ADAPTIVE LOCKING LAYER TEMPERATURE CONTROL FOR AN ELECTRIC MOTOR

Abstract

A motor control system comprises an inverter with a diode and a transistor for generating an alternating current in response to a pulse-width modulated direct current with a fixed amplitude, and a processor configured to set 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.

Description

INTRODUCTION

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

[0002] Electric motors are used in electric vehicles (EVs) to convert electrical energy from the battery into mechanical energy to turn the wheels. Typically, 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 inexpensive to manufacture. Induction motors are also very efficient and can provide high torque output. PMSMs are more expensive than induction motors, but they are also more efficient and offer better performance. PMSMs are often used in high-performance EVs such as sports cars and race cars. Modern EVs typically have two electric motors, one for each axle, but some EVs may have a single motor located under the hood or four motors, one for each wheel.

[0003] While the need to protect vehicle operation under damaging or destructive operating conditions is clear, it is desirable that any vehicle protection algorithms be optimally utilized to provide maximum protection for the vehicle while continuing to provide service to a driver. It is desirable to eliminate the aforementioned problems 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 appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. SUMMARY

[0004] Disclosed herein are vehicle control methods and vehicle control systems and related electrical systems for providing motor systems, methods of manufacturing, and methods of operating such systems, and 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 systems for compensating for thermal stress in a three-phase inverter encountered during stall conditions for vehicle electric motors are presented. [THIS SECTION IS TO BE COMPLETED AFTER APPROVAL OF THE CLAIMS]BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The exemplary embodiments are described below in conjunction with the following drawing figures, wherein like reference numerals denote like elements; Fig. 1 shows a control system associated with a vehicle according to various embodiments; Fig. 2A, Fig. 2B are diagrams illustrating cross-sectional views of an exemplary electric machine from two different perspectives, according to various embodiments; Fig. 3 illustrates an exemplary EV propulsion configuration according to various embodiments; Fig. 4 is a block diagram illustrating an adaptive junction temperature control system for an electric motor according to various embodiments; and Fig. 5 is a flowchart illustrating a method of operating an exemplary adaptive junction temperature controller for electric motor systems according to various embodiments. DETAILED DESCRIPTION

[0006] The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description.As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and / or processor device, individually or in any combination, including without limitation: an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an electronic circuit, a processor (shared, dedicated, or group) and memory executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.

[0007] Embodiments of the present disclosure may be described herein in terms of functional and / or logic block components and various processing steps. It should be appreciated that such block components may be implemented by a number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may utilize various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, lookup tables, or the like, that may perform a variety of functions under the control of one or more microprocessors or other control devices.Furthermore, those skilled in the art will recognize that embodiments of the present disclosure may be practiced in connection with any number of systems, and that the systems described herein are merely exemplary embodiments of the present disclosure.

[0008] 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 systems (and the individual operational components of the systems) may not be described in detail here. Furthermore, the connecting lines shown in the various figures included herein are intended to represent example functional relationships and / or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure.

[0009] In Fig. 1, a control system 100 is associated with a vehicle 10 (also referred to herein as a "host vehicle") according to various embodiments. In general, the control system (or simply "system") 100 provides control of various actions of the vehicle 10 (e.g., torque control) determined by reinforcement learning (RL) that is or may be stored in a DNN-type model that controls operation in response to data from vehicle inputs, for example, as further described below in connection with Fig. 2-4 described in more detail.

[0010] In various exemplary embodiments, the system 100 provides a process using an algorithm that controls torque and speed in embedded controller software of the system 100 of the host vehicle 10, enabling DNNs to be used for an ACC behavior prediction model. The system 100 enables learning the driver's preference for following distance for various vehicles, such as a target vehicle, and classifying the driver's preference 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 target vehicles according to a performance preference using online and historical driver and environmental information.

[0011] As in Fig. 1, 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 encloses 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 respective associated tires.

[0012] In various embodiments, the vehicle 10 is autonomous or semi-autonomous, and the control system 100 and / or components thereof are integrated 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 recognized that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, an aircraft, and the like, may also be used.

