Adaptive junction temperature control for electric motor

Abstract

A motor control system includes an inverter having a diode and a transistor for generating an alternating current in response to a pulse width modulated direct current having a fixed amplitude; and a processor configured to adjust a zero vector of the pulse width modulated direct current in response to the diode temperature and the transistor temperature such that the diode temperature equals the transistor temperature.

Description

Technical Field

[0001] The present disclosure relates generally to controlling vehicle systems in response to predictions of thermal stresses in an electric motor, and more particularly to a method and apparatus for adaptive junction temperature control for enhanced stall torque and noise vibration and harshness performance under low speed, high load conditions of an electric vehicle. Background Art

[0002] Electric motors are used in electric vehicles (EVs) to convert electrical energy from the battery into mechanical energy to turn the wheels. Generally, there are two main types of electric motors used in EVs: induction motors and permanent magnet synchronous motors (PMSMs). Induction motors are the most common type of electric motor used in EVs. They are relatively simple and cheap to manufacture. Induction motors are also very efficient, and they can provide high torque output. PMSMs are more expensive than induction motors, but they are also more efficient and provide better performance. PMSMs are often used in high-performance EVs, such as sports cars and racing cars. Modern EVs usually have two electric motors, one for each axle, but some EVs can have a single motor located under the hood or four motors, one for each wheel.

[0003] While the need to protect vehicle operation during destructive or disruptive operating conditions is clear, it is desirable to optimally use any vehicle protection algorithm to provide maximum protection to the vehicle while continuing to provide service to the driver. It is desirable to overcome the above-described problems in order to provide systems and methods for vehicle system protection and continued vehicle operation for vehicle propulsion and driver assistance systems. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. Summary of the invention

[0004] Disclosed herein are vehicle control methods and systems and related electrical systems for supplying motor systems, methods for manufacturing such systems, and methods for operating such systems, as well as motor vehicles and other equipment, such as aircraft, ships, wind turbines, and other EVs equipped with onboard propulsion systems. By way of example and not limitation, various embodiments of a system for balancing thermal stresses in a three-phase inverter that occurs during a stall condition of an electric vehicle motor are presented. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Exemplary embodiments will be described below with reference to the following drawings, wherein like reference numerals denote like elements, and in the drawings:

[0006] Figure 1 shows a control system associated with a vehicle according to various embodiments;

[0007] Figure 2A and 2B is a diagram depicting a cross-sectional view of an exemplary electric machine according to various embodiments from two different angles;

[0008] Figure 3 shows an exemplary EV drive configuration according to various embodiments;

[0009] Figure 4 shows a block diagram illustrating an adaptive junction temperature control system for an electric motor according to various embodiments; and

[0010] Figure 5 A flow chart illustrating a method for operating an exemplary adaptive junction temperature control for an electric motor system in accordance with various embodiments is shown. DETAILED DESCRIPTION

[0011] The following detailed description is merely exemplary in nature and is not intended to limit application and use. In addition, there is no intention to be bound by any express or implied theory presented in the aforementioned technical field, background technology, invention content or the following specific embodiments. As used herein, the term "module" refers to any hardware, software, firmware, electronic control component, processing logic and / or processor device, alone or in any combination, including but not limited to: application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), electronic circuits, processors (shared, dedicated or grouped) and memories that execute one or more software or firmware programs, combinational logic circuits and / or other suitable components that provide the described functions.

[0012] Embodiments of the present disclosure may be described herein according to functions and / or logic block components and various processing steps. It should be understood that such block components may be implemented by any number of hardware, software and / or firmware components configured to perform a specified function. For example, embodiments of the present disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of the present disclosure.

[0013] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning, image analysis, and other functional aspects of the system (and the various operating components of the system) may not be described in detail herein. In addition, the connecting lines shown in the various figures included herein are intended to represent example functional relationships and / or physical connections between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of the present disclosure.

[0014] refer to Figure 1 According to various embodiments, the control system 100 is associated with a vehicle 10 (also referred to herein as a "host vehicle"). Generally, the control system (or simply "system") 100 provides control of various actions (e.g., torque control) of the vehicle 10 established through reinforcement learning (RL) stored or can be stored in a DNN type model that controls operation in response to data from vehicle inputs, for example, as further described in more detail below in conjunction with Figures 2-4.

