Control device, control system, control method, storage medium, and electric vehicle

Abstract

The control system (101-501) controls the operation of a power conversion device (20) that performs power conversion between a motor (30) that drives a vehicle and a power supply (10), the control system (101-501) having: a data acquisition unit (61, 61a) that acquires data from a device within the vehicle; and a control unit (64) that reduces the drive frequency of a switching element possessed by the power conversion device (20) in a case where it is judged, based on the data acquired by the data acquisition unit (61a), that it is a state in which the driver can allow noise.

Description

Technical Field

[0001] This invention relates to control devices, control systems, control methods, storage media, and electric vehicles. Background Technology

[0002] As a conventional control device for electric vehicles, Patent Document 1 discloses a method that determines the number of power converters to be driven and the frequency of the carrier signal used to generate the drive signal based on the total inflow current to multiple power converters. In this control device, the vehicle load is predicted based on information about a predetermined driving path. If the predicted load is greater than the current load, the determined number of drives is increased, and the determined frequency is decreased. By increasing the number of power converters to be driven in advance before the current load increases based on a prediction of future loads and decreasing the carrier frequency in the event of a anticipated high load, heat generation in the power converters can be suppressed. The information about the predetermined driving path is typically the gradient information of the predetermined driving path. Additionally, it is also described that the number of drives can be determined based on the accelerator pedal opening, motor output, and the target output of the motor, instead of the total inflow current.

[0003] On the other hand, Patent Document 2 discloses a control device for an electric vehicle, which includes: a detection unit for detecting the occurrence of load operations such as the charging and discharging of an energy storage device, which cause a temperature rise in the switching element; and a limit setting unit for setting a limit value in power conversion to suppress the current flowing through the switching element in accordance with the amount of temperature change in the switching element during each load operation. According to Patent Document 2, the temperature rise phenomenon that causes the temperature change that generates thermal stress in the switching element occurs during driver acceleration, engine starting, or large vehicle deceleration, due to load operations such as the charging and discharging of the main battery. According to the control device described in Patent Document 2, if the temperature rise increases when the load operation is detected, the current flowing through the switching element can be limited by suppressing the battery current or by prohibiting charging and discharging, thereby suppressing the amount of temperature change caused by the heating of the switching element.

[0004] Furthermore, Patent Document 2 also describes the following: Generally, the power loss in a switching element increases with the switching frequency, resulting in a more severe rise in element temperature. When detecting load operation, if the element current or battery current is greater than a threshold, the upper limit of the switching frequency is lowered by reducing the switching frequency of the converter to be lower than the default value. This serves as a state quantity for estimating the amount of temperature rise during load operation. For example, the element current and battery current are obtained, and based on the obtained state quantity, a limit value for power conversion is set.

[0005] Patent Document 1: Japanese Patent Application Publication No. 2020-088870

[0006] Patent Document 2: Japanese Patent Application Publication No. 2012-019587 Summary of the Invention

[0007] As described in Patent Documents 1 and 2, the heating of the switching elements can be suppressed by reducing the driving frequency of the switching elements used in the power conversion device. However, if the driving frequency of the switching elements is reduced, a new problem arises: the driving sound enters the audible range, generating noise. Patent Documents 1 and 2 completely disregard this noise problem.

[0008] The present invention was proposed to solve the aforementioned problems, and its purpose is to reduce the discomfort caused by noise generated from power conversion devices, and to suppress the heating of switching elements and improve the driving efficiency.

[0009] The control system of the present invention controls the operation of a power conversion device that performs power conversion between an electric motor and a power source, the electric motor driving a vehicle. The control system includes: a data acquisition unit that acquires data from devices inside the vehicle; and a control unit that reduces the driving frequency of the switching elements of the power conversion device when it is determined, based on the data acquired by the data acquisition unit, that the driver can tolerate the noise.

[0010] The effects of the invention

[0011] Since the control system of the present invention has a control unit, which determines, based on data obtained by the data acquisition unit, that the driver can tolerate the noise, reduces the driving frequency of the switching elements of the power conversion device, thereby alleviating discomfort from the noise generated from the power conversion device and suppressing the heating of the switching elements and improving the driving efficiency. Attached Figure Description

[0012] Figure 1 This is a block diagram showing the overall structure of the control system in Implementation Method 1.

[0013] Figure 2 It is a diagram showing the hardware structure of the control device.

[0014] Figure 3 This is a flowchart illustrating the operation of the control device in Implementation Method 1.

[0015] Figure 4 This is a block diagram showing the overall structure of the control system in Implementation Method 2.

[0016] Figure 5This is a flowchart illustrating the operation of the control device in Embodiment 2.

[0017] Figure 6 This is a block diagram showing the overall structure of the control system in Implementation Method 3.

[0018] Figure 7 This is a flowchart illustrating the operation of the control device 60 in Embodiment 3.

[0019] Figure 8 This is a flowchart illustrating the operation of the control device in a variation of Embodiment 3.

[0020] Figure 9 This is a block diagram showing the overall structure of the control system in Implementation Method 4.

[0021] Figure 10 This is a flowchart illustrating the operation of the control device in Embodiment 4.

[0022] Figure 11 This is a block diagram showing the overall structure of the control system in Implementation 5.

[0023] Figure 12 This is a schematic diagram showing the structure of the power conversion device in Embodiment 5.

[0024] Figure 13 This is a flowchart illustrating the operation of the control device in Embodiment 5.

[0025] Figure 14 This is a flowchart illustrating the operation of the control device in a variation of Embodiment 5.

[0026] Figure 15 This is a block diagram showing the structure of the learning device in Embodiment 6.

[0027] Figure 16 This is a flowchart of the learning process of the learning device in Implementation 6.

[0028] Figure 17 This is a block diagram showing the structure of the control device in Embodiment 6.

[0029] Figure 18 This is a flowchart regarding the inference process of the control device in Implementation 6.

[0030] Figure 19 This is a schematic diagram illustrating the three-layer neural network in Implementation Method 6. Detailed Implementation

[0031] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Furthermore, the drawings are merely schematic, and the dimensions and positions of the images shown in different drawings may not be accurately depicted and can be appropriately modified. Additionally, in the following description, the same structural elements are illustrated with the same reference numerals, and their names and functions are the same or identical. Therefore, detailed descriptions of them may sometimes be omitted.

[0032] Implementation Method 1

[0033] Figure 1 This is a block diagram showing the overall structure of the control system 101 in Embodiment 1 of the present invention. Although the diagram is omitted, the control system 101 is, for example, installed in a hybrid electric vehicle, an electric vehicle, or an electric vehicle that uses electric drive, and generates or controls the driving force for driving the electric vehicle. Figure 1 As shown, the control system 101 includes a power supply 10, a power conversion device 20, a motor 30, a semiconductor device 40, an accelerometer position sensor 51, a vehicle speed sensor 52, and a control device 60.

[0034] Power supply 10 is a DC power supply that supplies DC power to power conversion device 20. Power supply 10 can be composed of various power sources, such as a DC system, solar cells, or batteries, or it can be composed of a rectifier circuit or an AC / DC converter connected to an AC system. Alternatively, power supply 10 can also be composed of a DC / DC converter that converts DC power output from a DC system into a specified power.

[0035] The power conversion device 20 is a three-phase inverter connected between the power source 10 and the motor 30, which converts the DC power supplied from the power source 10 into AC power and supplies it to the motor 30. For example... Figure 1 As shown, the power conversion device 20 includes a main conversion circuit 21, a drive circuit 22, and a control circuit 23. The main conversion circuit 21 converts the DC power input from the power source 10 into AC power and outputs it to the motor 30. The drive circuit 22 outputs drive signals to drive the switching elements disposed within the semiconductor device 40 constituting the main conversion circuit 21. The control circuit 23 outputs control signals to the drive circuit 22 to control the drive circuit 22.

[0036] The electric motor 30 is a three-phase AC motor driven by AC power supplied from the power conversion device 20. The electric motor 30 drives the electric vehicle it is mounted on, generating a driving force.

[0037] Details of the power conversion device 20 will be described. The semiconductor device 40 constituting the main conversion circuit 21 includes switching elements and freewheeling diodes (not shown). By switching the switching elements on and off, the DC power supplied from the power source 10 is converted into AC power and supplied to the motor 30. The specific circuit structure of the main conversion circuit 21 varies, but the main conversion circuit 21 in this embodiment is a two-level three-phase full-bridge circuit, which can be composed of six switching elements and six freewheeling diodes connected in anti-parallel to the switching elements. The six switching elements are connected in series in pairs to form upper and lower bridge arms, and each upper and lower bridge arm constitutes a phase (U phase, V phase, W phase) of the full-bridge circuit. Furthermore, the output terminals of each upper and lower bridge arm, i.e., the three output terminals of the main conversion circuit 21, are connected to the motor 30.

[0038] Here, the switching element is, for example, a power semiconductor element such as an IGBT (Insulated Gate Bipolar Transistor) or a MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor), and the freewheeling diode is, for example, a semiconductor element that has formed a PIN diode or an SBD (Schottky barrier diode) or a FWD (Free Wheel Diode), but it is not limited to these if it has the same function.

[0039] In addition, silicon is typically used as the semiconductor material for switching elements and freewheeling diodes, but it is not particularly limited. For example, so-called wide-bandgap semiconductors with a wider bandgap than silicon can also be used. Examples of wide-bandgap semiconductors include silicon carbide, gallium nitride, aluminum nitride, aluminum gallium nitride, gallium oxide, and diamond.

[0040] Furthermore, the main conversion circuit 21 can be configured by setting six semiconductor devices 40 with a pair of switching elements and freewheeling diodes, or by setting three semiconductor devices 40 with two sets of switching elements and freewheeling diodes constituting the upper and lower bridge arms, or by setting one semiconductor device 40 with six switching elements and freewheeling diodes; any configuration is acceptable.

[0041] The drive circuit 22 generates drive signals to drive the switching elements of the semiconductor device 40 and supplies them to the control electrodes of the switching elements of the semiconductor device 40. Specifically, according to the control signals from the control circuit 23 (described later), drive signals that turn the switching elements on and drive signals that turn the switching elements off are output to the control electrodes of each switching element. When the switching element is kept on, the drive signal is a voltage signal greater than or equal to the threshold voltage of the switching element (on signal); when the switching element is kept off, the drive signal is a voltage signal less than or equal to the threshold voltage of the switching element (off signal).

[0042] Control circuit 23 controls the switching elements of semiconductor device 40 to supply the desired power to motor 30. Specifically, it calculates the time (on-time) during which each switching element of semiconductor device 40 should be in the on state based on the power to be supplied to motor 30. For example, the main converter circuit 21 can be controlled by PWM control, which modulates the on-time of the switching elements in accordance with the voltage to be output. Furthermore, a control command (control signal) is output to drive circuit 22, causing an on signal to be output to the switching element that should be in the on state at each time, and an off signal to be output to the switching element that should be in the off state. Drive circuit 22 outputs the on signal or off signal as a drive signal to the control electrode of each switching element according to the control signal.

[0043] Furthermore, in Embodiment 1, the power conversion device 20 is a two-level three-phase inverter, but the power conversion device 20 of the present invention is not limited to this. Power conversion between the motor 30 and the power supply 10 can be performed by driving switching elements; it can also be a three-level or multi-level three-phase inverter, or a single-phase inverter when supplying power to a single-phase load. Additionally, when supplying power to a DC load, a DC / DC converter or an AC / DC converter can be used as the power conversion device 20.

[0044] An accelerator position sensor 51 is installed inside the electric vehicle to detect the accelerator opening degree A. As is well known, acceleration and deceleration-stop commands issued by the driver are input through the operation of the accelerator pedal and brake pedal. The accelerator position sensor 51 is typically installed on the accelerator pedal of a car to detect the position of the accelerator pedal pressed by the driver and measure the amount of pressure applied to the accelerator pedal. The accelerator position sensor 51 outputs a voltage signal representing the amount of pressure applied to the accelerator pedal by the driver to the control device 60.

[0045] Vehicle speed sensor 52 is installed inside the electric vehicle to detect the vehicle speed. Vehicle speed sensor 52 is typically a speed sensor located on the axle connected to the tires, used to convert the speed detected by the speed sensor into vehicle speed. Vehicle speed sensor 52 is also electrically connected to control device 60, similar to accelerator position sensor 51. Vehicle speed sensor 52 outputs an output signal indicating the detected vehicle speed to control device 60.

[0046] Furthermore, the structure and operation of the accelerometer position sensor 51 and the vehicle speed sensor 52 are well known, so further detailed descriptions are omitted.

[0047] The control device 60 is an electronic control unit (ECU) that controls the operation of the power conversion device 20. In Embodiment 1, the control device 60 determines whether the noise level is acceptable to the driver based on the predicted temperature of the switching element of the semiconductor device 40 and the speed of the electric vehicle. The control device 60 includes a data acquisition unit 61, a storage unit 62, a frequency switching determination unit 63, and an inverter control unit 64.

[0048] The data acquisition unit 61 acquires data from devices installed inside the electric vehicle. In Embodiment 1, the data acquisition unit 61 acquires data on the accelerator opening A and the vehicle speed of the electric vehicle from the accelerator position sensor 51 and the vehicle speed sensor 52 installed inside the electric vehicle.