[0013] As shown, the vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a fuel tank venting 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. The propulsion system 20, in various embodiments, 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 vehicle wheels 16 and 18 according to selectable speed ratios. According to various embodiments, the transmission system 22 may include a stepped automatic transmission, a continuously variable transmission, or other suitable transmissions.

[0014] The braking system 26 is configured to provide braking torque to the vehicle wheels 16 and 18. In various embodiments, the braking system 26 may include friction brakes, brake-by-wire, a regenerative braking system such as an electric machine, and / or other suitable braking systems.

[0015] The steering system 24 influences the position of the vehicle wheels 16 and / or 18. Although illustrated 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.

[0016] The controller 34 includes at least a processor 44 (and a neural network 33) and a computer-readable storage device or storage media 46. As indicated above, in various embodiments, the controller 34 (e.g., the processor 44 thereof) provides data concerning a projected future path of the vehicle 10, including projected future steering instructions, to the steering control system 84 in advance for use in controlling the steering for a limited period of time if communications with the steering control system 84 become unavailable. In various embodiments, the controller 34 also provides communications to the steering control system 84 via the communication system 36, which is further described below, for example, via a communication bus and / or a transmitter (in Fig. 1 not shown).

[0017] In various embodiments, the controller 34 includes at least one processor 44 and a computer-readable storage device or storage media 46. The processor 44 may be any custom-built or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among multiple processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or generally any device for executing instructions. The computer-readable storage device or storage media 46 may include, for example, volatile and non-volatile memory in read-only memory (ROM), random access memory (RAM), and retained memory (RAM).KAM is a persistent or non-volatile memory that can be used to store multiple neural networks along with various operating variables while the processor 44 is off. The computer-readable storage device or storage media 46 can be implemented using any of a number of known memory devices, such as PROMs (programmable read-only memory), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or any other electrical, magnetic, optical, or combination memory devices capable of storing data, some of which may be executable instructions used by the controller 34 in controlling the vehicle 10.

[0018] The instructions may comprise one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor 44, receive and process signals from the sensor system 28, perform logic, calculations, methods, and / or algorithms for automatically controlling the components of the vehicle 10, and generate control signals that are transmitted to the actuator system 30 to automatically control the components of the vehicle based on the logic, calculations, methods, and / or algorithms. Although only one controller 34 in Fig. 1, embodiments of the vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and that cooperate to process the sensor signals, perform the logic, calculations, methods and / or algorithms, and generate control signals to automatically control features of the vehicle 10.

[0019] As in Fig. 1, the vehicle 10 generally includes, in addition to the aforementioned steering system 24 and controller 34, a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially encloses 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 respective associated tires.

[0020] In various embodiments, the vehicle 10 is an autonomous vehicle, and the control system 100 and / or components thereof are integrated 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 recognized that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, an aircraft, and the like, may also be used.

[0021] Controller 34 includes a vehicle controller that operates based on the output of the neural network model 33. In an exemplary embodiment, a feedforward operation may be applied to an adjustment factor, which is the continuous output of the neural network models 33, to generate a control action for the desired torque or other similar action (for example, in the case of continuous neural network models 33, the continuous APC / SPARK prediction values ​​are outputs).

[0022] In various embodiments, one or more user input devices 27 receive inputs from one or more passengers (and the driver 11) of the vehicle 10. In various embodiments, the inputs include a desired destination for the vehicle 10. In certain embodiments, one or more input devices 27 include an interactive touchscreen in the vehicle 10. In certain embodiments, one or more input devices 27 include a speaker for receiving audio information from the passengers. In certain other embodiments, one or more input devices 27 may include one or more other types of devices and / or may be coupled to a user device (e.g., smartphone and / or other electronic devices) of the passengers.

[0023] The sensor system 28 includes one or more sensors 40a-40n that detect 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, radars, lidars, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and / or other sensors.

[0024] 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 fuel tank venting system 31, the 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 interior and / or exterior vehicle features that are Fig. 1, such as various doors, a trunk, and cabin features such as air, music, lighting, touch screen display components (such as those used in conjunction with navigation systems), and the like.

