Machine position sensor data processing method and system

Abstract

Machine position sensor data processing methods and systems are provided. A system for determining an angular position of a rotor within an electric motor includes a resolver for generating a first alternating current and a second alternating current in response to rotation of the rotor; and a position filter for performing synchronous frame filtering on the first alternating current and the second alternating current to generate a first angular position, performing motion state filtering on the first angular position to determine a first rotation angle and a second rotation angle, and performing harmonic error decoupling on the first angular position in response to the first rotational angle and the second rotational angle to generate a refined angular position.

Description

Technical Field

[0001] This disclosure generally relates to vehicles, systems, and methods for detecting the machine position of rotating objects in automotive systems. In particular, a general method for compensating for non-ideal position sensor behavior in alternating current (AC) machines is disclosed. Background Technology

[0002] Autonomous and semi-autonomous vehicles are able to sense their environment and navigate based on that sensed environment. These vehicles use sensing devices such as radar, lidar, and image sensors to perceive their environment. The vehicle system further uses information from Global Positioning System (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and / or drive-by-wire systems to navigate the vehicle. Vehicle automation has been categorized into numerical levels from zero to five, with zero corresponding to no automation and five corresponding to full automation without human control. Various automated driver assistance systems, such as cruise control, adaptive cruise control, and parking assistance systems, correspond to lower levels of automation, while truly “driverless” vehicles correspond to higher levels of automation.

[0003] Position sensors are components in autonomous and semi-autonomous vehicle systems, providing information about the positions of various mechanical elements. These sensors play a crucial role in optimizing vehicle performance, enhancing safety features, and enabling Advanced Driver Assistance Systems (ADAS). By accurately measuring linear or rotational displacement, position sensors provide real-time data to the vehicle's control unit. This data is used to control engine timing, throttle position, steering angle, and many other functions. For example, the throttle position sensor measures the driver's input to the accelerator pedal, enabling the engine control module to adjust fuel injection and ignition timing accordingly. Similarly, the crankshaft position sensor measures the rotational position of the crankshaft to determine engine speed and timing.

[0004] Several types of position sensors are commonly used in automotive applications, each with its own advantages and limitations. Potentiometers, Hall effect sensors, and magnetoresistive sensors are the most prevalent. Potentiometers utilize resistive elements and wipers to measure position, while Hall effect sensors detect changes in magnetic fields. Magnetoresistive sensors, on the other hand, utilize changes in resistance in response to magnetic fields. The widespread adoption of position sensors has greatly facilitated advancements in automotive technology. These sensors are integral to the operation of safety systems such as anti-lock braking systems (ABS) and electronic stability control (ESC). Furthermore, they enable the development of fuel-efficient engines, advanced driver assistance systems, and autonomous driving technologies. As the automotive industry continues to innovate, position sensors will remain crucial in shaping the future of vehicle technology.

[0005] Furthermore, position sensors are used to implement ADAS and autonomous driving technologies. These systems rely on accurate and reliable position data to make informed decisions regarding vehicle control and navigation. For example, steering angle sensors provide information about driver steering input, allowing the vehicle's control system to adjust stability control and lane keeping assist. Additionally, wheel speed sensors measure the rotational speed of each wheel for use in anti-lock braking systems and traction control. Therefore, it is desirable to provide systems and methods for detecting images of objects that are near, tall, and rare, for constructing an image corpus for object detection. Furthermore, other desirable features and characteristics of the invention will become apparent from the following detailed description and appended claims, taking into account the accompanying drawings and the foregoing technical and background information. Summary of the Invention

[0006] This document discloses a vehicle system propulsion method and system for providing a vehicle propulsion system, along with related control logic, a method for manufacturing such a propulsion system, a method for operating such a propulsion system, and a motor vehicle equipped with the propulsion system. By way of example and not limitation, various embodiments are presented for providing a motor positioning sensor and a motor position processing system for compensating for non-ideal position sensor behavior in an AC motor of the motor vehicle disclosed herein.

[0007] According to one aspect of this disclosure, a method for determining the angular position of an electric motor includes: receiving from sensors a first alternating current and a second alternating current sensed in response to rotation of a rotor within the electric motor, wherein the first alternating current is phase-shifted by 90 degrees with the second alternating current; performing synchronization frame filtering on the first alternating current and the second alternating current to generate a first angular position; performing motion state filtering on the first angular position to determine a first rotation angle and a second rotation angle; performing harmonic error decoupling on the first angular position in response to the first rotation angle and the second rotation angle to generate a refined angular position; controlling an inverter in response to the refined angular position to generate a three-phase alternating current; controlling the electric motor in response to the three-phase alternating current; and using the electric motor to propel a vehicle along a motion path.

[0008] According to another aspect of this disclosure, the motion state filtering is operable to interpolate between a plurality of discrete values ​​of the first alternating current and the second alternating current to generate a first angular position.

[0009] According to another aspect of this disclosure, the refined angular position forms the values ​​of the refined first refined AC current and the refined second refined AC current, and the first refined AC current and the second refined AC current are supplied to the inverter controller for controlling the inverter to modify the characteristics of the three-phase AC current.

[0010] According to another aspect of this disclosure, harmonic error decoupling is configured to transform the first rotation angle and the second rotation angle to a rotating reference frame in order to mitigate high-frequency quantization noise and multiple harmonic distortions.

