Multi-working-condition cooperative control new energy heavy truck driving motor intelligent speed limiting system and method

Abstract

The invention discloses an intelligent speed limiting system and method for a driving motor of a multi-working-condition cooperative control new energy heavy truck. Synchronous data are sent to a working condition recognition layer through a CAN bus; the working condition identification layer is used for receiving the data of the multi-sensor fusion layer, dynamically classifying the current working condition through a fuzzy rule base and Kalman filtering, and outputting a confidence label to the dynamic speed limit decision-making layer; the dynamic speed limit decision-making layer is used for predicting and controlling a rolling optimization motor rotating speed limit value through a model according to the working condition label, the motor efficiency MAP and the battery SOC state, and issuing a limit value instruction to the execution control layer; and the execution control layer is used for receiving the limit value instruction of the dynamic speed limit decision-making layer, adjusting the d / q axis current component through weak magnetic control, and coordinating regenerative braking and mechanical braking to realize rotating speed closed-loop control. By fusing multi-source sensor data and a working condition dynamic recognition technology, the system can sense complex road condition changes in real time and pre-judge the vehicle state.

Description

Technical Field

[0001] The present invention belongs to the technical field of driving motor speed limit, and specifically relates to an intelligent speed limit system and method for a new energy heavy truck driving motor based on multi-condition collaborative control. Background Technique

[0002] The speed limit system of the new energy heavy truck driving motor is a safety control system designed specifically for new energy vehicles. Its main function is to monitor and control the speed of the heavy truck driving motor in real time to ensure that the vehicle travels safely within the specified speed range. This system usually adopts high-precision sensors, advanced control algorithms and actuators, and can intelligently adjust the motor output power according to the vehicle driving state and driver operation instructions to achieve the speed limit function. The speed limit system not only improves the driving safety of new energy heavy trucks, but also improves the vehicle's endurance by optimizing energy consumption, meets the requirements of energy conservation and emission reduction, is an important achievement of technological innovation in the field of new energy vehicles, and is of great significance for promoting the green development of the logistics and transportation industry.

[0003] However, the traditional scheme relies on single-sensor data and cannot comprehensively respond to the dynamic changes of slope, load and road adhesion, resulting in the motor deviating from the efficient range for a long time and frequent safety hazards; at the same time, the fixed speed limit strategy and the braking system are designed separately, which is difficult to maximize the energy recovery efficiency and lacks a collaborative protection mechanism under abnormal conditions, and is prone to risks such as motor overheating or braking failure. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent speed limit system and method for a new energy heavy truck driving motor based on multi-condition collaborative control to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: An intelligent speed limit system for a new energy heavy truck driving motor based on multi-condition collaborative control, the system includes: A multi-sensor fusion layer for real-time collecting and preprocessing motor temperature, road slope, wheel speed and accelerator pedal signals, and sending the synchronized data to the working condition recognition layer through the CAN bus; data synchronization is based on the IEEE 1588 protocol to achieve microsecond-level timestamp alignment of multi-sensors, and after filtering, it is encapsulated into CAN frames (ID: 0x300~0x303) and transmitted once every 10ms.

[0006] A working condition recognition layer for receiving the data of the multi-sensor fusion layer, dynamically classifying the current working condition through a fuzzy rule base and Kalman filtering, and outputting a confidence label to the dynamic speed limit decision layer; A dynamic speed limit decision layer for rolling optimizing the motor speed limit value through model predictive control (MPC) according to the working condition label, motor efficiency MAP diagram and battery SOC state, and sending the limit value instruction to the execution control layer; The execution control layer is used to receive the limit value instruction from the dynamic speed limit decision layer, adjust the d / q axis current components through field weakening control, and coordinate the regenerative braking and mechanical braking to achieve closed-loop speed control. Preferably, a time-triggered communication mechanism is adopted between the working condition identification layer and the dynamic speed limit decision layer to ensure the strict matching of the timing of the working condition label and the limit value instruction, and avoid control delay.

[0007] The execution control layer is hard-wired to the vehicle EBS system to exchange braking pressure demand signals in real time, realizing seamless switching between regenerative braking and mechanical braking.