[0025] The data storage device 32 stores data for use in automatically controlling the vehicle 10, including storing data from a DNN constructed by the RL, which is used to predict driver behavior for vehicle control. In various embodiments, the data storage device 32 stores a machine learning model of a DNN and other data models constructed by the RL. The model constructed by the RL may be for a DNN behavior prediction model or an RL-constructed model (see Fig. 2, DNN prediction model or RL prediction model). In an exemplary embodiment, no separate training is required for the DNN; rather, the DNN behavior prediction model (i.e., DNN prediction model) is implemented with a set of learned features. In various embodiments, the neural network (i.e., DNN behavior prediction model) may be built using RL or trained using a supervised learning methodology by a remote system and communicated or deployed (wirelessly and / or in a wired manner) within the vehicle 10 and stored in the data storage device 32. The DNN behavior prediction model may also be trained via supervised or unsupervised learning based on input vehicle data from host vehicle operations and / or collected data about a host vehicle operating environment.

[0026] 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 within the data storage device 32—i.e., a set of road segments (geographically associated with one or more of the defined maps) that together define a route the user can take to travel from a starting location (e.g., the user's current location) to a destination 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.

[0027] The controller 34 implements the logic model built by RL or for the DNN based on the DNN behavior model trained with a set of values. It includes at least one processor 44 and a computer-readable storage device or storage media 46. The processor 44 may be any custom-built or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among multiple processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or, in general, any device for executing instructions. The computer-readable storage device or storage media 46 may include, for example, volatile and non-volatile memory in read-only memory (ROM), random access memory (RAM), and hold memory (RAM).KAM is a persistent or non-volatile random access memory that can be used to store various operating variables while the processor 44 is powered off. The computer-readable storage device or storage media 46 can be implemented using any of a number of known memory devices, such as PROMs (programmable read-only memory), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or any other electrical, magnetic, optical, or combination memory devices capable of storing data, some of which may be executable instructions used by the controller 34 in controlling the vehicle 10.

[0028] The instructions may comprise one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor 44, receive and process signals from the sensor system 28, perform logic, calculations, methods, and / or algorithms for automatically controlling the components of the vehicle 10, and generate control signals that are transmitted to the actuator system 30 to automatically control the components of the vehicle based on the logic, calculations, methods, and / or algorithms. Although only one controller 34 in Fig. 1, embodiments of the vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and that cooperate to process the sensor signals, perform the logic, calculations, methods and / or algorithms, and generate control signals to automatically control features of the vehicle 10.

[0029] The communication system 36 is configured to wirelessly communicate (with respect to Fig. 2). In an exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate over a wireless local area network (WLAN) using IEEE 802.11 standards or using cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communication (DSRC) channel, are also contemplated within the scope of the present disclosure. DSRC channels refer to short- to medium-range, one-way or two-way wireless communication channels specifically designed for automotive use, and a corresponding set of protocols and standards.

[0030] In various embodiments, communication system 36 is used for communications between controller 34, including data related to a projected future path of vehicle 10, including projected future steering instructions. In various embodiments, communication system 36 may also facilitate communications between steering control system 84 and / or other systems and / or devices.

[0031] In certain embodiments, the communication system 36 is further 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 other systems and / or devices. The communication system 36 may, for example, 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, passenger devices (e.g., the user device 54 of Fig. 2) and / or one or more sources of remote information (e.g. GPS data, traffic information, weather information, and so on).

[0032] Fig. 2A and Fig. 2B are diagrams illustrating cross-sectional views of an example electric machine 200 from two different perspectives. The example electric machine 200 includes a housing 202 surrounding the example electric machine 200 and a stator stack 204 within the housing 202. The example stator stack 204 includes a plurality of windings 206 around an inner perimeter of the stator stack 204. Enclosed within the example stator stack 204 is a rotor stack 208. The example rotor stack 208 includes a plurality of magnets 210 and a rotor hub 212. The example rotor stack 208 is configured to rotate within the stator stack 204 using a set of bearings 214. The example stator stack 204 and the example rotor stack 208 are enclosed within the housing 202 by an end cover 216. The example electric machine 200 further includes a resolver 218 for measuring the degrees of rotation of the rotor stack 208.

