Position feedback system for parking motor of speed reducer of electric automobile

Abstract

The invention relates to the technical field of electric vehicle transmission and control, in particular to an electric vehicle speed reducer parking motor position feedback system. The system aims at solving the problems that the position feedback precision is reduced, the reliability is insufficient and the redundancy is deficient in a severe environment in the existing scheme. According to the system, through redundancy design of a main position sensing module (a magnetoelectric encoder) and an auxiliary position sensing module (an inductive sensor) and in combination with a multi-source data fusion module (based on Kalman filtering), anomaly detection and fault tolerance, position prediction and compensation and a temperature compensation and self-adaptive calibration mechanism, accurate feedback of the position of the parking motor rotor is achieved. According to the system, the accuracy and robustness of position feedback and the reliability of the system can be remarkably improved, the service life of a parking mechanism is prolonged, and the parking safety is improved.

Description

Technical Field

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

[0002] The field of electric vehicle technology includes multiple branches such as electric drive, battery management, intelligent control, and energy recovery. The core content of this technology field is to use electric energy to drive the vehicle, design a system that can independently charge and discharge, efficiently utilize energy, and provide an intelligent driving experience. With the advancement of battery technology, motor control algorithms and power electronic devices, electric vehicle technology has been widely used, covering multiple application scenarios such as passenger cars, commercial vehicles, and special vehicles. The development of this field has not only promoted the advancement of science and technology, but also brought profound changes to various industries, including energy, transportation and other fields.

[0003] The electric vehicle reducer parking motor position feedback system achieves 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 the problems of inaccurate parking motor position information acquisition, feedback delays, and insufficient system reliability during electric vehicle parking. This paper proposes an optimized position feedback method. By combining high-precision sensors with intelligent algorithms, this method enables real-time and accurate sensing 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 independently or collaboratively provides position information based on its own characteristics and capabilities, thereby improving the safety and intelligence of the overall parking system. This method also ensures the timeliness and accuracy of position feedback by optimizing data processing and transmission strategies.

[0004] Existing technologies mostly rely on a single sensor type for parking motor position feedback, lacking the ability to effectively suppress 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 and wide temperature differences). For example, during long-term parking or parking on a slope, the feedback signal may deviate due to external interference or the sensor's inherent characteristics. However, due to the lack of multi-source redundancy or adaptive calibration mechanisms, parking safety cannot be fully guaranteed. Position information judgment is often based on fixed thresholds rather than the dynamic evolution of actual operating data, making it difficult to accurately reflect the wear status of the parking motor and mechanism, thereby hindering preventive maintenance and fault diagnosis. The lack of analysis of the impact of sensor failures makes it impossible to assess the conflict between the failure of key position sensors and the execution of parking commands, resulting in limited parking functions in emergency situations. There is no comparison mechanism between the operating data and the parking mechanism status data, which makes it difficult to detect potential mechanical wear or jamming, posing a safety hazard. In terms of position monitoring, existing technologies mainly rely on coarse-grained state recognition, ignoring the position accuracy and response speed analysis of fine-grained segments, making it difficult to adapt to the control needs under high safety and high reliability requirements. The problem is particularly prominent in the position feedback of the electric vehicle reducer parking motor, which directly affects the overall parking performance and vehicle safety. Summary of the Invention

[0005] The present invention provides a position feedback system for a parking motor of an electric vehicle reducer, aiming to solve the technical problems faced by existing parking motor position feedback solutions for electric vehicles in harsh working environments, such as wide temperature ranges, strong electromagnetic interference, severe vibration, and long-term operation wear. Conventional parking motor position feedback systems usually rely on a single type of sensor, such as a Hall sensor or an optical encoder. These sensors are easily interfered with in certain environments, resulting in unstable or distorted output signals, which in turn affects the precise control of the parking mechanism and may cause parking failures, increased wear, and 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.

