A cooperative allocation method and system based on wheel-side electric drive bridge

Abstract

This invention provides a cooperative allocation method and system based on a wheel-side electric drive axle. The method includes collecting vehicle operating conditions and performing anti-interference observation and estimation of the vehicle; updating the MPC weight matrix online and optimizing the updated MPC weight matrix; solving for the vehicle's longitudinal driving force and target sway torque within the same optimization domain based on the optimized MPC weight matrix, and decomposing them into the target torque of the left wheel motor, the target torque of the right wheel motor, and the regenerative braking allocation or hydraulic braking allocation; receiving a time-stamped target vector and attaching a timeout or retransmission strategy to the target vector; executing the attached timeout or retransmission strategy, and performing limited slip and torque tracking based on the target torque of the left wheel motor, the target torque of the right wheel motor, and the regenerative braking allocation or hydraulic braking allocation. This invention can significantly improve low-adhesion stability and avoid the situation of asynchronous control between the left and right wheels.

Description

Technical Field

[0001] This invention relates to the field of drive axle control technology, and in particular to a cooperative allocation method and system based on wheel-side electric drive axles. Background Technology

[0002] In the electrification of commercial vehicles, two parallel development paths exist: electric drive axles and distributed drive systems. One type is the centralized electric drive axle, which highly integrates the motor, reducer, and differential. This type is compact, efficient, and easy to arrange within the vehicle, and is already widely mass-produced in light trucks and other models. The other type is distributed / wheel-side (including wheel-end and wheel-hub) drive, where the motor drives independently close to the wheel. This results in a shorter transmission chain, faster response, and easier implementation of independent torque control and energy recovery optimization. Distributed drive systems are widely recognized as a key underlying capability for improving vehicle active safety and energy efficiency due to their fast motor dynamic response, flexible chassis layout, and ease of expansion to multi-axle / multi-wheel independent drive. However, challenges remain, such as increased unsprung mass and high system design and manufacturing difficulty and cost associated with hub direct drive. Engineering has led to compromise solutions such as fixed or oscillating structures using "wheel-side motor + reducer" to balance efficiency, handling stability, and manufacturability. In passenger and light commercial vehicle scenarios, typical wheel-side / hub products (such as ZF AVE130) achieve high transmission efficiency and energy recovery capabilities through two-stage reduction and highly integrated cooling braking design, providing a reference path for light truck platforms. Meanwhile, industry research into control technologies such as "electronic differential," "regenerative braking collaborative control," and "four / two-wheel torque distribution" is deepening, emphasizing maximizing energy recovery while meeting stability and braking regulations, and working in conjunction with ABS / ESC. Overall, wheel-side electric drive axles for light trucks, to handle complex load changes and multiple operating conditions (low adhesion, wet skid, slope, urban distribution start-stop, etc.), require faster network and control response, more robust adhesion / load recognition, and a tighter "electronic differential-regenerative braking" coupling strategy.

[0003] Centralized / integrated electric drive axle: The motor and reducer / differential are integrated into one unit, resulting in low transmission loss and a compact structure. It is suitable for improving the range and NVH performance of light truck chassis. Domestic manufacturers have developed multi-platform products and put them into mass production.

[0004] Wheel-side / hub drive axle: The motor is positioned close to the wheel, eliminating the need for a long drive chain and facilitating independent drive of the left and right wheels and energy recovery. Fixed and swing-type wheel-side structures can effectively control the equivalent unsprung mass; hub direct drive offers high integration but has stricter requirements for quality, heat dissipation, and sealing.

[0005] Electronic differential control: Replaces the mechanical differential with an independent motor, and achieves differential and steering assistance through speed / torque control. It can be extended to differential / longitudinal and lateral force coupling control of dual / four motor systems.

[0006] Regenerative braking coordinated control: Under the premise of meeting driving braking requirements and stability, optimize the distribution of electric motor braking and hydraulic braking, and coordinate switching and compensation with ABS / ESC to improve recovery efficiency and ensure emergency braking stability;

[0007] Layered vehicle control structure: A multi-layered architecture of management layer—top layer—bottom layer is used for setting target dynamic performance, traction / yaw moment distribution and actuator control, often supplemented by slip ratio control, observer estimation and other methods.