[0049] The storage unit 62 stores the data used by the frequency switching determination unit 63 for determination. More specifically, the storage unit 62 stores a prediction model and a relational expression. The prediction model predicts the future load of the motor 30 or the power conversion device 20 based on data obtained from equipment inside the electric vehicle. The relational expression calculates the temperature of the switching element based on the load of the motor 30 or the power conversion device 20 and the characteristics of the switching element. In Embodiment 1, the storage unit 62 stores a prediction model that predicts the future load of the motor 30 or the power conversion device 20 based on data of the accelerator opening A of the electric vehicle. Here, the correlation between the data of the accelerator opening A of the electric vehicle and the future load of the motor 30 or the power conversion device 20 can also be preset based on experiments, experience, or simulations.

[0050] Furthermore, the storage unit 62 stores data for each driving mode, which associates the prescribed driving mode of the electric vehicle with a result obtained in advance from judging whether the driver can tolerate the sound generated from the power conversion device 20 in that driving mode. In Embodiment 1, the storage unit 62 stores data for each vehicle speed, which associates the vehicle speed data of the electric vehicle with a result obtained in advance from judging whether the driver can tolerate the sound generated from the power conversion device 20 at that vehicle speed.

[0051] Here, the data that links the driving mode with the result of judging whether the driver can tolerate the sound produced at this time can be modeled by measuring the sound inside the cabin of the electric vehicle during test driving in advance during development, and the permissible range can be set by collecting the opinions of multiple people on the sound produced during actual driving through questionnaires and other means.

[0052] Furthermore, the level of noise that the driver can tolerate in each driving mode can be determined based on driving test results, the test driver's driving experience, and the tendency of each vehicle manufacturer to adjust the relationship between acceleration requirements and noise levels. For example, if a car prioritizes quietness, the setting might be configured to change the drive frequency only when the accelerator opening A changes significantly. Conversely, if a car, like a sports car, is intended to produce noise during acceleration, the setting might be configured to switch the drive frequency in the early stages. Thus, it is possible to predefine the level of noise the driver can tolerate, i.e., a driving mode that prioritizes acceleration over quietness.

[0053] The frequency switching determination unit 63 determines whether the noise level is acceptable to the driver based on the data acquired by the data acquisition unit 61. In Embodiment 1, the frequency switching determination unit 63 determines whether the noise level is acceptable to the driver based on data of the electric vehicle's accelerator opening A and the electric vehicle's speed.

[0054] More specifically, the frequency switching determination unit 63 predicts the future temperature of the switching element based on the accelerator opening A data acquired by the data acquisition unit 61, the prediction model stored in the storage unit 62, and the relational formula. Furthermore, if the predicted temperature of the switching element exceeds a predetermined value, the frequency switching determination unit 63 determines whether the current driving state of the electric vehicle is consistent with the predetermined driving mode stored in the storage unit 62 based on the vehicle speed data acquired by the data acquisition unit 61, and determines whether the noise level is acceptable to the driver based on this determination result.

[0055] Furthermore, in Embodiment 1, the frequency switching determination unit 63 may also be configured to calculate the change in accelerator opening dA / dt based on the accelerator opening A data of the electric vehicle acquired by the data acquisition unit 61, determine whether the change in accelerator opening dA / dt exceeds a predetermined threshold, and thereby determine whether the predicted temperature of the switching element exceeds a predetermined value. If the change in accelerator opening dA / dt exceeds the predetermined threshold, and the driver requests rapid acceleration, it can be determined that a high load will be applied to the switching element in the future, leading to a temperature rise. In this case, the storage unit 62 may also be configured such that a threshold used for determining the change in accelerator opening dA / dt is stored in advance, and the frequency switching determination unit 63 determines whether the change in accelerator opening dA / dt exceeds the threshold stored in the storage unit 62.

[0056] Furthermore, in Embodiment 1, the frequency switching determination unit 63 can also be configured to determine whether the noise level is acceptable to the driver by determining whether the vehicle speed of the electric vehicle, acquired by the data acquisition unit 61, exceeds a predetermined threshold. If the vehicle speed exceeds the predetermined threshold, it can be determined that the driver can tolerate the increased noise level due to the vehicle transitioning to a high-speed driving state where noise is acceptable, or that the electric vehicle is already in a high-speed driving state. In this case, the configuration can be such that the storage unit 62 stores the threshold value used for speed determination in advance, and the frequency switching determination unit 63 determines whether the vehicle speed exceeds the threshold value stored in the storage unit 62.

[0057] The inverter control unit 64 controls the operation of the power conversion device 20 by outputting commands related to the target output of the motor 30, the energizing current of the switching elements, and the drive frequency to the control circuit 23. Furthermore, when the frequency switching determination unit 63 determines that the driver can tolerate the noise level, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements in the power conversion device 20. That is, the inverter control unit 64 reduces the drive frequency of the switching elements when the change in the accelerator opening dA / dt of the electric vehicle exceeds a predetermined value and the vehicle speed exceeds a predetermined value.

[0058] Figure 2This diagram illustrates the hardware structure of the control device 60 in Embodiment 1. The control device 60 is configured to include a transceiver 66, a processor (CPU): Central Processing Unit (CPU) 67, a read-only memory (ROM) 68, and a random access memory (RAM) 69. The control device 60 outputs instructions for controlling the operation of the power conversion device 20 by processing a predetermined program pre-stored in the memory 68 by the processor 67. The transceiver 66 transmits and receives signals with various devices connected to the control device 60 and the power conversion device 20.

[0059] In the control device 60, various functional modules are implemented by executing a predetermined program stored in the memory 68 by the processor 67. The control module includes a data acquisition unit 61, a frequency switching determination unit 63, and an inverter control unit 64. Furthermore, the aforementioned storage unit 62 corresponds to the memory 68 and memory 69.

[0060] Furthermore, each functional module of the control device 60 can be implemented by the processor 67 executing software processing according to the pre-set program as described above, or it can be configured to perform prescribed numerical-logic operations on at least a portion of the modules by hardware such as electronic circuits having functions equivalent to those of each functional module.

[0061] In addition, in this embodiment, the operation control of the power conversion device 20 and the switching of the drive frequency of the switching element are performed by a single control device 60. However, the same control structure can also be achieved by the coordinated operation of multiple control devices (ECUs).

[0062] Figure 3 This is a flowchart illustrating the operation of the control device 60 in Embodiment 1. While the electric vehicle equipped with the control system 101 is in motion, the control device 60 always performs appropriate actions, either at a predetermined time or during a specified period. Figure 3 The process shown is as follows.

[0063] In step S1, the data acquisition unit 61 acquires an output signal from the accelerator position sensor 51, representing the voltage corresponding to the amount of accelerator pedal depressed by the driver, and uses this signal as data for the accelerator opening A of the electric vehicle. Additionally, the data acquisition unit 61 acquires an output signal from the vehicle speed sensor 52, representing the vehicle speed of the electric vehicle, and uses this signal as data for the vehicle speed.

[0064] Next, in step S2, the frequency switching determination unit 63 predicts the future temperature of the switching element based on the data of the accelerator opening A of the electric vehicle obtained by the data acquisition unit 61, the prediction model for predicting the future load of the motor 30 or the power conversion device 20 stored in the storage unit 62, and the relationship between the load of the motor 30 or the power conversion device 20 stored in the storage unit 62 and the characteristics of the switching element. It then determines whether the predicted temperature exceeds a predetermined value.

[0065] In addition, the frequency switching determination unit 63 can also calculate the change in accelerator opening dA / dt based on the data of accelerator opening A, determine whether the change in accelerator opening dA / dt exceeds a predetermined threshold, and thereby determine whether the predicted temperature of the switching element exceeds a predetermined value.

[0066] In step S2, if the change in the accelerator opening of the electric vehicle, dA / dt, does not exceed a predetermined threshold, that is, if the predicted temperature of the switching element is determined not to exceed a predetermined value (step S2 is No), the control device 60 will... Figure 3 The process is now complete.

[0067] On the other hand, in step S2, if the change in the accelerator opening of the electric vehicle, dA / dt, exceeds a predetermined threshold, that is, if it is determined that the predicted temperature of the switching element exceeds a predetermined value (step S2 is Yes), the determination process proceeds to step S3.

[0068] In step S3, the frequency switching determination unit 63 determines whether the current driving state of the electric vehicle is consistent with the prescribed driving mode stored in the storage unit 62 based on the vehicle speed data of the electric vehicle acquired by the data acquisition unit 61. If the current driving state of the electric vehicle is consistent with the prescribed driving mode, the frequency switching determination unit 63 determines whether the noise is permissible for the driver based on the determination result of whether the driver can tolerate the sound generated, which is associated with the prescribed driving mode stored in the storage unit 62.

[0069] In addition, the frequency switching determination unit 63 can also determine whether the electric vehicle's speed exceeds a predetermined threshold and whether the noise level is acceptable to the driver.

[0070] In step S3, if the speed of the electric vehicle does not exceed a predetermined threshold, i.e., if it is determined that the noise level is not permissible by the driver (step S3 is No), the control device 60 will... Figure 3 The process is now complete.

[0071] On the other hand, in step S3, if the speed of the electric vehicle exceeds a predetermined threshold, that is, if it is determined that the driver can tolerate the noise (step S3 is Yes), the process proceeds to step S4.

[0072] In step S4, based on the determination result of the frequency switching determination unit 63, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements of the power conversion device 20. Based on this command, the control circuit 23 outputs a control signal to the drive circuit 22, and the drive circuit 22 outputs a drive signal to the switching elements to reduce the drive frequency, thereby actually reducing the drive frequency of the switching elements. Then, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements. Figure 3 The process is now complete.

[0073] Next, the drive control of switching elements in conventional power conversion devices will be explained. For example, as described in Patent Documents 1 and 2, generally, in terms of power loss in switching elements, the loss increases with the increase of the drive frequency of the switching element. Therefore, by reducing the drive frequency of the switching element, the heating of the switching element can be suppressed. This is because the switching loss generated in the switching element is obtained by multiplying the loss generated in one switching cycle by the number of repetitions. Therefore, the higher the drive frequency, the more repetitions there are, and conversely, the lower the drive frequency, the fewer the repetitions, and the less the switching loss.

[0074] Here, the range of sound audible to humans is typically 20Hz to 20kHz. However, in the field of inverter control, for example, sound can be heard up to 2kHz to 5kHz, but becomes inaudible or unnoticed from around 8kHz. Therefore, when the drive frequency of the switching elements is reduced, the drive frequency enters the aforementioned range of sound audible to humans, and the drive sound of the power conversion device is thus identified as noise.

[0075] To address the aforementioned issues, it is considered to set the switching timing of the drive frequency near the allowable limit of the switching element. Furthermore, silicon semiconductors typically have a heat resistance temperature of 150°C, while silicon carbide semiconductors have a temperature resistance of around 200°C. However, a guaranteed operating temperature is usually specified depending on the semiconductor used. Therefore, it is conceivable to control the frequency by switching it just before reaching the aforementioned heat resistance temperature or guaranteed operating temperature, considering sensor errors and processing time delays on the system side, and switching at a value of approximately 100°C. However, in this case, the heat load accompanying the temperature rise of the switching element accumulates in the switching element, which may degrade the switching element and shorten its lifespan.

[0076] On the other hand, when the switching time of the drive frequency is advanced, as a countermeasure against noise, for example in electric vehicles, additional components such as the wall (partition) separating the engine compartment under the hood where the power conversion device is installed from the passenger compartment, and the installation of sound-absorbing materials under the hood are required.

[0077] Furthermore, when the power conversion device is an inverter, if the drive frequency of the switching elements is switched during inverter operation, there is a problem that the drive pulse width of the switching elements increases at the moment of switching, which may cause a short circuit. As a countermeasure, methods such as switching the drive frequency in stages are needed. However, if the temperature of the switching elements rises sharply, there is a risk that the switching frequency will be delayed, and the switching elements will become overheated.

[0078] In contrast, the control system 101 of Embodiment 1 has a control device 60, which includes a data acquisition unit 61 that acquires data from devices inside the electric vehicle; and an inverter control unit 64 that reduces the drive frequency of the switching elements of the power conversion device 20 when it is determined, based on the data acquired by the data acquisition unit 61, that the driver can tolerate the noise.

[0079] The control system 101 of Embodiment 1 reduces the drive frequency of the switching elements when the driver can tolerate the noise. Therefore, the driver will not perceive the sound generated by the power conversion device 20 as noise, which reduces the loss of the switching elements and suppresses heat generation. Thus, discomfort from the noise generated by the power conversion device can be reduced, and heat generation of the switching elements can be suppressed and drive efficiency improved.

[0080] Furthermore, according to the control system 101 of Embodiment 1, based on data from existing sensors such as the accelerometer position sensor 51 and the vehicle speed sensor 52, the system predicts the operation of the switching element of the power conversion device 20 to reach a high temperature. Without impairing the driver's drivability, the system performs switching control by reducing the drive frequency to lower the temperature of the switching element before it actually reaches a high temperature. This prevents delays in drive frequency switching as is common in the past.

[0081] In addition, semiconductors are usually specified with heat resistance temperature or operating protection temperature. However, when the temperature of the switching element is still low, the drive frequency is changed by reducing the load for safety precautions. Therefore, high-temperature operation of the switching element can be reliably avoided, ensuring safe operation within the specified temperature range.

[0082] Furthermore, when the switching element is a MOSFET made of silicon carbide (SiC) or similar material, the MOSFET typically experiences increased losses due to its resistive characteristics as the temperature rises. In contrast, according to Embodiment 1, the effect of reducing losses by suppressing the temperature of the switching element is also achieved.

[0083] Furthermore, if it is determined that the noise level is acceptable to the driver, the drive frequency can be actively reduced regardless of the temperature of the switching element or the load condition, with the aim of reducing the aforementioned switching losses and the MOSFET-specific losses associated with temperature rise.