[0006] The present invention provides an electric vehicle speed reducer parking motor position feedback system, 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 electric vehicle reducer's parking motor rotor. This module is specifically configured as a magnetoelectric encoder, comprising 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. The magnetoresistive array sensor senses changes in magnetic field intensity and outputs an analog or digital signal that precisely corresponds to the angular position of the motor rotor. The magnetoelectric encoder offers the advantages of a compact structure, strong anti-fouling capabilities, and fast response speed, capable of directly providing high-resolution incremental or absolute position information. Furthermore, the magnetoresistive array sensor utilizes the anisotropic magnetoresistive (AMR) or giant magnetoresistive (GMR) effect to achieve higher sensitivity and a wider operating temperature range.

[0008] The auxiliary position sensing module is used to provide redundant position information independent of the primary position sensing module, thereby improving 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 comprises 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, generating an alternating magnetic field. As the conductive rotor target rotates with the motor, eddy currents induce currents in the magnetic field, which react on the magnetic field distribution of the stator coil, causing changes in the induced voltage or impedance of the receiving coil. After demodulation and processing, the induced signal from the receiving coil accurately reflects the angular position of the conductive rotor target. This inductive position sensor offers 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 magnetoelectric 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, with an optimized mounting location to accurately reflect critical temperature points that affect position sensor performance. The temperature data output by the temperature sensing module is used for subsequent temperature compensation to correct for temperature-related drift in the position sensor's characteristics.

[0010] The signal preprocessing module is used to perform preliminary processing on the raw position signals output by the primary and auxiliary position sensing modules, 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 is responsible for amplifying, filtering, and performing 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 denoising and smoothing on the digital signals, for example, using a low-pass filter or a moving average filter to eliminate high-frequency noise and random fluctuations, ensuring high purity of the data input to the subsequent fusion algorithm.

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

[0012] Specifically, the multi-source data fusion module performs the following steps: First, the system state vector is defined, including, for example, the current angular position and angular velocity of the parking motor.

[0013] Furthermore, a discrete-time state transfer model and measurement model of motor motion are established.

[0014] Then, in each sampling period, the prediction step of the Kalman filter is executed to predict the current state based on the optimal state estimate at the previous moment and the control input of the motor. The prediction formula is as follows: in, Indicates Based on the observation value at each moment Predictive estimation of the state at each moment; Represents the state transition matrix, which describes the system state from The evolution from moment to moment k; Indicates The optimal state estimate at time; represents the control input matrix; Indicates The control input vector (such as motor drive current or voltage) applied at each moment.

[0015] Furthermore, the Kalman filter update step is performed to fuse the real-time measurements and predictions of the primary and secondary position sensing modules to correct the state estimate. The update formula is as follows: in, Indicates The optimal state estimate at time; represents the Kalman gain, which determines the degree to which the measurement value corrects the state estimate; Indicates The measurement vector at the moment (from the primary and secondary position sensors); Represents the measurement matrix, which maps the state vector to the measurement vector space.

[0016] Through the above-mentioned Kalman filtering process, 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 parking motor fusion position data.

[0017] The anomaly detection and fault tolerance module is used to monitor the operating status of the primary and auxiliary position sensing modules in real time and identify possible 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. The module also performs signal integrity checks, such as detecting whether the signal exceeds the valid range, remains unchanged for a long time, or experiences jumps. Upon detecting a sensor failure or an abnormal output, the anomaly detection and fault tolerance module instructs the multi-source data fusion module to temporarily or permanently exclude the data from the abnormal sensor and rely solely on healthy sensor data 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 failure status to the vehicle's main control system.

[0018] The position prediction and compensation module is designed to provide temporary position estimates or further compensate for fused position data in specific situations (such as temporary sensor failure, communication delays, or high-speed motor motion). This module uses a predictive algorithm based on motor control commands and motor dynamics models to provide inputs, including current, voltage, or PWM control signals from the parking motor driver. This module combines known motor parameters (such as torque constant and moment of inertia) with the equations of motion to predict the motor's angular position at the next instant. This predictive capability is particularly important when sensor data is temporarily unavailable or during high-speed dynamic changes, smoothing the position output and reducing control lag. Furthermore, the module can fine-tune the fused position data, for example by accounting for accumulated errors caused by gear backlash or mechanical wear.