[0011] According to another aspect of this disclosure, the sensor is a resolver, and the first alternating current is a quantized value of a sinusoidal current, and the second alternating current is a quantized value of a cosine current.

[0012] According to another aspect of this disclosure, the synchronization frame filtering further includes reducing the frequency of the first alternating current to zero to obtain a first direct current (DC) value, and reducing the frequency of the second alternating current to zero to obtain a second DC value, wherein the first DC value and the second DC value are passed through a low-pass filter to remove high-frequency components from the first DC value to generate a first filtered DC value and a second filtered DC value, and wherein a first angular position is determined in response to the first filtered DC value and the second filtered DC value.

[0013] According to another aspect of this disclosure, the motion state filtering is further configured to detect a first angle error and a second angle error in response to a first angular position, and to extract a high-frequency component from the first angle error to generate a first rotation angle, and to extract a low-frequency component from the second angle error to generate a second rotation angle.

[0014] According to another aspect of this disclosure, the motion state filtering is further configured to filter multiple harmonic distortions from a first angular position to determine a first rotation angle and a second rotation angle.

[0015] According to another aspect of this disclosure, the second rotation angle is coupled back to the synchronization frame filter, and the second rotation angle is used to generate a subsequent angular position in response to a subsequent first alternating current and a second subsequent alternating current.

[0016] According to another aspect of this disclosure, a system for determining the angular position of a rotor within an electric motor includes: a sensor for generating a first alternating current and a second alternating current; a position filter for performing synchronous frame filtering on the first and second alternating currents to generate a first angular position, performing motion state filtering on the first angular position to determine a first rotation angle and a second rotation angle, and performing harmonic error decoupling on the first angular position in response to the first and second rotation angles to generate a refined angular position; an inverter controller for controlling an inverter to generate a three-phase alternating current in response to the refined angular position; and an electric motor for propelling a vehicle in response to the three-phase alternating current.

[0017] According to another aspect of this disclosure, the refined angular position forms the values ​​of the refined first refined AC current and the refined second refined AC current, and the first refined AC current and the second refined AC current are supplied to the inverter controller for controlling the inverter to modify the characteristics of the three-phase AC current.

[0018] According to another aspect of this disclosure, the position filter is further configured to interpolate between a plurality of discrete values ​​of the first alternating current and the second alternating current to generate a first angular position.

[0019] According to another aspect of this disclosure, the position filter is further configured to transform the first rotation angle and the second rotation angle to a rotating reference frame in order to mitigate high-frequency quantization noise and multiple harmonic distortions.

[0020] According to another aspect of this disclosure, the sensor is a rotary transformer, and the first alternating current is a quantized value of a sinusoidal current, and the second alternating current is a quantized value of a cosine current, and the first and second alternating currents are generated in response to the rotation of the rotor.

[0021] According to another aspect of this disclosure, the position filter is further configured to reduce the frequency of the first alternating current to zero to obtain a first DC value, and to reduce the frequency of the second alternating current to zero to obtain a second DC value, wherein the first DC value and the second DC value are passed through a low-pass filter to remove high-frequency components from the first DC value to generate a first filtered DC value and a second filtered DC value, and wherein a first angular position is determined in response to the first filtered DC value and the second filtered DC value.

[0022] According to another aspect of this disclosure, the position filter is further configured to detect a first angle error and a second angle error in response to a first angular position, and to extract a high-frequency component from the first angle error to generate a first rotation angle, and to extract a low-frequency component from the second angle error to generate a second rotation angle.

[0023] According to another aspect of this disclosure, the position filter is further configured to filter multiple harmonic distortions from a first angular position to determine a first rotation angle and a second rotation angle.

[0024] According to another aspect of this disclosure, the second rotation angle is used to generate a subsequent angular position in response to subsequent synchronization frame filtering in response to subsequent first and second alternating currents.

[0025] According to another aspect of this disclosure, an electric vehicle propulsion system includes: a battery for supplying DC current; an inverter for converting the DC current into three-phase alternating current (AC) in response to an inverter control signal; an electric motor for rotating a rotor within the motor in response to the three-phase AC current; a resolver for generating a first AC current and a second AC current in response to the rotation of the rotor; a position sensor for performing synchronous frame filtering on the first AC current and the second AC current to generate a first angular position, performing motion state filtering on the first angular position to determine a first rotation angle and a second rotation angle, performing harmonic error decoupling on the first angular position in response to the first rotation angle and the second rotation angle to generate a refined angular position; and an inverter controller for generating an inverter control signal in response to the refined angular position.

[0026] According to another aspect of this disclosure, the synchronization frame filtering further includes reducing the frequency of a first alternating current to zero to obtain a first DC value, and reducing the frequency of a second alternating current to zero to obtain a second DC value, wherein the first DC value and the second DC value are passed through a low-pass filter to remove a first high-frequency component from the first DC value to generate a first filtered DC value and a second filtered DC value, wherein a first angular position is determined in response to the first filtered DC value and the second filtered DC value, wherein motion state filtering is further configured to detect a first angular error and a second angular error in response to the first angular position, and to extract a second high-frequency component from the first angular error to generate a first rotation angle, and to extract a low-frequency component from the second angular error to generate a second rotation angle, and wherein harmonic error decoupling is configured to transform the first rotation angle and the second rotation angle to a rotating reference frame to mitigate high-frequency quantization noise and multiple harmonic distortions. Attached Figure Description

[0027] Exemplary embodiments will now be described in conjunction with the following figures, wherein the same numerals denote the same elements, and wherein:

[0028] Figure 1 This is a functional block diagram illustrating an autonomous or semi-autonomous vehicle system utilizing an AC machine position sensor data processing method according to various embodiments.