[0015] In various exemplary embodiments, the system 100 provides a process using an algorithm that controls torque and speed in the embedded controller software of the host vehicle 10 of the system 100, thereby allowing DNN to be used for ACC behavior prediction models. The system 100 enables learning of the driver's preferences for following distances of different vehicles such as a target vehicle, and classifies the driver's preferences based on driving scenarios; e.g., traffic signs, stop-and-go traffic, city driving, etc. The system 100 uses a Q matrix to build a knowledge base for the target vehicle following the performance preferences by utilizing online and historical driver and environment information.

[0016] like Figure 1 As shown, the vehicle 10 generally includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially surrounds the components of the vehicle 10. The body 14 and the chassis 12 may together form a frame. The wheels 16-18 are each rotatably coupled to the chassis 12 near a respective corner of the body 14. In various embodiments, the wheels 16, 18 comprise a wheel assembly that also includes a tire associated therewith.

[0017] In various embodiments, the vehicle 10 is autonomous or semi-autonomous, and the control system 100 and / or its components are incorporated into the vehicle 10. The vehicle 10 is, for example, a vehicle that is automatically controlled to transport passengers from one location to another. The vehicle 10 is depicted as a passenger car in the illustrated embodiment, but it should be understood that any other vehicle may be used, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), boats, airplanes, etc.

[0018] As shown, the vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a tank purge system 31, one or more user input devices 27, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In various embodiments, the propulsion system 20 may include an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell propulsion system. The transmission system 22 is configured to transfer power from the propulsion system 20 to the wheels 16 and 18 according to a selectable speed ratio. According to various embodiments, the transmission system 22 may include a stepped ratio automatic transmission, a continuously variable transmission, or other suitable transmission.

[0019] Braking system 26 is configured to provide braking torque to wheels 16 and 18. In various embodiments, braking system 26 may include friction brakes, brake-by-wire brakes, a regenerative braking system such as an electric motor, and / or other suitable braking systems.

[0020] The steering system 24 affects the position of the wheels 16 and / or 18. Although depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system 24 may not include a steering wheel.

[0021] The controller 34 includes at least one processor 44 (and neural network 33) and a computer-readable storage device or medium 46. As described above, in various embodiments, the controller 34 (e.g., its processor 44) provides data related to the expected future path of the vehicle 10 in advance to the steering control system 84, including expected future steering instructions, for controlling the steering for a limited period of time in the event that communication with the steering control system 84 becomes unavailable. Moreover, in various embodiments, the controller 34 communicates with the steering control system 84 via the communication system 36 (e.g., via a communication bus and / or a transmitter ( Figure 1 Not shown)) provides communication to the steering control system 84.

[0022] In various embodiments, the controller 34 includes at least one processor 44 and a computer readable storage device or medium 46. The processor 44 can be any custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor of several processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or any device generally used to execute instructions. The computer readable storage device or medium 46 can include volatile and non-volatile storage devices such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a persistent or non-volatile memory that can be used to store multiple neural networks and various operating variables when the processor 44 is powered off. The computer readable storage device or medium 46 can be implemented using any of a number of known memory devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combination memory device capable of storing data used by the controller 34 in controlling the vehicle 10, some of which represent executable instructions.

[0023] The instructions may include one or more separate programs, each of which includes an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, execute logic, calculations, methods and / or algorithms for automatically controlling components of the vehicle 10, and generate control signals transmitted to the actuator system 30 to automatically control components of the vehicle 10 based on the logic, calculations, methods and / or algorithms. Although Figure 1 Only one controller 34 is shown, but embodiments of the vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication mediums and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control features of the vehicle 10.

[0024] like Figure 1 As shown, in addition to the above-mentioned steering system 24 and controller 34, the vehicle 10 generally includes a chassis 12, a body 14, front wheels 16 and rear wheels 18. The body 14 is arranged on the chassis 12 and substantially surrounds the components of the vehicle 10. The body 14 and the chassis 12 can together form a frame. The wheels 16-18 are each rotatably coupled to the chassis 12 near the corresponding corners of the body 14. In various embodiments, the wheels 16, 18 include a wheel assembly, which also includes a tire associated with each.

