Electric vehicle reducer parking motor position feedback system

Abstract

This application relates to the field of electric vehicle transmission and control technology, and in particular to a position feedback system for the parking motor of an electric vehicle reducer. This system aims to address the problems of decreased position feedback accuracy, insufficient reliability, and lack of redundancy in existing solutions under harsh environments. The system achieves accurate feedback of the parking motor rotor position through redundant design of a main position sensing module (magnetoelectric encoder) and an auxiliary position sensing module (inductive sensor), combined with a multi-source data fusion module (based on Kalman filtering), anomaly detection and fault tolerance, position prediction and compensation, and temperature compensation and adaptive calibration mechanisms. This system can significantly improve the accuracy, robustness, and reliability of position feedback, extend the service life of the parking mechanism, and enhance parking safety.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle transmission and control technology, and in particular to a position feedback system for a parking motor in an electric vehicle reducer. Background Technology

[0002] The field of electric vehicle technology encompasses multiple branches, including electric drive, battery management, intelligent control, and energy recovery. The core of this technology lies in designing systems that can autonomously charge and discharge, efficiently utilize energy, and provide an intelligent driving experience by driving vehicles with electric power. With advancements in battery technology, motor control algorithms, and power electronic devices, electric vehicle technology has been widely applied, covering multiple application scenarios such as passenger cars, commercial vehicles, and special vehicles. The development of this field has not only driven scientific and technological progress but has also brought profound changes to various industries, including energy and transportation.

[0003] The electric vehicle reducer parking motor position feedback system refers to the precise control and reliable engagement / disengagement of the vehicle's parking mechanism by accurately monitoring the operating status and position information of the parking motor. This paper addresses issues such as inaccurate parking motor position information acquisition, feedback delays, and insufficient system reliability during electric vehicle parking. An optimized position feedback method is proposed, combining high-precision sensors with intelligent algorithms to enable real-time and accurate perception and feedback of the parking motor's position. In this method, the system collects data such as the motor's rotation angle and speed, and combines this with the vehicle's actual operating status to achieve precise control of the parking mechanism. Each feedback link provides position information independently or collaboratively based on its own characteristics and capabilities, thereby improving the overall safety and intelligence level of the parking system. The method also optimizes data processing and transmission strategies to ensure the timeliness and accuracy of position feedback.

[0004] Existing technologies for parking motor position feedback largely rely on a single sensor type, lacking effective suppression of signal noise and drift under complex operating conditions. This makes it difficult to detect position deviations of the parking mechanism in extreme environments (such as severe vibration or wide temperature differences). For example, during prolonged parking or parking on a slope, feedback signals may deviate due to external interference or the sensor's inherent characteristics. However, without multi-source redundancy or adaptive calibration mechanisms, parking safety cannot be fully guaranteed. Position information judgments are often based on fixed thresholds rather than the dynamic evolution of actual operating data, making it difficult to accurately reflect the wear state of the parking motor and mechanism, thus hindering preventative maintenance and fault diagnosis. The lack of sensor failure impact analysis means that conflicts between critical position sensor failures and parking command execution cannot be assessed, resulting in limited parking functionality in emergency situations. The lack of a comparison mechanism between operational data and parking mechanism status data makes it difficult to detect potential mechanical wear or jamming, creating safety hazards. In terms of position monitoring, existing technologies mainly rely on coarse-grained status recognition, neglecting the analysis of position accuracy and response speed in fine-grained sections. This makes it difficult to meet the control requirements under high safety and high reliability conditions. The problem is particularly prominent in the position feedback of the parking motor in the electric vehicle reducer, directly affecting the overall parking performance and vehicle safety. Summary of the Invention

[0005] This invention provides a parking motor position feedback system for electric vehicle reducers, aiming to solve the technical problems of decreased accuracy, insufficient reliability, and lack of redundancy faced by existing electric vehicle parking motor position feedback schemes under harsh operating environments, such as wide temperature ranges, strong electromagnetic interference, severe vibration, and long-term wear. Traditional parking motor position feedback systems typically rely on a single type of sensor, such as Hall sensors or optical encoders. These sensors are easily interfered with under certain environments, leading to unstable or distorted output signals, which in turn affects the precise control of the parking mechanism, potentially causing parking failures, accelerated wear, or even safety hazards. This invention significantly improves the accuracy, robustness, and system reliability of parking motor position feedback by introducing multimodal sensor fusion, intelligent signal processing, and adaptive compensation mechanisms.

[0006] The present invention discloses a position feedback system for a parking motor of an electric vehicle reducer, comprising a main position sensing module, an auxiliary position sensing module, a temperature sensing module, a signal preprocessing module, a multi-source data fusion module, an anomaly detection and fault tolerance module, a position prediction and compensation module, and a communication interface module.

[0007] The main position sensing module is used to accurately detect the real-time angular position of the parking motor rotor in an electric vehicle's reducer. This module is specifically configured as a magnetoelectric encoder, which includes a multi-pole permanent magnet ring integrated on the motor shaft and a fixed magnetoresistive array sensor. The multi-pole permanent magnet ring generates a periodically changing magnetic field as it rotates, and the magnetoresistive array sensor outputs an analog or digital signal by sensing changes in the magnetic field strength. This signal corresponds precisely to the angular position of the motor rotor. The magnetoelectric encoder has the advantages of compact structure, strong anti-fouling capability, and fast response speed, and can directly provide high-resolution incremental or absolute position information. Furthermore, the magnetoresistive array sensor employs the anisotropic magnetoresistive (AMR) or giant magnetoresistive (GMR) effect principle to achieve higher sensitivity and a wider operating temperature range.