[0029] Figure 2 This is a functional block diagram illustrating an exemplary EV drive system including an AC machine position sensor data processing method according to various embodiments;

[0030] Figure 3 This is a system diagram illustrating exemplary block diagrams of a position sensor according to various embodiments of an AC machine position sensor data processing method; and

[0031] Figure 4This is a data flow diagram schematically illustrating a flowchart of a method for performing an AC machine position sensor data processing method according to various embodiments. Detailed Implementation

[0032] The following detailed description is merely exemplary in nature and is not intended to limit application and use. Furthermore, it is not intended to be bound by any express or implied theory set forth in the foregoing technical fields, background art, summary of the invention, or the detailed description below. As used herein, the term "module" refers to any hardware, software, firmware, electronic control components, processing logic, and / or processor device, individually or in any combination, including but not limited to: application-specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or grouped) and memories executing one or more software or firmware programs, combinational logic circuits, and / or other suitable components that provide the described functionality.

[0033] Embodiments of this disclosure are described herein according to functional and / or logical block components and various processing steps. It should be understood that such block components can be implemented by any number of hardware, software, and / or firmware components configured to perform specified functions. For example, embodiments of this disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which can perform various functions under the control of one or more microprocessors or other control devices. Furthermore, those skilled in the art will understand that embodiments of this disclosure can be practiced in combination with any number of systems, and the systems described herein are merely exemplary embodiments of this disclosure.

[0034] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the system (and its various operating components) are not described in detail herein. Furthermore, the connecting lines shown in the various figures included herein are intended to represent exemplary functional relationships and / or physical couplings between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of this disclosure.

[0035] The system and method described in this paper provide a robust object detection system by creating an image corpus for training an object detector that focuses on near, tall, and rare objects in a long-tailed distribution, such as objects that are not frequently observed. In the automotive domain, common objects such as vehicles, pedestrians, and road signs are frequently encountered. The disclosed method uses relative depth, eye gaze estimation, and frequent object detection to find images with tall, near, and rare objects without using a specific query or rare object detector to find such frames. In particular, a system and method using a two-stage approach to detect and classify rare, near, and tall objects within an image are proposed. The initial stage employs a combination of gaze estimation and depth estimation techniques to identify potential regions of interest. This stage prioritizes geometric and salient cues over semantic object recognition. Subsequently, a frequent object detector is applied to these regions. By comparing the detected objects with a database of common objects, the system can effectively isolate rare instances that may represent unusual or uncommon scenes. This approach is capable of detecting anomalous objects that may pose a potential risk to autonomous vehicle systems.

[0036] refer to Figure 1 According to various embodiments, the vehicle system generally shown at 100 is associated with vehicle 10. Typically, vehicle system 100 includes object detection system 200 configured to detect the positions of static, dynamic, common, and uncommon neighboring objects. Vehicle 10 typically includes chassis 12, body 14, front wheels 16, and rear wheels 18. Body 14 is disposed on chassis 12 and substantially surrounds the components of vehicle 10. Body 14 and chassis 12 may collectively form a frame. Wheels 16-18 are each rotatably coupled to chassis 12 near a corresponding angle of body 14.

[0037] In some embodiments, vehicle 10 is an autonomous vehicle, and static object detection system 200 is incorporated into autonomous vehicle 10 (hereinafter referred to as autonomous vehicle 10). This description focuses on exemplary applications in autonomous vehicle applications. However, it should be understood that the static object detection system 200 described herein is contemplated for use in semi-autonomous motor vehicles.

[0038] For example, autonomous vehicle 10 is a vehicle that is automatically controlled to transport passengers from one location to another. In the illustrated embodiment, vehicle 10 is depicted as a passenger car, but it should be understood that any other means of transportation may be used, including motorcycles, trucks, sports utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc. In an exemplary embodiment, autonomous vehicle 10 is a so-called Level 4 or Level 5 automation system. Level 4 system means "high automation," referring to the driving mode-specific performance of the automated driving system for all aspects of a dynamic driving task, even if the human driver does not respond appropriately to intervention requests. Level 5 system means "full automation," referring to the full-time performance of the automated driving system for all aspects of a dynamic driving task under all road and environmental conditions that can be managed by a human driver.

[0039] As shown, the autonomous vehicle 10 typically includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, 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 motor such as a traction motor, and / or a fuel cell propulsion system. The transmission system 22 is configured to transmit power from the propulsion system 20 to the vehicle wheels 16-18 according to a selectable speed ratio. According to various embodiments, the transmission system 22 may include a step-ratio automatic transmission, a continuously variable transmission (CVT), or other suitable transmission. The braking system 26 is configured to provide braking torque to the vehicle wheels 16-18. In various embodiments, the braking system 26 may include friction brakes, brake-by-wire brakes, regenerative braking systems such as electric motors, and / or other suitable braking systems. The steering system 24 affects the position of the vehicle wheels 16-18. Although depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of this disclosure, the steering system 24 may not include a steering wheel.