[0033] In Fig. 3 shows an exemplary EV propulsion configuration 300 according to exemplary embodiments of the present disclosure. EVs are configured with one or more electric motors. For example, all-wheel-drive EVs may have a motor for each axle. This enables better traction and control on slippery surfaces. The most common types of electric motors used in EVs are permanent magnet synchronous motors (PMSMs) and induction motors. PMSMs are the most efficient type of electric motor and are used in most modern EVs due to their high torque output and low noise and vibration levels. PMSMs use permanent magnets to create a rotating magnetic field that drives the rotor. Less expensive induction motors are less efficient than PMSMs but can be more reliable.Induction motors use a rotating magnetic field to create a current in the rotor windings, which causes the rotor to rotate.

[0034] The electric motor 310 is typically powered using three-phase alternating current (AC). Electric motors can be powered by either single-phase or three-phase AC electricity. Single-phase induction motors are less common than three-phase induction motors and are typically used in smaller applications. Three-phase induction motors are more common and are used in larger applications such as electric vehicles and industrial machinery. Three-phase electric motors are more efficient than single-phase motors and can also produce more torque. This makes them ideal for use in applications where high power and efficiency are required, such as electric vehicles.

[0035] The exemplary three-phase inverter 320 is supplied with a DC voltage from a direct current (DC) voltage source, such as a battery 330, and converts the DC electricity into three-phase alternating current (AC) electricity 340. The three-phase AC electricity 340 has three separate voltages that are 120 degrees out of phase. To power the electric motor 310, the inverter 320 supplies the motor with three-phase AC electricity 340 to the stator windings of the electric motor 310, which creates a rotating magnetic field in the stator. The rotating magnetic field then induces a current in the rotor windings, causing the rotor to rotate. The rotor speed is determined by the frequency of the three-phase AC electricity 340 supplied by the inverter 320. The higher the frequency, the faster the rotor rotates.Inverter 320 may also control the torque output of electric motor 310 by adjusting the voltage of three-phase AC electricity 340. In some example embodiments, the switching signals applied to the plurality of transistors S1-S6 and the DC voltage may then be controlled in response to the estimated position of the rotor of electric motor 310.

[0036] In some exemplary embodiments, the plurality of transistors S1-S6 may be arranged in a bridge configuration. The bridge circuit may consist 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 generates three-phase AC voltages 340 at the output of the bridge circuit. The three-phase AC voltages are then supplied to the stator windings of the electric motor 310. The rotating magnetic field in the stator induces a current in the rotor windings, causing the rotor to rotate.

[0037] Body diodes D1-D6 in a three-phase 320 inverter provide a path for the inductive load current to flow during dead time. Dead time is a short period of time between when one switch is turned off and the other switch in the same phase is turned on to prevent burnout, which occurs when both switches are turned on simultaneously, creating a short circuit. In IGBT-based three-phase 320 inverters, body diodes participate in conduction. Since IGBTs are not bidirectional, diodes conduct to continue current flow. For positive current, an upper IGBT and a lower diode conduct, and for negative current, a lower IGBT and an upper diode conduct. Therefore, their role is not limited to dead time for an IGBT-based inverter, and diodes and their thermal characteristics are important for managing the thermal stress on the inverter.

[0038] Problems can arise when the electric motor 310 experiences a stall condition, such as when starting movement with a heavy load attached or starting movement on a hill. Stall conditions occur when electrical power is applied to one or more of the three-phase windings, but the motor 310 is not rotating. When a motor 310 stalls, all of the electrical power applied to it is converted to heat. This can quickly overheat the motor 310, causing damage to the windings, insulation, and other components. Likewise, the amount of current a motor 310 draws when unable to rotate is typically much higher than the running current of the motor 310. This high current draw can overload the controller of the motor 310 and other electrical components.Damage resulting from a stoppage of the motor 310 will damage the shaft, bearings, and other components of the motor 310 and reduce the overall performance and service life of the motor 310.

