METHOD AND SYSTEM FOR PROCESSING POSITION ESSENTIAL DATA

Abstract

System for determining an angular position of a rotor within an electric motor, comprising a resolver for generating a first alternating current and a second alternating current in response to a 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 rotation angle and the second rotation angle to generate a refined angular position.

Description

introduction

[0001] The present description relates generally to vehicles, systems, and methods for object rotation machine position detection in a motor vehicle system. In particular, a method for a generalized approach to compensating for non-ideal position sensor behavior in alternating current (AC) machines is disclosed.

[0002] Autonomous and semi-autonomous vehicles are capable of perceiving their surroundings and navigating based on this perception. Such vehicles perceive their environment using sensing devices such as radar, lidar, image sensors, and the like. The vehicle system also 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 ranging from zero, representing no automation with complete human control, to five, representing complete automation without any 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 that provide information about the position of various mechanical elements. These sensors play a crucial role in optimizing vehicle performance, improving 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 units. This data is used to control engine timing, throttle position, steering angle, and numerous other functions. For example, a throttle position sensor measures the driver's input to the accelerator pedal, allowing the engine control module to adjust fuel injection and ignition timing accordingly.Similarly, a crankshaft position sensor measures the rotational position of the crankshaft, which is used 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 among the most widely used. Potentiometers use a resistive element and a wiper to measure position, while Hall-effect sensors detect changes in magnetic fields. Magnetoresistive sensors, on the other hand, utilize changes in electrical resistance in response to a magnetic field. The widespread use of position sensors has significantly contributed to the advancement of automotive technology. These sensors are integral to the operation of safety systems such as anti-lock braking systems (ABS) and electronic stability control (ESC).In addition, 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 enable ADAS and autonomous driving technologies. These systems rely on accurate and reliable positional data to make informed decisions regarding vehicle control and navigation. For example, a steering angle sensor provides information about the driver's steering input, allowing the vehicle's control systems to adjust stability control and lane keeping assist. Additionally, wheel speed sensors measure the rotational speed of each wheel, which is used for anti-lock braking systems and traction control. Accordingly, it is desirable to provide systems and methods for capturing images of nearby, tall, and rare objects to construct an image body for object detection.Furthermore, other desirable features and characteristics of the present invention will become apparent from the following detailed description and the attached claims in conjunction with the attached drawings and the aforementioned technical field and background. Summary

[0006] This document discloses methods and systems for driving vehicle systems and associated control logic for providing vehicle drive systems, methods for manufacturing and operating such drive systems, and motor vehicles equipped with drive systems. As an example, and not as a limitation, various embodiments of systems for providing an electric motor position sensor and an electric motor position processing system for compensating for non-ideal position sensor behavior in AC motors in a motor vehicle disclosed herein are presented.

[0007] According to one aspect of the present description, a method for determining an angular position of an electric motor comprises receiving, from a sensor, a first alternating current and a second alternating current, detected in response to a rotation of a rotor within the electric motor, wherein the first alternating current is phase-shifted by ninety degrees from the second alternating current; 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; 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 controlling an inverter.to generate a three-phase alternating current in response to the refined angular position, to control the electric motor in response to the three-phase alternating current, and to drive a vehicle along a motion path with the electric motor.

[0008] According to another aspect of the present description, where the motion state filtering is effective to interpolate between a multitude of discrete values ​​of the first AC current and the second AC current in order to generate the first angular position.

[0009] According to another aspect of the present description, wherein the refined angular position forms a value in a refined first refined AC and a second refined AC, and wherein the first refined AC and a second refined AC are fed to an inverter controller to control the inverter in order to modify a property of the three-phase AC.

[0010] According to another aspect of the present description, the harmonic error decoupling is configured to transform the first rotation angle and the second rotation angle into a rotating reference frame in order to attenuate high-frequency quantization noise and a variety of harmonic distortions.

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

[0012] According to another aspect of the present description, wherein the synchronous frame filtering further comprises reducing a frequency of the first AC current to zero to obtain a first direct current (DC) value, and reducing a frequency of the second AC current to zero to obtain a second DC value, and wherein the first DC value and the second DC value are passed through a low-pass filter to remove a high-frequency component from the first DC value in order to produce 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.

[0013] According to another aspect of the present description, wherein the 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 high-frequency component from the first angular error to generate the first rotation angle, and to extract a low-frequency component from the second angular error to generate the second rotation angle.

[0014] According to another aspect of the present description, the motion state filtering is further configured to filter out a variety of harmonic distortions from the first angular position in order to determine the first rotation angle and the second rotation angle.