[0040] Sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external and / or internal environment of the autonomous vehicle 10. Sensing devices 40a-40n may include, but are not limited to, radar, lidar, global positioning system, optical cameras 140a-140n, thermal cameras, ultrasonic sensors, and / or other sensors. Optical cameras 140a-140n are mounted on vehicle 10 and arranged to capture images (e.g., image sequences in video form) of the environment surrounding vehicle 10. In the illustrated embodiment, two front cameras 140a and 140b are arranged to image wide-angle, near-field and narrow-angle, far-field views, respectively. A left-side camera 140c, a right-side camera 140e, and a rear-side camera 140d are also shown. The number and position of the various cameras 140a-140n are merely exemplary, and other arrangements are contemplated.

[0041] Sensor system 28 includes one or more of the following sensors for detecting the position of static, dynamic, common, and uncommon nearby objects. Sensor system 28 may include a steering angle sensor (SAS), a wheel speed sensor (WSS), an inertial measurement unit (IMU), a global positioning system (GPS), an engine sensor, and a throttle and / or brake sensor. Sensor system 28 provides measurements of translational velocity and angular velocity in input vector 204.

[0042] Actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, propulsion system 20, transmission system 22, steering system 24, and braking system 26. In various embodiments, vehicle features may further include interior and / or exterior vehicle features, such as, but not limited to, doors, trunk, and cabin features, such as air, music, lighting, etc. (not numbered).

[0043] Data storage device 32 stores data used for automatically controlling the autonomous vehicle 10. In various embodiments, data storage device 32 stores a map defining the navigable environment. It is understood that data storage device 32 may be part of controller 34, separate from controller 34, or part of controller 34 and a separate system.

[0044] 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, central processing unit (CPU), graphics processing unit (GPU), auxiliary processor among several processors associated with the controller 34, semiconductor-based microprocessor (in the form of a microchip or chipset), macroprocessor, any combination thereof, or any device typically used for executing instructions. For example, the computer-readable storage device or medium 46 can include volatile and non-volatile storage in read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is persistent or non-volatile memory that can be used to store various operational 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 programmable read-only memory (PROM), electrical PROM, electrically erasable PROM, flash memory, or any other electrical, magnetic, optical, or combined memory device capable of storing data, some of which represents executable instructions used by the controller 34 in controlling the autonomous vehicle 10.

[0045] The instructions may include one or more separate programs, each including an ordered list of executable instructions for implementing logical functions. When executed by processor 44, the instructions receive and process signals from sensor system 28, execute logic, calculations, methods, and / or algorithms for automatically controlling components of autonomous vehicle 10, and generate control signals to actuator system 30 to automatically control components of autonomous vehicle 10 based on logic, calculations, methods, and / or algorithms. Although Figure 1 Only one controller 34 is shown in the figure, but embodiments of the autonomous vehicle 10 may include any number of controllers 34, which communicate through any suitable communication medium or combination of communication media and cooperate to process sensor signals, perform logic, calculations, methods and / or algorithms, and generate control signals to automatically control the features of the autonomous vehicle 10.

[0046] In various embodiments, one or more instructions of controller 34 are embodied in object detection system 200 and, when executed by processor 44, are configured to implement the methods and systems described herein for detecting the locations of static, dynamic, common and uncommon neighboring objects.

[0047] Communication system 36 is configured to wirelessly communicate information to and from other entities 48, such as, but not limited to, other vehicles, infrastructure, remote systems, and / or personal devices. In an exemplary embodiment, communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) or by using cellular data communication. However, additional or alternative communication methods, such as dedicated short-range communication (DSRC) channels, are also considered to be within the scope of this disclosure. A DSRC channel refers to a one-way or two-way short-to-medium-range wireless communication channel specifically designed for automotive use and corresponding set of protocols and standards.

[0048] As will be understood, the subject matter disclosed herein provides certain enhanced features and functionalities to a vehicle 10 that can be considered a standard or baseline autonomous vehicle. To this end, the autonomous vehicle can be modified, enhanced, or otherwise supplemented to provide the additional features described in more detail below. The subject matter described herein concerning the static object detection system 200 is applicable not only to autonomous driving applications but also to other driving systems with one or more automated features that utilize automated traffic object detection, particularly the location of static traffic objects, to control the automated features of vehicle 10.

[0049] According to an exemplary autonomous driving application, controller 34 implements autonomous driving system 70. That is, it utilizes appropriate software and / or hardware components of controller 34 (e.g., processor 44 and computer-readable storage device 46) to provide autonomous driving system 70 for use in conjunction with vehicle 10.

[0050] In various embodiments, the instructions of the autonomous driving system 70 can be organized by functions, modules, or systems. For example, such as Figure 2 As shown, the autonomous driving system 70 may include a computer vision system 74, a positioning system 76, a guidance system 78, and a vehicle control system 80. It will be understood that in various embodiments, instructions may be organized into any number of systems (e.g., combined, further segmented, etc.), as this disclosure is not limited to this example.

[0051] In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and / or path of objects, as well as characteristics of the environment of the vehicle 10. In various embodiments, the computer vision system 74 may combine information from multiple sensors, including but not limited to cameras, LiDAR, radar, and / or any number of other types of sensors. The computer vision system 74 includes an object detection module and an object detection system 200.

[0052] The positioning system 76 processes sensor data and other data to determine the position of vehicle 10 relative to its environment (e.g., local position relative to a map, precise position relative to a road lane, vehicle heading, speed, etc.). The guidance system 78 processes sensor data and other data to determine the path that vehicle 10 should follow. The vehicle control system 80 generates control signals for controlling vehicle 10 based on the determined path. The positioning system 76 can process various types of positioning data when determining the position of vehicle 10, including inertial measurement unit data, global positioning system data, real-time motion correction data, cellular and other wireless data, etc.