[0008] The auxiliary position sensing module provides redundant position information independent of the main position sensing module to improve the overall robustness and reliability of the system. This module is specifically configured as an inductive position sensor, operating based on the principle of electromagnetic induction. The inductive position sensor includes a stator coil structure fixedly mounted inside the motor housing and a conductive rotor target attached to the motor shaft. The stator coil structure typically consists of multiple sets of planar coils, such as copper traces on a printed circuit board, including an excitation coil and multiple receiving coils. A high-frequency AC signal is applied to the excitation coil to generate an alternating magnetic field. When the conductive rotor target rotates with the motor, due to the eddy current effect, it generates an induced current in the magnetic field, which reacts on the magnetic field distribution of the stator coil, causing a change in the induced voltage or impedance of the receiving coils. After demodulation and processing, the induced signal from the receiving coils can accurately reflect the angular position of the conductive rotor target. The inductive position sensor has advantages such as insensitivity to external magnetic field interference, strong vibration resistance, immunity to dust and oil contamination, and stable performance over a wide temperature range, forming a complementary redundancy mechanism with the magneto-electric encoder.

[0009] The temperature sensing module is used to monitor the ambient temperature inside or near the parking motor in real time. This module is specifically configured as a high-precision thermistor or digital temperature sensor, and its installation position is optimized to accurately reflect the key temperature points affecting the position sensor's performance. The temperature data output by the temperature sensing module will be used for subsequent temperature compensation processing to correct for position sensor characteristic drift caused by temperature changes.

[0010] The signal preprocessing module is used to perform preliminary processing on the raw position signals output by the main position sensing module and the auxiliary position sensing module, as well as the temperature signal output by the temperature sensing module. This module includes an analog signal conditioning circuit and a digital signal processing unit. The analog signal conditioning circuit amplifies, filters, and performs analog-to-digital conversion on the analog signals to improve signal quality and meet the requirements of subsequent digital processing. The digital signal processing unit performs noise reduction and smoothing on the digital signals, for example, by using low-pass filters or moving average filters to eliminate high-frequency noise and random fluctuations, ensuring that the data input to the subsequent fusion algorithm has high purity.

[0011] The multi-source data fusion module is the core component of this invention. It intelligently integrates redundant position data from the main position sensing module and the auxiliary position sensing module to generate a more accurate and robust fused position estimate. The core algorithm of this module is based on Kalman filtering or extended Kalman filtering. Kalman filtering can effectively fuse noisy measurement data from different sensors and, combined with the system's dynamic model, provide an optimal estimate of the system's state.

[0012] Specifically, the multi-source data fusion module performs the following steps:

[0013] First, define the system state vector, which may include the current angular position and angular velocity of the parking motor.

[0014] Furthermore, a discrete-time state transition model and a measurement model for motor motion are established.

[0015] Subsequently, within each sampling period, a Kalman filter prediction step is performed to predict the current state based on the optimal state estimate from the previous time step and the motor control input. The prediction formula is as follows:

[0016]

[0017] in, Indicates in Based on the time observation values Prediction and estimation of state at any given time; The state transition matrix describes the transition of the system state from state to state. The evolution from time k to time k; Indicates in Optimal state estimation at time t; Represents the control input matrix; Indicates in The control input vector (e.g., motor drive current or voltage) applied at any time.

[0018] Furthermore, a Kalman filter update step is performed, fusing the real-time measurements and predicted values ​​from the primary and secondary position sensing modules to correct the state estimate. The update formula is as follows:

[0019]

[0020] in, Indicates in Optimal state estimation at time t; The Kalman gain represents the degree to which the measurement corrects the state estimate. Indicates in Measurement vector at time (from primary and secondary position sensors); The measurement matrix represents the mapping of the state vector to the measurement vector space.

[0021] Through the Kalman filtering process described above, the multi-source data fusion module can dynamically adjust the weights of different sensor data, effectively suppress noise, and compensate for possible sensor drift, thereby outputting highly accurate and stable fused parking motor position data.

[0022] The anomaly detection and fault tolerance module monitors the operating status of the main position sensing module and the auxiliary position sensing module in real time and identifies potential sensor failures or anomalies. This module determines consistency by comparing the position data output by the two sensors. When the absolute difference between the position values ​​output by the two sensors exceeds a preset tolerance threshold, the system triggers an anomaly alarm. Furthermore, this module performs signal integrity checks, such as detecting whether the signal exceeds its effective range, remains unchanged for an extended period, or exhibits abrupt changes. Once a sensor failure or output anomaly is detected, the anomaly detection and fault tolerance module instructs the multi-source data fusion module to temporarily or permanently exclude the data from the faulty sensor and rely solely on data from healthy sensors for position estimation, thereby achieving fault-tolerant operation of the system and ensuring the continuity and reliability of position feedback. In the event of failure of both sensors, the system activates the position prediction and compensation module and reports the fault status to the vehicle's main control system.