[0008] A data verification module is set between the multi-sensor fusion layer and the working condition identification layer. Through CRC verification and redundant data comparison, the integrity and reliability of the transmitted data are ensured.

[0009] The dynamic speed limit decision layer integrates the dynamic update function of the motor efficiency MAP diagram, and automatically adjusts the threshold of the high-efficiency working range according to motor aging or environmental temperature changes.

[0010] The execution control layer supports manual priority switching by the driver. In case of emergency, the driver is allowed to override the speed limit instruction through the pedal and restore direct torque control.

[0011] Preferably, the following are provided inside the multi-sensor fusion layer: Temperature sensor: A PT1000 platinum resistor embedded in the motor winding, which converts the temperature signal through a high-precision ADC. Gradient sensor: Composed of a MEMS accelerometer and a gyroscope, and the gradient angle is calculated through a quaternion fusion algorithm. Wheel speed sensor: A Hall effect sensor, which calculates the wheel speed difference Δω by combining FFT filtering. Throttle pedal sensor: Collects the pedal potentiometer signal and calculates the throttle opening change rate dθ / dt.

[0012] Preferably, the following are provided inside the multi-sensor fusion layer: Temperature sensor: A PT1000 platinum resistor embedded in the motor winding, which converts the temperature signal through a high-precision ADC. Gradient sensor: Composed of a MEMS accelerometer and a gyroscope, and the gradient angle is calculated through a quaternion fusion algorithm. Wheel speed sensor: A Hall effect sensor, which calculates the wheel speed difference Δω by combining FFT filtering. Throttle pedal sensor: Collects the pedal potentiometer signal and calculates the throttle opening change rate dθ / dt.

[0013] Preferably, the dynamic speed limit decision layer includes: MPC Optimization Module: Based on the motor dynamics model and the energy consumption equation, it iteratively solves the optimal rotational speed limit sequence within the next 50 ms every 10 ms. Safety Constraint Module: Enforces the limit values to satisfy the conditions that the motor temperature ≤ 150 °C, the battery feedback current ≤ the SOC threshold, and the wheel speed difference Δω ≤ 15%. Limit Smoothing: Uses a first-order inertial filter to gradually transition the limit values and avoid sudden changes in rotational speed (slope limit: ±50 rpm / ms).

[0014] Preferably, the execution control layer includes: Field Weakening Control Module: Dynamically adjusts the d-axis current component according to the deviation between the actual rotational speed and the limit value to suppress the motor back electromotive force. Regenerative Braking Coordination Module: Calculates the maximum feedback current based on the slope angle and the battery SOC, and when insufficient, the EBS system supplements the hydraulic braking force. Control Closed-loop: Real-time corrects the q-axis current through a PID regulator to ensure that the actual rotational speed tracks the limit value target.

[0015] Preferably, the system includes an exception handling unit for: When the motor temperature > 150 °C or the wheel speed difference Δω > 20%, triggers an emergency speed reduction command to forcibly limit the rotational speed to the safety threshold. Sends an alarm signal to the dashboard via the CAN bus to prompt the driver to intervene.

[0016] Preferably, the intelligent speed limit method for a new energy heavy truck drive motor based on multi-condition collaborative control includes the following steps: S1: Multi-sensor synchronous acquisition and data preprocessing: Synchronously acquires data on motor temperature, road slope, wheel speed, and throttle opening change rate through the multi-sensor fusion layer, aligns the protocol timestamps, performs median filtering, moving average, and outlier removal, and encapsulates it into a specified frame format for transmission to the working condition recognition layer.

[0017] S2: Working condition fuzzy classification and dynamic verification: The working condition recognition layer receives the preprocessed data, matches the slope, wheel speed difference, and throttle change rate based on the fuzzy rule base, outputs the coarsely classified working condition type, and verifies the working condition confidence level and generates the final working condition label by filtering and fusing the on-vehicle weight sensor data and the historical wheel speed average value.

[0018] S3: Model prediction optimization and speed limit decision-making: The dynamic speed limit decision-making layer constructs a prediction model based on the working condition label, combines the motor efficiency characteristics and the battery state, iteratively solves the optimal rotational speed limit, forcibly satisfies the temperature rise, wheel speed difference, and efficiency constraints, and uses a filtering algorithm to smooth the limit value output.