[0008] In summary, existing technologies suffer from insufficient response and coordination: under conditions of rapid load changes and low adhesion, torque distribution typically relies on conservative torque limiting / anti-slip strategies; network and control closed-loop delays make it difficult for torque coordination to keep up in a timely manner; and the coupling control between torque, regeneration, and braking is not tight enough, affecting stability and recovery efficiency (industry reports also point out that distributed drive control is complex and has high barriers to entry for core algorithms).

[0009] The existing solution is not adaptable to the working conditions / states: It is not sufficient to jointly identify and adaptively allocate multiple constraints such as "axle load transfer - road surface adhesion - actuator thermal state". Especially in the scenario of light trucks with a large "no-load - full-load" span and frequent start-stop in urban distribution, it is easy to find that the same calibration cannot take into account both energy consumption and handling stability.

[0010] Insufficient fault tolerance and synchronization in system integration: Under independent control of multiple controllers, if there is a lack of high-precision synchronous clock and channel-level redundancy, problems such as asynchronous control of left and right wheels or premature / late triggering of degradation strategies may occur during transient slippage and sudden changes in attachment. Summary of the Invention

[0011] Therefore, the purpose of this invention is to provide a cooperative allocation method and system based on wheel-side electric drive axles to overcome the shortcomings of the prior art.

[0012] In a first aspect, the present invention provides a cooperative allocation method based on a wheel-side electric drive axle, the method comprising:

[0013] Collect vehicle operating conditions and perform anti-interference observation and estimation on the vehicle to identify low-adjacent signals;

[0014] Based on the operating conditions and the low-level signal, risk indicators are calculated and the MPC weight matrix is ​​updated online, and the updated MPC weight matrix is ​​optimized.

[0015] Based on the optimized MPC weight matrix, the longitudinal driving force and target sway torque of the vehicle are solved in the same optimization domain, and the longitudinal driving force and target sway torque of the vehicle are decomposed into the target torque of the left wheel motor, the target torque of the right wheel motor, and the regenerative braking distribution or hydraulic braking distribution.

[0016] The target vector is received by the MCU and aligned with the time stamp, and a timeout or retransmission strategy is attached to the target vector;

[0017] The system executes a timeout or re-triggered strategy and performs limited slip and torque tracking based on the target torque of the left wheel motor, the target torque of the right wheel motor, and the regenerative braking distribution or the hydraulic braking distribution.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: by optimizing the MPC weight matrix and solving within the same optimization domain, the lateral control is no longer limited by the serial triggering logic, reducing sideslip and understeer, thereby significantly improving low adhesion stability. By decomposing the longitudinal driving force of the entire axle and the target sway torque into the target torque of the left wheel motor, the target torque of the right wheel motor, and the regenerative braking distribution or hydraulic braking distribution, "forced hydraulic intervention" can be reduced, significantly improving energy recovery under typical urban distribution start-stop and downhill conditions, extending the driving range and reducing brake pad wear. Furthermore, by using the attached timeout or re-launch strategy, it can follow the axle load transfer and adhesion changes in real time, avoiding the situation of asynchronous control of the left and right wheels.

[0019] Furthermore, the operating conditions include VCU reading vehicle speed, steering wheel angle, pedal opening, IMU, wheel speed, axle pressure, sprung load, temperature, and SOC.

[0020] Furthermore, the step of performing anti-interference observation and estimation on the vehicle to identify low-level signals includes:

[0021] Based on the fusion of motor electrical parameters with wheel speed using an anti-interference slip observer or an attached observer, low-attachment signals can be identified;

[0022] Dynamic axle load estimation is performed based on longitudinal acceleration, lateral acceleration, suspension updates of the front and rear axles, pressure updates of the front and rear axles, and left and right wheel normal loads, and an axle load transfer model is constructed.

[0023] Furthermore, the step of optimizing the updated MPC weight matrix includes:

[0024] Minimize the updated MPC weight matrix and impose constraints on the MPC weight matrix.