[0025] In various embodiments, the vehicle 10 is an autonomous vehicle, and the control system 100 and / or its components are incorporated into the vehicle 10. The vehicle 10 is, for example, a vehicle that is automatically controlled to transport passengers from one location to another. The vehicle 10 is depicted as a passenger car in the illustrated embodiment, but it should be understood that any other vehicle may be used, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), boats, airplanes, etc.

[0026] The controller 34 comprises a vehicle controller that operates based on the output of the neural network 33 model. In an exemplary embodiment, a feed-forward operation may be applied to an adjustment factor that is a continuous output of the neural network 33 model to generate a control action for a desired torque or other similar action (in the case of a continuous neural network 33 model, for example, a continuous APC / SPARK prediction value is the output).

[0027] In various embodiments, the one or more user input devices 27 receive input from one or more passengers of the vehicle 10 (and the driver 11). In various embodiments, the input includes a desired travel destination for the vehicle 10. In certain embodiments, the one or more input devices 27 include an interactive touch screen in the vehicle 10. In certain embodiments, the one or more input devices 27 include a speaker for receiving audio information from the passenger. In certain other embodiments, the one or more input devices 27 may include one or more other types of devices and / or user devices (e.g., smart phones and / or other electronic devices) that can be connected to the passenger.

[0028] The sensor system 28 includes one or more sensors 40a-40n that sense observable conditions of the external environment and / or the internal environment of the vehicle 10. The sensors 40a-40n include, but are not limited to, radar, LIDAR, global positioning system, optical camera, thermal camera, ultrasonic sensor, inertial measurement unit, and / or other sensors.

[0029] The actuator system 30 includes one or more actuators 42a-42n that control one or more vehicle features such as, but not limited to, the canister purge system 31, the air intake system 38, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle 10 may also include Figure 1 Interior and / or exterior vehicle features not shown, such as various doors, trunk, and cabin features such as air, music, lighting, touch screen display components (such as those used in conjunction with a navigation system), etc.

[0030] The data storage device 32 stores data for automatically controlling the vehicle 10, including storing data of the DNN established by RL for predicting driver behavior for vehicle control. In various embodiments, the data storage device 32 stores machine learning models of the DNN and other data models established by RL. The model established by RL can occur in the DNN behavior prediction model or the RL establishment model (see Figure 2, DNN prediction model or RL prediction model). In an exemplary embodiment, the DNN does not require separate training, but implements the DNN behavior prediction model (i.e., DNN prediction model) with a set of learning functions. In various embodiments, the neural network (i.e., DNN behavior prediction model) can be established by RI or trained by a remote system through a supervised learning method, and transmitted or provided in the vehicle 10 (wirelessly and / or in a wired manner) and stored in the data storage device 32. The DNN behavior prediction model can also be trained via supervised or unsupervised learning based on input vehicle data of the main vehicle operation and / or sensory data about the main vehicle operating environment.

[0031] The data storage device 32 is not limited to control data, as other data may also be stored in the data storage device 32. For example, route information may also be stored in the data storage device 32, i.e., a set of road segments (geographically associated with one or more defined maps) that together define a route that a user may take to travel from a starting location (e.g., the user's current location) to a target location. As will be appreciated, the data storage device 32 may be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system.

[0032] The controller 34 implements a logic model established by RL or a logic model established for the DNN based on a DNN behavior model that has been trained with a set of values, and includes at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 can be any custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor in several processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or any device generally used to execute instructions. The computer-readable storage device or medium 46 may include volatile and non-volatile storage devices such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a persistent or non-volatile processor that can be used to store various operating variables when the memory 44 is powered off. The computer readable storage device or medium 46 may be implemented using any of a number of known memory devices, such as a PROM (programmable read-only memory), an EPROM (electrical PROM), an EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combination memory device capable of storing data used by the controller 34 in controlling the vehicle 10, some of which represent executable instructions.

[0033] The instructions may include one or more separate programs, each of which includes an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, execute logic, calculations, methods and / or algorithms for automatically controlling components of the vehicle 10, and generate control signals transmitted to the actuator system 30 to automatically control components of the vehicle 10 based on the logic, calculations, methods and / or algorithms. Although Figure 1 Only one controller 34 is shown, but embodiments of the vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication mediums and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control features of the vehicle 10.