[0015] According to another aspect of the present description, wherein the second rotation angle is fed back to the synchronous frame filtering and wherein the second rotation angle is used to generate a subsequent angular position in response to a subsequent first alternating current and a subsequent second alternating current.

[0016] According to another aspect of the present description, a system for determining the angular position of a rotor within an electric motor comprises a sensor for generating a first alternating current and a second alternating current, 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, 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 controlling an inverter to generate a three-phase alternating current in response to the refined angular position.and the electric motor to propel a vehicle in response to the three-phase alternating current.

[0017] According to another aspect of the present description, wherein the refined angular position forms a value in a refined first refined AC and a second refined AC, and wherein the first refined AC and a second refined AC are supplied to the inverter control for controlling the inverter in order to modify a property of the three-phase AC.

[0018] According to another aspect of the present description, the position filter is further configured to interpolate between a variety of discrete values ​​of the first AC current and the second AC current to generate the first angular position.

[0019] According to another aspect of the present description, the position filter is further configured to transform the first rotation angle and the second rotation angle into a rotating reference frame in order to attenuate high-frequency quantization noise and a variety of harmonic distortions.

[0020] According to another aspect of the present description, wherein the sensor is a resolver and 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 and wherein the first alternating current and the second alternating current are generated in response to a rotation of the rotor.

[0021] According to another aspect of the present description, wherein the position filter is further configured to reduce a frequency of the first AC current to zero in order to obtain a first DC current value, and to reduce a frequency of the second AC current to zero in order to obtain a second DC current value, and wherein the first DC current value and the second DC current value are passed through a low-pass filter to remove a high-frequency component from the first DC current value in order to produce a first filtered DC current value and a second filtered DC current value, and wherein the first angular position is determined in response to the first filtered DC current value and the second filtered DC current value.

[0022] According to another aspect of the present description, wherein the position filter 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 high-frequency component from the first angular error to generate the first rotation angle, and to extract a low-frequency component from the second angular error to generate the second rotation angle.

[0023] According to another aspect of the present description, the position filter is further configured to filter out a variety of harmonic distortions from the first angular position in order to determine the first rotation angle and the second rotation angle.

[0024] According to another aspect of the present description, the second rotation angle is used to generate a subsequent angular position in response to a subsequent first alternating current and a subsequent second alternating current in response to a subsequent synchronous frame filtering.

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

[0026] According to another aspect of the present description, wherein the synchronous frame filtering further comprises reducing a frequency of the first AC current to zero to obtain a first DC value, and reducing a frequency of the second AC current to zero to obtain a second DC value, and 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 in order to produce 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, and wherein the 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 the first rotation angle, and to extract a low-frequency component from the second angular error to generate the second rotation angle, and wherein the harmonic error decoupling is configured to transform the first rotation angle and the second rotation angle into a rotating reference frame to attenuate high-frequency quantization noise and a variety of harmonic distortions. Brief description of the drawings

[0027] The exemplary embodiments are described below in conjunction with the following drawing figures, where the same reference numerals denote the same elements and where: Fig. 1 is a functional block diagram illustrating an autonomous or semi-autonomous vehicle system that uses a method for processing position sensor data from an alternating current (AC) machine according to various embodiments; Fig. 2 is a functional block diagram illustrating an exemplary EV drive system that includes the method for processing position sensor data from an AC machine according to various embodiments; Fig. 3 is a system diagram illustrating an exemplary block diagram of a position sensor, which specifies the method for processing position sensor data of an AC machine according to various embodiments; and Fig. 4 is a data flow diagram that schematically represents a flow diagram that specifies a method for carrying out a method for processing position sensor data of an AC machine according to various embodiments. Detailed description

[0028] The following detailed description is merely exemplary and is not intended to limit applications 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: application-specific integrated circuit (ASIC), 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 providing the described functionality.

[0029] Embodiments of the present description may be described herein with respect to functional and / or logical block components and various processing steps. It is understood that such block components may be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the present description may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, or the like, which can perform a variety of functions under the control of one or more microprocessors or other control devices.Furthermore, the person skilled in the art will recognize that embodiments of the present description can be practiced in conjunction with any number of systems and that the systems described herein are merely exemplary embodiments of the present description.

[0030] For the sake of brevity, conventional techniques relating to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent exemplary 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 exist in an embodiment of the present description.