[0025] Furthermore, the expression for the minimized updated MPC weight matrix is:

[0026] J=J stability +J energy +J smooth ;

[0027] In the formula, J Let represent the objective function of the overall optimization problem.J stability This represents the stability-related cost term. J energy This represents the cost associated with energy recovery. J smooth This represents the cost associated with smoothness / actuator wear.

[0028] Furthermore, the step of receiving the target vector with time-stamp alignment from the MCU includes:

[0029] The time-aligned target vector is sent down to the left and right MCUs via the VCU.

[0030] Furthermore, after the steps of performing limited slip and torque tracking based on the target torque of the left wheel motor, the target torque of the right wheel motor, and the regenerative braking distribution amount or the hydraulic braking distribution amount, the method further includes:

[0031] Monitor link latency, packet loss rate, and sensor quality when executing timeout or retransmission policies, and determine whether the execution exceeds the limits.

[0032] If so, then the left and right wheel motors will be degraded for limited slip and the swing torque will be mitigated as needed.

[0033] Secondly, the present invention also provides a cooperative distribution system based on a wheel-side electric drive axle, the system comprising:

[0034] The acquisition module is used to collect vehicle operating conditions and perform anti-interference observation and estimation of the vehicle to identify low-level signals;

[0035] An optimization module is used to calculate risk indicators and update the MPC weight matrix online based on the operating conditions and the low-level signal, and to optimize the updated MPC weight matrix.

[0036] The decomposition module is used to solve the longitudinal driving force of the entire axle and the target sway torque of the vehicle in the same optimization domain based on the optimized MPC weight matrix, and decompose the longitudinal driving force of the entire axle and the target sway torque into the target torque of the left wheel motor, the target torque of the right wheel motor and the regenerative braking distribution amount or the hydraulic braking distribution amount.

[0037] The receiving module is used to receive a time-aligned target vector based on the MCU and to attach a timeout or retransmission strategy to the target vector;

[0038] The execution module is used to execute a timeout or re-trigger strategy, and to perform limited slip and torque tracking based on the target torque of the left wheel motor, the target torque of the right wheel motor and the regenerative braking distribution amount or the hydraulic braking distribution amount.

[0039] Thirdly, the present invention also provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described cooperative allocation method based on a wheel-side electric drive bridge.

[0040] Fourthly, the present invention also provides a vehicle including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described cooperative allocation method based on the wheel-side electric drive axle. Attached Figure Description

[0041] Figure 1 This is a flowchart of the collaborative allocation method based on the wheel-side electric drive bridge in the first embodiment of the present invention;

[0042] Figure 2 This is a structural diagram of the cooperative distribution system based on the wheel-side electric drive bridge in the second embodiment of the present invention;

[0043] Figure 3 This is a structural diagram of the vehicle in the third embodiment of the present invention.

[0044] Explanation of key component symbols:

[0045] 11. Acquisition Module; 12. Optimization Module; 13. Decomposition Module; 14. Receiving Module; 15. Execution Module;

[0046] 10. Memory; 20. Processor; 30. Computer program.

[0047] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0048] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0049] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0051] Example 1

[0052] Please see Figure 1 The figure shows a cooperative allocation method based on a wheel-side electric drive axle in the first embodiment of the present invention, the method comprising steps S1 to S5:

[0053] S1 collects vehicle operating conditions and performs anti-interference observation and estimation of the vehicle to identify low-level signals;

[0054] It should be noted that the vehicle's operating conditions include vehicle speed, steering wheel angle, pedal opening, IMU, wheel speed, axle pressure, sprung load, temperature, and SOC (State of Charge).

[0055] Specifically, step S1 includes steps S11 to S12:

[0056] S11, based on the anti-interference slip observer or the attachment observer, integrates the motor electrical participation with the wheel speed to identify low-attachment signals;

[0057] S12, based on longitudinal acceleration, lateral acceleration, suspension updates of front and rear axles, pressure updates of front and rear axles, and left and right wheel normal loads, dynamic axle load estimation is performed, and an axle load transfer model is constructed;

[0058] It should be explained that, in specific implementation, the following components are included: Wheel-side electric drive axle module: each side is equipped with a high-speed motor and a two-stage reduction structure, integrating an inverter and a local controller (MCU); Vehicle controller (VCU): runs a multi-constraint adaptive coupled MPC control algorithm, responsible for global dynamic target calculation and control command generation; Sensors and network: including IMU, wheel speed, axle load sensors, etc., using industrial Ethernet (EtherCAT+TSN) with time synchronization and redundancy.