[0084] In addition, as mentioned above, the reduction in drive frequency will cause the inverter noise to increase. The switching operation is carried out in a state where the driver can tolerate the noise, so it can prevent the driver from getting tired due to noise and impairing driving performance, and can reduce the amount of sound-absorbing materials and the like installed to prevent the driver from hearing the inverter noise.

[0085] That is, the control system 101 according to Embodiment 1 can take into account both the driver's drivability and the safety of the device, and since unnecessary sound-absorbing materials can be omitted, the cost of automobiles can also be reduced.

[0086] Furthermore, in the control system 101 of Embodiment 1, the main conversion circuit 21 is configured as follows: it is composed of a semiconductor device 40 having one or more switching elements and a freewheeling diode. The drive signal from the drive circuit 22 is supplied to the switching elements of the semiconductor device 40, but it is not limited to this. For example, the semiconductor device 40 may also be configured to include, in addition to the group of one or more switching elements and a freewheeling diode, a single package containing the drive circuit 22, other protection circuits, etc., which is called an IPM (Intelligent Power Module).

[0087] Furthermore, in the control system 101 of Embodiment 1, the inverter control unit 64 is configured to output commands such as the drive frequency of the switching elements to the control circuit 23, the control circuit 23 outputs control signals to the drive circuit 22, and the drive circuit 22 outputs drive signals to the switching elements, but it is not limited to this. For example, the inverter control unit 64 may also be configured to replace the drive circuit 22 and output drive signals to each switching element constituting the main conversion circuit 21 for driving them. In this case, Figure 3In step S4, the inverter control unit 64 directly outputs a drive signal that actually reduces the drive frequency to the switching element, instead of outputting a command to the control circuit 23 to reduce the drive frequency of the switching element. With this configuration, the advantage of not requiring both the drive circuit 22 and the control circuit 23 is achieved. Furthermore, in this case, the drive signal that actually reduces the drive frequency is equivalent to a command that reduces the drive frequency of the switching element.

[0088] Furthermore, in the control system 101 of Embodiment 1, the data acquisition unit 61 is configured to directly acquire data from the accelerometer position sensor 51 and the vehicle speed sensor 52, but it is not limited to this. For example, it can also be configured such that the control system 101 further includes a higher-level controller (not shown), which acquires data from devices such as the accelerometer position sensor 51 and the vehicle speed sensor 52 installed in the electric vehicle, and outputs the acquired data to the data acquisition unit 61.

[0089] Furthermore, these variations can also be applied in the following embodiments.

[0090] Implementation Method 2

[0091] Figure 4 This is a block diagram showing the overall structure of the control system 201 in Embodiment 2. The control system 201 in Embodiment 2 differs from the control system 101 in Embodiment 1, using data obtained from the navigation device 53 instead of data obtained from the accelerometer position sensor 51. Furthermore, the control system 201 in Embodiment 2 is largely common to the control system 101 in Embodiment 1; therefore, the following description focuses on the differences from the control system 101, and descriptions of structures and operations common to the control system 101 are appropriately omitted.

[0092] like Figure 4 As shown, the control system 201 of Embodiment 2 includes a power supply 10, a power conversion device 20, a motor 30, a semiconductor device 40, a vehicle speed sensor 52, a navigation device 53, and a control device 60.

[0093] The navigation device 53 is installed inside the electric vehicle and includes a location retrieval system such as GPS (Global Positioning System) and map data. The navigation device 53 is configured to determine the vehicle's current position on a map based on location information obtained via GPS, and to output the current position and map information overlaid on a display device (not shown). The navigation device 53 stores road information such as road gradient and speed limits. The navigation device 53 is configured to use GPS to obtain latitude, longitude, and altitude information related to the electric vehicle's current position, and based on this information, to generate road-related information such as gradient information, road information, and various other information, which is then output to the display device (not shown). The gradient information is related to the absolute gradient of the road surface on which the electric vehicle is traveling.

[0094] In addition, the navigation device 53 searches for a path from the current location to the destination set by the user, and displays the information of the explored path, i.e., the predetermined driving path, on the display device, thereby providing a prompt to the user (driver). Furthermore, the predetermined driving path refers to the portion of the route from the current location to the destination when a destination has been set, which is the part of the path the electric vehicle will travel on. When no destination has been set, it refers to the road ahead of the electric vehicle.

[0095] Furthermore, the navigation device 53 may be a device consisting only of a display device and a human-machine interface mounted inside the vehicle, with the main body of the device, including the storage medium for storing data and the program, consisting of an external device (server) connected wirelessly. Alternatively, the navigation device 53 may be a device that determines the current location and predetermined driving route of the electric vehicle in conjunction with a mobile terminal, smartwatch, or similar device owned by the driver. In this case, the electric vehicle may also have a structure that includes an interface device for communication with the mobile terminal or smartwatch, through which data related to the predetermined driving route is input to the data acquisition unit 61.

[0096] The navigation device 53 is electrically connected to the control device 60 and outputs data related to the predetermined driving path of the electric vehicle to the control device 60.

[0097] Furthermore, since the structure and operation of the navigation device 53 are well known, further detailed descriptions are omitted.

[0098] In Embodiment 2, the data acquisition unit 61 of the control device 60 acquires data related to the predetermined travel path of the electric vehicle from the navigation device 53. This data includes information related to the slope of the road surface the electric vehicle will travel on. Here, the road surface the electric vehicle will travel on includes at least the road surface the electric vehicle is currently traveling on, and further includes the concept of roads the electric vehicle may travel on in the near future. Additionally, similarly to Embodiment 1, the data acquisition unit 61 acquires data on the electric vehicle's speed from the vehicle speed sensor 52 installed within the electric vehicle.

[0099] In Embodiment 2, the storage unit 62 stores a prediction model that predicts the future load of the motor 30 or the power conversion device 20 based on data related to the predetermined travel path of the electric vehicle. Here, the correlation between the data related to the predetermined travel path of the electric vehicle and the future load of the motor 30 or the power conversion device 20 can also be set in advance based on experiments, experience, or simulations.

[0100] Furthermore, similarly to Embodiment 1, storage unit 62 stores data for each vehicle speed. This data associates the electric vehicle's speed data with a result obtained in advance from judging whether the driver can tolerate the sound generated from the power conversion device 20 at that speed. Here, the data that associates the driving mode with the result of judging whether the driver can tolerate the sound generated at that time can be created in the same way as in Embodiment 1.

[0101] In Embodiment 2, the frequency switching determination unit 63 predicts that the load of the electric vehicle will increase in the future based on the data related to the predetermined driving path obtained by the data acquisition unit 61, the prediction model stored by the storage unit 62, and the relationship, and predicts the temperature of the switching element at that time.

[0102] Furthermore, in Embodiment 2, the frequency switching determination unit 63 predicts whether the load on the electric vehicle will increase in the future based on information about the slope of the road surface the electric vehicle will travel on, which is included in data related to the predetermined travel path. That is, the frequency switching determination unit 63 can also be configured to analyze information related to the slope of the road surface the electric vehicle will travel on, and determine whether it is predicted that the electric vehicle will enter an uphill section in the future; in other words, whether it is presumed that the electric vehicle will travel on an uphill section in the near future. This allows for a determination of whether the predicted temperature of the switching element exceeds a predetermined value. If it is predicted that the electric vehicle will enter a high-slope uphill section, it is predicted that the load on the electric vehicle will increase in the future. Therefore, it can be determined that a high load will be applied to the switching element in the future, leading to a temperature increase. In this case, the structure can also be configured such that the storage unit 62 stores a threshold value for determining the road surface slope in advance, and the frequency switching determination unit 63 determines whether the slope of the road surface the electric vehicle will travel on exceeds the threshold value stored in the storage unit 62.

[0103] Alternatively, the frequency switching determination unit 63 can be configured to determine that the predicted load is greater than the current load if the slope of the road surface ahead (the predetermined driving path) is greater than the slope of the road surface directly below the electric vehicle, and to determine that the predicted load is less than the current load if the slope of the road surface ahead is less than the slope of the road surface directly below the vehicle.

[0104] Furthermore, similar to Embodiment 1, when the predicted temperature of the switching element exceeds a predetermined value, the frequency switching determination unit 63 determines whether the noise level is acceptable to the driver based on the vehicle speed data. If the vehicle speed exceeds the predetermined value, it can be determined that the driver is accelerating to climb the hill, and therefore it can be determined that the noise level is acceptable to the driver.

[0105] Similar to Embodiment 1, when the frequency switching determination unit 63 determines that the driver can tolerate the noise, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements of the power conversion device 20. That is, the inverter control unit 64 predicts the increase in load on the electric vehicle based on data related to the predetermined travel path of the electric vehicle, and reduces the drive frequency of the switching elements when the vehicle speed exceeds a predetermined value.

[0106] Figure 5 This is a flowchart illustrating the operation of the control device 60 in Embodiment 2. In step S11, the data acquisition unit 61 acquires data related to the predetermined travel path of the electric vehicle from the navigation device 53 and acquires the vehicle speed data of the electric vehicle from the vehicle speed sensor 52.

[0107] In step S12, the frequency switching determination unit 63 predicts the future temperature of the switching element based on data related to the predetermined driving path, a prediction model stored in the storage unit 62 that predicts the future load of the motor 30 or power conversion device 20, and a temperature relationship formula of the switching element based on the load of the motor 30 or power conversion device 20 and the characteristics of the switching element stored in the storage unit 62, and determines whether the predicted temperature exceeds a predetermined value.

[0108] In addition, the frequency switching determination unit 63 can also analyze information related to the slope of the road surface on which the electric vehicle travels based on data related to the predetermined travel path, determine whether the slope of the road surface on which the electric vehicle travels exceeds a predetermined threshold, and thereby determine whether the predicted temperature of the switching element exceeds a predetermined value.

[0109] In step S12, if the slope of the road surface on which the electric vehicle is traveling does not exceed a predetermined threshold, that is, if it is determined that the predicted temperature of the switching element does not exceed a predetermined value (step S12 is No), the control device 60 will... Figure 5 The process is now complete.

[0110] On the other hand, in step S12, if the slope of the road surface on which the electric vehicle is traveling exceeds a predetermined threshold, that is, if it is determined that the predicted temperature of the switching element exceeds a predetermined value (step S12 is Yes), the determination process proceeds to step S13.

[0111] In step S13, the frequency switching determination unit 63 determines whether the current driving state of the electric vehicle is consistent with the prescribed driving mode stored in the storage unit 62 based on the vehicle speed data of the electric vehicle. If the current driving state of the electric vehicle is consistent with the prescribed driving mode, the frequency switching determination unit 63 determines whether the noise is permissible for the driver based on the determination result of whether the driver can tolerate the sound generated, which is associated with the prescribed driving mode stored in the storage unit 62.

[0112] In addition, the frequency switching determination unit 63 can also determine whether the electric vehicle's speed exceeds a predetermined threshold and whether the noise level is acceptable to the driver.

[0113] In step S13, if the speed of the electric vehicle does not exceed a predetermined threshold, that is, if it is determined that the noise is not permissible by the driver (step S13 is No), the control device 60 will... Figure 5 The process is now complete.

[0114] On the other hand, in step S13, if the speed of the electric vehicle exceeds a predetermined threshold, that is, if it is determined that the driver can tolerate the noise (step S13 is Yes), the process proceeds to step S14.

[0115] In step S14, based on the determination result of the frequency switching determination unit 63, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements of the power conversion device 20. Based on this command, the control circuit 23 outputs a control signal to the drive circuit 22, and the drive circuit 22 outputs a drive signal to the switching elements to reduce the drive frequency, thereby actually reducing the drive frequency of the switching elements. Then, Figure 5 The process is now complete.

[0116] In the control system 201 of Embodiment 2, the same effect as that described in Embodiment 1 can also be obtained.

[0117] Furthermore, in the control system 201 of Embodiment 2, the frequency switching determination unit 63 determines whether the noise level is acceptable to the driver based on the vehicle speed data of the electric vehicle, but is not limited to this. For example, the control system 201 may also be configured such that the data acquisition unit 61 acquires acceleration data of the electric vehicle from an acceleration sensor installed in the electric vehicle, and the frequency switching determination unit 63 determines whether the acceleration of the electric vehicle exceeds a predetermined threshold, thereby determining whether the noise level is acceptable to the driver. When using acceleration data, if the acceleration of the electric vehicle exceeds a predetermined value, it can be determined that the driver has chosen to accelerate and climb a hill, and therefore it can be determined that the noise level is acceptable to the driver. In this case, it may also be configured such that the storage unit 62 stores the threshold value used for acceleration determination in advance, and the frequency switching determination unit 63 determines whether the acceleration of the electric vehicle exceeds the threshold value stored in the storage unit 62.

[0118] Furthermore, in the control system 201 of Embodiment 2, the frequency switching determination unit 63 determines whether it predicts that the load on the electric vehicle will increase in the future, i.e., whether it predicts that the electric vehicle will enter an uphill section, based on the slope information of the road surface on which the electric vehicle travels, included in the data related to the predetermined driving path provided by the navigation device 53. However, it is not limited to this. For example, it can be based on the slope information provided by the navigation device 53, or it can be based on the slope information obtained by the control device 60 as a result of parsing the position information of the electric vehicle provided by the navigation device 53. In these cases, the data related to the predetermined driving path refers to the slope information or the position information of the electric vehicle.