[0023] The position prediction and compensation module is used to provide temporary position estimates or further compensate for fused position data under specific conditions (such as transient sensor failure, communication delays, or high-speed motor movement). This is achieved through a prediction algorithm based on motor control commands and a motor dynamics model. The module uses the current, voltage, or PWM control signal from the parking motor driver as input, combined with known motor parameters (such as torque constant and moment of inertia) and equations of motion, to predict the motor's angular position at the next moment. This predictive capability is particularly important when sensor data is temporarily unavailable or during high-speed dynamic changes, as it can smooth position output and reduce control lag. Furthermore, the module can fine-tune the fused position data, for example, by correcting for accumulated errors caused by gear transmission backlash or mechanical wear.

[0024] The communication interface module is used to transmit processed and fused information such as the parking motor position, speed, and system status (e.g., sensor health status, fault codes) to the vehicle controller or parking brake controller of the electric vehicle via a standard in-vehicle communication bus (e.g., CAN bus or LIN bus). The communication interface module ensures the real-time performance, integrity, and reliability of data transmission, meeting the stringent requirements of in-vehicle networks for data transmission.

[0025] In one embodiment of the present invention, the system further includes an adaptive calibration module. When the vehicle starts or the parking motor is running under no-load, the adaptive calibration module executes a specific calibration sequence, such as allowing the motor to complete a full rotation cycle, while simultaneously recording data from the primary and auxiliary sensors. This data is then compared with a preset reference position or a known physical limit to dynamically correct sensor zero-point drift or gain error. This calibration process can be performed periodically or triggered when sensor performance degradation is detected, ensuring the long-term accuracy of the system.

[0026] In one embodiment of the present invention, the multi-source data fusion module further introduces a temperature compensation mechanism based on Kalman filtering. Specifically, the real-time temperature data acquired by the temperature sensing module is input into the multi-source data fusion module. Based on a pre-established sensor temperature characteristic curve or lookup table, the fusion algorithm performs temperature correction on the original outputs of the main position sensing module and the auxiliary position sensing module before fusion. For example, the magnetic field strength of a magnetoelectric encoder may weaken as the temperature rises, and the impedance of an inductive sensor may also change with temperature. Through temperature compensation, the influence of temperature on the sensor output accuracy is eliminated, ensuring that the position feedback accuracy remains consistent over a wide temperature range.

[0027] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0028] Significantly improves position feedback accuracy and stability: This invention adopts multi-modal sensor fusion technology, which intelligently fuses the data of the main magneto-electric encoder and the auxiliary inductive position sensor through advanced algorithms such as Kalman filtering. This effectively suppresses the inherent noise and drift of a single sensor. Especially in dynamic or complex environments, it can provide more accurate and stable parking motor position information than traditional single sensor systems.

[0029] Significantly enhances system robustness and reliability: Introduces a dual-sensor redundancy design, combined with anomaly detection and fault tolerance mechanisms. When one sensor fails or is severely interfered with, the system can automatically switch to or primarily rely on data from the healthy sensor, thereby ensuring continuous position feedback and normal system operation, and preventing parking function interruption or malfunction due to the failure of a single sensor.

[0030] Excellent environmental adaptability: Both the magnetoelectric encoder and the inductive position sensor possess resistance to various environmental factors. The magnetoelectric encoder is highly resistant to contamination, while the inductive position sensor is insensitive to external magnetic fields, dust, oil, and temperature changes. The combination of these two technologies allows the system to maintain high accuracy and reliability even under harsh conditions encountered by the parking motor of an electric vehicle's reducer, including a wide temperature range, severe vibration, electromagnetic interference, and oil and dust contamination.

[0031] Extended service life of parking mechanism: Precise parking motor position feedback ensures accurate engagement and disengagement of the parking mechanism, avoiding impact, wear and fatigue of mechanical parts (such as gears and pawls) caused by positional deviation, thereby significantly extending the service life of the parking system and reducing maintenance costs.

[0032] Enhancing vehicle parking safety: Accurate and reliable position information is fundamental to the safe operation of the parking brake system. This invention effectively avoids misoperation, incomplete engagement, or accidental release of the parking mechanism, thereby greatly improving the safety of electric vehicles when parked and ensuring the safety of passengers and the vehicle.

[0033] It features adaptive calibration and temperature compensation capabilities: The system integrates an adaptive calibration module and a temperature compensation mechanism, which can dynamically correct zero drift and gain errors caused by long-term operation or changes in ambient temperature, further ensuring the measurement accuracy of the system throughout its entire life cycle without the need for frequent manual calibration.

[0034] Optimized parking control performance: High-precision position feedback enables the parking motor controller to achieve more precise and smoother parking action control, improving the user experience and helping to optimize motor energy consumption, thus achieving more efficient parking functions. Attached Figure Description

[0035] Figure 1This is a schematic diagram of the overall technical solution architecture of the electric vehicle reducer parking motor position feedback system proposed in this invention;

[0036] Figure 2 This is a schematic diagram of the core principle framework of the multi-source data fusion algorithm in this invention;

[0037] Figure 3 This is a logical flowchart of the position signal processing and fault tolerance in this invention;

[0038] Figure 4 This is a schematic diagram of the core principle framework of the adaptive calibration and temperature compensation mechanism in this invention. Detailed Implementation