[0053] In various embodiments, controller 34 implements machine learning techniques to assist its functions, such as feature detection / classification, obstacle mitigation, route traversal, mapping, sensor integration, and ground condition determination. One such machine learning technique performs traffic object detection, thereby identifying, locating, and optionally determining the state of traffic objects for further processing by guidance system 78. Machine learning techniques can be implemented using deep convolutional neural networks. For example, traffic control devices (TCDs), such as traffic lights, can be identified and located, and their states can be determined. Feature detection and classification in two dimensions (2D) can be performed through object detection. Based on the state of the traffic light (e.g., red for stop or green for go), guidance system 78 and vehicle control system 80 work together to determine whether to stop or go at a traffic light. Three-dimensional (3D) localization of TCDs and other static traffic objects supports localization system 76 for vehicle 10, such as lane alignment of vehicle 10 and TCD.

[0054] As briefly mentioned above, the static object detection system can be included within the autonomous driving system 70 in an autonomous driving application, for example, operatively communicating with the computer vision system 74, positioning system 76, guidance system 78, and vehicle control system 80. The static object detection system 200 is configured to detect the positions of static, dynamic, common, and infrequent nearby objects, and the vehicle control system 80 responds to this by generating automatic control commands. The vehicle control system 80 works in conjunction with the actuator system 30 to traverse such a path.

[0055] Now go to Figure 2 An exemplary electric vehicle (EV) drive system 200 is illustrated according to an exemplary embodiment. The EV drive system 200 may include an electric motor, a position processor 205, an inverter controller 235, an inverter 240, and a battery. The position processor 205 may include a synchronization frame filter 220, a high-frequency filter 225, and a harmonic error decoupling unit 230.

[0056] The electric motor 210 is the primary actuator in the system. The motor converts electrical energy into mechanical energy to drive a load or perform a specific task. The performance of the motor is affected by factors such as voltage, current, and rotational speed. The inverter controller 235 is configured to regulate these parameters to achieve the desired motor behavior. Typically, the electric motor 210 includes a rotor (a rotating component wound with copper wire) and a stator (a stationary component containing magnets or electromagnets). When current is supplied to the rotor windings, it interacts with the magnetic field generated by the stator, producing torque that rotates the rotor. This rotational motion is transmitted to the wheels of the vehicle via a transmission system, enabling the vehicle to move.

[0057] In the EV drive system 200, three-phase alternating current (AC) generated by the inverter 240 is supplied to the electric motor 210. The inverter is typically configured with multiple high-voltage switching transformers that are switched on and off in a regular sequence to convert direct current (DC) from the battery into AC current. The inverter controller 235 carefully adjusts the switching rate and other inverter parameters in response to the desired vehicle speed and direction. The inverter controller 235 can adjust the inverter parameters to optimize the electric motor performance to meet the vehicle's driving needs, providing smooth and efficient acceleration, deceleration, and speed control.

[0058] To precisely control the motor 210, the inverter controller 235 needs to know the motor's angular position. Accurate position information allows the controller to precisely calculate and apply the necessary voltage and current waveforms to the motor windings to ensure precise control of motor torque and speed. For high-speed applications, field weakening techniques can be used to maintain high power output without exceeding the motor's voltage limits. By knowing the motor's position, the controller can optimally adjust the excitation current to weaken the magnetic field, thereby achieving higher speeds while maintaining efficiency. Monitoring the motor's position can further aid in the early detection of anomalies such as excessive vibration, misalignment, or mechanical damage, allowing the inverter controller 235 to initiate appropriate protective measures, such as reducing power or shutting down the motor, to prevent damage and ensure system reliability.

[0059] In some exemplary embodiments, the motor 210 may be configured with a resolver. The resolver is a rotary position sensor that generates two sinusoidal voltage signals, a sine and a cosine signal. These signals are 90 degrees out of phase with each other, and their amplitude and phase shift vary with the angular position of the resolver shaft. By measuring the amplitude and phase difference between the two signals, the position processor 205 can determine the angular position of the motor shaft. The position processor 205 can transform the sine and cosine signals to a rotating reference frame aligned with the synchronous speed of the motor. This transformation simplifies control and analysis by eliminating time-varying components. The filtering process removes unwanted high-frequency noise and interference from the signal, thereby improving the accuracy of the control system.

[0060] Generally, position sensing in resolvers and motors relies on the fact that sinusoidal and cosine signals are typically noisy and may include unwanted integer and non-integer position harmonics. Non-ideal signal characteristics can include signal offset, signal scaling errors (such as amplitude imbalance on the sine and cosine), imperfect orthogonality or orthogonality errors between the sensor's sine and cosine, additional spatial harmonics, mechanical eccentricity, and low-resolution noise from the sensor. Some of these non-ideal signal characteristics may be caused by electromagnetic radiation from the high-power motor stator in the resolver.