[0119] Furthermore, scenarios where the load on the electric vehicle will increase in the future are not limited to situations where the electric vehicle enters a steep uphill section. For example, in scenarios where the driving route changes from ordinary roads such as city streets to highways or suburban areas, it is also predicted that the load on the electric vehicle will be greater than the current load. In this case, the frequency switching determination unit 63 can also be configured to determine whether the load on the electric vehicle will increase in the future based on information about the driving route changing to highways or suburban areas, which is included in the data related to the predetermined driving route provided by the navigation device 53.

[0120] Implementation Method 3

[0121] Figure 6 This is a block diagram showing the overall structure of the control system 301 in Embodiment 3. The control system 301 in Embodiment 3 differs from the control system 101 in Embodiment 1. Instead of using data obtained from the accelerometer position sensor 51 and the vehicle speed sensor 52, it uses data obtained from the driver assistance device 54, the accelerometer position sensor 51, and the direction indicator 55. Furthermore, the control system 301 in Embodiment 3 is largely common to the control system 101 in Embodiment 1; therefore, the following description focuses on the differences from the control system 101, and descriptions of structures and operations common to the control system 101 are appropriately omitted.

[0122] like Figure 6 As shown, the control system 301 of Embodiment 3 includes a power supply 10, a power conversion device 20, a motor 30, a semiconductor device 40, an accelerometer position sensor 51, a driving assistance device 54, a direction indicator 55, and a control device 60.

[0123] Driving assistance device 54 refers to devices that assist in driving electric vehicles, such as ACC (Adaptive Cruise Control) or automated driving devices. ACC was developed with the premise of use on highways and dedicated motorway roads. It is a device that automatically maintains a fixed distance between the electric vehicle and other vehicles and keeps the electric vehicle at a prescribed speed. In conventional CC (Cruise Control), the vehicle can travel at a speed set by the driver, but the driver needs to brake to maintain a fixed distance. In contrast, ACC has a structure in which, through the coordinated action of sensors and the CPU, it can follow the vehicle in front while maintaining a fixed distance, and can automatically accelerate and brake. This is what is known as Level 2 of automated driving.

[0124] The accelerometer position sensor 51 is the same as that described in Embodiment 1. The direction indicator 55 is a device operated by the driver to indicate its direction to the surroundings when turning left or right or changing the route; it is a so-called direction indicator light.

[0125] The driver assistance device 54, the direction indicator 55, and the accelerometer position sensor 51 are all electrically connected to the control unit 60. The driver assistance device 54 outputs data related to the driving status of the electric vehicle to the control unit 60. In addition, the direction indicator 55 outputs data related to the direction of travel of the electric vehicle to the control unit 60.

[0126] Furthermore, the structure and operation of the driver assistance device 54 and the direction indicator 55 are well known, so further detailed descriptions are omitted.

[0127] In Embodiment 3, the data acquisition unit 61 of the control device 60 acquires data related to the driving state of the electric vehicle from the driving assistance device 54. This data includes information indicating that the electric vehicle is performing automated driving via ACC (Adaptive Cruise Control). Additionally, the data acquisition unit 61 acquires data related to the electric vehicle's direction of travel from the direction indicator 55. This data includes information indicating the direction the electric vehicle is taking when turning left or right, or changing its route. Furthermore, similarly to Embodiment 1, the data acquisition unit 61 acquires data on the electric vehicle's accelerator opening A from the accelerator position sensor 51.

[0128] In embodiment 3, the storage unit 62 stores data for various driving states of the electric vehicle. This data is associated with data related to the driving state of the electric vehicle and the result obtained in advance by judging whether the driver can allow the sound generated from the power conversion device 20 in that driving state.

[0129] In addition, in embodiment 3, the storage unit 62 stores a prediction model that predicts the future load of the motor 30 or the power conversion device 20 based on data related to the direction of travel of the electric vehicle and data on the accelerator opening A of the electric vehicle.

[0130] In embodiment 3, the frequency switching determination unit 63 determines whether the current driving state of the electric vehicle is consistent with the prescribed driving mode stored in the storage unit 62 based on data related to the driving state of the electric vehicle, and determines whether the noise is permissible for the driver based on the determination result.

[0131] Furthermore, in Embodiment 3, the frequency switching determination unit 63 acquires information from data related to the driving state of the electric vehicle, indicating that the electric vehicle is performing automated driving via ACC, and thereby determines whether the driver can tolerate noise. When automated driving via ACC is in progress, the electric vehicle is traveling at high speed, and it can be determined that the driver intends to tolerate noise.

[0132] In addition, in embodiment 3, the frequency switching determination unit 63 predicts the future temperature of the switching element based on data related to the direction of travel of the electric vehicle, data on the accelerator opening A of the electric vehicle, and the prediction model and formula stored in the storage unit 62.

[0133] Furthermore, in Embodiment 3, the frequency switching determination unit 63 predicts the future temperature of the switching element based on data related to the electric vehicle's direction of travel, including information indicating left or right turns or changes in the route, and the electric vehicle's accelerator opening A, and determines whether the predicted temperature of the switching element exceeds a predetermined value. In the state of autonomous driving implemented by ACC, if acceleration and turn signal operation are detected, it can be determined from the driver's intention that overtaking acceleration is being performed, and therefore it can be predicted that a high load will be applied to the switching element during this stage, causing the switching element to become hot. In this case, the structure can also be configured such that the storage unit 62 stores a threshold value used for determining the accelerator opening A in advance, the frequency switching determination unit 63 obtains information from the direction indicator 55 indicating that the electric vehicle is making left or right turns or changing its route, and determines whether the accelerator opening A exceeds the threshold value stored in the storage unit 62.

[0134] When the frequency switching determination unit 63 determines that the noise level is acceptable to the driver and the predicted temperature of the switching element exceeds a predetermined value, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching element in the power conversion device 20. That is, when the driver assistance device 54 is providing driving assistance to the electric vehicle and, based on data of the electric vehicle's accelerator opening A and data related to the electric vehicle's direction of travel, determines that the driver is overtaking, the inverter control unit 64 reduces the drive frequency of the switching element.

[0135] Figure 7 This is a flowchart illustrating the operation of the control device 60 in Embodiment 3. In step S21, the data acquisition unit 61 acquires data related to the driving state of the electric vehicle from the driving assistance device 54, acquires data related to the direction of travel of the electric vehicle from the direction indicator 55, and acquires data on the accelerator opening A of the electric vehicle from the accelerator position sensor 51.

[0136] In step S22, the frequency switching determination unit 63 determines whether the current driving state of the electric vehicle is consistent with the prescribed driving mode stored in the storage unit 62 based on data related to the driving state of the electric vehicle. If the current driving state of the electric vehicle is consistent with the prescribed driving mode, the frequency switching determination unit 63 determines whether the noise is permissible for the driver based on the determination result of whether the driver can tolerate the sound generated, which is associated with the prescribed driving mode stored in the storage unit 62.

[0137] In addition, the frequency switching determination unit 63 can also obtain information from data related to the driving state of the electric vehicle, indicating that the electric vehicle is performing autonomous driving by ACC, thereby determining whether the driver can tolerate the noise.

[0138] In step S22, if no information indicating that the electric vehicle is performing autonomous driving via ACC is obtained, i.e., if it is determined that the noise is not permissible by the driver (step S22 is No), the control device 60 will... Figure 7 The process is now complete.

[0139] On the other hand, in step S22, if information indicating that the electric vehicle is performing autonomous driving by ACC is obtained, that is, if it is determined that the driver can tolerate the noise (step S22 is Yes), the process proceeds to step S23.

[0140] In step S23, the frequency switching determination unit 63 predicts the future temperature of the switching element based on data related to the direction of travel of the electric vehicle and data on the accelerator opening A of the electric vehicle, a prediction model stored in the storage unit 62 that predicts the future load of the motor 30 or the power conversion device 20, and a relationship formula for the temperature of the switching element based on the load of the motor 30 or the power conversion device 20 stored in the storage unit 62 and the characteristics of the switching element, and determines whether the predicted temperature exceeds a predetermined value.

[0141] In addition, the frequency switching determination unit 63 can also obtain information such as the electric vehicle making left or right turns or changing its route, and determine whether the accelerator opening A exceeds a predetermined threshold, thereby determining whether the predicted temperature of the switching element exceeds a predetermined value.

[0142] In step S23, if no information indicating that the electric vehicle is making left or right turns or changing its route is obtained, or if the accelerator opening A does not exceed a predetermined threshold, i.e., if it is determined that the predicted temperature of the switching element does not exceed a predetermined value (step S23 is No), the control device 60 will... Figure 7 The process is now complete.

[0143] On the other hand, in step S23, if information is obtained that the electric vehicle is turning left or right or changing its route, and the accelerator opening A exceeds a predetermined threshold, that is, if it is determined that the predicted temperature of the switching element exceeds a predetermined value (step S23 is Yes), the process proceeds to step S24.

[0144] In step S24, based on the determination result of the frequency switching determination unit 63, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements of the power conversion device 20. Based on this command, the control circuit 23 outputs a control signal to the drive circuit 22, and the drive circuit 22 outputs a drive signal to the switching elements to reduce the drive frequency, thereby actually reducing the drive frequency of the switching elements. Then, Figure 7 The process is now complete.

[0145] In the control system 301 of Embodiment 3, the same effect as that described in Embodiment 1 can also be obtained.

[0146] Furthermore, in the control system 301 of Embodiment 3, when the driver assistance device 54 is providing driver assistance for the electric vehicle, and the inverter control unit 64 determines that the driver is overtaking based on data of the electric vehicle's accelerator opening A and data related to the electric vehicle's direction of travel, it reduces the drive frequency of the switching elements, but is not limited to this. For example, the driver assistance device 54 may be a device capable of automatically overtaking, and the inverter control unit 64 reduces the drive frequency of the switching elements when the driver assistance device 54 is overtaking.

[0147] In this scenario, for example, the driving assistance device 54 can work in conjunction with a navigation device to assist the driving of an electric vehicle, enabling it to automatically drive on a prescribed route, such as a highway, while maintaining a fixed distance from the vehicle in front and at a set speed limit. If a vehicle traveling at a speed lower than the set speed is ahead, the driving assistance device 54 will suggest to the driver if it determines that overtaking the vehicle is feasible. Furthermore, the device is configured such that if the driver agrees to the suggestion via a switch or similar means, it can automatically execute a series of actions, from changing lanes to overtaking the vehicle in front, and then returning to the original lane.

[0148] Furthermore, the driving assistance device 54 is electrically connected to the control device 60 and outputs data related to the driving status of the electric vehicle to the control device 60. The data acquisition unit 61 acquires data related to the driving status of the electric vehicle from the driving assistance device 54. This data includes information indicating that the driving assistance device 54 automatically performs overtaking maneuvers.

[0149] Figure 8 This is a flowchart illustrating the operation of the control device 60 in a variation of Embodiment 3. In step S31, the data acquisition unit 61 acquires data related to the driving state of the electric vehicle from the driving assistance device 54.

[0150] In step S32, the frequency switching determination unit 63 determines whether the current driving state of the electric vehicle is consistent with the prescribed driving mode stored in the storage unit 62 based on data related to the driving state of the electric vehicle. If the current driving state of the electric vehicle is consistent with the prescribed driving mode, the frequency switching determination unit 63 determines whether the driver can tolerate the noise based on the determination result of whether the driver can tolerate the sound generated, which is associated with the prescribed driving mode stored in the storage unit 62.

[0151] In addition, the frequency switching determination unit 63 predicts the future temperature of the switching element based on data related to the driving state of the electric vehicle, a prediction model stored in the storage unit 62 that predicts the future load of the motor 30 or the power conversion device 20, and a temperature relationship formula for the switching element based on the load of the motor 30 or the power conversion device 20 stored in the storage unit 62 and the characteristics of the switching element, and determines whether the predicted temperature exceeds a predetermined value.

[0152] Furthermore, the frequency switching determination unit 63 can also acquire information from data related to the driving state of the electric vehicle, indicating that the driving assistance device 54 is automatically overtaking, thereby determining whether the noise level is acceptable to the driver. Additionally, the frequency switching determination unit 63 can also acquire information from data related to the driving state of the electric vehicle, indicating that the driving assistance device 54 is automatically overtaking, thereby determining whether the predicted temperature of the switching element exceeds a predetermined value. In other words, the frequency switching determination unit 63 can be configured to acquire information indicating that the driving assistance device 54 is automatically overtaking and then perform these determinations in a comprehensive manner.

[0153] In step S32, if no information indicating that the driving assistance device 54 is automatically overtaking is obtained, that is, if it is determined that the noise is not permissible by the driver, or if the predicted temperature of the switching element does not exceed a predetermined value (step S32 is No), the control device 60 will... Figure 8 The process is now complete.

[0154] On the other hand, in step S32, if information indicating that the driving assistance device 54 is automatically overtaking is obtained, that is, if it is determined that the driver can tolerate the noise, and the predicted temperature of the switching element exceeds a predetermined value (step S32 is Yes), the process proceeds to step S33.

[0155] In step S33, based on the determination result of the frequency switching determination unit 63, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements of the power conversion device 20. Based on this command, the control circuit 23 outputs a control signal to the drive circuit 22, and the drive circuit 22 outputs a drive signal to the switching elements to reduce the drive frequency, thereby actually reducing the drive frequency of the switching elements. Then, Figure 8 The process is now complete.

[0156] In a variation of embodiment 3, the same effect as that described in embodiment 1 can also be obtained.

[0157] Furthermore, in a variation of embodiment 3, since the driving frequency of the switching element can be reduced without waiting for data obtained from the direction indicator 55 and the accelerometer position sensor 51, the effects of heat suppression and driving efficiency of the switching element can be further improved, and the processing performed in the control device 60 can be simplified.