[0039] This invention provides a position feedback system for the parking motor of an electric vehicle reducer. To better understand this invention, the following will be discussed in conjunction with the accompanying drawings. Figure 1 The overall technical solution architecture diagram shown is attached. Figure 2 The diagram shows the core principle framework of the multi-source data fusion algorithm, and includes an appendix. Figure 3 The diagram shown below illustrates the logical flow framework for position signal processing and fault tolerance, along with its appendix. Figure 4 The core principle framework of the adaptive calibration and temperature compensation mechanism is illustrated in the schematic diagram shown. It should be noted that the accompanying drawings described in this specification are merely exemplary embodiments of the present invention, intended to help those skilled in the art understand the technical solution of the present invention, and are not intended to limit the present invention in any way. The present invention aims to solve the technical problems of decreased accuracy, insufficient reliability, and lack of redundancy faced by existing electric vehicle parking motor position feedback schemes under harsh working environments, such as wide temperature ranges, strong electromagnetic interference, severe vibration, and long-term wear. Traditional parking motor position feedback systems typically rely on a single type of sensor, such as a Hall sensor or optical encoder. These sensors are easily interfered with under certain environments, leading to unstable or distorted output signals, which in turn affects the precise control of the parking mechanism, potentially causing parking malfunctions, accelerated wear, or even safety hazards. The present invention significantly improves the accuracy, robustness, and system reliability of parking motor position feedback by introducing multimodal sensor fusion, intelligent signal processing, and adaptive compensation mechanisms.

[0040] This invention discloses a parking motor position feedback system for an electric vehicle reducer. Its overall architecture incorporates multiple collaborative core functional modules to ensure high-precision and high-reliability acquisition of the parking motor's real-time angular position information during electric vehicle parking. These modules include a main position sensing module, an auxiliary position sensing module, a temperature sensing module, a signal preprocessing module, a multi-source data fusion module, an anomaly detection and fault tolerance module, a position prediction and compensation module, and a communication interface module. These modules are tightly coupled through data flow and control signals, forming an intelligent system capable of self-adaptation, self-correction, and fault tolerance.

[0041] Specifically, the main position sensing module undertakes the core task of accurately detecting the real-time angular position of the parking motor rotor in an electric vehicle's reducer. As an important component of this invention, this module is specifically configured as a magnetoelectric encoder. The magnetoelectric encoder is structurally meticulously designed, with its core components including a multi-pole permanent magnet ring integrated on the motor shaft and a fixed magnetoresistive array sensor. The multi-pole permanent magnet ring undergoes special magnetization treatment, forming alternating magnetic pole distributions along its circumference. When the parking motor shaft rotates with the motor, the multi-pole permanent magnet ring also rotates synchronously, thereby generating a periodically changing magnetic field in the surrounding space. The magnetoresistive array sensor, typically composed of multiple magnetoresistive elements, is precisely fixed and installed in a suitable position inside the motor housing, enabling it to closely sense the changes in magnetic field strength caused by the rotation of the permanent magnet ring. By sensing the changes in magnetic field strength, the magnetoresistive array sensor converts the magnetic signal into an electrical signal and outputs an analog or digital signal that precisely corresponds to the angular position of the motor rotor. This magneto-electric encoder offers numerous advantages, including a compact structure that effectively saves installation space; strong resistance to contamination, maintaining stable performance even in harsh environments with dust, oil, and other pollutants; and fast response speed, enabling real-time capture of the motor rotor's rapid movements. It can directly provide high-resolution incremental or absolute position information, providing fundamental data for subsequent position calculations and control. Furthermore, to achieve higher sensitivity and a wider operating temperature range, the magnetoresistive array sensor preferably employs the anisotropic magnetoresistive (AMR) or giant magnetoresistive (GMR) effect principle. AMR and GMR sensors exhibit significant resistance changes with varying magnetic fields and good stability to temperature variations, ensuring that the main position sensing module can still output high-precision, high-reliability position signals under the wide operating temperature conditions encountered by electric vehicle reducers.

[0042] Furthermore, the auxiliary position sensing module provides redundant position information independent of the main position sensing module, aiming to significantly improve the overall robustness and reliability of the system. This module is specifically configured as an inductive position sensor, its working principle based on electromagnetic induction. The inductive position sensor comprises two main parts: a stator coil structure fixedly mounted inside the motor housing and a conductive rotor target attached to the motor shaft. The stator coil structure typically consists of multiple sets of planar coils, such as copper traces implemented using printed circuit board (PCB) technology. These coil arrays include an excitation coil and multiple receiving coils. During operation, a high-frequency AC signal is passed through the excitation coil, thereby generating an alternating magnetic field in the surrounding space. When the conductive rotor target rotates with the motor, due to the eddy current effect in the electromagnetic induction principle, it generates an induced current in the alternating magnetic field. These induced currents then react on the original magnetic field distribution of the stator coil, causing a corresponding change in the induced voltage or impedance of the receiving coil. After subsequent demodulation and processing, the induced signal from the receiving coil can accurately reflect the angular position of the conductive rotor target. The inductive position sensor and the magneto-electric encoder form a complementary redundancy mechanism, with significant advantages including: insensitivity to external magnetic field interference, as its operating principle relies on eddy current effects rather than external magnetic field strength; strong vibration resistance due to its robust structure and non-contact measurement method; immunity to dust and oil contamination, as its non-contact design makes it less susceptible to environmental pollutants; and stable performance over a wide temperature range, as its electromagnetic induction principle allows it to maintain high measurement accuracy even at extreme temperatures. This complementarity ensures that even when the main position sensing module experiences performance degradation due to specific environmental factors (such as extreme electromagnetic interference), the auxiliary position sensing module can still provide reliable position data, thereby greatly improving the system's fault tolerance and overall reliability.