[0019] S4: Field-weakening control and current closed-loop execution: The execution control layer receives the rotational speed limit instruction, dynamically adjusts the current component to suppress the back electromotive force or release the torque potential, and tracks the torque demand through the closed-loop controller to output the current component to achieve rotational speed limitation.

[0020] S5: Regenerative braking coordination and mechanical compensation: The execution control layer calculates the theoretical feedback power and preferentially distributes the regenerative braking force. When the feedback braking force is insufficient, it triggers the mechanical braking system to supplement the hydraulic braking force to maintain the linear matching of the total braking force and the pedal stroke.

[0021] Preferably, in the step S2, the working condition identification layer receives the preprocessed data, and the executed content includes: Based on the fuzzy rule base, match the slope α, wheel speed difference Δω, and throttle change rate dθ / dt, and output the rough classification of working conditions (uphill heavy load / muddy road section / downhill feedback); Fuse the data of the vehicle-mounted weight sensor and the historical average wheel speed through Kalman filtering to verify the confidence of the working condition. If it is ≥70%, generate the final working condition label and the estimated value of the adhesion coefficient, otherwise mark it as "to be confirmed".

[0022] Preferably, in the step S3, the dynamic speed limit decision layer, according to the working condition label, combines the motor efficiency MAP diagram and the battery SOC state. The specific steps include: Construct an MPC prediction model (including the motor dynamics equation T_motor = KtIq - Jdω / dt and the energy consumption equation Ploss = Id²Rd + Iq²Rq); Solve the optimal rotational speed limit for the next 50ms every 10ms, and forcefully satisfy the constraint conditions of temperature rise ≤150°C, Δω ≤15%, and efficiency ≥85%; Use first-order inertial filtering (N_limit(k) = 0.7N_limit(k - 1) + 0.3N_MPC(k)) to smooth the limit output.

[0023] Preferably, in the step S4, the execution control layer receives the rotational speed limit instruction and executes the following steps: The field-weakening control module dynamically adjusts the d-axis current: if N_actual > N_limit, increase I_d to suppress the back electromotive force; if N_actual < N_limit, decrease I_d to release the torque potential; The q-axis current closed-loop tracks the torque demand T_req through the PID controller and outputs the I_q current component.

[0024] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are: 1. In the present invention, by fusing multi-source sensor data with the working condition dynamic recognition technology, the system can perceive complex road condition changes in real time and predict the vehicle state. For example, in steep slope, muddy or heavy load scenarios, it can accurately match the motor output characteristics with the road adhesion conditions, effectively suppressing potential safety hazards such as wheel slippage and motor overheating. At the same time, the speed limit decision-making mechanism based on model prediction breaks through the limitations of traditional fixed threshold control, dynamically optimizes the speed range in combination with the battery state and motor efficiency characteristics, avoiding power interruption caused by insufficient torque on large slope sections and significantly reducing energy loss under inefficient working conditions, thus extending the vehicle's endurance.

[0025] 2. In the present invention, the dynamic coordination of field weakening control and regenerative braking in the execution layer can not only quickly respond to speed limit commands and smoothly adjust the motor output, but also maximize the energy recovery efficiency in downhill or braking scenarios, reducing mechanical braking wear. The combination of the abnormal handling mechanism and the driver override design further enhances the system reliability, ensuring controllable vehicle speed and stability under extreme working conditions. This intelligent control mode with multi-module linkage provides a highly adaptable, low-energy-consuming and safety-redundant power management solution for new energy heavy trucks in complex transportation scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the overall flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] Referring to Figure 1 , a new energy heavy truck drive motor intelligent speed limit system based on multi-condition collaborative control, the system includes: A multi-sensor fusion layer for real-time collecting and preprocessing signals of motor temperature, road slope, wheel speed and accelerator pedal, and sending the synchronized data to the working condition recognition layer through the CAN bus; data synchronization is realized based on the IEEE 1588 protocol to align the microsecond-level timestamps of multi-sensors, and after filtering, it is encapsulated into CAN frames (ID: 0x300~0x303) and transmitted once every 10 ms.