[0158] Implementation Method 4

[0159] Figure 9 This is a block diagram showing the overall structure of the control system 401 in Embodiment 4. The control system 401 in Embodiment 4 differs from the control system 101 in Embodiment 1, using data obtained from the fuel gauge 56 and battery capacity gauge 57 instead of data obtained from the accelerator position sensor 51 and vehicle speed sensor 52. Furthermore, the control system 401 in Embodiment 4 is largely common to the control system 101 in Embodiment 1; therefore, the following description focuses on the differences from the control system 101, and descriptions of structures and operations common to the control system 101 are appropriately omitted.

[0160] In embodiment 4, the electric vehicle equipped with the control system 401 is a hybrid electric vehicle equipped with both a gasoline engine and a battery. Furthermore, the control device 60 determines whether the noise level is acceptable to the driver based on the future driving range of the hybrid electric vehicle.

[0161] like Figure 9 As shown, the control system 401 of Embodiment 4 includes a power supply 10, a power conversion device 20, a motor 30, a semiconductor device 40, a fuel gauge 56, a battery capacity gauge 57, and a control device 60.

[0162] Fuel gauge 56 is a metering instrument used in hybrid vehicles to detect and display the remaining fuel level in the gasoline engine and other components. Its primary purpose is to allow the driver to monitor the current fuel level.

[0163] Battery capacity meter 57 is a sensor configured to detect the remaining capacity, or SOC (State of Charge), of a battery (not shown) installed in a hybrid vehicle. Furthermore, the battery is a rechargeable storage battery that functions as a power supply source for driving the electric motor 30.

[0164] The fuel gauge 56 and the battery capacity gauge 57 are electrically connected to the control device 60, and the control device 60 always monitors the remaining fuel level of the hybrid vehicle detected by the fuel gauge 56 and the remaining battery capacity detected by the battery capacity gauge 57.

[0165] Furthermore, since the structure and operation of the fuel gauge 56 and the battery capacity gauge 57 are well known, further detailed explanations are omitted.

[0166] In embodiment 4, the data acquisition unit 61 of the control device 60 acquires data on the remaining fuel level of the hybrid vehicle from the fuel gauge 56 and data on the remaining battery capacity of the hybrid vehicle from the battery capacity gauge 57.

[0167] In embodiment 4, the storage unit 62 stores a model that determines whether the noise level is acceptable to the driver based on data on the remaining fuel level of the hybrid vehicle and the remaining battery capacity of the hybrid vehicle.

[0168] In embodiment 4, the frequency switching determination unit 63 determines whether the noise level is acceptable to the driver based on data on the remaining fuel level of the hybrid vehicle, data on the remaining battery capacity of the hybrid vehicle, and a model stored in the storage unit 62.

[0169] Furthermore, in embodiment 4, the frequency switching determination unit 63 can also be configured to determine whether the driver can tolerate noise by judging whether the remaining fuel level of the hybrid vehicle is lower than a predetermined threshold. Alternatively, the frequency switching determination unit 63 can also be configured to determine whether the driver can tolerate noise by judging whether the remaining battery capacity is lower than a predetermined threshold. When the remaining fuel level or the remaining battery capacity is lower than the predetermined threshold, it can be determined that the fuel or battery capacity is insufficient, and the driver desires an extended driving range. That is, since it is a state where high load on switching elements should be avoided to prevent increased losses, it can be determined that the driver can tolerate noise. In this case, it can also be configured such that the storage unit 62 stores the threshold values ​​used for judging the remaining fuel level and battery capacity of the hybrid vehicle, and the frequency switching determination unit 63 determines whether the remaining fuel level or battery capacity of the hybrid vehicle exceeds the threshold values ​​stored in the storage unit 62.

[0170] Here, the state where the remaining fuel level is below a predetermined threshold is exemplified by a stage where a warning light illuminates when the fuel gauge detects a low fuel level, prompting the driver to refuel promptly. The warning light is activated in a system that detects the height of a float in the fuel tank using sensors and switches, and illuminates when the detected value exceeds a predetermined threshold. Furthermore, the timing of the warning light's activation is typically based on a point where the remaining fuel in the tank allows for a driving distance of approximately 5 to 10 km.

[0171] Furthermore, the same applies when the remaining battery capacity is below a predetermined threshold, such as when a warning light illuminates when the remaining driving range calculated based on the State of Charge (SOC) is low. Moreover, the state where the remaining fuel or battery capacity is below the predetermined threshold is not limited to the above; it could also be structured such that, anticipating future fuel or battery capacity shortages, a pre-determined low fuel or battery capacity is identified at a stage earlier than the warning light illuminates.

[0172] Similar to Embodiment 1, when the frequency switching determination unit 63 determines that the driver can tolerate the noise, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements of the power conversion device 20. That is, when the fuel level or battery capacity of the hybrid vehicle is lower than a predetermined value, the inverter control unit 64 reduces the drive frequency of the switching elements.

[0173] Figure 10This is a flowchart illustrating the operation of the control device 60 in Embodiment 4. In step S41, the data acquisition unit 61 acquires data on the remaining fuel level of the hybrid vehicle from the fuel gauge 56 and data on the remaining battery capacity of the hybrid vehicle from the battery capacity gauge 57.

[0174] In step S42, the frequency switching determination unit 63 determines whether the noise level is acceptable to the driver based on the data of the remaining fuel level of the hybrid vehicle, the data of the remaining battery capacity of the hybrid vehicle, and the model stored in the storage unit 62.

[0175] Furthermore, the frequency switching determination unit 63 can also be configured to determine whether the noise level is acceptable to the driver by judging whether the remaining fuel level of the hybrid vehicle is below a predetermined threshold. Alternatively, the frequency switching determination unit 63 can also be configured to determine whether the noise level is acceptable to the driver by judging whether the remaining battery capacity is below a predetermined threshold.

[0176] In step S42, if the remaining fuel level of the hybrid vehicle is not lower than a predetermined threshold and the remaining battery capacity is not lower than a predetermined threshold, that is, if it is determined that the noise level is not acceptable to the driver (step S42 is No), the control device 60 will... Figure 10 The process is now complete.

[0177] On the other hand, in step S42, if the remaining fuel of the hybrid vehicle is lower than a predetermined threshold, or the remaining capacity of the battery is lower than a predetermined threshold, that is, if it is determined that the driver can tolerate the noise (step S42 is Yes), the process proceeds to step S43.

[0178] In step S43, based on the determination result of the frequency switching determination unit 63, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements of the power conversion device 20. Based on this command, the control circuit 23 outputs a control signal to the drive circuit 22, and the drive circuit 22 outputs a drive signal to the switching elements to reduce the drive frequency, thereby actually reducing the drive frequency of the switching elements. Then, Figure 10 The process is now complete.

[0179] In the control system 401 of Embodiment 4, the same effect as that described in Embodiment 1 can also be obtained.

[0180] Furthermore, as described above, reducing the driving frequency of the switching elements can suppress heat generation and losses in the switching elements. Therefore, by reducing the driving frequency when the fuel level and battery capacity are low, it is possible to avoid the switching elements becoming under high load and increasing losses. This also results in improved fuel and battery utilization efficiency, extending the driving range of the hybrid vehicle. In other words, the control system 401 according to Embodiment 4 can balance driver drivability and device safety, and also achieve an increased driving range.

[0181] Furthermore, in Embodiment 4, the electric vehicle equipped with the control system 401 is a hybrid electric vehicle equipped with both a gasoline engine and a battery, but it is not limited to this. For example, the electric vehicle may also be an electric vehicle equipped only with lead-acid batteries, nickel-metal hydride batteries, lithium-ion batteries, etc., or a fuel cell vehicle equipped with a battery that is a fuel cell that uses hydrogen fuel. In this case, the data acquisition unit 61 only obtains the remaining capacity data of the battery from the battery capacity meter, the frequency switching determination unit 63 determines whether the driver can tolerate the noise based on the remaining capacity data of the battery, and the inverter control unit 64 reduces the driving frequency of the switching elements of the power conversion device 20 based on the determination result of the frequency switching determination unit 63. The same effect as described above can be obtained in this structure.

[0182] Implementation Method 5

[0183] Figure 11 This is a block diagram showing the overall structure of the control system 501 in Embodiment 5. The control system 501 in Embodiment 5 differs from the control system 101 in Embodiment 1, using data obtained from the temperature sensor 42 and the current sensor 43 instead of the data obtained from the accelerometer position sensor 51 and the vehicle speed sensor 52. Furthermore, the control system 501 in Embodiment 5 is largely common to the control system 101 in Embodiment 1; therefore, the following description focuses on the differences from the control system 101, and descriptions of structures and operations common to the control system 101 are appropriately omitted.

[0184] like Figure 11 As shown, the control system 501 of Embodiment 5 includes a power supply 10, a power conversion device 20, a motor 30, a semiconductor device 40, and a control device 60. The control device 60 is configured to be electrically connected to the semiconductor device 40 and is capable of transmitting and receiving data.

[0185] Figure 12 This is a schematic diagram showing the structure of the power conversion device 20 in embodiment 5. (As shown...) Figure 12 As shown, the semiconductor device 40 includes a switching element 41, a temperature sensor 42, and a current sensor 43.

[0186] Temperature sensor 42 detects the element temperature Ts of switching element 41. In embodiment 5, temperature sensor 42 is an on-chip temperature sensor disposed within the chip of switching element 41. However, temperature sensor 42 is not limited to being disposed within the chip of switching element 41; it can be configured to be disposed within the main conversion circuit 21 and capable of measuring the element temperature Ts of switching element 41. For example, a temperature sensor built into a semiconductor device 40 configured as an Intelligent Power Module (IPM) can be used as such a temperature sensor 42.

[0187] The current sensor 43 detects the current value Is flowing through the switching element 41. In Embodiment 5, the current sensor 43 is an on-chip current sensor that detects the current value Is flowing through a current sensing region disposed within the chip of the switching element 41. Furthermore, the current sensor 43 is not limited to being disposed within the chip of the switching element 41; it can be configured to be disposed in the main conversion circuit 21 and capable of measuring the current value Is flowing through the switching element 41. For example, the current sensor 43 can be configured to detect the current value Is flowing through the switching element 41 via a shunt resistor (not shown) connected internally or externally to the semiconductor device 40.

[0188] The temperature sensor 42 and the current sensor 43 are electrically connected to the control device 60, and the control device 60 always monitors the element temperature Ts of the switching element 41 detected by the temperature sensor 42 and the current value Is of the switching element 41 detected by the current sensor 43.

[0189] Furthermore, the structure and operation of the temperature sensor 42 and the current sensor 43 are well known, so further detailed descriptions are omitted.

[0190] like Figure 12 As shown, the main conversion circuit 21 includes a semiconductor device 40, a frequency divider circuit 25, a switch 26, and a switch 27. The frequency divider circuit 25 divides the frequency of the drive signal input from the drive circuit 22 and outputs it. For example, a 1 / 2 frequency divider circuit that divides the frequency of the input drive signal by 1 / 2 or a 1 / 3 frequency divider circuit that divides it by 1 / 3 can be used as the frequency divider circuit 25. Switches 26 and 27 are opened and closed according to the command from the control circuit 23, switching between a path where the drive signal from the drive circuit 22 first passes through the frequency divider circuit 25 and is then supplied to the switching element 41, and a path where it is directly supplied to the switching element 41. Normally, switch 26 is in the open state and switch 27 is in the closed state, and the drive signal from the drive circuit 22 is directly supplied to the control electrode of the switching element 41.

[0191] In the power conversion device 20 of Embodiment 5, by employing a structure that uses a frequency divider circuit 25 provided in the main conversion circuit 21 to divide the drive signal, it is possible to prevent the control device 60 from detecting an abnormality and performing control processing, outputting a command to switch the drive frequency, and receiving the command to actually switch the operation of the switching element 41, thus preventing adverse situations such as the switching element 41 becoming too hot and deteriorating during the processing delay. Furthermore, the structure and operation of the frequency divider circuit 25 are known, for example, as described in Japanese Patent Application Publication No. 6-140923, therefore further detailed description is omitted.

[0192] In control circuit 23, the element temperature Ts of switching element 41 is input from temperature sensor 42, and the current value Is flowing through switching element 41 is input from current sensor 43. Furthermore, control circuit 23 is set with predetermined threshold values. When the element temperature Ts of switching element 41 and the temperature change dTs / dt exceed these threshold values, a command is output to switch the open and closed states of switches 26 and 27. Thus, the drive signal from drive circuit 22 is supplied to the control electrode of switching element 41 via frequency divider circuit 25.

[0193] In Embodiment 5, the control device 60 determines whether the noise level is acceptable to the driver based on the element temperature Ts of the switching element 41 and the temperature change dTs / dt. In Embodiment 5, the data acquisition unit 61 of the control device 60 acquires the element temperature Ts data of the switching element 41 from the temperature sensor 42.

[0194] In embodiment 5, the storage unit 62 stores a prediction model that predicts whether the switching element 41 will become overheated in the future based on data of the element temperature Ts of the switching element 41. Here, as mentioned above, the heat resistance temperature of silicon semiconductors is typically 150°C. Generally, an operating protection temperature is specified according to the semiconductor used, but relative to this, the switching control is usually set to a value of around 100°C to take into account sensor errors and processing time delays on the system side. The temperature at which this switching control is performed is set according to the response of the system performing the switching control, etc. In the case of a system with a slow response, a low temperature is set, and in the opposite case, a value close to 150°C is set. Examples of systems with a slow response include those with a lot of noise, a large filter time constant for filtering signals, and slow processing timing of microcomputers.