[0034] The communication system 36 is configured to wirelessly transmit information to and from other entities 48, such as, but not limited to, other vehicles ("V2V" communications), infrastructure ("V2I" communications), remote transportation systems, and / or user devices (described in more detail with respect to FIG. 2). In an exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or by using cellular data communications. However, additional or alternative communication methods such as dedicated short-range communications (DSRC) channels are also considered within the scope of the present disclosure. A DSRC channel refers to a one-way or two-way short-range to medium-range wireless communication channel designed specifically for automotive use and a corresponding set of protocols and standards.

[0035] In various embodiments, the communication system 36 is used for communication between the controllers 34, including data related to the expected future path of the vehicle 10, including expected future steering instructions. Moreover, in various embodiments, the communication system 36 can facilitate communication between the steering control system 84 and / or more other systems and / or devices.

[0036] In certain embodiments, the communication system 36 is also configured for communication between the sensor system 28, the input device 27, the actuator system 30, one or more controllers (e.g., the controller 34), and / or more other systems and / or devices. For example, the communication system 36 may include any combination of a controller area network (CAN) bus and / or direct wiring between the sensor system 28, the actuator system 30, one or more controllers 34, and / or one or more other systems and / or devices. In various embodiments, the communication system 36 may include one or more transceivers for communicating with one or more devices and / or systems of the vehicle 10, a passenger's device (e.g., the user device 54 of FIG. 2), and / or one or more remote information sources (e.g., GPS data, traffic information, weather information, etc.).

[0037] Figure 2A and Figure 2B 2 is a diagram depicting a cross-sectional view of an exemplary motor 200 from two different angles. The exemplary motor 200 includes a housing 202 surrounding the exemplary motor 200 and a stator stack 204 within the housing 202. The exemplary stator stack 204 includes a plurality of windings 206 around the inner circumference of the stator stack 204. Enclosed within the exemplary stator stack 204 is a rotor stack 208. The exemplary rotor stack 208 includes a plurality of magnets 210 and a rotor hub 212. The exemplary rotor stack 208 is configured to rotate within the stator stack 204 using a set of bearings 214. The exemplary stator stack 204 and the exemplary rotor stack 208 are enclosed within the housing 202 by an end cap 216. The exemplary motor 200 also includes a resolver 218 for measuring the degree of rotation of the rotor stack 208.

[0038] Now go to Figure 3 , shows an exemplary EV drive configuration 300 according to an exemplary embodiment of the present disclosure. An EV is configured with one or more electric motors. For example, an all-wheel drive EV may have a motor for each axle. This allows for better traction and control on slippery surfaces. The most common types of electric motors used in EVs are permanent magnet synchronous motors (PMSM) and induction motors. PMSM is the most efficient type of electric motor and is used in most modern EVs due to its high torque output and low noise and vibration levels. PMSM uses permanent magnets to create a rotating magnetic field that drives the rotor. Lower cost induction motors are less efficient than PMSMs, but may be more reliable. Induction motors use a rotating magnetic field to induce current in the rotor windings, which causes the rotor to turn.

[0039] The electric motor 310 is typically powered using three-phase alternating current (AC). Single-phase or three-phase AC power can be supplied to the electric motor. Single-phase induction motors are less common than three-phase induction motors, and they are typically used in smaller applications. Three-phase induction motors are more common and are used in larger applications, such as EVs and industrial machinery. Three-phase electric motors are more efficient than single-phase electric motors and are also able to produce greater torque. This makes them well suited for use in applications that require high power and efficiency, such as EVs.