[0029] A working condition recognition layer for receiving the data from the multi-sensor fusion layer, dynamically classifying the current working condition through a fuzzy rule base and Kalman filtering, and outputting a confidence label to the dynamic speed limit decision layer; The dynamic speed limit decision-making layer is used to roll-optimize the motor speed limit value according to the working condition label, the motor efficiency MAP diagram, and the battery SOC state through model predictive control (MPC), and send the limit instruction to the execution control layer; The execution control layer is used to receive the limit instruction from the dynamic speed limit decision-making layer, adjust the d / q axis current components through field weakening control, and coordinate the regenerative braking and mechanical braking to achieve speed closed-loop control; A time-triggered communication mechanism is adopted between the working condition identification layer and the dynamic speed limit decision-making layer to ensure that the timing of the working condition label and the limit instruction strictly matches, avoiding control delay.

[0030] The execution control layer is connected to the vehicle EBS system through a hard wire to exchange the braking pressure demand signal in real time, realizing seamless switching between regenerative braking and mechanical braking.

[0031] A data verification module is set between the multi-sensor fusion layer and the working condition identification layer. Through CRC verification and redundant data comparison, the integrity and reliability of the transmitted data are ensured.

[0032] The dynamic speed limit decision-making layer integrates the dynamic update function of the motor efficiency MAP diagram, and automatically adjusts the threshold of the high-efficiency working range according to motor aging or environmental temperature changes.

[0033] The execution control layer supports manual priority switching by the driver. In case of emergency, the driver is allowed to override the speed limit instruction through the pedal and restore direct torque control.

[0034] Inside the multi-sensor fusion layer, there are: Temperature sensor: A PT1000 platinum resistor embedded in the motor winding, converting the temperature signal through a high-precision ADC; Gradient sensor: Composed of a MEMS accelerometer and a gyroscope, calculating the gradient angle through a quaternion fusion algorithm; Wheel speed sensor: A Hall effect sensor, calculating the wheel speed difference Δω by combining FFT filtering; Throttle pedal sensor: Collecting the pedal potentiometer signal and calculating the throttle opening change rate dθ / dt.

[0035] The working condition identification layer includes: Fuzzy rule classification module: Matching the preset rule library according to the gradient angle (α), wheel speed difference (Δω), and throttle change rate (dθ / dt), and initially determining the working condition type (uphill heavy load / muddy road section / downhill feedback); Kalman filter verification module: Fusing the data of the on-vehicle weight sensor and the historical average value of the wheel speed difference to improve the working condition classification accuracy, and outputting the final label with a confidence level ≥70%; Data transmission: The working condition label and key parameters (such as road surface adhesion coefficient) are sent to the dynamic speed limit decision-making layer through a high-speed serial bus.

[0036] The functions of the operating condition recognition layer are as follows: Operating condition classification: Dynamically recognize the current driving scenario (uphill with heavy load, muddy road section, downhill feedback, etc.) based on sensor data.

[0037] State estimation: Eliminate noise interference through a filtering algorithm and output a high-confidence operating condition determination result.

[0038] Implementation method: Fuzzy rule base: Define the fuzzy sets and rule base of the input variables (gradient, wheel speed difference, throttle change rate). For example, if the gradient > 15%, the wheel speed difference < 5%, and the throttle increases slowly → determine it as an uphill heavy load operating condition.

[0039] Kalman filter: Integrate multi-sensor data to improve the estimation accuracy of the gradient angle and wheel speed difference.

[0040] The working logic of the operating condition recognition layer includes: 1. Coarse classification by fuzzy rules Input parameters: Gradient angle α, wheel speed difference Δω, throttle change rate dθ / dt

[0041] 2. Fine refinement by Kalman filter Perform secondary verification on the fuzzy classification result: Uphill operating condition: Verify whether the load coefficient K_load (provided by the vehicle weight sensor) > the threshold.

[0042] Muddy road section: Combine historical data (such as the average value of Δω in the past 10s) to confirm the low adhesion state.

[0043] Output the final operating condition label and confidence level (0 - 100%). If the confidence level < 70%, mark it as "to be confirmed".