[0195] The prediction model stored in the storage unit 62 is preferably created taking into account the aforementioned conditions. Alternatively, the prediction model or threshold can be set based on prior experiments, experience, or simulations.

[0196] Furthermore, in Embodiment 5, the storage unit 62 stores a model used to determine whether the noise level is acceptable to the driver based on data regarding the temperature change dTs / dt of the switching element 41. Moreover, the relationship between the temperature change dTs / dt of the switching element 41 and whether the driver can tolerate the noise generated at that time can be determined in the same manner as described in Embodiment 1. That is, the relationship between the temperature change dTs / dt of the element and specified temperatures such as the semiconductor's operating temperature can be calculated and modeled based on test driving during the development of the electric vehicle and simulation results of the electric vehicle.

[0197] In embodiment 5, the frequency switching determination unit 63 predicts the future increase in the load of the electric vehicle based on the element temperature Ts of the switching element 41 and the prediction model stored in the storage unit 62, and predicts the temperature of the switching element at that time.

[0198] Alternatively, the storage unit 62 may store a threshold value for determining the element temperature Ts in advance, and the frequency switching determination unit 63 may determine whether the predicted temperature of the switching element exceeds a predetermined value by determining whether the element temperature Ts of the switching element 41 exceeds the threshold value stored in the storage unit 62.

[0199] Furthermore, in Embodiment 5, when the predicted temperature of the switching element exceeds a predetermined value, the frequency switching determination unit 63 determines whether the noise is permissible for the driver based on the change in the element temperature of the switching element 41, dTs / dt, calculated from the element temperature Ts data of the switching element 41, and the model stored in the storage unit 62.

[0200] Furthermore, in Embodiment 5, the frequency switching determination unit 63 can also be configured to determine whether the noise level is acceptable to the driver by judging whether the change in the element temperature dTs / dt of the switching element 41 exceeds a predetermined threshold. If the change in the element temperature dTs / dt of the switching element 41 exceeds the predetermined threshold, it can be determined that the electric vehicle is experiencing a large load change, reaching a high-load driving state where the driver can tolerate the noise. In this case, the structure can also be such that the storage unit 62 stores the threshold value used for determining the change in element temperature dTs / dt in advance, and the frequency switching determination unit 63 calculates the change in element temperature dTs / dt based on the element temperature Ts data of the switching element 41, and determines whether the calculated change in element temperature dTs / dt exceeds the threshold value stored in the storage unit 62.

[0201] Similar to Embodiment 1, when the frequency switching determination unit 63 determines that the noise level is acceptable to the driver, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching element in the power conversion device 20. Specifically, the inverter control unit 64 reduces the drive frequency of the switching element 41 when the element temperature Ts of the switching element 41 exceeds a predetermined value, and when the change in element temperature dTs / dt of the switching element 41 exceeds a predetermined value.

[0202] Figure 13 This is a flowchart illustrating the operation of the control device 60 in Embodiment 5. In step S51, the data acquisition unit 61 acquires data on the element temperature Ts of the switching element 41 from the temperature sensor 42.

[0203] In step S52, the frequency switching determination unit 63 predicts the future temperature of the switching element based on the element temperature Ts of the switching element 41 from the temperature sensor 42 and the prediction model stored in the storage unit 62 that predicts whether the switching element 41 will become high temperature in the future, and determines whether the predicted temperature exceeds a predetermined value.

[0204] In addition, the frequency switching determination unit 63 can also determine whether the element temperature Ts of the switching element 41 exceeds a predetermined threshold, thereby determining whether the predicted temperature of the switching element exceeds a predetermined value.

[0205] In step S52, if the element temperature Ts of the switching element 41 does not exceed a predetermined threshold, that is, if it is determined that the predicted temperature of the switching element does not exceed a predetermined value (step S52 is No), the control device 60 will... Figure 13 The process is now complete.

[0206] On the other hand, in step S52, if the element temperature Ts of the switching element 41 exceeds a predetermined threshold, that is, if it is determined that the predicted temperature of the switching element exceeds a predetermined value (step S52 is Yes), the determination process in step S53 is entered.

[0207] In step S53, the frequency switching determination unit 63 determines whether the noise is permissible for the driver based on the change in element temperature dTs / dt of the switching element 41 calculated from the element temperature Ts data of the switching element 41 and the model stored in the storage unit 62.

[0208] In addition, the frequency switching determination unit 63 can also determine whether the noise level is acceptable to the driver by determining whether the change in element temperature dTs / dt, calculated based on the element temperature Ts data of the switching element 41, exceeds the threshold stored in the storage unit 62.

[0209] In step S53, if the speed of the electric vehicle does not exceed a predetermined threshold, i.e., if it is determined that the noise level is not permissible by the driver (step S53 is No), the control device 60 will... Figure 13 The process is now complete.

[0210] On the other hand, in step S53, if the speed of the electric vehicle exceeds a predetermined threshold, that is, if it is determined that the driver can tolerate the noise (step S53 is Yes), the process proceeds to step S54.

[0211] In step S54, based on the determination result of the frequency switching determination unit 63, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the drive frequency of the switching element 41 of the power conversion device 20. Based on this command, the control circuit 23 outputs a control signal to the drive circuit 22, and the drive circuit 22 outputs a drive signal to the switching element to reduce the drive frequency, thereby actually reducing the drive frequency of the switching element. Then, Figure 13 The process is now complete.

[0212] Furthermore, in Embodiment 5, when the inverter control unit 64 reduces the drive frequency of the switching element 41, the power conversion device 20 outputs a command to the control circuit 23 to reduce the drive frequency of the switching element 41, and simultaneously or before that, divides the frequency of the drive signal driving the switching element 41. That is, in Embodiment 5, the element temperature Ts of the switching element 41 detected by the temperature sensor 42 is also supplied to the control circuit 23 in step S51. Moreover, when the element temperature Ts of the switching element 41 exceeds a predetermined threshold, and the change in element temperature dTs / dt exceeds a predetermined threshold, the control circuit 23 outputs a command to switch the open and closed states of switches 26 and 27. As a result, the drive signal of the drive circuit 22 is divided by the frequency divider circuit 25, and the divided drive signal is supplied to the control electrode of the switching element 41.

[0213] In the control system 501 of Embodiment 5, the same effect as that described in Embodiment 1 can also be obtained.

[0214] Furthermore, in step S51 of embodiment 5, if the element temperature Ts of the switching element 41 and the change in element temperature dTs / dt exceed a predetermined threshold, the control system 501 divides the drive signal supplied to the switching element 41 via the frequency divider circuit 25. This prevents the control device 60 from detecting an abnormality, performing control processing, outputting a command to switch the drive frequency, and receiving that command to actually switch the switching element 41, thus preventing a processing delay. Therefore, it prevents the switching element 41 from becoming too hot and deteriorating during the processing delay.

[0215] Furthermore, in Embodiment 5, the control system 501 reduces the driving frequency of the switching element 41 based on the element temperature Ts data of the switching element 41 obtained from the temperature sensor 42, but is not limited thereto. For example, the control system 501 may also reduce the driving frequency of the switching element 41 based on the current value Is flowing through the switching element 41 obtained from the current sensor 43.

[0216] In this case, the data acquisition unit 61 acquires the current value Is flowing through the switching element 41 from the current sensor 43. The storage unit 62 stores the prediction model or threshold used to determine the current value Is, and the model or threshold used to determine the change in current value dIs / dt. The frequency switching determination unit 63 determines whether the predicted temperature of the switching element exceeds a predetermined value based on the current value Is, the prediction model, or the threshold. In addition, the frequency switching determination unit 63 determines whether the noise level is acceptable to the driver based on the change in current value dIs / dt, the model, or the threshold. When the current value Is of the switching element 41 exceeds a predetermined value, and the change in current value dIs / dt exceeds a predetermined value, the inverter control unit 64 reduces the driving frequency of the switching element 41.

[0217] The element temperature Ts of the switching element 41 increases due to the heat generated by the switching operation used for power conversion. Therefore, the change in element temperature dTs / dt mainly depends on the current flowing through the switching element 41 during switching. Thus, the element temperature Ts of the switching element 41 can be predicted based on the current value Is flowing through it, and the change in element temperature dTs / dt can be predicted based on the change in current value dIs / dt. Therefore, judging whether the noise level is acceptable to the driver based on the current value Is flowing through the switching element 41 and the change in current value dIs / dt is synonymous with judging whether the noise level is acceptable to the driver based on the element temperature Ts of the switching element 41 and the change in temperature dTs / dt.

[0218] Figure 14This is a flowchart illustrating the operation of the control device 60 in a modified example of Embodiment 5. In step S61, the data acquisition unit 61 acquires data on the current value Is flowing through the switching element 41 from the current sensor 43. In step S62, the frequency switching determination unit 63 determines whether the current value Is flowing through the switching element 41 exceeds a predetermined threshold. Additionally, in step S63, the frequency switching determination unit 63 determines whether the change in current value dIs / dt exceeds a predetermined threshold. Then, in step S64, if the current value Is of the switching element 41 exceeds a predetermined value, and the change in current of the switching element 41 dIs / dt exceeds a predetermined value, the inverter control unit 64 outputs a command to the control circuit 23 to reduce the driving frequency of the switching element 41.

[0219] In a variation of embodiment 5, the same effect as that described in embodiment 1 can also be obtained.

[0220] Implementation Method 6

[0221] In embodiments 1 to 5, the control device 60 determines whether the predicted temperature of the switching element exceeds a predetermined value based on data obtained from various devices installed in the electric vehicle, prediction models stored in the storage unit 62, or threshold values. Additionally, the control device 60 determines whether the noise level is acceptable to the driver based on data, models, or threshold values ​​obtained from various devices installed in the electric vehicle. Here, the prediction models and threshold values ​​used for the determination are set based on questionnaires conducted during test drives during development, or are set in advance based on experiments, experience, or simulations. In embodiment 6, the case where the prediction models and threshold values ​​used for the determination are created or determined using AI (Artificial Intelligence) machine learning will be described. Furthermore, the case where AI is applied to the creation of the prediction models in embodiment 1 will be described below, but it can also be applied to other embodiments in the same way.

[0222] <Learning Phase>

[0223] Figure 15This is a block diagram illustrating the structure of a learning device 70 used to create a trained model for use in the control device 60a of Embodiment 6. The learning device 70 is installed inside an electric vehicle and generates a trained model using learning data. This trained model is used to infer the result of a judgment on whether the driver can tolerate noise (hereinafter referred to as the noise tolerance judgment result). The learning data includes data obtained from devices installed in the electric vehicle under a specified driving mode of the electric vehicle, and a result obtained in advance from a judgment on whether the driver can tolerate the sound generated from the power conversion device 20 under that driving mode (hereinafter referred to as the tolerance judgment result). The learning device 70 includes a learning data acquisition unit 71, a model generation unit 72, and a trained model storage unit 73.

[0224] The learning data acquisition unit 71 acquires data on the accelerator opening A in the prescribed driving mode of the electric vehicle, the vehicle speed data, and data that is associated with the result of a prior judgment on whether the driver can tolerate the sound generated from the power conversion device 20 at that speed, and uses this data as learning data.

[0225] The model generation unit 72 learns the noise permissibility judgment result based on learning data created from the combination of the accelerator opening A and vehicle speed of the electric vehicle under a specified driving mode output from the learning data acquisition unit 71 and the permissibility judgment result at that time. That is, a trained model is generated, which infers the optimal noise permissibility judgment result based on the accelerator opening A and vehicle speed under the specified driving mode of the electric vehicle and the permissibility judgment result. Here, the learning data is data that correlates the accelerator opening A and vehicle speed under the specified driving mode with the noise permissibility judgment result. Furthermore, the correlation between the data used as learning data can be performed before or after acquisition by the learning data acquisition unit 71.

[0226] The learning algorithm used by the model generation unit 72 can employ well-known algorithms such as teacher-assisted learning, teacherless learning, and reinforcement learning. As an example, the application of a neural network will be explained.

[0227] The model generation unit 72, for example, learns the decision result for noise-allowed purposes through a neural network model, using so-called teacher-guided learning. Here, teacher-guided learning refers to a method that learns the features present in the learning data by assigning a set of input and result (label) data to the learning device 70, and then infers the result based on the input.

[0228] A neural network consists of the following layers: an input layer composed of multiple neurons, an intermediate layer (hidden layer) composed of multiple neurons, and an output layer composed of multiple neurons. The intermediate layer can be one layer or more than two layers.

[0229] For example, if it is like Figure 19 In a three-layer neural network as shown, if multiple inputs are fed into the input layer (X1-X3), the value is multiplied by weight W1 (w11-w16) and fed into the intermediate layer (Y1-Y2). The result is then multiplied by weight W2 (w21-w26) and output from the output layer (Z1-Z3). The output varies depending on the values ​​of weights W1 and W2.

[0230] In this invention, the neural network learns the results of a judgment on whether the driver can tolerate noise, based on the learning data and so-called teacher-guided learning. The learning data is created by combining the accelerator opening A and vehicle speed of the electric vehicle in a specified driving mode obtained by the learning data acquisition unit 71 with the judgment result on whether the noise is permissible at that time.

[0231] That is, the neural network learns by adjusting the weights W1 and W2 in a way that the result output from the output layer when the accelerator opening A and the vehicle speed of the electric vehicle are input into the input layer is close to the allowable / disallowable result.

[0232] The model generation unit 72 generates and outputs the trained model by performing the learning process described above.