[0040] The exemplary three-phase inverter 320 supplies a DC voltage from a direct current (DC) voltage source (such as a battery 330) and converts the DC electricity into a three-phase alternating current (AC) electricity 340. The three-phase AC power 340 has three separate voltages that are 120 degrees out of phase. In order to power the electric motor 310, the inverter 320 supplies the three-phase AC power 340 to the stator winding of the electric motor 310, which generates a rotating magnetic field in the stator. Then, the rotating magnetic field induces current in the rotor winding, which causes the rotor to rotate. The speed of the rotor is determined by the frequency of the three-phase AC power 340 supplied by the inverter 320. The higher the frequency, the faster the rotor rotates. The inverter 320 can also control the torque output of the electric motor 310 by adjusting the voltage of the three-phase AC power 340. In some exemplary embodiments, the switching signal and the DC voltage applied to the plurality of transistors S1-S6 can then be controlled in response to the estimated position of the rotor of the electric motor 310.

[0041] In some exemplary embodiments, a plurality of transistors S1-S6 may be arranged in a bridge configuration. The bridge circuit may be composed of six semiconductor switches S1-S6, such as insulated gate bipolar transistors (IGBTs) or metal oxide semiconductor field effect transistors (MOSFETs). The switches are turned on and off in a sequence that produces a three-phase AC voltage 340 at the output of the bridge circuit. The three-phase AC voltage is then provided to the stator windings of the electric motor 310. The rotating magnetic field in the stator induces current in the rotor windings, which causes the rotor to rotate.

[0042] The body diodes D1-D6 in the three-phase inverter 320 provide a path for the inductive load current to flow during the dead time. Dead time is a short period of time between when one switch is turned off and when another switch in the same phase is turned on, preventing shoot through, which is when two switches are turned on at the same time, creating a short circuit. In the IGBT-based three-phase inverter 320, the body diodes play an equal role in the conduction. Since the IGBT is not bidirectional, the diodes conduct to continue the current flow. For positive current, the upper IGBT and the lower diode conduct, and for negative current, the lower IGBT and the upper diode conduct. Therefore, their role is not limited to the dead time of the IGBT-based inverter, so the diodes and their thermal characteristics are important for managing the thermal stress of the inverter.

[0043] Problems arise when the electric motor 310 experiences a stall condition, such as when starting to move while pulling a heavy object or starting to move on a slope. A stall condition occurs when power is applied to one or more of the three-phase windings but the motor 310 is not rotating. When the motor 310 stalls, all power applied to it is converted into heat. This can cause the motor 310 to overheat quickly, causing damage to the windings, insulation, and other components. Similarly, the amount of current drawn by the motor 310 when it cannot rotate is typically much higher than the operating current of the motor 310. This high current draw can overload the controller and other electrical components of the motor 310. Damage caused by the stall of the motor 310 can damage the shaft, bearings, and other components of the motor 310 and reduce the overall performance and life of the motor 310.

[0044] PWM modulation (such as adaptive temperature balanced PWM modulation) is used to reduce the thermal stress on the semiconductor switches S1-S6 to allow acceptable switching noise levels and reduce potential damage due to motor stall. However, when stalled, the use of discontinuous PWM may lead to NVH problems and insufficient heat distribution. Inadequate heat distribution also occurs under continuous PWM, but there are no NVH problems. Thermal stress may become unevenly distributed between the switching transistors S1-S6 and the body diodes D1-D6, with the hottest device varying based on the rotor position. In discontinuous PWM, the modulating signal is clamped to the DC rail for a portion of each cycle. This can be done by using a discontinuous triangle carrier or by using a continuous triangle carrier and adding a zero-sequence voltage to it.

[0045] In order to address the unevenly distributed thermal stress and potential NVH issues caused by the use of discontinuous PWM, the thermal distribution of various components can be estimated, and the zero vector and therefore the duty cycle distribution between devices can be adaptively changed to achieve a more uniform thermal distribution between the switching transistors S1-S6 and the body diodes D1-D6. For example, in the exemplary EV driver 300, the steady-state position drives the 3-phase AC current so that -1120A flows through the first phase and +560A flows through the second and third phases. Here, the first phase is the highest current-carrying phase. When the current flowing through the first phase is negative, the conductive devices of the first phase are the body diode D1 and the transistor S4. In some exemplary embodiments based on the states of the inverter 320 and the motor 310 for the space vector pulse width modulation SVPWM scheme, the first phase duty cycle is 0.45 for the upper device and 0.55 for the lower device. For this exemplary duty cycle distribution, transistor S4 may be turned on more than body diode D1, while the detected temperature indicates that body diode D1 has a higher temperature, such as 160°C, and transistor S4 has a lower temperature, such as 145°C. This uneven heat distribution may be the result of differences in thermal characteristics between body diode D1 and transistor S4. The exemplary algorithm is constructed to rebalance the heat between body diode D1 and transistor S4 by adaptively changing the zero vector. In some exemplary embodiments, the duty cycle is redistributed so that the body diode D1 duty cycle is reduced from 0.45 to 0.4, and transistor S4 is increased from 0.55 to 0.6. This redistribution results in a balanced temperature, for example, ~155°C. In some exemplary embodiments, the exemplary algorithm may balance the heat dissipation between the upper and lower devices of the same highest current carrying phase, or may balance the heat dissipation between the hottest devices of the highest current carrying phase and the second highest current carrying phase.