[0019] The communication interface module transmits processed and integrated information, including parking motor position and speed, and system status (e.g., sensor health and fault codes), to the electric vehicle's vehicle controller or parking brake controller via a standard in-vehicle communication bus (e.g., CAN or LIN). This ensures real-time, complete, and reliable data transmission, meeting the stringent requirements of in-vehicle networks.

[0020] As one embodiment of the present invention, the system further includes an adaptive calibration module. During vehicle startup or when the parking motor is running at no load, the module performs a specific calibration sequence, such as allowing the motor to complete a full rotation cycle. The module simultaneously records data from the primary and auxiliary sensors and compares it to preset reference positions or known physical limits to dynamically correct for sensor zero drift or gain errors. This calibration process can be performed periodically or triggered upon detection of sensor performance degradation to ensure long-term system accuracy.

[0021] As an embodiment of the present invention, the multi-source data fusion module further introduces a temperature compensation mechanism on the basis of 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 the 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 the magnetoelectric encoder may weaken as the temperature rises, and the impedance of the inductive sensor may also change with temperature. Through temperature compensation, the influence of temperature on the output accuracy of the sensor is eliminated, so that the accuracy of the position feedback can be kept consistent within a wide temperature range.

[0022] Compared with the prior art, the advantages and positive effects of the present invention are: Significantly improve position feedback accuracy and stability: The present invention adopts multimodal sensor fusion technology and intelligently fuses the data of the main magnetoelectric encoder and auxiliary inductive position sensor through advanced algorithms such as Kalman filtering, effectively suppressing the noise and drift inherent in a single sensor. Especially in dynamically changing or complex environments, it can provide more accurate and stable parking motor position information than traditional single-sensor systems.

[0023] Significantly enhanced system robustness and reliability: A redundant design with primary and secondary sensors, combined with anomaly detection and fault tolerance mechanisms, allows the system to automatically switch to or primarily rely on data from healthy sensors if one sensor fails or is severely interfered with. This ensures continuous position feedback and system operation, preventing interruptions or malfunctions of the parking function due to a single sensor failure.

[0024] Excellent Environmental Adaptability: The magnetoelectric encoder and inductive position sensor each have their own resistance to different 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 fluctuations. This combination of the two ensures that the system maintains high accuracy and reliability despite the wide temperature ranges, severe vibration, electromagnetic interference, oil, and dust encountered by electric vehicle speed reducer parking motors.

[0025] Extending the service life of the parking mechanism: Accurate parking motor position feedback ensures precise engagement and disengagement of the parking mechanism, avoiding impact, wear and fatigue of mechanical components (such as gears and pawls) caused by position deviation, thereby significantly extending the service life of the parking system and reducing maintenance costs.

[0026] Improved parking safety: Accurate and reliable position information is essential for the safe operation of the parking brake system. This invention effectively prevents misoperation, incomplete engagement, or accidental release of the parking mechanism, significantly improving parking safety for electric vehicles and protecting both the driver and the vehicle.

[0027] Adaptive calibration and temperature compensation capabilities: The system integrates an adaptive calibration module and temperature compensation mechanism, which can dynamically correct the zero drift and gain error of the sensor caused by long-term operation or ambient temperature changes, further ensuring the measurement accuracy of the system throughout its life cycle without the need for frequent manual calibration.

[0028] Optimized parking control performance: High-precision position feedback enables the parking motor controller to achieve more refined and smooth parking control, improving the user experience and helping to optimize motor energy consumption for more efficient parking functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1This is a schematic diagram of the overall technical solution architecture of the electric vehicle reducer parking motor position feedback system proposed by the present invention; Figure 2 This is a schematic diagram of the core principle framework of the multi-source data fusion algorithm in the present invention; Figure 3 It is a logical flow framework diagram of position signal processing and fault tolerance in the present invention; Figure 4 This is a schematic diagram of the core principle framework of the adaptive calibration and temperature compensation mechanism in the present invention. DETAILED DESCRIPTION