[0233] The trained model storage unit 73 stores the trained model output from the model generation unit 72. The trained model generated thereby is based on data obtained from devices installed in the vehicle under a specified driving mode of the electric vehicle (i.e., the accelerator opening A and vehicle speed of the electric vehicle), and the result of a prior judgment on whether the driver can tolerate noise in each driving mode (i.e., the allowability judgment result). The control device 60a, described later, is activated to output a judgment result on whether the driver can tolerate noise (i.e., the noise allowance judgment result).

[0234] Next, use Figure 16 This indicates that the learning process is performed by the learning device 70. Figure 16 This is a flowchart related to the learning process of the learning device 70.

[0235] In step S71, the learning data acquisition unit 71 acquires the accelerator opening A and vehicle speed of the electric vehicle under a specified driving mode, as well as the current permission determination result. Furthermore, it is assumed that the accelerator opening A, vehicle speed, and current permission determination result are acquired simultaneously; however, as long as the accelerator opening A, vehicle speed, and permission determination result can be input in a related manner, the data for the accelerator opening A, vehicle speed, and permission determination result can also be acquired separately at different time points.

[0236] In step S72, the model generation unit 72 learns from the noise permissibility judgment results through so-called teacher-guided learning based on the learning data, and generates a trained model. The learning data is created based on the combination of accelerator opening A and vehicle speed with permissibility judgment results obtained by the learning data acquisition unit 71.

[0237] In step S73, the trained model storage unit 73 stores the trained model generated by the model generation unit 72.

[0238] <Application Phase>

[0239] Figure 17 This is a block diagram illustrating the structure of the control device 60a in Embodiment 6. The control device 60a is installed inside the electric vehicle. During a specified driving mode of the electric vehicle, it acquires data from devices installed within the electric vehicle, uses a trained model to infer a noise tolerance determination result based on the data acquired during that driving mode, and outputs a noise tolerance determination result based on the acquired data. The control device 60a is, for example, an electronic control unit (ECU) provided in the control system 101 described in Embodiment 1 above, replacing the control device 60, and has the same function as the control device 60, controlling the operation of the power conversion device 20. The control device 60a includes an inference data acquisition unit 61a, a storage unit 62a, a frequency switching determination unit 63a, and an inverter control unit 64.

[0240] The inference data acquisition unit 61a acquires the accelerator opening A data from the accelerator position sensor 51 and the vehicle speed data of the electric vehicle from the vehicle speed sensor 52.

[0241] The storage unit 62a stores the trained model created by the learning device 70.

[0242] The frequency switching determination unit 63a uses the trained model stored in the storage unit 62a to infer the noise allowance determination result obtained from the trained model. That is, by inputting the accelerator opening A data obtained by the inference data acquisition unit 61a and the electric vehicle speed data into the trained model, it can output the noise allowance determination result inferred based on the accelerator opening A and the vehicle speed.

[0243] Furthermore, in Embodiment 6, it is explained that a noise allowance determination result is output using a trained model learned by the model generation unit 72 during the test driving of an electric vehicle. However, a trained model can also be obtained from an external source, such as another electric vehicle, and a noise allowance determination result can be output based on the trained model.

[0244] Next, use Figure 18This describes the process of using control device 60a to obtain a noise tolerance determination result and switching the drive frequency of the switching element based on the determination result.

[0245] In step S81, it is deduced that the data acquisition unit 61a acquires the accelerator opening A data from the accelerator position sensor 51 and the vehicle speed data of the electric vehicle from the vehicle speed sensor 52.

[0246] In step S82, the frequency switching determination unit 63a inputs the accelerator opening A data and the electric vehicle speed data into the trained model stored in the storage unit 62a to obtain the noise allowance determination result.

[0247] In step S83, the frequency switching determination unit 63a outputs the noise allowance determination result obtained by the trained model to the inverter control unit 64.

[0248] In step S84, the inverter control unit 64, based on the output noise tolerance determination result, outputs a command to the control circuit 23 to reduce the drive frequency of the switching elements in the power conversion device 20. This effectively reduces the drive frequency of the switching elements.

[0249] In embodiment 6, the same effects as those described in embodiments 1 to 5 can also be obtained.

[0250] Furthermore, in Embodiment 6, the case where teacher-assisted learning is applied to the learning algorithm used by the model generation unit 72 is described, but it is not limited to this. Regarding the learning algorithm, in addition to teacher-assisted learning, reinforcement learning, unassisted learning, or semi-teacher-assisted learning can also be applied.

[0251] In addition, the trained model storage unit 73 can be a memory of the learning device 70 or the control device 60a, or it can be an external memory or a memory of other devices.

[0252] Furthermore, the trained model generated by the model generation unit 72 is not limited to being stored in the trained model storage unit 73. For example, the trained model may also be stored on a storage medium that can be read by a computer, such as an optical disc. In this case, the trained model generated by the model generation unit 72 is stored on the storage medium rather than in the trained model storage unit 73. Moreover, the control device 60a can store the trained model obtained from the storage medium in the storage unit 62a for inferring the noise permissibility determination result as described above.

[0253] Additionally, the learning device 70 is used to learn the noise tolerance judgment results during the test driving of the electric vehicle, but it is not limited to being installed inside the electric vehicle. The control device 60a is also used to infer the noise tolerance judgment results during the driving of the electric vehicle using the trained model generated by the learning device 70, but it is not limited to being installed inside the electric vehicle. These learning devices 70 and control devices 60a can, for example, be devices connected to the electric vehicle via a network and prepared separately from the electric vehicle. Alternatively, the learning devices 70 and control devices 60a can also be built into the electric vehicle. Furthermore, the learning devices 70 and control devices 60a can also reside on a cloud server.

[0254] Furthermore, it is not limited to the structure of the learning device 70 and the control device 60a being connected to the electric vehicle via a network and residing on a cloud server. Alternatively, it may be a structure in which any one of their functions, namely the learning data acquisition unit 71, the model generation unit 72, the trained model storage unit 73, the inference data acquisition unit 61a, the storage unit 62a, the frequency switching determination unit 63a, and the inverter control unit 64, is connected to the electric vehicle via a network and resides on a cloud server.

[0255] Furthermore, the model generation unit 72 can also learn the noise tolerance judgment results based on learning data created for multiple electric vehicles. In addition, the model generation unit 72 can obtain learning data from multiple electric vehicles used in the same country or region, or it can use learning data collected from multiple electric vehicles operating independently in different countries or regions to learn the noise tolerance judgment results. Furthermore, electric vehicles from which learning data is collected can be added to the list of objects or removed from the list of objects at any point. Moreover, the learning device 70, which learns the noise tolerance judgment results for a specific electric vehicle, can be applied to different electric vehicles to relearn and update the noise tolerance judgment results for those different electric vehicles.

[0256] In addition, the learning algorithm used by the model generation unit 72 can be deep learning, which learns by extracting the feature quantities themselves, or it can be machine learning performed by other known methods, such as gene programming, functional logic programming, support vector machines, etc.

[0257] The following describes a variation of Embodiment 6. The variation of Embodiment 6 is similar to Embodiment 6 in that, when determining whether a noise level is permissible for the driver, instead of using a prediction model or threshold set based on questionnaire results or simulation results from test driving, a pre-trained model generated using AI machine learning is used. Furthermore, the variation of Embodiment 6 strengthens the learning for noise levels that are unacceptable to the driver, thereby correcting the inference action based on the noise permissibility determination result by reducing the driving frequency of the switching element in noise-unacceptable states.

[0258] For example, when AI is applied to the creation of a prediction model in Implementation 1, data that links the accelerator opening A in a specified driving mode, the vehicle speed data of the electric vehicle, and the permission determination result in that driving mode are used as learning data, and as described above, a trained model can be generated by the machine learning of the model generation unit 72.

[0259] In this situation, when the data used for training is extremely short—for example, when the time between transitioning from a state where switching the drive frequency of a switching element is deemed necessary to a state where switching the drive frequency of that switching element is deemed unnecessary—specifically, when a driver's acceleration operation is immediately released after pressing the accelerator, resulting in a high load on the switching element ending in an extremely short time, it cannot be determined that the driver intends to tolerate noise. Therefore, it is inappropriate to conclude that switching the drive frequency in such a situation is not permissible for the driver. Therefore, for data in such cases, the inference actions in the trained model need to be corrected to output a noise-permissible determination result that indicates a state where noise is not permissible for the driver.

[0260] Here, regarding data where the time from the state where switching of the drive frequency of the switching element is determined to be required to the state where switching of the drive frequency of the switching element is determined not to be required (or, the duration of the state where a high load is applied to the switching element; hereinafter referred to as the switching processing time) is very short (hereinafter referred to as "no-switching processing data"), if it is associated with a permissibility judgment result of whether the driver cannot tolerate noise, then directly using this no-switching processing data as training data will not cause any problem. On the other hand, if the no-switching processing data is associated with a permissibility judgment result of whether the driver can tolerate noise, then the data needs to be corrected when used as training data.

[0261] The following describes a method for correcting the inference actions in the trained model. First, a threshold tb is preset to determine whether a switching of drive frequency is required. The model generation unit 72 of the learning device 70 learns using learning data, which includes, for example, data relating the accelerator opening A in a specified driving mode, the vehicle speed data, the permissibility determination result of whether the driver can tolerate noise in that driving mode, and the switching processing time in that driving mode.

[0262] At this point, the model generation unit 72 determines whether the switching processing time in the driving mode is less than or equal to a preset threshold tb. Furthermore, if the switching processing time in the driving mode is longer than the threshold tb, the model generation unit 72 learns that the driver can tolerate noise based on the permissibility determination result. On the other hand, if the switching processing time in the driving mode is less than or equal to the threshold tb, regardless of the permissibility determination result, the model generation unit 72 learns that the driver cannot tolerate noise. Thus, the model generation unit 72 generates a trained model for inferring a more appropriate noise permissibility determination result.

[0263] The trained model generated in this way, when given input data on the accelerator opening A under a specified driving mode, the electric vehicle's speed, and the switching processing time for that driving mode, outputs a noise tolerance judgment result based on the accelerator opening A data and the electric vehicle's speed data if the switching processing time is longer than a threshold tb. Conversely, if the switching processing time is less than or equal to the threshold tb, it outputs a noise tolerance judgment result indicating that the noise is not permissible for the driver, regardless of the accelerator opening A data or the electric vehicle's speed data for that driving mode.

[0264] In other words, in the trained model described above, when input data that does not require switching processing and has a very short switching processing time, the output is a noise tolerance determination result that is not a noise level that the driver can tolerate. The frequency switching determination unit 63a of the control device 60a uses the trained model generated therefrom to infer the noise tolerance determination result obtained from the trained model as described above.

[0265] In a variation of embodiment 6, the same effects as those described in embodiments 1 to 6 can also be obtained.

[0266] Furthermore, in a variation of embodiment 6, by utilizing a trained model for inferring a more appropriate noise tolerance determination result, it is possible to more appropriately balance the response to noise generated from the power conversion device 20, the suppression of heat generation of switching elements, and the improvement of drive efficiency.

[0267] Furthermore, in the variations of Embodiment 6 described above, the application of AI to the creation of the prediction model of Embodiment 1 was explained, but it is not limited to this. For example, in the case of application to Embodiment 2, the model generation unit 72 of the learning device 70 learns by using data that associates the information on the slope of the road surface on which the electric vehicle travels, the speed data of the electric vehicle, the current permissibility determination result, and the data indicating that the electric vehicle is not traveling on an uphill section, which is included in the data on the predetermined driving path, as learning data. Thus, regardless of the permissibility determination result that the driver can tolerate noise, it learns a state that the driver cannot tolerate noise. As data indicating that the electric vehicle is not traveling on an uphill section, examples include data indicating that the electric vehicle deviates from the predetermined driving path before entering the uphill section, or stops abruptly due to emergency braking, etc., indicating that it is not actually traveling on an uphill section.

[0268] The resulting trained model, in addition to the information on the slope of the road surface where the electric vehicle travels and the speed data of the electric vehicle contained in the data about the predetermined driving path, also obtains data indicating that the electric vehicle is not traveling on an uphill section. Under normal circumstances, based on the data related to the predetermined driving path, it makes the assumption that the electric vehicle will travel on an uphill section in the near future. Even if it is determined to be a state where the driver can tolerate the noise, it will output a noise tolerance judgment result indicating that the driver cannot tolerate the noise.

[0269] Furthermore, for example, when applied to Embodiment 3 or its variations, the model generation unit 72 of the learning device 70 learns using learning data. This learning data includes information indicating that the electric vehicle is performing autonomous driving enabled by the driver assistance device, (if necessary) data related to the electric vehicle's direction of travel and the electric vehicle's accelerator opening A, the current permission determination result, and data associated with the switching processing time of the driving mode. Similarly, when the switching processing time is longer than a threshold tb, based on the permission determination result that the driver can tolerate noise, a state where the driver can tolerate noise is learned. On the other hand, when the switching processing time of the driving mode is less than or equal to the threshold tb, regardless of the permission determination result that the driver can tolerate noise, a state where the driver cannot tolerate noise is learned.

[0270] In this example, as a case of very short switching processing time, specifically, it is an example of the driver accelerating and then releasing the accelerator, or the driver disengaging the automatic driving enabled by the driver assistance device immediately after making the judgment that the driver assistance device is automatically overtaking.

[0271] In the trained model generated as a result, even when the input data has a very short switching processing time and does not require switching processing, it also outputs a noise allowance judgment result that is not a state that the driver can tolerate.