[0044] The working logic of the multi-sensor fusion layer includes: 1. Multi-sensor synchronous sampling: The temperature, gradient, wheel speed, and throttle sensors perform synchronous sampling at a frequency of 1kHz, and trigger data capture through a hardware interrupt.

[0045] Timestamp alignment: Adopt the IEEE 1588 PTP protocol to ensure that the time deviation of each sensor data < 1ms.

[0046] 2. Data filtering and anomaly detection: Temperature data: Median filtering to eliminate pulse noise, and threshold detection (such as triggering an alarm when the temperature > 150°C).

[0047] Slope data: Complementary filtering fuses accelerometer and gyroscope signals, with a slope angle output range of -30° to +30° and a resolution of 0.1°.

[0048] Wheel speed data: Sliding average filtering is used to calculate the front and rear wheel speed difference Δω. If Δω>20%, it is marked as "suspected slip".

[0049] Throttle data: Calculate the rate of change of the throttle opening, dθ / dt, and distinguish between "slow increase" (dθ / dt < 10% / s) and "rapid increase" (dθ / dt ≥ 10% / s).

[0050] 3. Data encapsulation and transmission: The pre-processed data is packaged into CAN frames (ID: 0x300~0x303) and sent to the working condition identification module via the CAN bus with a transmission cycle of 10ms.

[0051] The dynamic speed limit decision layer includes: MPC optimization module: Based on the motor dynamics model and energy consumption equation, it solves the optimal speed limit sequence within the next 50ms every 10ms; Safety constraint module: mandatory limits meet the requirements of motor temperature ≤ 150°C, battery feedback current ≤ SOC threshold, and wheel speed difference Δω ≤ 15%; Limit smoothing: A first-order inertial filter is used to gradually transition the limit to avoid sudden changes in speed (slope limit: ±50rpm / ms).

[0052] The working logic of the dynamic speed limit decision layer is: 1.MPC rolling optimization Prediction model building: Motor dynamics model: T motor = K t * I q – J * dω / dt (K t is the torque constant, is the moment of inertia).

[0053] Energy consumption model: P loss =I d 2 ⋅R d +I q 2 ⋅R q (R d , R q is the resistance value of d / q).

[0054] 2. Constraint setting Safety margin: motor temperature ≤ 150°C, battery feedback current ≤ SOC limit value, wheel speed difference Δω ≤ 15%.

[0055] Efficiency First: Force the rotational speed to be in the high-efficiency range (efficiency ≥ 85%) of the motor efficiency MAP chart.

[0056] 3. Optimization Calculation Solve the objective function every 10 ms to generate a sequence of rotational speed limit values within the next  50 ms.

[0057] 4. Smooth Transition of Limits To avoid sudden changes in rotational speed, a first-order inertial filter is used to smooth the MPC output limit: N limit (k) = 0.7 * N limit (k - 1)+0.3 * N MPC (k) If the difference in limit values between adjacent cycles > 10%, trigger a gradual transition (slope limit: ±50 rpm / ms).

[0058] The execution control layer includes: Field-weakening control module: Dynamically adjust the d-axis current component according to the deviation between the actual rotational speed and the limit value to suppress the motor back electromotive force; Regenerative braking coordination module: Calculate the maximum feedback current based on the slope angle and the battery SOC, and when insufficient, the EBS system makes up the hydraulic braking force; Control closed-loop: Real-time correct the q-axis current through a PID regulator to ensure that the actual rotational speed tracks the limit value target.

[0059] The system includes an exception handling unit for: When the motor temperature > 150 °C or the wheel speed difference Δω > 20%, trigger an emergency speed reduction command to forcefully limit the rotational speed to a safe threshold; Send an alarm signal to the dashboard through the CAN bus to prompt the driver to intervene.

[0060] The working logic of the execution control layer includes: 1. Field-weakening Control and Current Distribution d-axis current regulation: Dynamically adjust the d-axis current according to the difference between the rotational speed limit N_limit and the current rotational speed N_actual: If N_actual > N_limit, increase the d-axis demagnetizing current component I_d to suppress the back electromotive force If N_actual < N_limit, decrease I_d to release the torque potential of the motor q-axis current closed-loop: Track the torque demand T_req through a PI controller and output the q-axis current I_q.