[0030] The present invention provides a position feedback system for the parking motor of an electric vehicle reducer. Figure 1 The overall technical solution architecture diagram shown in the figure, Figure 2 The core principle framework diagram of the multi-source data fusion algorithm shown in the figure, Figure 3 The logical flow frame diagram of position signal processing and fault tolerance shown in the figure and the attached Figure 4 The core principle framework diagram of the adaptive calibration and temperature compensation mechanism shown in the figure is explained. It should be pointed out that the drawings described in this specification are only exemplary embodiments of the present invention and are intended to help those skilled in the art understand the technical solutions of the present invention, and do not constitute any limitation of the present invention. The present invention aims to solve the technical problems faced by existing electric vehicle parking motor position feedback solutions in harsh operating environments, such as wide temperature ranges, strong electromagnetic interference, severe vibration, and long-term operation wear, such as reduced accuracy, insufficient reliability, and lack of redundancy. Traditional parking motor position feedback systems generally rely on a single type of sensor, such as a Hall sensor or optical encoder. These sensors are easily interfered with in certain environments, resulting in unstable or distorted output signals, which in turn affects the precise control of the parking mechanism, potentially causing parking failures, increased wear, and 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.

[0031] The present invention proposes a position feedback system for an electric vehicle speed reducer parking motor. Its overall architecture design incorporates multiple core functional modules that work in concert to ensure high-precision and reliable acquisition of the parking motor's real-time angular position information during parking. These modules include a primary 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 flows and control signals, forming an intelligent system capable of self-adaptation, self-correction, and fault tolerance.

[0032] Specifically, the main position sensing module is responsible for the high-precision detection of the real-time angular position of the electric vehicle's parking motor rotor. As a key component of the present invention, this module is specifically configured as a magnetoelectric encoder. The magnetoelectric encoder is meticulously designed, with its core components consisting of a multi-pole permanent magnet ring integrated into the motor shaft and a fixed magnetoresistive array sensor. The multi-pole permanent magnet ring undergoes a special magnetization process to create an alternating magnetic pole distribution along its circumference. As the parking motor shaft rotates with the motor, the multi-pole permanent magnet ring also rotates synchronously, generating a periodically varying magnetic field in the surrounding space. The magnetoresistive array sensor, typically composed of multiple magnetoresistive elements, is precisely fixed in place within the motor housing, enabling it to closely sense the changes in magnetic field intensity caused by the rotation of the permanent magnet ring. By sensing these changes in magnetic field intensity, the magnetoresistive array sensor converts the magnetic signal into an electrical signal, outputting an analog or digital signal that precisely corresponds to the angular position of the motor rotor. This type of magnetoelectric encoder offers numerous advantages, including a compact structure that effectively saves installation space; strong anti-fouling properties, maintaining stable performance even in harsh environments with contaminants such as dust and oil; and a fast response speed, enabling real-time capture of the rapid motion of the motor rotor. It can directly provide high-resolution incremental or absolute position information, providing the underlying data for subsequent position calculation and control. Furthermore, to achieve higher sensitivity and a wider operating temperature range, the magnetoresistive array sensor preferably utilizes the anisotropic magnetoresistive (AMR) or giant magnetoresistive (GMR) effect principle. AMR and GMR sensors exhibit significant changes in resistance when the magnetic field changes and exhibit good temperature stability. This ensures that the main position sensing module can output a highly accurate and reliable position signal over the wide operating temperature range encountered by electric vehicle reducers.

[0033] Furthermore, the auxiliary position sensing module is used to provide redundant position information independent of the primary position sensing module, significantly improving the overall robustness and reliability of the system. This module is specifically configured as an inductive position sensor based on the electromagnetic induction effect. The inductive position sensor consists of two main components: 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 applied to the excitation coil, generating an alternating magnetic field in the surrounding space. As the conductive rotor target rotates with the motor, eddy currents, due to the electromagnetic induction principle, induce currents in the alternating magnetic field. These induced currents react to the original magnetic field distribution of the stator coil, causing corresponding changes in the induced voltage or impedance of the receiving coil. The induced signal from the receiving coil, after subsequent demodulation and processing, accurately reflects the angular position of the conductive rotor target. The inductive position sensor and magnetoelectric encoder form a complementary redundancy mechanism with significant advantages, including: insensitivity to external magnetic field interference, due to its operating principle relying on eddy current effects rather than external magnetic field strength; strong vibration resistance, due to its robust structure and non-contact measurement method; impervious to dust and oil, as its non-contact design makes it less susceptible to corrosion by environmental pollutants; and stable performance over a wide temperature range, as its electromagnetic induction principle enables high measurement accuracy even at extreme temperatures. This complementarity ensures that the auxiliary position sensor module can still provide reliable position data even when the performance of the primary position sensor module degrades due to certain environmental factors (such as extreme electromagnetic interference), significantly improving the system's fault tolerance and overall reliability.