[0272] In summary, the switching processing time from determining the switching frequency of the switching element to actually switching the driving frequency is very short. Therefore, in cases where switching processing is not actually required, such as when the electric vehicle is not driving on an uphill section and therefore does not require switching processing, even if the input data indicates that switching of the driving frequency of the switching element is not actually required, the trained model can still be configured to output a noise tolerance determination result that is not a noise level acceptable to the driver, regardless of other input data. The model generation unit 72 of the learning device 70 generates such a trained model, and the frequency switching determination unit 63a of the control device 60a can use this trained model to make a more appropriate inference of the noise tolerance determination result.

[0273] Furthermore, in Embodiment 6 and its variations, a separate device structure for the learning device 70 and the control device 60a has been described. However, it is also possible for the control device 60a to also function as the learning device 70, i.e., the control device 60a may contain the learning device 70. In this case, the learning data acquisition unit 71 and the inference data acquisition unit 61a can be configured with the same functions, for example, they can be implemented through common program processing. In addition, in this case, by using the same memory or the like to configure the trained model storage unit 73 and the storage unit 62a, it is not necessary to move the trained model from the memory or the like that storing the generated trained model, and therefore it is not necessary to send and receive the trained model via a storage medium or the like.

[0274] In addition, as mentioned above, the entire structure of the learning device 70 and the control device 60a, or a part thereof, may be connected to the electric vehicle via a network and exist on a cloud server.

[0275] <Finally>

[0276] Furthermore, in the embodiments described above, the material, size, shape, relative arrangement, or implementation conditions of each structural element are sometimes described. However, these are merely illustrative examples and are not limited to the contents described in each embodiment. Therefore, countless variations not illustrated can be imagined within the scope of each embodiment. For example, these include cases of modifying, adding, or omitting any structural element, as well as cases of extracting at least one structural element from at least one embodiment and combining it with structural elements from other embodiments.

[0277] Furthermore, as long as there is no contradiction, the structural element described as having "1" in the above embodiments can also have "more than or equal to 1". Moreover, each structural element is a conceptual unit, including the case where one structural element is composed of multiple structures and the case where one structural element corresponds to a part of a certain structure.

[0278] Furthermore, none of the descriptions in this specification acknowledge that they are prior art.

[0279] Furthermore, the various implementation methods can be freely combined, and appropriate modifications or omissions can be made to each implementation method.

[0280] Explanation of the label

[0281] 10 Power supply, 20 Power conversion device, 21 Main conversion circuit, 22 Drive circuit, 23 Control circuit, 25 Frequency divider circuit, 26 Switch, 27 Switch, 30 Motor, 40 Semiconductor device, 41 Switching element, 42 Temperature sensor, 43 Current sensor, 51 Accelerometer position sensor, 52 Vehicle speed sensor, 53 Navigation device, 54 Driver assistance device, 55 Direction indicator, 56 Fuel gauge, 57 Battery capacity gauge, 60, 60a Control device, 61 Data acquisition unit, 61a Inference data acquisition unit, 62, 62a Storage unit, 63, 63a Frequency switching determination unit, 64 Inverter control unit, 66 Transceiver device, 67 Processor, 68 Memory (ROM), 69 Memory (RAM), 70 Learning device, 71 Learning data acquisition unit, 72 Model generation unit, 73 The trained model storage unit, and control systems 101, 201, 301, 401, and 501 are then configured.

Claims

1. A control system for controlling the operation of a power conversion device that performs power conversion between an electric motor and a power source, the electric motor driving a vehicle. The control system has the following features: A data acquisition unit acquires data from a device installed inside the vehicle; The control unit, when determining, based on data obtained by the data acquisition unit, that the driver can tolerate the noise, reduces the driving frequency of the switching elements of the power conversion device. The storage unit stores data for each driving mode that associates the vehicle's specified driving mode with the results of a pre-determined judgment on whether the driver can tolerate the sound generated from the power conversion device in the driving mode. as well as The determination unit, based on the data obtained by the data acquisition unit, determines whether the current driving state of the vehicle is consistent with the driving mode. The storage unit stores prediction models and relational expressions. The predictive model forecasts the future load on the motor or the power conversion device based on data obtained from the device. This relationship is used to determine the temperature of the switching element based on the load of the motor or the power conversion device and the characteristics of the switching element. The determination unit predicts the future temperature of the switching element based on the prediction model and the relational formula. If the predicted temperature of the switching element exceeds a predetermined value, it determines whether the current driving state of the vehicle is consistent with the driving mode.

2. The control system according to claim 1, wherein, The control unit determines whether the noise level is acceptable to the driver based on the predicted temperature from the switching element and the vehicle speed.

3. The control system according to claim 1 or 2, wherein, The device comprises an accelerator position sensor for detecting the accelerator opening of the vehicle and a vehicle speed sensor for detecting the vehicle speed. The data acquisition unit acquires the accelerator opening data of the vehicle from the accelerator position sensor and the vehicle speed data from the vehicle speed sensor. When the change in the accelerator opening of the vehicle exceeds a predetermined value and the vehicle speed exceeds a predetermined value, the control unit reduces the driving frequency of the switching element.

4. The control system according to claim 1 or 2, wherein, The device is a navigation device with the vehicle's location information and a vehicle speed sensor for detecting the vehicle's speed. The data acquisition unit obtains data related to the vehicle's predetermined driving path from the navigation device and obtains the vehicle's speed data from the vehicle speed sensor. The control unit predicts the increase in load on the vehicle based on data related to the vehicle's predetermined driving path, and reduces the driving frequency of the switching element if the vehicle speed exceeds a predetermined value.

5. The control system according to claim 1 or 2, wherein, The device is a navigation device with the vehicle's location information and an acceleration sensor that detects the vehicle's acceleration. The data acquisition unit acquires data related to the vehicle's predetermined driving path from the navigation device and acquires the vehicle's acceleration data from the acceleration sensor. The control unit predicts the increase in load on the vehicle based on data related to the vehicle's predetermined driving path, and reduces the driving frequency of the switching element if the vehicle's acceleration exceeds a predetermined value.

6. The control system according to claim 4, wherein, The control unit predicts whether the vehicle's load will increase based on data from the vehicle's predetermined travel path, including information related to the slope of the road surface the vehicle is traveling on.

7. The control system according to claim 1 or 2, wherein, The device is a driving assistance system that assists in driving the vehicle. The data acquisition unit acquires data related to the driving status of the vehicle from the driving assistance device. When the driving assistance device is used for overtaking, the control unit reduces the driving frequency of the switching element.

8. The control system according to claim 1 or 2, wherein, The device comprises a driving assistance device for assisting the driving of the vehicle, an accelerator position sensor for detecting the accelerator opening of the vehicle, and a direction indicator for displaying the direction of travel of the vehicle. The data acquisition unit acquires data related to the driving state of the vehicle from the driving assistance device, data on the accelerator opening of the vehicle from the accelerator position sensor, and data related to the direction of travel of the vehicle from the direction indicator. When the driving assistance device is providing driving assistance to the vehicle, and the control unit determines that the driver is overtaking based on the vehicle's accelerator opening data and data related to the vehicle's direction of travel, the control unit reduces the driving frequency of the switching element.

9. The control system according to claim 7, wherein, The driving assistance device automatically performs driving operations to maintain a fixed distance between the vehicle and other vehicles and to drive the vehicle at a prescribed speed.

10. The control system according to claim 1, wherein, If the vehicle's future driving range is less than a predetermined threshold, the control unit determines that the noise level is acceptable to the driver.

11. The control system according to claim 1 or 10, wherein, The vehicle has an internal combustion engine that operates by fuel. The device is a fuel gauge that detects the remaining fuel level of the internal combustion engine, i.e., the remaining fuel level of the vehicle. The data acquisition unit obtains the vehicle's remaining fuel level data from the fuel gauge. When the vehicle's fuel level is lower than a predetermined value, the control unit reduces the driving frequency of the switching element.

12. The control system according to claim 1 or 10, wherein, The device is a battery capacity meter used to detect the remaining capacity of the vehicle's battery. The data acquisition unit obtains data on the remaining battery capacity of the vehicle from the battery capacity table. When the remaining capacity of the vehicle's battery is lower than a predetermined value, the control unit reduces the driving frequency of the switching element.

13. The control system according to claim 1, wherein, The control unit determines whether the noise level is acceptable to the driver based on the temperature of the switching element and the amount of temperature change.

14. The control system according to claim 1 or 13, wherein, The device is a temperature sensor that detects the temperature of the switching element. The data acquisition unit obtains the temperature data of the switching element from the temperature sensor. When the temperature of the switching element exceeds a predetermined value, and the amount of temperature change of the switching element exceeds a predetermined value, the control unit reduces the driving frequency of the switching element.

15. The control system according to claim 1 or 13, wherein, The device is a current sensor that detects the current value of the switching element. The data acquisition unit obtains the current value data of the switching element from the current sensor. When the current value of the switching element exceeds a predetermined value, and the change in the current value of the switching element exceeds a predetermined value, the control unit reduces the driving frequency of the switching element.

16. The control system according to claim 13, wherein, When the control unit reduces the driving frequency of the switching element, the power conversion device divides the frequency of the driving signal that drives the switching element.

17. A control device for controlling the operation of a power conversion device that performs power conversion between an electric motor and a power source, the electric motor driving a vehicle. The control device has: The data acquisition unit acquires data from devices installed inside the vehicle; The determination unit determines, based on the data acquired by the data acquisition unit, whether the noise level is acceptable to the driver. The control unit, when the determination unit determines that the driver can tolerate the noise, outputs a command to reduce the drive frequency of the switching elements of the power conversion device. as well as The storage unit stores data that, for each driving mode, correlates the vehicle's prescribed driving mode with pre-determined results of whether the driver can tolerate sounds generated by the power conversion device in that driving mode. The determination unit determines whether the current driving state of the vehicle is consistent with the driving mode based on the data obtained by the data acquisition unit. The storage unit stores prediction models and relational expressions. The predictive model forecasts the future load on the motor or the power conversion device based on data obtained from the device. This relationship is used to determine the temperature of the switching element based on the load of the motor or the power conversion device and the characteristics of the switching element. The determination unit predicts the future temperature of the switching element based on the prediction model and the relational formula. If the predicted temperature of the switching element exceeds a predetermined value, it determines whether the current driving state of the vehicle is consistent with the driving mode.

18. A control method for controlling the operation of a power conversion device that performs power conversion between an electric motor and a power source, the electric motor driving a vehicle. This control method has the following characteristics: The data acquisition step involves acquiring data from a device installed inside the vehicle; The determination step involves assessing, based on the acquired data, whether the noise level is acceptable to the driver. The control step involves reducing the drive frequency of the switching elements in the power conversion device when it is determined that the noise level is acceptable to the driver. as well as The storage step involves, for each driving mode, storing data that correlates the vehicle's designated driving mode with pre-defined results of a judgment on whether the driver can tolerate sounds generated by the power conversion device in that driving mode. The determination step, based on the data obtained in the data acquisition step, determines whether the vehicle's current driving state is consistent with the driving mode. The storage step stores the prediction model and the relational expression. The predictive model forecasts the future load on the motor or the power conversion device based on data obtained from the device. This relationship is used to determine the temperature of the switching element based on the load of the motor or the power conversion device and the characteristics of the switching element. The determination step predicts the future temperature of the switching element based on the prediction model and the relationship. If the predicted temperature of the switching element exceeds a predetermined value, it determines whether the current driving state of the vehicle is consistent with the driving mode.

19. A storage medium storing a program that is executed by a control device, the control device controlling the operation of a power conversion device that performs power conversion between an electric motor and a power source, the electric motor driving a vehicle. The program causes the control device to perform the following processing: Based on data obtained from devices installed in the vehicle, a determination is made as to whether the noise level is acceptable to the driver. If it is determined that the driver can tolerate the noise, an instruction is output to reduce the drive frequency of the switching elements of the power conversion device. as well as For each driving mode, data is stored that associates the vehicle's designated driving mode with a pre-determined result of whether the driver can tolerate sounds generated by the power conversion device in that driving mode. Based on the acquired data, a determination is made as to whether the vehicle's current driving status is consistent with the driving mode. It stores prediction models and formulas. The predictive model forecasts the future load on the motor or the power conversion device based on data obtained from the device. This relationship is used to determine the temperature of the switching element based on the load of the motor or the power conversion device and the characteristics of the switching element. Based on the prediction model and the relationship, the future temperature of the switching element is predicted. If the predicted temperature of the switching element exceeds a predetermined value, it is determined whether the current driving state of the vehicle is consistent with the driving mode.

20. An electric vehicle, comprising: power supply; An electric motor, which drives the vehicle; A power conversion device that performs power conversion between the power source and the motor; as well as The control device acquires data from equipment installed in the vehicle, and if, based on the acquired data, determines that the noise level is acceptable to the driver, reduces the driving frequency of the switching elements in the power conversion device. For each driving mode, the control device stores data that associates the vehicle's designated driving mode with pre-defined results of determining whether the driver can tolerate sounds generated by the power conversion device in that driving mode. Based on this data, the device also determines whether the vehicle's current driving state matches the specified driving mode. The control device stores a prediction model and a relational expression. The prediction model predicts the future load of the motor or the power conversion device based on data obtained from the device. The relational expression calculates the temperature of the switching element based on the load of the motor or the power conversion device and the characteristics of the switching element. The control device predicts the future temperature of the switching element based on the prediction model and the relationship. If the predicted temperature of the switching element exceeds a predetermined value, it determines whether the current driving state of the vehicle is consistent with the driving mode.

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