[0061] Ideally, the raw sine and cosine signals supplied to the position processor 205 by the resolver would have the same amplitude, zero offset, and be orthogonal, i.e., precisely phase-shifted by 90 degrees relative to each other. However, sensor misalignment, electromagnetic interference, and other factors can produce the type of position error addressed in this paper. Without correction, such errors can ultimately lead to current and torque ripple, affecting control functions within the EV drive system 200. To correct these sensor errors, the position processor 205 can employ a software-based solution for position measurement across various sensing technologies, including those with integrated digital signal processors (DSPs). By employing a synchronization frame filter 220, a high-frequency filter 225, and a harmonic error decoupler 230, the position processor 205 can mitigate quantization noise and other non-ideal signal characteristics. Furthermore, the position processor 205 can utilize motion state filters to correlate errors with non-ideal harmonic characteristics, enabling real-time learning and adaptation to these errors.

[0062] Synchronous frame filter 220 is configured to receive sine and cosine signals from motor 210 to eliminate quantization and high-frequency noise caused by sampling, thereby improving the accuracy and reliability of angle estimation. High-frequency noise is effectively filtered out by transforming the sinusoidal signal into a stationary DC component. The filtered DC signal is then converted back to a sinusoidal waveform, resulting in a smoother and more accurate representation of the original signal. This technique is particularly useful in applications where precise angle measurement is critical, as it mitigates the impact of noise and interference on the estimation process. Synchronous frame filtering helps mitigate discrete jumps caused by the sampling process of sensors with lower sampling rates.

[0063] High-frequency filter 225 is configured to filter the sine and cosine signals from motor 210 to remove any residual high-frequency components that may interfere with the control system. This filtering helps improve the robustness and accuracy of inverter controller 235, especially in the presence of electrical noise or other interference. High-frequency filter 225 can be a motion state filter with a low-pass response, thereby mitigating high-frequency noise. The error term of the motion state filter has a high-pass filter response, thereby removing DC components that contribute to the harmonic coefficients. This isolates the harmonic content, which is then Fourier integrated. By extracting the Fourier coefficients (A and B), the filter uses a dot product operation to reconstruct the harmonic signal. This process is applied iteratively over time to decouple the harmonic components from the original signal. High-frequency filter 225 can be implemented in two configurations: high bandwidth and low bandwidth. The high-bandwidth filter attenuates high-frequency noise, while the low-bandwidth filter extracts the harmonic content for Fourier integration. By cascading these filters, the system can accurately estimate the harmonic components and improve the overall signal quality. Additional filtering can be applied to the position signal to further refine the estimation process.

[0064] The harmonic error decoupler 230 compensates for harmonic distortion in the current and voltage waveforms of a motor. These distortions can degrade motor performance and introduce unwanted vibrations. The harmonic error decoupler 230 first identifies the dominant harmonic frequencies present in the signal. This can be done using techniques such as Fourier analysis or spectral analysis. Once the harmonic frequencies are identified, the decoupler applies filters to attenuate or eliminate these frequencies. These filters can be designed for specific frequency bands or specific harmonics. After filtering out harmonic distortion, the angular position signal is reconstructed, which is now clearer and less distorted.

[0065] Harmonic error decoupler 230 compensates for harmonic distortion present in the current and voltage waveforms of motor 210. These distortions can adversely affect angular position detection. By utilizing techniques such as Fourier analysis, harmonic error decoupler 230 identifies the dominant harmonic frequencies within the signal. It then applies filters to specific frequency bands or individual harmonics to attenuate or eliminate these frequencies. This filtering process produces a clearer angular position signal with less distortion, resulting in more accurate and reliable motor control. The angular position signal is then coupled to inverter controller 235 for controlling inverter 240 and motor 210.

[0066] Now go to Figure 3 An exemplary block diagram of a position sensor 300 indicating an AC machine position sensor data processing method according to an exemplary embodiment is shown. The position sensor 300 may include a synchronization frame filter 310, a motion state filter 320, and a harmonic error decoupler 330.

[0067] The synchronous frame filter 310 is a signal processing technique used to improve the accuracy and reliability of angle estimation. It works by transforming a sinusoidal signal into a stationary DC component, which is then filtered to remove high-frequency noise. The filtered DC signal is then converted back to a sinusoidal waveform, resulting in a smoother and more accurate representation of the original signal.

[0068] The synchronization frame filter 310 receives two input signals from a motor or other rotating equipment: a sine wave (Sin) and a cosine wave (Cos). These signals are typically obtained from sensors such as resolvers or encoders. The input signals are multiplied by a complex exponential term e. (-jθ) 312. This transformation shifts the frequency of the input signal to zero, effectively converting it into DC components. The DC components are then passed through a low-pass filter (K1 / S) 314, where K1 is a fixed characteristic impedance value and S is a complex frequency variable used in the Laplace transform analysis. This filter removes high-frequency noise and other unwanted components from the signal. The filtered DC signal is then multiplied by a complex exponential term e. (jθ) 316, this shifts their frequencies back to their original frequencies. This restores the sine wave waveform. Filtered sine and cosine waves are output from the filter. These signals are now clearer and less noisy than the original input signals. The filtered sine and cosine signals are used to calculate the angle using the arctangent function (atan2) 318. This improves the quantization error caused by low resolution and provides a more accurate angle estimate compared to using the original noisy signal. By removing high-frequency noise, the synchronization frame filter 310 can significantly improve the accuracy of angle estimation. The filter is less sensitive to noise and interference, making it more robust in noisy environments. The filter improves the signal-to-noise ratio of the input signal, resulting in clearer and more reliable measurements.