[0031] The systems and methods described herein provide a robust object detection system by generating a body of images for use by training object detectors that focus on near, high, and rare objects in a long-tailed distribution, such as objects that are observed less frequently. In the automotive field, objects such as vehicles, pedestrians, and traffic signs are frequently encountered. The disclosed method uses relative depth, gaze estimation, and frequent object detection to find images containing high, near, and rare objects without using specific queries or rare-object detectors to locate such frames. In particular, the systems and methods are proposed using a two-stage approach for detecting and classifying rare, near, and high objects within an image.The initial stage uses a combination of gaze estimation and depth estimation techniques to identify potential regions of interest. This stage prioritizes geometric and prominence cues over semantic object recognition. Subsequently, a common object detector is applied to these regions. By comparing the detected objects to a database of common objects, the system can effectively isolate rare cases that may indicate unusual or atypical scenarios. This approach enables the detection of anomalous objects that could pose potential risks to autonomous vehicle systems.

[0032] With reference to Fig. Figure 1 is a vehicle system, generally shown at Figure 100, associated with a vehicle 10 according to various embodiments. Generally, the vehicle system 100 includes an object detection system 200 configured to detect the positions of static, dynamic, common, and unusual objects in its vicinity. The vehicle 10 generally comprises a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and essentially 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.

[0033] In some embodiments, the vehicle 10 is an autonomous vehicle and the static object detection system 200 is integrated into the autonomous vehicle 10 (hereinafter referred to as the autonomous vehicle 10). The present description focuses on an exemplary application in autonomous vehicle applications. However, it is understood that the static object detection system 200 described herein is intended for use in semi-autonomous vehicles.

[0034] The autonomous vehicle 10, for example, is a vehicle that is automatically controlled to transport passengers from one place to another. In the illustrated embodiment, the vehicle 10 is depicted as a passenger car; however, it is understood that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), watercraft, aircraft, etc., can also be used. In an exemplary embodiment, the autonomous vehicle 10 is a so-called Level 4 or Level 5 automation system. A Level 4 system indicates "high automation" and refers to the driving-mode-specific performance of all aspects of the dynamic driving task by an automated driving system, even if a human driver does not respond appropriately to a request for intervention.A fifth-level system indicates "full automation" and refers to the full-time performance of all aspects of the dynamic driving task by an automated driving system under all road and environmental conditions that can be managed by a human driver.

[0035] As shown, the autonomous vehicle 10 generally comprises a drive 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 control unit 34, and a communication system 36. The drive system 20 may, in various embodiments, comprise an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell drive system. The transmission system 22 is configured to transmit power from the drive system 20 to the vehicle wheels 16-18 according to selectable speed ratios. According to various embodiments, the transmission system 22 may comprise a gear-ratio automatic transmission, a continuously variable transmission, or another suitable transmission. The braking system 26 is configured to provide braking torque to the vehicle wheels 16-18.The braking system 26 can, in various embodiments, comprise friction brakes, brake-by-wire, a regenerative braking system such as an electric motor, and / or other suitable braking systems. The steering system 24 influences the position of the vehicle wheels 16-18. Although it is shown for illustrative purposes as having a steering wheel, the steering system 24 may, in some embodiments considered within the scope of this description, not have a steering wheel.

[0036] The sensor system 28 comprises one or more detection devices 40a-40n that detect observable conditions of the external and / or internal environment of the autonomous vehicle 10. The detection devices 40a-40n may include, but are not limited to, radar, lidar, global positioning systems, optical cameras 140a-140n, thermal imaging cameras, ultrasonic sensors, and / or other sensors. The optical cameras 140a-140n are mounted on the vehicle 10 and are arranged to capture images (e.g., a sequence of images in the form of a video) of an environment surrounding the vehicle 10. In the illustrated embodiment, there are two front cameras 140a, 140b, each arranged to image a wide-angle, near-field of view and a narrow-angle, far-field of view, respectively. Furthermore, left-side and right-side cameras 140c, 140e and a rear camera 140d are illustrated.The number and position of the different cameras 140a-140n are merely exemplary and other arrangements are being considered.

[0037] The sensor system 28 includes one or more of the following sensors for use in detecting the positions of static, dynamic, common, and unusual objects in the vicinity. The 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. The sensor system 28 provides a measurement of the translational velocity and angular velocity in the input vector 204.

[0038] The actuator system 30 comprises one or more actuator devices 42a-42n that control one or more vehicle features, such as, but are not limited to, the drive system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may further include, but are not limited to, interior and / or exterior vehicle features, such as doors, a trunk, and cabin features, such as air conditioning, music, lighting, etc. (not numbered).