[0043] Furthermore, the temperature sensing module is used to monitor the ambient temperature inside or near the parking motor in real time. This module is specifically configured as a high-precision thermistor or digital temperature sensor. Its installation location is optimized, typically chosen near the key areas of the main and auxiliary position sensing modules to ensure accurate reflection of critical temperature points affecting position sensor performance. For example, the magnetic field strength of a magneto-electric encoder may weaken with increasing temperature, and the impedance of an inductive sensor may also change with temperature. The real-time temperature data output by the temperature sensing module is used as a critical input to subsequent temperature compensation processing to correct for position sensor characteristic drift caused by changes in ambient temperature, thereby ensuring consistent position feedback accuracy over a wide temperature range.

[0044] The signal preprocessing module serves as the entry point for the system data flow. Its core function is to perform preliminary processing on the raw position signals output from the main and auxiliary position sensing modules, as well as the temperature signal output from the temperature sensing module. This module typically comprises two main parts: an analog signal conditioning circuit and a digital signal processing unit. The analog signal conditioning circuit is responsible for performing a series of preliminary optimizations on the analog signals from the sensors. This includes amplifying weak sensor signals to improve the signal-to-noise ratio; filtering the signals, for example, using a low-pass filter to remove high-frequency noise and interference; and performing analog-to-digital conversion (ADC) to convert the continuous analog signal into a discrete digital signal to meet the requirements of subsequent digital processing. The accuracy and sampling rate of the ADC directly affect the quality of subsequent position estimation. After the ADC is completed, the digital signal is sent to the digital signal processing unit. This unit further performs denoising and smoothing processing on the digital signal, for example, using algorithms such as digital low-pass filters, moving average filters, or median filters to eliminate residual high-frequency noise and random fluctuations. Through these preprocessing steps, the system can ensure that the data input into the subsequent multi-source data fusion algorithm has high purity, accuracy, and consistency, thus laying a solid foundation for accurate location estimation.

[0045] The multi-source data fusion module is the core component of this invention. Its key function is to intelligently integrate redundant position data from the main position sensing module and the auxiliary position sensing module to generate a more accurate and robust fused position estimate. The core algorithm of this module is based on Kalman filtering or extended Kalman filtering. Kalman filtering is a highly efficient recursive filter that can effectively fuse noisy measurement data from different sensors and, combined with the system's dynamic model, provide an optimal estimate of the system's state. Its advantage lies in its ability to handle uncertainties in dynamic systems, providing a real-time, unbiased estimate of the system state with minimal variance.

[0046] Specifically, the multi-source data fusion module performs the following series of rigorous steps to achieve optimal location estimation:

[0047] First, the system needs to precisely define its system state vector. In the context of parking motor position feedback, this system state vector typically includes the current angular position and angular velocity of the parking motor. For example, the state vector can be represented as... ,in Represents the angular position. Represents angular velocity.

[0048] Furthermore, to support Kalman filtering, the system needs to establish a discrete-time state transition model and a measurement model for the motor motion. The state transition model describes the law of change of the motor state over time, for example, based on physical laws or empirical models, and can predict the position and speed of the motor at the next moment. The measurement model describes the relationship between sensor measurements and the actual system state, taking into account the inherent noise and characteristics of the sensors.

[0049] Subsequently, within each sampling period, the multi-source data fusion module performs a Kalman filter prediction step. This step predicts the current state based on the optimal state estimate from the previous time step and the motor control input. The prediction formula is as follows:

[0050]

[0051] in, Indicates in Based on the time observation values The prediction of the state at time k is the prior state estimate of the system at time k. This estimate is inferred based on the system's own dynamic model and past best state information. The state transition matrix is ​​a representation of the changes in the system state from state to state. Time's up A mathematical model of how time evolves. For a parking motor, this might include time intervals, the effect of angular velocity on angular position, etc. Indicates in The optimal state estimate at time step is obtained through fusion. The posterior estimate is obtained from the measurement data at time 1. This represents the control input matrix, which maps the effects of the control inputs to the system state space. Indicates in The control input vector applied at any given time, such as the current, voltage, or PWM control signal of the parking motor driver. This control input reflects the driving force or torque currently applied to the motor and has a direct impact on the motion state at the next moment.

[0052] Furthermore, after completing the prediction step, the multi-source data fusion module performs a Kalman filter update step. The key to this step is fusing the real-time measurements from the primary and secondary position sensing modules with the predicted values ​​to correct the previous state estimate and obtain a more accurate posterior state estimate. The update formula is as follows:

[0053]

[0054] in, Indicates in The optimal state estimate at time 1 is the posterior state estimate of the system after correction of the measured values, and it is also the most reliable state information at the current time. The Kalman gain is a dynamically adjusted weighting factor that determines the degree to which the measurement corrects for the state estimate. The Kalman gain is dynamically adjusted based on the prediction error covariance and the measurement noise covariance, assigning smaller weights to measurements with higher noise levels and larger weights to measurements with lower noise levels. Indicates in The measurement vector at time moment contains real-time position data from the primary position sensing module and the secondary position sensing module. The measurement matrix, which maps the system state vector to the measurement vector space, describes how the sensors observe the system state. For example, if the sensors directly measure angular position, the measurement matrix might be a simple selection matrix. Through the Kalman filtering process described above, the multi-source data fusion module can dynamically adjust the weights of different sensor data, effectively suppress noise, and compensate for possible sensor drift, thereby outputting highly accurate and stable fused parking motor position data. This dynamic weighted fusion mechanism ensures that the system always utilizes the most reliable data source for position estimation under different operating conditions, significantly improving the accuracy and robustness of position feedback.