[0046] Changing the zero vector of PWM involves employing different combinations of switch states to represent the zero vector. In a three-phase inverter, there are six possible switch states, and each switch state represents a different combination of phase voltages. The zero vector is represented by a switch state where all three phases are connected to the same DC rail. A common method of changing the zero vector of PWM is to use a space vector modulation (SVM) algorithm. Alternatively, a carrier-based PWM algorithm can be used to generate a PWM signal by comparing a reference voltage waveform with a triangular carrier waveform.

[0047] Now go to Figure 4 , a block diagram illustrating an adaptive junction temperature control system 400 for an electric drive 410 according to various embodiments is shown. The exemplary adaptive junction temperature control system 400 includes a physical system 407 and a discrete control system 405. The exemplary physical system may include an electric drive motor 410, a PWM inverter 415, and a position sensor 470 to determine the position of the electric drive motor 410. The discrete control system 405 includes a PWM generation portion 422 and an adaptive junction temperature control portion 442.

[0048] The PWM generating section 422 is configured to generate a PWM power to be supplied to the PWM inverter by using the pulse width modulation (PWM) inverter 415. The PWM power is operable to convert the DC voltage V dc The PWM generator 420 is converted into a PWM voltage, which is applied to the PWM inverter 415 to generate the three-phase AC power supplied to the electric drive 410. The duty cycle is the ratio of the on time to the off time of the voltage that determines the average voltage applied to the motor. By changing the duty cycle, the PWM generator 420 can control the speed and torque of the electric drive 410.

[0049] DC voltage V dc The battery is connected to the command generation module 430, which is configured to respond to the DC voltage V dc , electrical angular frequency ω e and controller cycle time T cmd The stator current I dq . Electrical angular frequency ω e Responsive to electric drive position The differential 435 of is then generated in response to the electric drive position received from the position sensor 470. The driving current I determined in proportion to 440 abc The error is used to correct the stator current I dq The corrected stator current is then regulated by the current regulator 425 and generated by the PWM generator 420 with an initial zero vector s abc PWM waveform.

[0050] The adaptive junction temperature control section 442 is configured to generate an adaptive zero vector s adapt To rotate the initial zero vector s abc To generate the final zero vector Sf abc . An adapted zero vector is generated in response to the estimated inverter losses 455 and the estimated junction temperature 450. The estimated inverter losses 455 may be determined using analytical, simulation, or experimental methods, and the estimated inverter losses 455 may be estimated for a particular inverter configuration in response to the predicted losses of the inverter components. These inverter losses 455 may be estimated for different operating conditions, and these inverter losses 455 may be stored in a memory or a lookup table, or these inverter losses 455 may be used to generate a prediction algorithm for estimating the inverter losses for the current inverter operating conditions. The estimated junction temperature 450 may be estimated for a particular transistor / body diode pair to determine a maximum junction temperature.

[0051] The zero vector adaptation 455 is responsive to the drive current I from the currently conducting body diode and transistor pair abc The peak current and the intermediate current are used to generate the adapted zero vector s adapt In some exemplary embodiments, the zero vector adaptation 455 may first estimate intermediate zero vectors in response to body diode and transistor conditional losses at peak currents, estimate junction temperature and diode temperature in response to these intermediate zero vectors, estimate intermediate zero vectors at intermediate currents, and then generate an adapted zero vector s in response to an optimal ratio of the intermediate zero vectors. adapt , resulting in a balanced junction temperature and diode temperature for a particular transistor and body diode pair. Then, in response to the adaptive zero vector s adapt and the initial zero vector s abc Generate the final zero vector. Then use the final zero vector s f To adjust the stator current I applied to the PWM inverter 415 dq .