[0039] The data storage device 32 stores data for use in the automatic control of the autonomous vehicle 10. In various embodiments, the data storage device 32 stores defined maps of the navigable environment. It is understood that the data storage device 32 can be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system.

[0040] The controller 34 comprises at least one processor 44 and a computer-readable memory device or computer-readable storage 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 assigned to the controller 34, microprocessor-based semiconductor processor (in the form of a microchip or chipset), macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable memory device or computer-readable storage 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 a persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is powered off. The computer-readable memory device or computer-readable storage medium 46 can be implemented using any 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 devices capable of storing data, some of which represent executable instructions used by the controller 34 in controlling the autonomous vehicle 10.

[0041] The instructions can comprise one or more separate programs, each containing an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, perform logic, calculations, procedures, and / or algorithms to automatically control the components of the autonomous vehicle 10, and generate control signals to the actuator system 30 to automatically control the components of the autonomous vehicle 10 based on the logic, calculations, procedures, and / or algorithms. Although in Fig. 1 where only one controller 34 is shown, embodiments of the autonomous vehicle 10 may have any number of controllers 34 which communicate via any suitable communication medium or combination of communication media and which work together to process the sensor signals, perform logic, calculations, procedures and / or algorithms and generate control signals to automatically control features of the autonomous vehicle 10.

[0042] In various embodiments, one or more instructions of the controller 34 are embodied in the object detection system 200 and, when executed by the processor 44, are configured to implement the methods and systems described herein for detecting the positions of static, dynamic, common and unusual objects in the vicinity.

[0043] The communication system 36 is configured to communicate information wirelessly to and from other entities 48, such as other vehicles, infrastructure, remote systems, and / or personal devices, but is not limited to this. In one exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate over a wireless local area network (WLAN) or using cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communications (DSRC) channel, are also considered within the scope of this description. DSRC channels refer to one-way or two-way short- to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards.

[0044] It is understood that the subject matter disclosed herein provides certain enhanced features and functionality for what may be considered a standard or baseline autonomous vehicle 10. For this purpose, an autonomous vehicle may be modified, enhanced, or otherwise supplemented to provide the additional features described in more detail below. The subject matter described herein, relating to 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 use automatic traffic object detection, in particular the position of static traffic objects, to control an automated feature of the vehicle 10.

[0045] According to an exemplary autonomous driving application, the controller 34 implements an autonomous driving system 70. That is, suitable software and / or hardware components of the controller 34 (e.g., the processor 44 and the computer-readable storage device 46) are used to provide an autonomous driving system 70 that is used in conjunction with the vehicle 10.

[0046] In various embodiments, the instructions of the autonomous driving system 70 can be organized according to function, module, or system. For example, the autonomous driving system 70, as shown in Fig. Figure 2 shows a computer vision system 74, a positioning system 76, a guidance system 78, and a vehicle control system 80. In various embodiments, the autonomous driving system 70 can be an autonomous navigation system. It is understood that in different embodiments, the instructions can be organized into any number of systems (e.g., combined, further subdivided, etc.), since the description is not limited to the examples shown.

[0047] In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, position, classification, and / or path of objects and features in the vehicle's environment 10. In various embodiments, the computer vision system 74 can integrate information from multiple sensors, including, but not limited to, cameras, lidar, radar, and / or any number of other sensor types. The computer vision system 74 includes an object detection module and the object detection system 200.

[0048] The positioning system 76 processes sensor data along with other data to determine the position (e.g., a local position relative to a map, a precise position relative to a lane of a road, vehicle direction, speed, etc.) of the vehicle 10 relative to its environment. The guidance system 78 processes sensor data along with other data to determine a path for the vehicle 10 to follow. The vehicle control system 80 generates control signals to steer the vehicle 10 according to the determined path. The positioning system 76 can process a variety of localization data when determining the position of the vehicle 10, including inertial measurement unit (IMU) data, global positioning system (GPS) data, real-time kinematic correction data, cellular and other wireless data, etc.

[0049] In various embodiments, the controller 34 implements machine learning techniques to support its functionality, such as feature detection / classification, obstacle mitigation, route crossing, mapping, sensor integration, ground truth determination, and the like. One such machine learning technique performs traffic object detection, thereby identifying and locating traffic objects and optionally determining their status for further processing by the guidance system 78. The machine learning technique can be implemented using a deep convolutional neural network. For example, a traffic control device (TCD), such as a traffic light, can be identified and located, and its light status determined. Feature detection and classification in two dimensions (2D) can be performed through object detection. Depending on the state of the traffic light (e.g.,The guidance system 78 and the vehicle control system 80 work together to determine whether to stop or change lanes at the traffic lights (red to stop or green to go). The three-dimensional (3D) position of the traffic control device (TCD) and other static traffic objects supports the positioning system 76 in localizing the vehicle 10, such as determining the lane alignment of the vehicle 10 and the TCD.