[0061] 2. Regenerative Braking Coordination Calculate the theoretical feedback power according to the slope α: P regen= m * g sinα* vη (η is the feedback efficiency) Dynamic allocation of feedback current and mechanical braking force: 1. Give priority to using motor feedback until the battery SOC or temperature limit is reached.

[0062] 2. The remaining braking force is supplemented by the EBS system according to the linear relationship of "pedal travel - hydraulic pressure".

[0063] 3. The remaining braking force is supplemented by the EBS system according to the linear relationship of "pedal travel - hydraulic pressure".

[0064] The intelligent speed limit method for the drive motor of new energy heavy trucks based on multi - condition collaborative control includes the following steps: S1: Multi - sensor synchronous acquisition and data pre - processing Synchronously acquire the data of motor temperature, road slope, wheel speed and throttle opening change rate through the multi - sensor fusion layer, use protocol - aligned timestamps, perform median filtering, moving average and outlier rejection, and encapsulate them into a specified frame format for transmission to the working condition recognition layer.

[0065] S2: Fuzzy classification and dynamic verification of working conditions The working condition recognition layer receives the pre - processed data, matches the slope, wheel speed difference and throttle change rate based on the fuzzy rule base, outputs the rough - classified working condition type, and verifies the working condition confidence level and generates the final working condition label by filtering and fusing the data of the vehicle weight sensor and the historical wheel speed mean value.

[0066] S3: Model prediction optimization and speed limit decision - making The dynamic speed limit decision - making layer constructs a prediction model and iteratively solves the optimal speed limit value according to the working condition label, combined with the motor efficiency characteristics and battery state, and forcibly satisfies the temperature rise, wheel speed difference and efficiency constraints, and uses a filtering algorithm to smooth the limit value output.

[0067] S4: Field - weakening control and current closed - loop execution The execution control layer receives the speed limit value instruction, dynamically adjusts the current component to suppress the back electromotive force or release the torque potential, and tracks the torque demand through the closed - loop controller to output the current component to achieve speed limitation.

[0068] S5: Regenerative braking coordination and mechanical compensation The execution control layer calculates the theoretical feedback power and preferentially allocates the regenerative braking force. When the feedback braking force is insufficient, it triggers the mechanical braking system to supplement the hydraulic braking force to keep the total braking force linearly matched with the pedal travel.

[0069] S6: Abnormal handling and priority arbitration The abnormal handling unit monitors the system status in real time. If it detects that the temperature or wheel speed difference exceeds the limit, it will force the speed reduction and send an alarm signal, support the driver to manually override the speed limit instruction, and freeze the optimization output.

[0070] In step S2, the working condition recognition layer receives the preprocessed data, and the executed content includes: Based on the fuzzy rule base, match the slope α, wheel speed difference Δω, and throttle change rate dθ / dt, and output the rough classification of working conditions (uphill with heavy load / muddy road / downhill feedback); Through Kalman filtering to fuse the vehicle load sensor data and the historical average wheel speed, verify the confidence of the working condition. If it is ≥70%, generate the final working condition label and the estimated value of the adhesion coefficient, otherwise mark it as "to be confirmed".

[0071] In step S3, the dynamic speed limit decision-making layer, according to the working condition label, combines the motor efficiency MAP diagram and the battery SOC state. The specific steps include: Construct an MPC prediction model (including the motor dynamics equation T_motor = KtIq - Jdω / dt and the energy consumption equation Ploss = Id²Rd + Iq²Rq); Solve the optimal speed limit for the next 50ms every 10ms, and forcefully meet the constraint conditions of temperature rise ≤150°C, Δω ≤15%, and efficiency ≥85%; Use first-order inertial filtering (N_limit(k) = 0.7N_limit(k - 1) + 0.3N_MPC(k)) to smooth the limit output.

[0072] In step S4, the execution control layer receives the speed limit instruction and executes the following steps: The field weakening control module dynamically adjusts the d-axis current: if N_actual > N_limit, increase I_d to suppress the back electromotive force; if N_actual < N_limit, decrease I_d to release the torque potential; The q-axis current closed-loop tracks the torque demand T_req through a PID controller and outputs the I_q current component.