[0039] PWM modulation, such as adaptive temperature compensation PWM modulation, is used to reduce the thermal stress on the solid-state switches S1-S6 to enable acceptable switching noise levels and reduce potential damage due to motor stall. However, during stalling, the use of discontinuous PWM can lead to NVH problems and improper thermal distribution. Improper thermal distribution also occurs with continuous PWM, but without NVH problems. The thermal stress can be unevenly distributed among the switching transistors S1-S6 and body diodes D1-D6, with the hottest device varying based on the rotor position. With discontinuous PWM, the modulation signal is clamped to the DC rail for a portion of each period.This can be done using a discontinuous triangular carrier wave or using a continuous triangular carrier wave and adding a zero sequence voltage to it.

[0040] To address the unevenly distributed thermal loads and potential NVH issues arising from the use of discontinuous PWM, the heat distribution of the various components can be estimated, and the zero vector, and therefore the duty cycle distribution between the devices, can be adaptively changed for more even heat distribution between the switching transistors S1-S6 and body diodes D1-D6. For example, in the exemplary EV drive 300, a stationary position drives 3-phase AC currents such that -1120 A flows through a first phase and +560 A flows through a 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 first-phase conducting devices are the body diode D1 and transistor S4.In a certain exemplary embodiment based on states of inverter 320 and motor 310 for a space vector pulse width modulation (SVPWM) scheme, the first phase duty cycle is 0.45 for upper devices and 0.55 for lower devices. For this exemplary duty cycle distribution, transistor S4 may conduct more than body diode D1, while detected temperatures indicate 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 a result of differences in thermal characteristics between body diode D1 and transistor S4. The exemplary algorithm is configured to rebalance the heat between body diode D1 and transistor S4 by adaptively changing the zero vector.In some example embodiments, the operating times are redistributed so that the operating time of body diode D1 decreases from 0.45 to 0.4 and that of transistor S4 increases from 0.55 to 0.6. This redistribution results in a balanced temperature, such as ~155°C. In some example embodiments, the example algorithm may equalize the heat distribution between the top and bottom devices of the same highest current-carrying phase, or may equalize the heat dissipation between the hottest devices of the highest and second-highest current-carrying phases.

[0041] Changing the zero vector for PWM involves using a different combination of switching states to represent the zero vector. In a three-phase inverter, there are six possible switching states, and each switching state represents a different combination of phase voltages. The zero vector is represented by the switching state in which all three phases are connected to the same DC rail. A common method to change the zero vector for PWM is to use a space vector modulation (SVM) algorithm. Alternatively, carrier-based PWM algorithms can be used to generate a PWM signal by comparing the reference voltage waveform with a triangular carrier waveform.

[0042] In Fig. Figure 4 shows a block diagram illustrating an adaptive junction temperature control system 400 for an electric drive 410 according to various embodiments. 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 a drive electric motor 410, a PWM inverter 415, and a position sensor 470 for determining a position of the drive electric motor 410. The discrete control system 405 includes a PWM generation section 422 and an adaptive junction temperature control section 442.

[0043] The PWM generation section 422 is configured to generate PWM power for supply to the PWM inverter using a pulse width modulation (PMW) inverter 415. This PWM power is effective to generate the DC voltage V dcThe battery 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 voltage's on-time to off-time, which determines the average voltage applied to the motor. By varying the duty cycle, the PWM generator 420 can control the speed and torque of the electric drive 410.

[0044] The DC voltage V dc is coupled from the battery to the command generation module 430, which is configured to control the stator current I dq in response to the DC voltage V dc , the electrical angular frequency ω e and the controller cycle time T cmd The electrical angular frequency ω e is in response to a differentiation 435 of the electrical drive position Ø e generated. The stator current l dqis then in response to errors from the drive current l abc corrected, which is determined as proportional 440 to the electrical drive position Ø e , which is received from the position sensor 470. The corrected stator current is then regulated by the current regulator 425 and the PWM waveform with an initial zero vector s abc is generated by the PWM generator 420.

[0045] The adaptive junction temperature control section 442 is configured to provide an adjusted zero vector s adapt to generate the initial zero vector s abe to rotate to the final zero vector Sf abcThe adjusted zero vector is generated in response to estimated inverter losses 455 and estimated junction temperatures 450. The estimated inverter losses 455 may be determined using analytical, simulated, or experimental methods and are estimated for the particular inverter configuration in response to predicted inverter component losses. These inverter losses 455 may be estimated for various operating conditions and may be stored in memory or a lookup table, or may be used to generate a prediction algorithm for estimating inverter losses for a current inverter operating condition. The estimated junction temperatures 450 may be estimated for particular transistor / body diode pairs to determine maximum junction temperatures.