[0052] Now go to Figure 5, a method 500 for operating an exemplary adaptive junction temperature control for an electric motor system according to various embodiments is shown. The exemplary method 500 is first operable to estimate inverter losses. The inverter losses in a three-phase PWM inverter for an EV drive motor may first be estimated by identifying inverter components. These components may include semiconductor switches (such as IGBTs or MOSFETs), gate drivers, body diodes, inductors, capacitors, and resistors. Next, the operating conditions of the inverter are considered, including input DC voltage, output AC voltage and frequency, modulation index, and load current. Then, when the semiconductor switch is turned on, the inverter losses may be estimated in response to the inverter components and the inverter operating conditions. These inverter losses may then be stored in a processor, etc., communicatively coupled to a memory or controller, for use in executing an adaptive temperature control for an electric motor algorithm.

[0053] Once the inverter losses are estimated, method 500 may then initiate 510 an algorithm. The algorithm may be initiated in response to a vehicle control system being activated, a vehicle operating state transitioning from a standby state to a running state, or a vehicle drive state transitioning from a "parked" state to a "drive" state, etc. In some exemplary embodiments, the algorithm is operable to detect a stall condition of the electric motor and perform adaptive temperature control in response to the detected stall condition.

[0054] The exemplary method can utilize continuous PWM with zero vector adaptation based on the model reference adaptive system (MRAS) of junction temperature estimation to achieve accurate thermal balance between all 12 devices to adapt to the change of rotor position while solving NVH problems with continuous PWM. This adaptive method promotes reliable operation of the electric drive during hill holding, heavy load take off or other motor stall conditions while minimizing both thermal stress and switching noise without any additional calibration work.

[0055] The method 500 is configured to first estimate 520 the junction temperature of each of the various inverter components. In some exemplary embodiments, the method 500 may estimate the temperature of each of the six transistors and each of the six body diodes of the three-phase inverter in response to the current conducted through each of the inverter components and the corresponding inverter losses. The losses and temperatures may be estimated separately for each device pair, transistor, and body diode, and the maximum of these temperatures may be estimated.

[0056] In some exemplary embodiments, if the maximum temperature T of the power module 525 is max If the threshold temperature is not exceeded, such as when the vehicle motor is not in a stall condition or low current / low duty cycle PWM power is being conducted, method 500 returns to estimating 520 the subsequent junction temperature.max exceeds the threshold temperature, the method 500 may then determine the conduction diode temperature T diode Is it not equal to the on-state transistor temperature T trans If the two temperatures are estimated to be equal, method 500 returns to estimating 520 a subsequent junction temperature.

[0057] If 530 conduction diode temperature T diode Not equal to the on-state transistor temperature T trans , indicating an unbalanced temperature distribution across the conduction element, the method 500 may then be operable to sort the three-phase currents based on magnitude to determine the critical phases. The method 500 may sort the phase currents A, B, and C based on current magnitude to determine the highest and second highest current carrying phases. The method then determines 540 whether the highest critical phase current phase is greater than zero. If the highest critical phase current phase is greater than zero, the method 500 calculates 545 a first intermediate zero vector using upper transistor and lower diode loss information. If the highest critical phase current has a positive direction, the upper IGBT and lower diode are conducting. The method calculates the first intermediate zero point vector taking into account the loss information and device parameters of the upper IGBT and lower diode of the phase conduction path. If the highest critical phase current phase is less than zero, the lower IGBT and upper diode are conducting. The method 500 then calculates 550 a first intermediate zero vector using lower transistor and upper diode loss information.