[0073] It can be seen from the above: In the present invention, by fusing multi-source sensor data and the working condition dynamic recognition technology, the system can real-time sense the complex road condition changes and predict the vehicle state. For example, in steep slope, muddy or heavy load scenarios, it can accurately match the motor output characteristics with the road adhesion conditions, effectively suppress safety hazards such as wheel slip and motor overheating. At the same time, the speed limit decision-making mechanism based on model prediction breaks the limitations of traditional fixed threshold control, dynamically optimizes the speed range in combination with the battery state and motor efficiency characteristics, not only avoids power interruption caused by insufficient torque in large slope sections, but also significantly reduces energy loss in inefficient working conditions and extends the vehicle's endurance.

[0074] In the present invention, the dynamic coordination between field-weakening control and regenerative braking in the execution layer can not only quickly respond to the speed limit command to smoothly adjust the motor output, but also maximize the energy recovery efficiency in downhill or braking scenarios and reduce mechanical braking wear. The combination of the abnormal handling mechanism and the driver override design further enhances the system reliability and ensures that a controllable vehicle speed and stability can still be maintained under extreme conditions. This intelligent control mode with multi-module linkage provides a power management solution with high adaptability, low energy consumption and safety redundancy for new energy heavy trucks in complex transportation scenarios.

[0075] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-condition collaborative control intelligent speed limit system for the drive motor of a new energy heavy truck, characterized in that: Including: A multi-sensor fusion layer, which is used to collect and preprocess the motor temperature, road gradient, wheel speed, and accelerator pedal signals in real time, and send the synchronized data to the working condition identification layer through the CAN bus; A working condition identification layer, which is used to receive the data from the multi-sensor fusion layer, dynamically classify the current working condition through a fuzzy rule base and Kalman filter, and output a confidence label to the dynamic speed limit decision-making layer; A dynamic speed limit decision-making layer, which is used to roll-optimize the motor speed limit according to the working condition label, motor efficiency MAP diagram, and battery SOC status through model predictive control, and send the limit instruction to the execution control layer; An execution control layer, which is used to receive the limit instruction from the dynamic speed limit decision-making layer, adjust the d / q axis current components through field-weakening control, and coordinate the regenerative braking and mechanical braking to achieve speed closed-loop control.

2. The intelligent speed limit system for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 1, wherein: A time-triggered communication mechanism is adopted between the working condition identification layer and the dynamic speed limit decision-making layer to ensure that the timing of the working condition label and the limit instruction strictly matches, avoiding control delay; The execution control layer is connected to the vehicle EBS system through a hard wire to exchange brake pressure demand signals in real time, realizing seamless switching between regenerative braking and mechanical braking; A data verification module is set between the multi-sensor fusion layer and the working condition identification layer. Through CRC verification and redundant data comparison, the integrity and reliability of the transmitted data are ensured; The dynamic speed limit decision-making layer integrates the dynamic update function of the motor efficiency MAP diagram, and automatically adjusts the threshold of the high-efficiency working range according to motor aging or environmental temperature changes; The execution control layer supports the driver's manual priority switching. In case of emergency, the driver is allowed to override the speed limit instruction through the pedal and restore direct torque control.

3. The intelligent speed limit system for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 1, characterized in that: The working condition identification layer includes: A fuzzy rule classification module: It preliminarily determines the working condition type according to the slope angle, wheel speed difference, and accelerator change rate by matching the preset rule base; A Kalman filter verification module: It fuses the data of the on-vehicle weight sensor and the historical average value of the wheel speed difference to improve the accuracy of working condition classification and outputs the final label with a confidence level ≥70%; Data transmission: The working condition label and key parameters are sent to the dynamic speed limit decision-making layer through a high-speed serial bus.

4. The intelligent speed limit system for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 1, wherein: The dynamic speed limit decision-making layer includes: An MPC optimization module: Based on the motor dynamics model and energy consumption equation, it rolls and solves the optimal speed limit sequence within the next 50 ms every 10 ms; A safety constraint module: It enforces the limit to satisfy the motor temperature ≤150 °C, battery feedback current ≤SOC threshold, and wheel speed difference Δω ≤15%; Limit smoothing: A first-order inertial filter is used to gradually transition the limit to avoid sudden speed changes.