[0034] In addition, the temperature sensing module is used to monitor the ambient temperature inside or near the parking motor in real time. The module is specifically configured as a high-precision thermistor or digital temperature sensor. Its installation position has been optimized and is usually selected in the key area close to the main position sensing module and the auxiliary position sensing module to ensure that the key temperature points that affect the performance of the position sensor can be accurately reflected. For example, the magnetic field strength of the magnetoelectric encoder may weaken as the temperature rises, and the impedance of the inductive sensor may also change with temperature. The real-time temperature data output by the temperature sensing module will be passed as a key input to the subsequent temperature compensation processing link to correct the position sensor characteristic drift caused by ambient temperature changes, thereby ensuring that the accuracy of the position feedback can remain consistent over a wide temperature range.

[0035] The signal preprocessing module is the entry point for the system's data flow. Its core function is to perform preliminary processing on the raw position signals output by the primary and auxiliary position sensing modules, as well as the temperature signal output by the temperature sensing module. This module's implementation typically consists of two main components: 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, such as using a low-pass filter to remove high-frequency noise and interference; and performing analog-to-digital conversion (ADC) to convert the continuous analog signals into discrete digital signals for subsequent digital processing. The accuracy and sampling rate of the ADC directly impact the quality of the subsequent position estimation. After the ADC is completed, the digital signals are fed into the digital signal processing unit. This unit further performs denoising and smoothing on the digital signals, 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 position estimation.

[0036] The multi-source data fusion module is a core component of the present invention. Its key function is to intelligently integrate redundant position data from the primary and auxiliary position sensing modules to generate a more accurate and robust fused position estimate. The module's core algorithm is based on a Kalman filter or an extended Kalman filter. The Kalman filter is a highly efficient recursive filter that effectively fuses noisy measurement data from different sensors and, combined with the system's dynamic model, generates an optimal estimate of the system's state. Its advantage lies in its ability to handle uncertainty in dynamic systems, providing a real-time, unbiased, and minimally skewed estimate of the system's state.

[0037] Specifically, the multi-source data fusion module performs the following rigorous steps to achieve optimal position estimation: First, the system needs to accurately define the system state vector. In the context of parking motor position feedback, the system state vector usually includes the current angular position and angular velocity of the parking motor. For example, the state vector can be expressed as ,in represents the angular position, Represents angular velocity.

[0038] Furthermore, to support Kalman filtering, the system needs to establish a discrete-time state transition model and measurement model for the motor's motion. The state transition model describes how the motor's state changes over time, for example, based on physical laws or empirical models, and can predict the motor's position and speed 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.

[0039] Then, within each sampling period, the multi-source data fusion module performs the prediction step of the Kalman filter. This step predicts the current state based on the optimal state estimate at the previous moment and the motor control input. The prediction formula is as follows: in, Indicates Based on the observation value at each moment The predicted estimate of the state at time k is the prior estimate of the system state at time k. This estimate is inferred based on the system's own dynamic model and the best state information in the past. Represents the state transition matrix, which is a matrix describing the system state from Time has come A mathematical model of how the moments evolve. For a parking motor, this might include time intervals, the effect of angular velocity on angular position, etc. Indicates The optimal state estimation at the moment is obtained by fusion The posterior estimate is obtained from the measurement data at time . represents the control input matrix, which maps the effects of the control input to the system state space. Indicates The control input vector applied at any given moment, such as the current, voltage, or PWM control signal of the parking motor driver, reflects the current driving force or torque applied to the motor and has a direct impact on the motion state at the next moment.

