates the engine sound of a virtual vehicle equipped with an internal combustion engine.

[0102] The mode information acquisition unit 110 transmits information about the selected control mode and information about the target virtual vehicle to the engine pseudo sound generation unit 160 .

[0103] The engine pseudo sound generation unit 160 functions when the control mode is the on-demand mode and the target virtual vehicle is a virtual vehicle equipped with an internal combustion engine. At this time, the sound control device 101b reads out a vehicle model 200 (target vehicle model) of the target virtual vehicle from the vehicle model database D10 based on information about the target virtual vehicle from the mode information acquisition unit 110. Parameters 201 are also set according to the target virtual vehicle. The engine pseudo sound generation unit 160 then generates an engine pseudo sound based on the virtual engine torque and virtual engine speed calculated using the target vehicle model.

[0104] The engine false sound generation unit 160 acquires the sound source of the engine false sound for the target virtual vehicle by referring to the storage device 103. The storage device 103 may store the sound source of the engine false sound for each virtual vehicle equipped with an internal combustion engine.

[0105] The pseudoengine sound generation unit 160 includes a process 161 for calculating engine sound pressure and a process 162 for calculating engine sound frequency. In process 161, the sound pressure of the pseudoengine sound is calculated from the virtual engine torque using a sound pressure map M11. The sound pressure map M11 is created so that the sound pressure increases as the virtual engine torque increases. In process 162, the frequency of the virtual engine sound is calculated from the virtual engine rotation speed using a frequency map M12. The frequency map M12 is created so that the frequency increases as the virtual engine rotation speed increases. The virtual engine torque and the virtual engine rotation speed change depending on the operation of the driving operation members by the driver.

[0106] The sound control device 101b outputs the pseudo engine sound generated by the pseudo engine sound generation unit 160 from the in-vehicle speaker 21. By performing sound control related to the pseudo engine sound by the sound control device 101b in this way, when the control mode is the on-demand mode, the driver can be given an even more realistic feeling as if he or she is driving the target virtual vehicle.

[0107] 6 Display Control Device The control device 101 according to this embodiment may function as a display control device that controls the HMI 20 to display the virtual engine speed and virtual gear position of the target virtual vehicle when the control mode is the on-demand mode, thereby providing the driver with an even more realistic feeling that they are driving the target virtual vehicle.

[0108] 7. Other The electric vehicle 100 according to the above embodiment is a front-wheel drive vehicle in which one electric motor 2 drives the front wheels. However, the technical features according to this embodiment can also be applied to electric vehicles in which two electric motors are arranged at the front and rear to drive the front and rear wheels, respectively. They can also be applied to electric vehicles in which each wheel is equipped with an in-wheel motor.

[0109] The technical features of this embodiment are not limited to battery electric vehicles, but can be widely applied to any electric vehicle that uses an electric motor as a driving power unit. For example, the technical features of this embodiment can be applied to hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) that have a mode in which they run solely on the driving force of the electric motor. They can also be applied to fuel cell electric vehicles (FCEVs) that supply electric energy generated by a fuel cell to the electric motor. [Explanation of symbols]

[0110] 2 electric motors 14 Battery 20 HMI 21 Car speakers 22 Accelerator pedal 24 Brake pedal 50 Sensor system 100 electric vehicles 101 Control device 102 Processing circuit 103 Storage device 104 Computer Programs 105 Data 200 vehicle models 201 Parameters

Claims

1. An electric vehicle having an electric motor as a drive source, a driving operation member used to drive the electric vehicle; a processing circuit; a storage device that stores a database that manages a plurality of vehicle models that are modeled based on a plurality of virtual vehicles that have different acceleration characteristics in response to a driver's driving operation; Equipped with The processing circuitry a target vehicle model corresponding to a target virtual vehicle selected by the driver from among the plurality of virtual vehicles is read from the database; calculating a virtual acceleration of the target virtual vehicle in response to the operation of the driving operation member using the target vehicle model based on the operation state of the driving operation member and the running state of the electric vehicle; performing an adjustment process of calculating an adjusted virtual acceleration by multiplying the virtual acceleration by a coefficient equal to or less than 1 according to the acceleration characteristic of the target virtual vehicle; The electric motor is controlled so that the acceleration of the electric vehicle is equal to the adjusted virtual acceleration. It is configured as follows: Electric car.

2. 10. The electric vehicle according to claim 1, In the adjustment process, the processing circuit When the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle, the coefficient is set to a value obtained by dividing the maximum acceleration that can be achieved by the electric vehicle by the maximum acceleration in the acceleration characteristics of the target virtual vehicle. It is configured as follows: Electric car.

3. 10. The electric vehicle according to claim 1, In the adjustment process, the processing circuit If the acceleration characteristic of the target virtual vehicle does not exceed the acceleration capability of the electric vehicle, the coefficient is set to 1. It is configured as follows: Electric car.

4. 10. The electric vehicle according to claim 1, In the adjustment process, the processing circuit While the electric vehicle can achieve the virtual acceleration, the coefficient is set to 1 regardless of the acceleration characteristics of the target virtual vehicle. It is configured as follows: Electric car.

5. 10. The electric vehicle according to claim 1, The driving operation member includes an accelerator pedal, In the adjustment process, the processing circuit When the acceleration characteristic of the target virtual vehicle exceeds the acceleration capacity of the electric vehicle, the value of the coefficient is changed based on the accelerator pedal opening and the vehicle speed of the electric vehicle. It is configured as follows: Electric car.

6. 6. An electric vehicle according to claim 1, The processing circuitry calculating a target driving force of the electric vehicle for adjusting the acceleration of the electric vehicle to the adjusted virtual acceleration; The motor torque output by the electric motor is changed so as to impart the target driving force to the electric vehicle. It is configured as follows: Electric car.

7. 6. An electric vehicle according to claim 1, each of the plurality of vehicle models has one or more parameters related to the acceleration characteristics; The processing circuitry is further configured to set the one or more parameters of the target vehicle model in response to the target virtual vehicle. Electric car.

8. A control device for an electric vehicle having an electric motor as a drive source, a processing circuit; a storage device that stores a database that manages a plurality of vehicle models that are modeled based on a plurality of virtual vehicles that have different acceleration characteristics in response to a driver's driving operation; Equipped with the electric vehicle includes a driving operation member used to drive the electric vehicle, The processing circuitry a target vehicle model corresponding to a target virtual vehicle selected by the driver from among the plurality of virtual vehicles is read from the database; calculating a virtual acceleration of the target virtual vehicle in response to the operation of the driving operation member using the target vehicle model based on the operation state of the driving operation member and the running state of the electric vehicle; performing an adjustment process of calculating an adjusted virtual acceleration by multiplying the virtual acceleration by a coefficient equal to or less than 1 according to the acceleration characteristic of the target virtual vehicle; The electric motor is controlled so that the acceleration of the electric vehicle is equal to the adjusted virtual acceleration. It is configured as follows: Control device.

9. The control device according to claim 8, In the adjustment process, the processing circuit When the acceleration characteristics of the target virtual vehicle exceed the acceleration capacity of the electric vehicle, the coefficient is set to a value obtained by dividing the maximum acceleration that can be achieved by the electric vehicle by the maximum acceleration in the acceleration characteristics of the target virtual vehicle. It is configured as follows: Control device.

10. The control device according to claim 8, In the adjustment process, the processing circuit If the acceleration characteristic of the target virtual vehicle does not exceed the acceleration capability of the electric vehicle, the coefficient is set to 1. It is configured as follows: Control device.

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