5. The intelligent speed limit system for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 1, characterized in that: The execution control layer includes: A field-weakening control module: It dynamically adjusts the d-axis current component according to the deviation between the actual speed and the limit to suppress the motor back electromotive force; A regenerative braking coordination module: It calculates the maximum feedback current based on the slope angle and battery SOC, and when it is insufficient, the EBS system makes up the hydraulic braking force; Control closed-loop: It uses a PID regulator to correct the q-axis current in real time to ensure that the actual speed tracks the limit target.

6. The intelligent speed limit system for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 1, characterized in that: The system also includes an abnormal handling unit, which is used for: When the motor temperature >150 °C or the wheel speed difference Δω >20%, it triggers an emergency speed reduction instruction to forcibly limit the speed to a safe threshold; Send an alarm signal to the instrument panel via the CAN bus to prompt the driver to intervene.

7. A multi-condition collaborative control intelligent speed limit method for the drive motor of a new energy heavy truck, characterized in that: It includes the following steps: S1: Multi-sensor synchronous acquisition and data preprocessing: Synchronously acquire the motor temperature, road slope, wheel speed, and throttle opening change rate data through the multi-sensor fusion layer. Use the protocol to align the timestamps, perform median filtering, moving average, and outlier removal, and encapsulate them into a specified frame format for transmission to the working condition recognition layer. S2: Working condition fuzzy classification and dynamic verification: The working condition recognition layer receives the preprocessed data, matches the slope, wheel speed difference, and throttle change rate based on the fuzzy rule base, outputs the roughly classified working condition type, and verifies the working condition confidence level and generates the final working condition label by filtering and fusing the vehicle weight sensor data and the historical wheel speed average value. S3: Model prediction optimization and speed limit decision-making: The dynamic speed limit decision-making layer constructs a prediction model and iteratively solves the optimal speed limit according to the working condition label, combined with the motor efficiency characteristics and battery state, and forcibly satisfies the constraints of temperature rise, wheel speed difference, and efficiency. Use a filtering algorithm to smooth the limit output. S4: Field-weakening control and current closed-loop execution: The execution control layer receives the speed limit instruction, dynamically adjusts the current component to suppress the back electromotive force or release the torque potential, and tracks the torque demand through the closed-loop controller to output the current component to achieve speed limitation. S5: Regenerative braking coordination and mechanical compensation: The execution control layer calculates the theoretical feedback power and preferentially distributes the regenerative braking force. When the feedback braking force is insufficient, trigger the mechanical braking system to supplement the hydraulic braking force to keep the total braking force linearly matched with the pedal stroke.

8. The intelligent speed limit method for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 7, wherein: In the step S2, the content executed by the working condition recognition layer when receiving the preprocessed data includes: Match the slope α, wheel speed difference Δω, and throttle change rate dθ / dt based on the fuzzy rule base, and output the roughly classified working condition. Verify the working condition confidence level by fusing the vehicle weight sensor data and the historical wheel speed average value through the Kalman filter. If it is ≥70%, generate the final working condition label and the adhesion coefficient estimation value, otherwise mark it as "to be confirmed".

9. The intelligent speed limit method for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 7, wherein: In the step S3, the specific steps of the dynamic speed limit decision-making layer according to the working condition label, combined with the motor efficiency MAP and the battery SOC state, include: Construct an MPC prediction model; iteratively solve the optimal speed limit for the next 50 ms every 10 ms, and forcibly satisfy the constraint conditions of temperature rise ≤150°C, Δω ≤15%, and efficiency ≥85%. Use a first-order inertial filter to smooth the limit output.

10. The intelligent speed limit method for the drive motor of a new energy heavy truck with multi-condition collaborative control according to claim 7, characterized in that: In the step S4, when the execution control layer receives the speed limit instruction, it performs the following steps: The field-weakening control module dynamically adjusts the d-axis current: If N_actual > N_limit, increase I_d to suppress the back electromotive force; if N_actual < N_limit, decrease I_d to release the torque potential. The q-axis current closed-loop tracks the torque demand T_req through the PID controller and outputs the I_q current component.

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