[0055] The anomaly detection and fault tolerance module is a crucial component for ensuring system reliability. Its core function is to monitor the operational status of the main and auxiliary position sensing modules in real time and identify potential sensor malfunctions or anomalies. This module is implemented based on a multi-verification and comparison mechanism. Specifically, it determines consistency by continuously comparing the differences between the position data output by the two sensors. When the absolute difference between the position values ​​output by the two sensors exceeds a preset tolerance threshold—for example, if the deviation between the position readings continuously exceeds a certain angle value (e.g., 0.5 degrees)—the system will immediately trigger an anomaly alarm, indicating a possible sensor malfunction or severe interference. Furthermore, this module can perform comprehensive signal integrity checks, such as detecting whether the sensor output signal exceeds its effective measurement range (e.g., exceeding or falling below limits), remains unchanged for an extended period (indicating potential signal jamming), or exhibits abnormal jumps (indicating potential transient interference or internal faults). Once a sensor malfunction or output anomaly is detected, the anomaly detection and fault tolerance module will, according to a preset fault tolerance strategy, instruct the multi-source data fusion module to temporarily or permanently exclude the data from the malfunctioning sensor and rely solely on data from healthy sensors for position estimation. For example, if the primary position sensor fails, the system will automatically switch to fusing data from only the auxiliary position sensor, thus achieving fault-tolerant operation and ensuring the continuity and reliability of position feedback. This prevents parking function interruption or malfunction due to the failure of a single sensor. If both sensors fail, the system will activate the position prediction and compensation module to provide a temporary position estimate and immediately report the fault status to the vehicle's main control system so that the vehicle can take appropriate safety measures.

[0056] The position prediction and compensation module plays a crucial role in specific situations (such as temporary sensor failure, communication delays, or high-speed motor movement). Through a prediction algorithm based on motor control commands and motor dynamics models, it provides temporary position estimates or further compensation for fused position data. This module utilizes real-time current, voltage, or PWM control signals from the parking motor driver as input. Combining known motor parameters (such as torque constant, moment of inertia, and coefficient of friction) and corresponding equations of motion (such as Newton's second law and the electromagnetic torque formula), the module can predict the motor's angular position and angular velocity in real time. For example, by analyzing the torque generated by the motor drive current and combining it with the motor's moment of inertia, the motor's angular acceleration can be calculated, thereby predicting its future angular velocity and angular position. This predictive capability is particularly important when sensor data is temporarily unavailable (such as during short-term disconnections or momentary interference) or when the motor is undergoing high-speed dynamic changes. It can smooth the position output curve, reduce control lag, and thus improve the responsiveness and stability of the control system. Furthermore, this module can also fine-tune and compensate for the position data after multi-source fusion. For example, through pre-established mechanical models or real-time learning, this module can consider and correct cumulative or periodic errors caused by factors such as gear transmission backlash, mechanical wear, or elastic deformation, further improving the overall accuracy of position feedback and ensuring precise engagement and disengagement of the parking mechanism.

[0057] The communication interface module serves as a bridge for data interaction between this system and external vehicle control systems. Its core function is to transmit key information such as the parking motor position, speed, and system status (e.g., sensor health status, fault codes, diagnostic information) after signal preprocessing, multi-source data fusion, and possible position prediction and compensation to the electric vehicle's vehicle controller or parking brake controller via a standard in-vehicle communication bus. Typical in-vehicle communication buses include CAN (Controller Area Network) or LIN (Local Interconnect Network). The communication interface module ensures the real-time performance, integrity, and reliability of data transmission, meeting the stringent requirements of in-vehicle networks. It encapsulates internal data into message formats conforming to the bus protocol and sends them at predetermined intervals or upon event triggering. Simultaneously, this module can also receive instructions or query requests from external control systems, enabling bidirectional data flow and control command interaction. For example, the vehicle controller can send a query command requesting the current parking motor position, or when the parking brake controller needs to perform a parking / release action, the system provides precise position feedback to support its closed-loop control.