[0050] As briefly mentioned above, the static object detection system can be integrated into the automated driving system 70 in autonomous driving applications, for example, in operational communication with the computer vision system 74, the positioning system 76, the guidance system 78, and the vehicle control system 80. The static object detection system 200 is configured to detect the positions of static, dynamic, common, and unusual objects in the vicinity, and the vehicle control system 80 responds to this to generate an automated control command. The vehicle control system 80 works in conjunction with the actuator system 30 to execute such a trajectory.

[0051] With reference to Fig. Figure 2 illustrates an exemplary electric vehicle (EV) drive system 200 according to exemplary embodiments. 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 is configured to determine the position of the electric motor. The position processor 205 may include a synchronous frame filter 220, a high-frequency filter 225, and a harmonic error decoupler 230.

[0052] The electric motor 210 is the primary actuator in the system. The motor converts electrical energy into mechanical energy, drives a load, or performs a specific task. The motor's performance is influenced by factors such as voltage, current, and rotational speed. The inverter controller 235 is configured to regulate these parameters to achieve the desired electric motor behavior. Typically, an electric motor 210 comprises a rotor, a rotating component wound with copper wire, and a stator, a stationary component containing magnets or electromagnets. When an electric current is supplied to the rotor windings, it interacts with the magnetic field generated by the stator, resulting in a torque that rotates the rotor. This rotational motion is transmitted to the vehicle's wheels via a transmission system, enabling the vehicle to move.

[0053] In the EV drive system 200, the electric motor 210 is supplied with three-phase alternating current (AC) generated by the inverter 240. The inverter is typically configured with a variety of high-voltage switching transformers that are switched on and off in a regular sequence to convert direct current (DC) from the battery into alternating current. The switching rate and other inverter parameters are carefully controlled by the inverter controller 235 in response to the desired vehicle speed and direction. The inverter controller 235 can adjust the inverter parameters to optimize the electric motor's performance to meet the vehicle's driving requirements, providing smooth and efficient acceleration, deceleration, and speed control.

[0054] To precisely control the electric motor 210, the inverter controller 235 requires knowledge of 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, ensuring precise control of the motor's torque and speed. For high-speed applications, field weakening techniques can be employed 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, enabling higher speeds while maintaining efficiency.Furthermore, monitoring the motor's position can aid in the early detection of anomalies such as excessive vibration, misalignment, or mechanical damage, enabling the inverter control 235 to initiate appropriate protective measures, such as reducing power or shutting down the motor, to prevent damage and ensure system reliability.

[0055] In some exemplary embodiments, the electric motor 210 can be configured with a resolver. Resolvers are rotary position sensors that generate two sinusoidal voltages, sine and cosine signals. These signals are phase-shifted by 90 degrees relative to 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 into 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 signals, thus improving the accuracy of the control system.

[0056] A common drawback of resolvers and electric motor position sensors is that the sine and cosine signals are often noisy and can exhibit unwanted integer and non-integer position harmonics. These non-ideal signal characteristics can include signal offsets, signal scaling errors such as amplitude imbalance between sine and cosine, imperfect orthogonality or quadrature errors between the sensor's sine and cosine signals, additional multiple spatial harmonics, mechanical eccentricity, and low-resolution sensor noise. Some of these non-ideal signal characteristics can result from electromagnetic emission from the high-power motor stator within the resolver.

[0057] Ideally, the raw sine and cosine signals provided by the turret to the position processor 205 would have the same amplitude, zero offset, and be orthogonal, i.e., phase-shifted by exactly 90 degrees relative to each other. However, sensor misalignment, electromagnetic interference, and other factors can generate the types of position errors discussed herein. If left uncorrected, such errors can ultimately lead to current and torque ripple, impairing the control functionality within the EV drive system 200. To correct these sensor errors, the position processor 205 can employ a software-based position measurement solution across various sensing technologies, including those with integrated digital signal processors (DSPs).By using a synchronous frame filter 220, a high-frequency filter 225, and a harmonic error decoupler 230, the position processor 205 can attenuate 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 properties, enabling real-time learning and adaptation to these errors.