[0069] The filtered angle is then coupled to motion state filter 320. Motion state filter 320 is initially operable to generate two error signals θ_err1 and θ_err2, representing the difference between the estimated angle and the true angle. High-bandwidth motion state filter 322 processes the first error signal θ_err1 to extract the high-frequency components of the angle error to generate a first observed rotation angle θ_obs1. θ_obs1 is then coupled to harmonic error decoupler 330 and can also be coupled to the control system input. Low-bandwidth motion state filter 324 processes the second error signal θ_err2 to extract the low-frequency components of the angle error and generates a second observed rotation angle θ_obs2. This filtering can be used to capture slow changes in angle. The decoupler identifies and mitigates the effects of harmonic distortion in the signal by filtering out specific frequency components associated with harmonic distortion. This second observed rotation angle is then coupled back as input to synchronization frame filter 310. By combining synchronous frame filtering and motion state filtering using harmonic error decoupling, this system can achieve highly accurate and reliable angle estimation, even in challenging environments with noise and interference.

[0070] Harmonic error decoupler 330 is configured to receive a first observed rotation angle θ_obs1 from a high-bandwidth motion state filter 322 and a second error signal θ_err2 from the motion state filter 320. Nth-order harmonic error decoupling is a signal processing technique used to isolate and mitigate the effects of harmonic distortion in a signal. Harmonic distortion is unwanted frequency components that can originate from various sources, such as nonlinear components in electronic circuits or mechanical systems, and errors (such as offsets, gains, and orthogonality on sine and cosine waves) also manifest as harmonics. Harmonics on sine and cosine waves may appear as harmonic errors at position. Harmonic error decoupler 330 first identifies the dominant harmonic frequencies present in the signal. This can be done using techniques such as Fourier analysis or spectral analysis. Once the harmonic frequencies are identified, harmonic error decoupler 330 applies filters to attenuate or eliminate these frequencies. These filters can be designed for specific frequency bands or specific harmonics. After filtering out harmonic distortion, the remaining signal is reconstructed, which is now clearer and less distorted.

[0071] Complex exponent e jnθ332 is then configured to receive θ_obs1 from the high-bandwidth motion state filter 322 and generate a harmonic signal of the nth frequency. This nth frequency harmonic signal is then coupled to a cross product block 334 and a dot product block 338. The cross product block 334 calculates the cross product between the second error signal θ_err2 and the nth frequency harmonic signal to identify the phase and amplitude of the nth harmonic component. The amplitude of the nth harmonic component is then coupled to a low-pass filter to smooth the estimated harmonic coefficients. This filtered nth harmonic component is then coupled to a dot product block 338, which is based on the complex exponential term e. jnθ 332 Calculate the dot product between the filtered nth harmonic component and the harmonic signal at the nth frequency, thus obtaining the result. The position signal is represented in the form of a signal. This position signal is then coupled to the inverter controller for controlling the motor, and also coupled back to the input of the motion state filter 320 to generate two error signals θ_err1 and θ_err2, which may be caused by non-ideal sensor behavior (such as nonlinearity, hysteresis, and temperature sensitivity).

[0072] Continue to refer to Figure 4 The diagram illustrates a flowchart of a method 400 for performing an AC machine position sensor data processing method according to an exemplary embodiment. Method 400 is first operable to receive sine and cosine sensor signals 410 from a rotating device. In some exemplary embodiments, the rotating device may be a three-phase motor in an electric vehicle application; however, the method can be equally applied to any application requiring the measurement of the angular position of a rotating object.

[0073] In response to receiving sine and cosine signals, method 400 then performs synchronous frame filtering 415 on the sine and cosine signals to determine the angular position of the rotating device. Errors will be introduced into the angular position for various reasons, such as offset errors, which manifest as shifts in the sine and cosine signals, causing first harmonic distortion in the calculated position; gain errors (where the sine and cosine signals are not perfectly scaled); second harmonic phase errors or orthogonality problems; and / or errors in position measurement, which may cause second harmonic distortion. For example, sensor noise (another important factor) can introduce various harmonics depending on the noise source. This noise may be inherent to the sensor itself, affected by temperature variations, or influenced by external systems (such as magnetic flux from a motor). Depending on the source, these harmonics can occur at different frequencies, such as the 5th, 7th, or higher harmonics.

[0074] To mitigate these errors, a two-pronged approach can be employed. The first component addresses undersampled sensor signals by interpolating between sampling points. This interpolation technique, facilitated by synchronous frame transform, converts sine and cosine signals into stationary DC components, filters them, and then rotates them back to obtain smoother interpolated values. The second component focuses on harmonic error decoupling, acting as a filter to attenuate high-frequency quantization noise and specific harmonics.

[0075] A synchronous frame filter 415 can be used to determine the rotation angle from noisy sine and cosine signals generated by a rotating sensor, such as a resolver. This method involves transforming the signals into a rotating reference frame, where they become stationary DC components. The DC components are cleaned up by filtering out high-frequency noise and interference. The filtered signal is then transformed back into the original stationary reference frame, producing a smooth and accurate estimate of the rotation angle. This technique is particularly effective in handling low sampling rates and noisy environments, making it a valuable tool for precise position sensing in a variety of applications.