[0058] In a preferred embodiment of the invention, the system further includes an adaptive calibration module. This module is designed to address zero-point drift or gain error issues that may arise from long-term sensor operation or environmental changes, thereby ensuring the long-term accuracy of the system. The adaptive calibration module is activated under specific conditions, such as when the vehicle starts, when the parking motor is running under no-load, or when the system detects a potential decline in sensor performance. Once activated, the module executes a specific calibration sequence. For example, it can instruct the parking motor to complete a full rotation cycle or reciprocate within a preset range. During this process, the adaptive calibration module synchronously records raw data from the primary position sensing module and the auxiliary position sensing module. This real-time recorded data is then compared with a preset reference position (e.g., an absolute position measured by a high-precision external device) or a known physical limit point. Through this comparison, the system can dynamically calculate and correct the sensor's zero-point offset and linear gain error. For example, if the sensor outputs a non-zero reading when the motor is at its physical zero position, the system will calculate the corresponding zero-point compensation value; if the sensor outputs a reading that deviates proportionally from the actual angle when the motor rotates a known angle, the system will calculate a gain correction factor. The calibration process can be performed periodically, for example, at certain mileage intervals or working hours, or actively triggered when the system detects a declining trend in sensor performance through the anomaly detection and fault tolerance module, thereby ensuring that the system can maintain high-precision position feedback throughout its entire life cycle.

[0059] As a further embodiment of the present invention, the multi-source data fusion module introduces a temperature compensation mechanism based on Kalman filtering. This mechanism aims to eliminate the influence of ambient temperature changes on the output accuracy of the position sensor, ensuring consistent position feedback accuracy over a wide temperature range. Specifically, real-time temperature data acquired by the temperature sensing module is continuously input into the multi-source data fusion module. Based on pre-established sensor temperature characteristic curves or lookup tables, the fusion algorithm first performs temperature correction on the original measurements before fusing the original outputs of the main and auxiliary position sensing modules. For example, the magnetic field strength of a magneto-electric encoder may weaken with increasing temperature, causing deviations in its output position value; similarly, the coil impedance and conductivity of the conductive rotor target of an inductive sensor may change with temperature, affecting the accuracy of its sensing signal. Through the temperature compensation algorithm, the system can accurately reverse these temperature-induced characteristic drifts based on real-time temperature data. For example, polynomial fitting or piecewise linear interpolation methods can be used to map the actual reading of the sensor at the current temperature to its equivalent reading at a standard reference temperature. This temperature compensation mechanism, integrated into the fusion algorithm, ensures that the sensor data entering the fusion process is temperature-calibrated and highly consistent, regardless of changes in the external ambient temperature. This significantly improves the accuracy and stability of position feedback over a wide temperature range.

[0060] The electric vehicle reducer parking motor position feedback system described in this invention successfully solves the technical challenges of insufficient position feedback accuracy, poor reliability, and lack of redundancy in harsh environments by leveraging the collaborative work of its core functional modules, including multimodal sensor fusion, intelligent signal processing, anomaly detection and fault tolerance, position prediction and compensation, and adaptive calibration and temperature compensation. This system provides accurate, stable, robust, and adaptive parking motor position information, significantly improving the safety, reliability, and service life of the electric vehicle parking system, and bringing a comprehensive technological upgrade to the parking function of electric vehicles.

[0061] As described above, the electric vehicle reducer parking motor position feedback system according to embodiments of the present invention can be implemented in various terminal devices. In one example, the electric vehicle reducer parking motor position feedback system can be integrated into the terminal device as a software module and / or a hardware module. For example, the system can be a software module in the terminal device's operating system, responsible for the acquisition, fusion, processing, and transmission of position data; or it can be an application developed for the terminal device, providing a user interface and diagnostic functions. Of course, the system can also be one of many hardware modules of the terminal device, for example, its core algorithm and control logic can be implemented in the form of an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA), directly integrated into the vehicle's electronic control unit (ECU) or parking brake controller.

[0062] Alternatively, in another example, the electric vehicle reducer parking motor position feedback system and the terminal device can also be separate devices. In this configuration, the electric vehicle reducer parking motor position feedback system exists as a standalone sensor unit or intelligent module, connected to the terminal device, such as the vehicle's central gateway, vehicle controller, or in-vehicle infotainment system, via wired and / or wireless networks. Through this connection, the system can transmit interactive information, such as real-time location data, system status, and fault codes, according to agreed-upon data formats and communication protocols. This separate design provides greater system integration flexibility and ease of maintenance.

[0063] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation. For example, some functions of the signal preprocessing module can be integrated into the various sensor modules, or the multi-source data fusion module and the anomaly detection and fault tolerance module can be more tightly coupled into a unified intelligent processing unit.

[0064] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. For example, the multi-source data fusion algorithm can run on a central processing unit, while sensor data acquisition is performed by microcontrollers distributed near the motor. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs. For example, in applications with extreme cost or space constraints, certain non-core functional modules can be simplified or optimized.

[0065] Furthermore, the functional modules in the various embodiments of this invention can be integrated into a single processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit. The integrated unit can be implemented in hardware, such as through customized chip design or a dedicated processor, or it can be implemented as a combination of hardware and software functional modules, such as running corresponding algorithm software on a general-purpose microcontroller. This flexibility allows the invention to adapt to different application scenarios and cost budgets.

[0066] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. For example, in addition to Kalman filtering, other advanced fusion algorithms such as particle filtering, unscented Kalman filtering, or deep learning-based sensor fusion methods can also be employed. The specific type of sensor can also be replaced according to technological advancements or cost-effectiveness, as long as similar functionality and complementarity can be achieved.

[0067] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0068] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The term "second class" is used to indicate names and does not indicate any specific order.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit of the technical solutions of the present invention.