[0040] Furthermore, after completing the prediction step, the multi-source data fusion module performs a Kalman filter update step. The key to this step is to fuse the real-time measurements and predictions from the primary and auxiliary position sensing modules to correct the previous state estimate and obtain a more accurate posterior state estimate. The update formula is as follows: in, Indicates The optimal state estimate at the moment is the system posterior state estimate after correction by the measured value, and is also the most reliable state information at the current moment. represents the Kalman gain, a dynamically adjusted weighting factor that determines the extent to which measurements modify the state estimate. The Kalman gain is dynamically adjusted based on the prediction error covariance and the measurement noise covariance, giving less weight to noisy measurements and more weight to less noisy measurements. Indicates The measurement vector at the moment, which contains the real-time position data from the main position sensing module and the auxiliary position sensing module. represents the measurement matrix, which maps the system state vector to the measurement vector space and describes how the sensor observes the system state. For example, if the sensor directly measures angular position, the measurement matrix may be a simple selection matrix. Through the above-mentioned Kalman filtering process, 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 parking motor fused position data. This dynamic weighted fusion mechanism ensures that the system always uses the most reliable data source for position estimation under different operating conditions, significantly improving the accuracy and robustness of position feedback.

[0041] The anomaly detection and fault tolerance module is a key component in ensuring system reliability. Its core function is to monitor the operating status of the primary and auxiliary position sensing modules in real time and identify potential sensor failures or anomalies. This module is implemented based on a multi-checksum comparison mechanism. Specifically, it continuously compares the differences between the position data output by the two sensors to determine their consistency. If the absolute difference between the position values ​​output by the two sensors exceeds a preset tolerance threshold—for example, if the position readings consistently deviate by more than a certain angle (e.g., 0.5 degrees)—the system immediately triggers an anomaly alarm, indicating a possible sensor failure or severe interference. Furthermore, this module performs comprehensive signal integrity checks, such as detecting whether sensor output signals exceed their valid measurement range (e.g., over- or under-range), remain unchanged for extended periods (indicating possible signal jamming), or exhibit unusual transitions (indicating possible transient interference or internal failure). Upon detecting a sensor failure or abnormal output, the anomaly detection and fault tolerance module, based on a preset fault tolerance strategy, instructs the multi-source data fusion module to temporarily or permanently exclude the data from the anomalous sensor and rely solely on healthy sensor data for position estimation. For example, if the primary position sensor fails, the system automatically switches to using only the data from the secondary position sensor for fusion, achieving fault-tolerant operation and ensuring continuous and reliable position feedback, thereby avoiding interruption or malfunction of the parking function due to a single sensor failure. In the event of both sensors failing, the system activates the position prediction and compensation module to provide a temporary position estimate and immediately reports the fault to the vehicle's main control system so that the vehicle can take appropriate safety measures.

[0042] The position prediction and compensation module plays a key role in specific situations (such as temporary sensor failure, communication delays, or high-speed motor motion). Using a predictive algorithm based on motor control commands and motor dynamics models, it provides temporary position estimates or further compensates for fused position data. This module uses the real-time current, voltage, or PWM control signal from the parking motor driver as input. Combining known motor parameters (such as torque constant, moment of inertia, friction coefficient) with corresponding equations of motion (such as Newton's second law and the motor electromagnetic torque formula), the module predicts the motor's angular position and velocity at the next instant 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 inferred, thereby predicting its future angular velocity and position. This predictive capability is particularly important when sensor data is temporarily unavailable (such as during a brief disconnection or momentary interference) or when the motor is experiencing high-speed dynamic changes. It smoothes the position output curve, reduces control lag, and thus improves the responsiveness and stability of the control system. Furthermore, the module can fine-tune and compensate the multi-source fused position data. For example, through pre-established mechanical models or real-time learning, the module can consider and correct cumulative errors or periodic errors caused by factors such as gear transmission clearance, mechanical wear or elastic deformation, further improving the overall accuracy of position feedback and ensuring precise engagement and disengagement of the parking mechanism.