[0046] The zero vector adjustment 455 generates the adjusted zero vector s adapt in response to the peak current and the average current of the drive current l abc for the currently conducting body diode and transistor pair. In some example embodiments, zero vector adjustment 455 may first estimate an intermediate zero vector in response to body diode and transistor condition losses at peak currents, estimate the junction temperatures and diode temperatures in response to these intermediate zero vectors, estimate intermediate zero vectors at intermediate currents, and then adjust the adjusted zero vector s adapt in response to an optimal ratio of the intermediate zero vectors, resulting in balanced junction and diode temperatures for specific transistor and body-diode pairs. The final zero vector is then adjusted in response to the adjusted zero vector s adapt and the initial zero vector s abcgenerated. The final zero vector s f is then used to determine the stator current l dq which is applied to the PWM inverter 415.

[0047] In Fig.5 shows a method 500 for operating an exemplary adaptive junction temperature controller for electric motor systems according to various embodiments. The exemplary method 500 is first operative to estimate inverter losses. Inverter losses in a 3-phase PWM inverter for an EV traction motor may first be estimated by identifying the inverter components. These components may include semiconductor switches such as IGBTs or MOSFETs, gate drivers, body diodes, inductors, capacitors, and resistors. The inverter operating conditions are next considered, including the DC input voltage, AC output voltage and frequency, modulation index, and load current. The inverter losses may then be estimated in response to the inverter components and the inverter operating conditions when the semiconductor switches are turned on.These inverter losses can then be stored in a memory or the like that is communicatively coupled to a processor or controller for performing adaptive temperature control for the electric motor algorithm.

[0048] Once the inverter losses are estimated, method 500 may next initiate the algorithm 510. The algorithm may be initiated in response to a vehicle control system being initiated, a vehicle running state transitioning from a standby state to a running state, or a vehicle driving state transitioning from a "peak" state to a "drive" state, or the like. In some example embodiments, the algorithm is operative to detect a stall condition of an electric motor and perform adaptive temperature control in response to the detected stall condition.

[0049] The exemplary method can use continuous PWM with zero-vector adjustment based on junction temperature estimation-based adaptive model reference systems (MRAS) to achieve accurate thermal balancing among all 12 devices, adapting to changing rotor position, while addressing NVH issues using continuous PWM. This adaptive method promotes reliable operation of the electric drives during hill starts, heavy-duty starts, or other engine-down conditions while minimizing both thermal stress and switching noise without any additional calibration effort.

[0050] The method 500 is configured to first estimate 520 junction temperatures for each of the various inverter components. In some example embodiments, the method 500 may estimate the temperature of each of the six transistors and each of the six body diodes of a three-phase inverter in response to the currents conducted by each of the inverter components and the corresponding inverter losses. Loss and temperature may be estimated individually for each device pair, transistor and body diode, and the maximum of these temperatures may be estimated.

[0051] In a certain exemplary embodiment, when 525 the maximum temperature of the power module T transdoes not exceed a threshold temperature, such as when the vehicle engine is not in a stalled state or PWM power is being conducted at low current / low duty cycle, the method 500 returns to estimating 520 subsequent junction temperatures. If the maximum power module temperature T max exceeds the threshold temperature, the method 500 may next determine whether the temperature T diode the conducting diode is not equal to the temperature T trans of the conducting transistor. If the two temperatures are estimated to be equal, the method 500 returns to estimating 520 subsequent junction temperatures.