Claims

1. A position feedback system for a parking motor in an electric vehicle reducer, characterized in that, include: The main position sensing module is used to detect the real-time angular position of the rotor of the parking motor of the electric vehicle reducer. The main position sensing module is configured as a magnetoelectric encoder, which includes a multi-pole permanent magnet ring integrated on the motor shaft and a fixed magnetoresistive array sensor. An auxiliary position sensing module is used to provide redundant position information independent of the main position sensing module. The auxiliary position sensing module is configured as an inductive position sensor, which includes a stator coil structure fixedly installed inside the motor housing and a conductive rotor target attached to the motor shaft. The signal preprocessing module is used to perform preliminary processing on the raw position signals output by the main position sensing module and the auxiliary position sensing module to optimize signal quality; The multi-source data fusion module is used to intelligently integrate redundant position data from the main position sensing module and the auxiliary position sensing module to generate accurate and robust fused position estimates. The multi-source data fusion module dynamically adjusts the weights of different sensor data and suppresses noise. The anomaly detection and fault tolerance module is used to monitor the operating status of the main position sensing module and the auxiliary position sensing module in real time, and to identify sensor faults or anomalies. The communication interface module is used to transmit the processed and fused parking motor position, speed, and system status information to the vehicle controller or parking brake controller of the electric vehicle.

2. The electric vehicle reducer parking motor position feedback system according to claim 1, characterized in that, The magnetoresistive array sensor outputs a signal by sensing the periodically changing magnetic field strength generated when the multipole permanent magnet ring rotates. The magnetoresistive array sensor adopts the principle of anisotropic magnetoresistive or giant magnetoresistive effect.

3. The electric vehicle reducer parking motor position feedback system according to claim 1, characterized in that, The stator coil structure includes an excitation coil and multiple receiving coils. The excitation coil is supplied with a high-frequency AC signal to generate an alternating magnetic field. When the conductive rotor target rotates with the motor, due to the eddy current effect, the conductive rotor target generates an induced current in the alternating magnetic field and reacts on the magnetic field distribution of the stator coil, causing a change in the induced voltage or impedance of the receiving coil. The induced signal of the receiving coil, after demodulation and processing, accurately reflects the angular position of the conductive rotor target.

4. The electric vehicle reducer parking motor position feedback system according to claim 1, characterized in that, The signal preprocessing module includes an analog signal conditioning circuit and a digital signal processing unit. The analog signal conditioning circuit is responsible for amplifying, filtering and converting analog signals to digital signals, while the digital signal processing unit performs noise reduction and smoothing on digital signals.

5. The electric vehicle reducer parking motor position feedback system according to claim 1, characterized in that, The multi-source data fusion module is specifically used for: Define a system state vector, which includes the current angular position and angular velocity of the parking motor; Establish a discrete-time state transition model and a measurement model for the motor motion; Within each sampling period, a Kalman filter prediction step is performed to predict the current state based on the optimal state estimate of the previous moment and the control input of the motor. as well as The Kalman filter update step is performed to fuse the real-time measurement values ​​and predicted values ​​of the main position sensing module and the auxiliary position sensing module, correct the state estimation, and obtain the fused position estimate.

6. The electric vehicle reducer parking motor position feedback system according to claim 5, characterized in that, The prediction step performed by the multi-source data fusion module has the following prediction formula: ,in, Indicates in Based on the time observation values Prediction and estimation of state at any given time; Represents the state transition matrix; Indicates in Optimal state estimation at time t; Represents the control input matrix; Indicates in The control input vector applied at any given time.

7. The electric vehicle reducer parking motor position feedback system according to claim 5, characterized in that, The update steps performed by the multi-source data fusion module have the following update formula: ,in, Indicates in Optimal state estimation at time t; Indicates Kalman gain; Indicates in The measurement vector at time; This represents the measurement matrix.

8. The electric vehicle reducer parking motor position feedback system according to claim 1, characterized in that, The anomaly detection and fault tolerance module determines the consistency of the position data output by the main position sensing module and the auxiliary position sensing module by comparing their differences. When the absolute difference between the position values ​​output by the two sensors exceeds a preset tolerance threshold, an anomaly alarm is triggered. The anomaly detection and fault tolerance module also performs a signal integrity check to detect whether the signal exceeds the effective range, remains unchanged for a long time, or undergoes a jump.

9. The electric vehicle reducer parking motor position feedback system according to claim 1, characterized in that, It includes a temperature sensing module and a position prediction and compensation module. The temperature sensing module is used to monitor the ambient temperature inside or near the parking motor in real time, and the temperature data output by the temperature sensing module is used for subsequent temperature compensation processing. The position prediction and compensation module provides temporary position estimation or further compensation for fused position data through a prediction algorithm based on motor control commands and motor dynamics model.

10. The electric vehicle reducer parking motor position feedback system according to claim 9, characterized in that, The system includes an adaptive calibration module. When the vehicle starts or the parking motor is running under no-load, the adaptive calibration module executes a calibration sequence and records the data from the main position sensing module and the auxiliary position sensing module. The data is then compared with a preset reference position or a known physical limit to dynamically correct the zero-point drift or gain error of the sensors. The multi-source data fusion module introduces a temperature compensation mechanism on the basis of Kalman filtering. The real-time temperature data acquired by the temperature sensing module is input into the multi-source data fusion module. Based on a pre-established sensor temperature characteristic curve or lookup table, the fusion algorithm performs temperature correction on the original outputs of the main position sensing module and the auxiliary position sensing module before fusion.

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