[0043] The communication interface module bridges data exchange between the system and external vehicle control systems. Its core function is to transmit key information, including parking motor position, speed, and system status (e.g., sensor health, fault codes, diagnostic information), via a standard on-board communication bus to the electric vehicle's vehicle controller or parking brake controller. Typical on-board communication buses include the CAN (Controller Area Network) or LIN (Local Interconnect Network). The communication interface module ensures real-time, integrity, and reliability of data transmission, meeting the stringent data transmission requirements of on-board networks. It encapsulates internal data into messages compliant with the bus protocol and transmits them periodically or upon event triggering. The module also receives commands or queries from external control systems, enabling bidirectional data flow and control command interaction. For example, the vehicle controller can send a query requesting the current parking motor position, or the parking brake controller can provide precise position feedback to support closed-loop control when it needs to execute a parking / release action.

[0044] As a preferred embodiment of the present invention, the system further includes an adaptive calibration module. This module is designed to address zero drift or gain error that may occur in sensors due to long-term 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 is started, when the parking motor is running without load, or when the system detects potential degradation in sensor performance. Once activated, the module performs a specific calibration sequence. For example, it may 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 and auxiliary position sensing modules. This real-time data is then compared with a preset reference position (for example, an absolute position measured by a high-precision external device) or a known physical limit point. This comparison enables the system to dynamically calculate and correct the sensor zero offset and linear gain error. For example, if the sensor output reading is non-zero when the motor is at the physical zero position, the system calculates the corresponding zero compensation value. If the sensor output reading is proportional to the actual angle when the motor rotates a known angle, the system calculates a gain correction factor. The calibration process can be performed periodically, for example, at certain mileage or operating hours, or can be actively triggered when the system detects a downward trend in sensor performance through an anomaly detection and fault tolerance module, thereby ensuring that the system maintains high-precision position feedback throughout its life cycle.

[0045] As a further embodiment of the present invention, the multi-source data fusion module incorporates a temperature compensation mechanism based on Kalman filtering. This mechanism aims to eliminate the impact 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 a pre-established sensor temperature characteristic curve or lookup table, the fusion algorithm first performs temperature correction on the raw outputs of the primary and auxiliary position sensing modules before fusing them. For example, the magnetic field strength of a magnetoelectric encoder may weaken with increasing temperature, resulting in deviations in its output position values. Similarly, the coil impedance of an inductive sensor and the conductivity of a conductive rotor target may vary with temperature, affecting the accuracy of its sensing signal. The temperature compensation algorithm enables the system to accurately reverse these temperature-induced characteristic drifts based on real-time temperature data. For example, methods such as polynomial fitting or piecewise linear interpolation can be used to map the actual sensor reading at the current temperature to its equivalent reading at a standard reference temperature. This temperature compensation mechanism integrated into the fusion algorithm ensures that regardless of changes in the external ambient temperature, the sensor data entering the fusion process is temperature-calibrated and highly consistent, thereby significantly improving the position feedback accuracy and stability over a wide temperature range.

[0046] The electric vehicle speed reducer parking motor position feedback system described in this embodiment successfully addresses the technical challenges of existing technologies, such as insufficient position feedback accuracy, poor reliability, and lack of redundancy in harsh environments, through the collaborative operation 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. The system provides accurate, stable, robust, and adaptive parking motor position information, significantly improving the safety, reliability, and service life of electric vehicle parking systems and bringing a comprehensive technological upgrade to electric vehicle parking functions.

[0047] As described above, the electric vehicle retarder parking motor position feedback system according to an embodiment of the present invention can be implemented in various terminal devices. In one example, the electric vehicle retarder parking motor position feedback system can be integrated into the terminal device as a software module and / or hardware module. For example, the system can be a software module within the terminal device's operating system, responsible for collecting, fusing, processing, and transmitting position data; or it can be an application developed specifically for the terminal device, providing a user interface and diagnostic functions. Of course, the system can also be one of the terminal device's many hardware modules, such as implementing its core algorithm and control logic in the form of an application-specific integrated circuit (ASIC) or field-programmable gate array (FPGA), and directly integrated into the vehicle's electronic control unit (ECU) or parking brake controller.

[0048] Alternatively, in another example, the electric vehicle reducer parking motor position feedback system and the terminal device can also be discrete devices. In this configuration, the electric vehicle reducer parking motor position feedback system exists as an independent sensor unit or intelligent module and is connected to the terminal device, such as the vehicle's central gateway, vehicle controller, or in-vehicle infotainment system, via a wired and / or wireless network. Through this connection, the system can transmit interactive information such as real-time position data, system status, fault codes, etc. according to the agreed data format and communication protocol. This discrete design provides greater system integration flexibility and maintenance convenience.