[0058] After calculating the first intermediate zero vector, the method then determines 555 whether the diode temperature T diode Exceeding the transistor temperature T trans If the diode temperature T diode If the transistor temperature is exceeded, the method 500 calculates 560 a second intermediate zero vector using the loss information of the diode in the highest critical phase and the second highest critical phase. If the diode is the hottest device, the method calculates the second intermediate zero vector taking into account the loss information and device parameters of the conducting diode in the highest and second highest phases. If the diode temperature T diode If the transistor temperature is not exceeded, the method 500 calculates 565 a second intermediate zero vector using loss information of the transistors in the highest critical phase and the second highest critical phase. If the IGBT is the hottest device, the method calculates the second intermediate zero vector taking into account loss information and device parameters of the conducting IGBTs in the highest and second highest phases.

[0059] Once the first intermediate zero vector and the second intermediate vector are calculated, the method 500 can generate 570 an adapted zero vector in response to the estimated inverter losses, the estimated junction temperature and / or the estimated current. In some exemplary embodiments, the method 500 can first determine the peak current and the intermediate current of the conductive element based on the total drive current. Then, the method 500 determines the first intermediate zero vector in response to the ratio of the diode conduction loss and the transistor conduction loss at the peak current. The method then determines the second intermediate zero vector in response to the ratio of the transistor conduction loss at the peak current to the transistor conduction loss at the intermediate current or the ratio of the diode conduction loss at the intermediate current to the diode conduction loss at the peak current, depending on which component temperature is greater. Then, the adapted zero vector is determined in response to the maximum and minimum values ​​of the first intermediate zero vector and the second intermediate zero vector.

[0060] The method 500 is then operable to apply 575 the adjusted zero vector to the drive current having the initial zero vector to generate a drive current having a final zero vector. The final zero vector is applied 545 to the PWM inverter so that an adapted PWM three-phase drive current having an adjusted zero vector is generated 580 to balance the temperature distribution on the conducting diodes and switches within the PWM inverter. The PWM three-phase drive current is then coupled to the drive motor to power the drive motor 585, and the method 500 returns to estimating 520 the subsequent junction temperature.

[0061] Although at least one exemplary embodiment has been presented in the foregoing specific embodiments, it should be understood that there are a large number of variations. It should also be understood that one or more exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of the present disclosure in any way. On the contrary, the foregoing specific embodiments will provide a convenient roadmap for implementing one or more exemplary embodiments for those skilled in the art. It should be understood that various changes may be made to the functions and arrangements of the elements without departing from the scope of the present disclosure as set forth in the attached claims and their legal equivalents.

Claims

1. A motor control system, comprising: an inverter having a diode and a transistor for generating an alternating current in response to a pulse width modulated direct current having a fixed amplitude; as well as A processor is configured to adjust a zero vector of the pulse width modulated direct current in response to a diode temperature and a transistor temperature such that the diode temperature is equal to the transistor temperature. 2 . The motor control system of claim 1 , further comprising a memory for storing a first component loss of the transistor and a second component loss of the diode. 3 . The motor control system of claim 1 , wherein an alternating current is coupled to a stator winding of the electric motor to drive a rotor of the electric motor. 4 . The motor control system of claim 1 , wherein the zero vector is adjusted in response to first component losses of the transistor, second component losses of the diode, and a portion of a pulse width modulated direct current coupled through the transistor and the diode. 5 . The motor control system of claim 1 , wherein the diode temperature is determined by a first temperature sensor and the transistor temperature is determined by a second temperature sensor. 6 . The motor control system of claim 1 , wherein the zero vector is adjusted by increasing a transistor conduction time and decreasing a diode conduction time.

7. The motor control system of claim 1 further comprising an electric motor driven by an alternating current, and wherein the processor is configured to adjust the zero vector in response to detecting a stall condition of the electric motor. 8 . The motor control system of claim 1 , wherein the zero vector is adjusted in response to diode conduction losses, diode voltage drops, transistor conduction losses, and transistor voltage drops.

9. The motor control system of claim 1, wherein a negative temperature coefficient thermistor is used to determine at least one of a diode temperature and a transistor temperature.

10. A method of generating an alternating current, comprising: generating a pulse width modulated current having a first zero vector; estimating a diode temperature and a transistor temperature in an inverter in response to the pulse width modulated current; adjusting a first zero vector of the pulse width modulation current to generate an adapted pulse width modulation current so that a diode temperature is equal to a transistor temperature; generating, by the inverter, an alternating current in response to the adapted pulse width modulated current; as well as Connects AC current to the electric motor.

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