[0058] The synchronous frame filter 220 is configured to receive the sine and cosine signals from the electric motor 210 in order to eliminate quantization and high-frequency noise due to sampling, thereby improving the accuracy and reliability of the angle estimation. By transforming sinusoidal signals into steady-state DC components, high-frequency noise can be effectively filtered out. The filtered DC signals are then converted back into sinusoidal waveforms, resulting in a smoother and more accurate representation of the original signal. This technique is particularly useful in applications where precise angle measurements are critical, as it mitigates the effects of noise and interference on the estimation process. Synchronous frame filtering is also useful for attenuating the discrete jumps caused by the sampling process of lower-sampling-rate sensors.

[0059] The high-frequency filter 225 is configured to filter the sine and cosine signals from the electric motor 210 to remove any remaining high-frequency components that could interfere with the control system. This filtering helps improve the robustness and accuracy of the inverter control 235, especially in the presence of electrical noise or other disturbances. The high-frequency filter 225 can be a motion-state filter with a low-pass response by attenuating high-frequency noise. An error term of a motion-state filter exhibits a high-pass filter response, thereby removing a DC component that is helpful for harmonic coefficients. This isolates the harmonic content, which is then subjected to Fourier integration. By extracting the Fourier coefficients (A and B), the filter reconstructs the harmonic signal using a scalar product operation.This process is applied iteratively over time to decouple the harmonic component from the original signal. The high-frequency filter 225 can be implemented in two configurations: a high bandwidth and a 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.

[0060] The 230 harmonic error decoupler compensates for harmonic distortions in the current and voltage waveforms of the motor. These distortions can degrade the motor's performance and introduce unwanted vibrations. The 230 harmonic error decoupler 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 to target specific frequency bands or specific harmonics. After filtering out the harmonic distortions, the angular position signal is reconstructed, resulting in a cleaner and less distorted signal.

[0061] The harmonic error decoupler 230 compensates for harmonic distortions present in the current and voltage waveforms of the electric motor 210. These distortions can adversely affect angular position detection. Using techniques such as Fourier analysis, the harmonic error decoupler 230 identifies the dominant harmonic frequencies within the signal. It then applies filters to attenuate or eliminate these frequencies, targeting specific frequency bands or individual harmonics. This filtering process results in a cleaner and less distorted angular position signal, enabling more accurate and reliable motor control. The angular position signal is then coupled to the inverter controller 235 for use in controlling the inverter 240 and the electric motor 210.

[0062] With reference to Fig. Figure 3 shows an exemplary block diagram of a position sensor 300, which indicates the method for processing position sensor data from an AC machine according to exemplary embodiments. The position sensor 300 can include a synchronous frame filter 310, a motion state filter 320, and a harmonic error decoupler 330.

[0063] The synchronous frame filter 310 is a signal processing technique used to improve the accuracy and reliability of angle estimation. It works by transforming sinusoidal signals into stationary DC components, which can then be filtered to remove high-frequency noise. The filtered DC signals are then converted back into sinusoidal waveforms, resulting in a smoother and more accurate representation of the original signal.

[0064] The synchronous frame filter 310 receives two input signals: a sine wave (sin) and a cosine wave (cos) from the electric motor or other rotating device. These signals are typically received by sensors such as resolvers or encoders. The input signals are processed with a complex exponential term e (-jθ) 312 multiplied. This transformation shifts the frequency of the input signals to zero, effectively converting them into DC components. The DC components are then passed through a low-pass filter (K1 / s) 314, where K1 is the fixed characteristic impedance value and S is the complex frequency variable used in Laplace transform analysis. This filter removes high-frequency noise and other unwanted components from the signal. The filtered DC signals are then multiplied by the complex exponential term e (jθ)316 multiplies the signal, shifting its frequency back to the original frequency. This restores the sinusoidal waveforms. The filtered sine and cosine waves are output by the filter. These signals are now cleaner 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 quantization errors due to low resolution and provides a more accurate angle estimate compared to using the original, noisy signals. By removing high-frequency noise, the synchronous frame filter 310 can significantly improve the accuracy of the 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 signals, resulting in clearer and more reliable measurements.

[0065] The filtered angle is then coupled to the motion state filter 320. The motion state filter 320 initially generates two error signals θ_err1 and θ_err2, representing the difference between the estimated angle and the true angle. The high-bandwidth motion state filter 322 processes the first error signal θ_err1 to extract high-frequency components of the angular error, generating a first observed rotation angle θ_obs1. θ_obs1 is then coupled to the harmonic error decoupler 330 and can also be coupled to a control system input. The low-bandwidth motion state filter 324 processes the second error signal θ_err2 to extract low-frequency components of the angular error and generate a second observed rotation angle θ_obs2. This filter is useful for detecting slow changes in the angle.The decoupler identifies and attenuates 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 fed back as an input to the synchronous frame filter 310. By combining synchronous frame filtering and motion-state filtering with harmonic error decoupling, this system can achieve highly accurate and reliable angle estimation, even in challenging environments with noise and interference.