[0049] In the several embodiments provided herein, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical functional division, and actual implementation may employ other division methods. For example, some functions of the signal preprocessing module can be integrated into each sensor module, or the multi-source data fusion module and the anomaly detection and fault tolerance module can be more tightly coupled to form a unified intelligent processing unit.

[0050] The modules described as separate components may or may not be physically separate, and 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 may run on a central processing unit, while sensor data acquisition is performed by microcontrollers distributed near the motor. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. For example, in applications with extreme cost or space constraints, certain non-core functional modules may be streamlined or optimized.

[0051] In addition, the functional modules in various embodiments of the present 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 above-mentioned integrated units can be implemented in the form of hardware, such as through customized chip design or dedicated processors, or in the form of hardware plus software functional modules, such as running the corresponding algorithm software on a general-purpose microcontroller. This flexibility enables the present invention to adapt to different application scenarios and cost budgets.

[0052] 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 present invention may be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. 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 may also be employed. The specific type of sensor may also be substituted based on technological advancements or cost-effectiveness, as long as similar functionality and complementarity are achieved.

[0053] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0054] 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. Second-order terms are used to indicate names and do not imply any particular order.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit of the technical solutions of the present invention.

Claims

1. A position feedback system for a parking motor of an electric vehicle reducer, characterized in that: include: A main position sensing module, used to detect the real-time angular position of the rotor of the electric vehicle reducer parking motor. 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, configured to provide redundant position information independent of the main position sensing module, the auxiliary position sensing module being configured as an inductive position sensor, the inductive position sensor comprising a stator coil structure fixedly mounted inside the motor housing and a conductive rotor target attached to the motor shaft; The signal preprocessing module is used to preliminarily process the original position signals output by the main position sensing module and the auxiliary position sensing module to optimize the signal quality; A multi-source data fusion module is used to intelligently integrate redundant position data from the primary and auxiliary position sensing modules 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. Anomaly detection and fault tolerance module, which is used to monitor the operating status of the main position sensing module and the auxiliary position sensing module in real time and identify sensor failures or anomalies; The communication interface module is used to transmit the processed and integrated 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 intensity generated when the multi-polar permanent magnet ring rotates. The magnetoresistive array sensor adopts the principle of anisotropic magnetoresistive effect 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. A high-frequency AC signal is introduced into the excitation coil 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 the induced voltage or impedance of the receiving coil to change. The induced signal of the receiving coil is demodulated and processed to accurately reflect 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 analog-to-digital conversion of the analog signal, and the digital signal processing unit performs denoising and smoothing on the digital signal.

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: defining a system state vector, the system state vector including a current angular position and an angular velocity of the parking motor; Establishing a discrete-time state transfer model and a measurement model for the motor motion; In each sampling period, a prediction step of the Kalman filter is executed to predict the state at the current moment based on the optimal state estimate at 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 the predicted values ​​of the primary position sensing module and the auxiliary position sensing module, and the state estimate is corrected to 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 Based on the observation value at each moment Predictive estimation of the state at each moment; represents the state transition matrix; Indicates The optimal state estimate at time t; represents the control input matrix; Indicates The control input vector applied at time instant.

7. The electric vehicle reducer parking motor position feedback system according to claim 5, characterized in that: The update step performed by the multi-source data fusion module has the following update formula: ,in, Indicates The optimal state estimate at time t; represents the Kalman gain; Indicates The measurement vector of the moment; 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 the 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 valid range, remains unchanged for a long time, or jumps.

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. 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 re-compensates the fused position data through a prediction algorithm based on motor control instructions 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 is started or the parking motor is running at no load, the adaptive calibration module executes a calibration sequence and records the data of the main position sensing module and the auxiliary position sensing module, and compares them with a preset reference position or a known physical limit to dynamically correct the sensor zero drift or gain error. The multi-source data fusion module introduces a temperature compensation mechanism on the basis of Kalman filtering. The real-time temperature data obtained 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.

Images