[0076] In response to the rotation angle determined by the synchronization frame filter 415, method 400 is next operable to perform motion state filtering 420 on the received rotation angle. Motion state filtering 420 can be employed to enhance the accuracy of the received rotation angle, especially in the case of undersampled signals, by generating a smoother and more continuous representation of the underlying motion through interpolation between discrete sensor measurements. Motion state filtering utilizes a mathematical model to predict the system behavior between sampling points. The filter then combines these predictions with the actual sensor measurements to obtain a more accurate estimate of the true motion state. This approach effectively mitigates the effects of noise and quantization errors typically associated with undersampled data, thereby improving position estimation and overall system performance.

[0077] In response to the refined rotation angle determined by motion state filtering 420, method 400 is next operable to perform harmonic error decoupling 425 on the refined rotation angle. Harmonic error decoupling 425 can be employed to enhance the accuracy of sensor data by mitigating high-frequency quantization noise and specific harmonic distortion. This method involves transforming a sinusoidal angular position signal into a rotating reference frame. In this transformation frame, the harmonic components become stationary, allowing a low-pass filter to be applied to selectively attenuate unwanted frequencies. By judiciously selecting the filter cutoff frequency, broadband noise and discrete harmonic interference are suppressed without compromising the fundamental information contained in the basic signal. Subsequently, the filtered signal is transformed back into the original reference frame, producing a refined and more accurate representation of the fundamental angular position signal.

[0078] Method 400 can then be operated to control inverter 430 using an inverter controller or similar means in response to a refined angular position. In an electric vehicle propulsion system, the inverter controller precisely controls the motor using angular position signals measured by a resolver or rotation sensor. The rotation sensor provides analog signals representing the motor's rotational position and speed. The inverter controller processes these signals to determine the optimal phase and amplitude of the AC voltage applied to the motor's stator windings. By synchronizing the AC voltage with the motor's rotor position, the controller ensures efficient energy transfer and precise torque delivery. This enables smooth and controlled acceleration, deceleration, and precise speed regulation of the motor.

[0079] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that numerous variations exist. It should also be understood that the exemplary embodiments or multiple exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing the exemplary embodiments or multiple exemplary embodiments. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the scope of this disclosure as set forth in the appended claims and their legal equivalents.

Claims

1. A method for determining the angular position of an electric motor, comprising: The sensor receives a first alternating current and a second alternating current sensed in response to the rotation of the rotor within the motor, wherein the first alternating current and the second alternating current are phase-shifted by 90 degrees. Perform synchronous frame filtering on the first AC current and the second AC current to generate the first corner position; Motion state filtering is performed on the first angular position to determine the first rotation angle and the second rotation angle; In response to the first rotation angle and the second rotation angle, harmonic error decoupling is performed on the first angular position to generate a refined angular position; The inverter is controlled in response to the refined angular position to generate three-phase alternating current; The motor is controlled in response to the three-phase alternating current; The electric motor propels the vehicle along the motion path.

2. The method for determining the angular position of a motor according to claim 1, wherein, The motion state filtering is operable to interpolate between multiple discrete values ​​of the first AC current and the second AC current to generate the first angular position.

3. The method for determining the angular position of a motor according to claim 1, wherein, The refined angle position forms the value of a refined first refined AC current and a refined second refined AC current, wherein the first refined AC current and the second refined AC current are supplied to an inverter controller for controlling the inverter to modify the characteristics of the three-phase AC current.

4. The method for determining the angular position of a motor according to claim 1, wherein, The harmonic error decoupling is configured to transform the first rotation angle and the second rotation angle to a rotating reference frame in order to reduce high-frequency quantization noise and multiple harmonic distortions.

5. The method for determining the angular position of a motor according to claim 1, wherein, The sensor is a rotary transformer, wherein the first alternating current is a quantized value of a sine wave current, and the second alternating current is a quantized value of a cosine wave current.

6. The method for determining the angular position of an electric motor according to claim 1, wherein, The synchronization frame filtering further includes reducing the frequency of the first alternating current to zero to obtain a first DC value, and reducing the frequency of the second alternating current to zero to obtain a second DC value, wherein the first DC value and the second DC value are passed through a low-pass filter to remove high-frequency components from the first DC value to generate a first filtered DC value and a second filtered DC value, and wherein the first angular position is determined in response to the first filtered DC value and the second filtered DC value.

7. The method for determining the angular position of a motor according to claim 1, wherein, The motion state filtering is further configured to detect a first angle error and a second angle error in response to the first angular position, and to extract a high-frequency component from the first angle error to generate the first rotation angle, and to extract a low-frequency component from the second angle error to generate the second rotation angle.

8. The method for determining the angular position of an electric motor according to claim 1, wherein, The motion state filtering is further configured to filter multiple harmonic distortions from the first angular position to determine the first rotation angle and the second rotation angle.

9. The method for determining the angular position of an electric motor according to claim 1, wherein, The second rotation angle is coupled back to the synchronization frame filter, and wherein the second rotation angle is used to generate a subsequent angular position in response to a subsequent first AC current and a second subsequent AC current.

10. A system for determining the angular position of a rotor within an electric motor, comprising: A sensor for generating a first alternating current (AC) and a second AC current; A position filter is used to perform synchronous frame filtering on the first AC current and the second AC current to generate a first angular position, perform motion state filtering on the first angular position to determine a first rotation angle and a second rotation angle, and perform harmonic error decoupling on the first angular position in response to the first rotation angle and the second rotation angle to generate a refined angular position. Inverter controller, configured to control the inverter to generate three-phase AC current in response to the refined angular position; and The electric motor is used to propel the vehicle in response to the three-phase AC current.

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