[0066] The harmonic error decoupling 330 is configured to receive the first observed rotation angle θ_obs1 from the high-bandwidth motion state filter 322 and the 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 attenuate the effects of harmonic distortion in a signal. Harmonic distortion consists of unwanted frequency components that can arise from various sources, such as nonlinear components in electronic circuits or mechanical systems. Errors such as offset, gain, and orthogonality on sine and cosine waves also appear as harmonics. Harmonics on sine and cosine waves can manifest as harmonic errors at position. The harmonic error decoupling 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, the 330 harmonic error decoupler applies filters to attenuate or eliminate these frequencies. These filters can be designed to target specific frequency bands or specific harmonics. After filtering out the harmonic distortion, the remaining signal is reconstructed, resulting in a cleaner and less distorted signal.

[0067] The complex exponential term ejnθ 332 is next configured to receive θ_obs1 from the high-bandwidth motion state filter 322 and generate a harmonic signal at the nth frequency. This harmonic signal at the nth frequency is then coupled to the cross-product block 334 and the scalar product block 338. The cross-product block 334 calculates the cross product between the second error signal θ_err2 and the harmonic signal at the nth frequency 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 with the scalar product block 338, which calculates the scalar product between the filtered nth harmonic component and the harmonic signal at the nth frequency from the complex exponential term ejnθ 332, resulting in a signal that is the position signal in the form A. n cos(n ) + Bnsin(n ). This position signal is then coupled to the inverter control for use in controlling the electric motor and is also fed back to the input of the motion state filter 320 for use in generating the two error signals θ_err1 and θ_err2, which can result from non-ideal sensor behavior such as non-linearity, hysteresis and temperature sensitivity.

[0068] With further reference to Fig.Figure 4 shows a flowchart that specifies a method 400 for carrying out a method for processing position sensor data from an AC machine according to exemplary embodiments. The method 400 is initially effective in receiving the sine and cosine sensor signals from a rotating device 410. In some exemplary embodiments, the rotating device may be a three-phase electric motor in an electric vehicle application, but the method can be applied equally to any application requirement measurement of the angular position of a rotating object.

[0069] In response to receiving the sine and cosine signals, the procedure 400 next performs synchronous frame filtering 415 on the sine and cosine signals to determine an angular position of the rotating device. Errors are introduced into the angular position for various reasons, such as offset errors, which manifest as a shift in the sine and cosine signals, leading to a first harmonic distortion in the calculated position; gain errors, where the sine and cosine signals are not perfectly scaled, introducing second harmonic phase errors or orthogonality problems; and / or errors in the position measurement, which can contribute to second harmonic distortions. For example, sensor noise, another significant factor, can introduce different harmonics depending on the noise source.This noise can be inherent to the sensor itself, influenced by temperature fluctuations, or caused by external systems such as the magnetic flux of motors. These harmonics can appear at different frequencies, such as the 5th, 7th, or higher harmonics, depending on the source.

[0070] 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 transformation, converts the sine and cosine signals into steady-state DC components, filters them, and then reverses them to obtain smoother, interpolated values. The second component focuses on harmonic error decoupling, which acts as a filter to attenuate high-frequency quantization noise and specific harmonics.

[0071] Synchronous frame filtering 415 can be used to determine the rotation angle from noisy sine and cosine signals generated by a rotation 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 then cleaned by filtering out high-frequency noise and interference. The filtered signals are subsequently transformed back into the original stationary reference frame, resulting in a smooth and accurate estimate of the rotation angle. This technique is particularly effective at low sampling rates and in noisy environments, making it a valuable tool for precise position sensing in various applications.

[0072] In response to the rotation angle determined by the synchronous frame filtering 415, the procedure 400 then takes effect to perform a motion-state filtering 420 on the received rotation angle. The motion-state filtering 420 can be used to improve the accuracy of the received rotation angle, particularly in the case of undersampled signals, by interpolating between discrete sensor measurements, producing a smoother and more continuous representation of the underlying motion. The motion-state filtering uses a mathematical model that predicts the system's behavior between sampling points. The filter then combines these predicted values ​​with the actual sensor measurements, resulting in a more accurate estimate of the true motion state.This approach effectively mitigates the effects of noise and quantization errors commonly associated with undersampled data, resulting in improved position estimation and overall system performance.