[0052] If 530 is the temperature T diode the conducting diode is not equal to the temperature T transof the conducting transistor, indicating an unbalanced temperature distribution across the entire conducting elements, the method 500 next operates to sort the three-phase currents based on amplitude to determine the critical phases. The method 500 may sort the phase currents A, B, and C based on the current amplitudes to determine the phases carrying the highest and second-highest currents. The method then determines whether 540 the phase with the highest critical phase current is greater than zero. If the phase of the highest critical phase current is greater than zero, the method 500 calculates 545 the first intermediate zero vector using the loss information of the upper transistor and the lower diode. If the highest critical phase current has a positive direction, the upper IGBT and the lower diode are conducting.The method calculates the first intermediate zero vector given loss information and device parameters of the upper IGBT and the lower diode for this phase conduction path. If the phase of the highest critical phase current is less than zero, the lower IGBT and the upper diode are conducting. The method 500 then calculates 550 the first intermediate zero vector using the loss information of the lower transistor and the upper diode.

[0053] After the first intermediate zero vector is calculated, the method then determines 555 whether the diode temperature T diode the transistor temperature T trans If the diode temperature T diodeexceeds the transistor temperature, the method 500 calculates 560 the 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, then the method calculates the second intermediate zero vector considering loss information and device parameters of the conducting diodes in the highest and second highest phases. If the diode temperature T diode If the transistor temperature does not exceed 565, the method 500 calculates the second intermediate zero vector using the loss information of the transistor 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 considering loss information and device parameters of the conducting IGBTs in the highest and second highest phases.

[0054] Once the first intermediate zero vector and the second intermediate vector are calculated, the method 500 then generates 570 an adjusted zero vector in response to the estimated inverter losses, the estimated junction temperatures, and / or the estimated electrical currents. In some example embodiments, the method 500 may first determine the peak current and the average current for the conductive elements from the total drive current. The method 500 then determines a first intermediate zero vector in response to a ratio of the diode conduction loss and the transistor conduction loss at the peak current.The method next determines a second intermediate zero vector in response to a ratio of the transistor conduction loss at peak current and the transistor conduction loss at average current, or the ratio of the diode conduction loss at average current and the diode conduction loss at peak current, depending on which component temperature is greater. The adjusted zero vector is then determined in response to a maximum and minimum of the first intermediate zero vector and the second intermediate zero vector.

[0055] The method 500 is next operative to apply the adjusted zero vector to the drive current with an initial zero vector 575 to generate a drive current with a final zero vector. The final zero vector is applied 545 to the PWM inverter so that adjusted PWM three-phase drive currents with the adjusted zero vector are generated 580 to equalize the temperature distribution across the conducting diodes and switches within the PWM inverter. The PWM three-phase drive currents are then coupled to the drive motor to power the drive motor 585, and the method 500 returns to estimating 520 subsequent junction temperatures.

[0056] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be recognized that a tremendous number of variations exist. It should also be recognized that the exemplary embodiment or exemplary embodiments are merely examples and are in no way intended to limit the scope, applicability, or configuration of the disclosure. Rather, the foregoing detailed description provides those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes in the function and arrangement of elements may be made without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.

Claims

[1] Engine control system, which includes: an inverter comprising 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 a diode temperature and a transistor temperature such that the diode temperature is equal to the transistor temperature. [2] The engine control system of claim 1, further comprising a memory for storing a first component loss for the transistor and a second component loss for the diode. [3] The motor control system of claim 1, wherein the alternating current is coupled to a stator winding of an electric motor to drive a rotor of the electric motor. [4] The engine control system of claim 1, wherein the zero vector is adjusted in response to a first component loss for the transistor, a second component loss for the diode, and a portion of the pulse width modulated DC current coupled through the transistor and the diode. [5] The engine 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 engine 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 the alternating current, wherein the processor is configured to adjust the zero vector in response to detection of a stall condition of an electric motor. [8] The engine control system of claim 1, wherein the zero vector is adjusted in response to a diode conduction loss, a diode voltage drop, a transistor conduction loss, and a transistor voltage drop. [9] The engine control system of claim 1, wherein the diode temperature and / or the transistor temperature are determined using a negative temperature coefficient thermistor. [10] A method for generating an alternating current, comprising: Generating a pulse width modulated current with a first zero vector; Estimating a diode temperature and a transistor temperature in an inverter in response to the pulse width modulated current; Adjusting the first zero vector of the pulse width modulated current to produce an adjusted pulse width modulated current such that the diode temperature is equal to the transistor temperature; generating an alternating current in response to the adjusted pulse-width modulated current through the inverter; and Coupling the alternating current with an electric motor.

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