[0073] In response to the refined rotation angle determined by the motion-state filtering 415, the method 400 is then applied to perform harmonic error decoupling 430 on the refined rotation angle. Harmonic error decoupling 430 can be used to improve the accuracy of sensor data by attenuating high-frequency quantization noise and specific harmonic distortions. This method involves transforming the sinusoidal angular position signal into a rotating reference frame. In this transformed frame, the harmonic components become stationary, allowing the application of low-pass filters to selectively attenuate unwanted frequencies. By carefully selecting the filter cutoff frequency, both broadband noise and discrete harmonic disturbances are suppressed without affecting the essential information contained in the fundamental signal.The filtered signals are then transformed back into the original reference frame, resulting in a refined and more accurate representation of the underlying angular position signal.

[0074] Method 400 is the next effective step in controlling the inverter 430 using an inverter controller or the like in response to the refined angular position. In an electric vehicle propulsion system, the inverter controller uses the angular position signals measured by a resolver or a rotary sensor to precisely control the electric motor. The rotary 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 alternating voltage applied to the motor's stator windings. By synchronizing the alternating 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 control of the electric motor.

[0075] While at least one exemplary embodiment has been presented in the foregoing detailed description, it is understood that a large number of variations exist. It is also understood that the exemplary embodiment or embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this description in any way. Rather, the foregoing detailed description provides the person skilled in the art with a suitable roadmap for implementing the exemplary embodiment or embodiments. It is understood that various modifications to the function and arrangement of elements can be made without deviating from the scope of the description as set out in the appended claims and their legal equivalents.

Claims

[1] Method for determining an angular position of an electric motor, comprising: Received by a sensor, a first alternating current and a second alternating current, detected in response to a rotation of a rotor within the electric motor, the first alternating current being phase-shifted by ninety degrees from the second alternating current; Performing synchronous frame filtering on the first AC current and the second AC current to generate a first angular position; Performing motion state filtering at the first angular position to determine a first rotation angle and a second rotation angle; Performing harmonic error decoupling at 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 to generate a three-phase alternating current in response to the refined angular position; Controlling the electric motor in response to the three-phase alternating current; propelling a vehicle along a path of movement with the electric motor. [2] Method for determining an angular position of an electric motor according to claim 1, wherein the motion state filtering is effective to interpolate between a plurality of discrete values ​​of the first alternating current and the second alternating current to generate the first angular position. [3] Method for determining an angular position of an electric motor according to claim 1, wherein the refined angular position forms a value in a refined first refined alternating current and a second refined alternating current and wherein the first refined alternating current and a second refined alternating current are supplied to an inverter control for controlling the inverter in order to modify a property of the three-phase alternating current. [4] Method for determining an angular position of an electric motor according to claim 1, wherein the harmonic error decoupling is configured to transform the first rotation angle and the second rotation angle into a rotating reference frame in order to attenuate high-frequency quantization noise and a variety of harmonic distortions. [5] Method for determining an angular position of an electric motor according to claim 1, wherein the sensor is a resolver and wherein the first alternating current is a quantized value of a sinusoidal wave current and the second alternating current is a quantized value of a cosine wave current. [6] Method for determining an angular position of an electric motor according to claim 1, wherein the synchronous frame filtering further comprises reducing a frequency of the first AC current to zero to obtain a first DC value, and reducing a frequency of the second AC current to zero to obtain a second DC value, and wherein the first DC value and the second DC value are passed through a low-pass filter to remove a high-frequency component from the first DC value in order 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] Method for determining an angular position of an electric motor according to claim 1, wherein the 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 high-frequency component from the first angular error to generate the first rotation angle and to extract a low-frequency component from the second angular error to generate the second rotation angle. [8] Method for determining an angular position of an electric motor according to claim 1, wherein the motion state filtering is further configured to filter a plurality of harmonic distortions from the first angular position in order to determine the first rotation angle and the second rotation angle. [9] Method for determining an angular position of an electric motor according to claim 1, wherein the second rotation angle is fed back to the synchronous frame filtering and wherein the second rotation angle is used to generate a subsequent angular position in response to a subsequent first alternating current and a subsequent second alternating current. [10] System for determining an angular position of a rotor within an electric motor, comprising: a sensor for generating a first alternating current (AC current) and a second AC current; a position filter to perform 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; an inverter controller for controlling an inverter to generate a three-phase alternating current in response to the refined angular position; and the electric motor to propel a vehicle in response to three-phase alternating current.

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