[0059] It is worth noting that the anti-interference observation module simultaneously utilizes the electromagnetic quantity of the motor and the wheel speed-acceleration information to identify the slip ratio inflection point and the change in tire longitudinal stiffness in order to determine low adhesion and update the upper limit of adhesion and the slip ratio limit.

[0060] It is worth noting that the rear axle features wheel-side motors on both sides (peak power 75–100kW each) + a two-stage planetary / fixed-axis hybrid reducer (total speed ratio 8–12), an integrated inverter and MCU, and water glycol cooling; the brakes are drive-by-wire hydraulic (EHB) with ABS / ESC interfaces; the oVCU (ASIL-D design) is equipped with an IMU, steering angle / pedal sensor, wheel speed sensor, and airbag pressure sensor; the battery BMS provides SoC and power limits; EtherCAT backbone + TSN time synchronization, dual-ring network redundancy; and messages include absolute timestamps and receipts.

[0061] S2, calculate the risk index and update the MPC weight matrix online based on the operating conditions and the low-level signal, and optimize the updated MPC weight matrix;

[0062] Specifically, step S2 includes step S21:

[0063] S21, minimize the updated MPC weight matrix and constrain the MPC weight matrix;

[0064] Understandably, the risk indicators are calculated, consisting of yaw rate deviation, sideslip angle estimation error, slip rate exceeding limits, and low attachment signal, satisfying the adaptive principle of increasing stability weights for high risks and increasing energy weights for low risks. In this embodiment, the indicators include yaw rate / slip angle error, slip rate exceeding limits, and low attachment signal, and the MPC weight matrix is ​​updated online. Weight scheduling follows the online update of the graded risk threshold R. When R increases, stability objectives are prioritized and slip and regeneration initiation slopes are limited. When R decreases, energy recovery weights are increased and regeneration upper limits are relaxed without exceeding attachment and regulatory constraints.

[0065] It is worth noting that the updated MPC weight matrix is ​​first minimized, as shown in the expression:

[0066] J=J stability +J energy +J smooth ;

[0067] In the formula, J The objective function represents the overall optimization problem, which is used to comprehensively measure vehicle stability, recoverable energy and smoothness of operation under given constraints, so as to find the optimal control quantity allocation scheme. J stability This represents a stability-related cost term used to quantify deviations and risks in vehicle dynamic stability. It mainly includes yaw rate error, sideslip angle error, tire slip rate deviation from the target value, and other indicators related to lateral / longitudinal dynamic stability. An increase in this term indicates that the vehicle is more unstable. The optimization objective is to minimize this term to ensure driving safety.J energy This represents the cost of energy recovery, used to measure the benefit of electric motor braking under safety and operational constraints, or the inverse of energy loss. Optimization tends to maximize regenerated energy (equivalent to minimizing negative recovery benefits or directly treating recovery targets as negative costs), thereby improving overall vehicle energy efficiency and range. J smooth This represents the cost associated with ride comfort / actuator wear, used to penalize abrupt changes and high-frequency variations in control commands, including torque slope, rate of change of braking force, and frequency of hydraulic braking intervention. Minimizing this item is beneficial for ride comfort, NVH performance, and component life.

[0068] and constraints |λi|≤λmax(μ,Nzi,Ttemp)、 Battery power limit, braking regulations / ABS interface, network latency margin; and solving for Fx, Mz, and {Tleft,Tright,Bregen_left / right,Bhyd_left / right} ,in, λi Indicates the first i The slip ratio of a wheel describes the degree of slippage of the tire under longitudinal force. The slip ratio is usually defined as the ratio of the relative difference between the wheel linear velocity and the vehicle forward speed, and is used to reflect the tire's adhesion utilization rate under driving or braking. λ Generally, it refers to a slip ratio vector or a single slip ratio variable. It can also represent the set of slip ratios of all wheels or the slip ratio of a particular wheel. λmax This represents the maximum absolute value of the permissible slip ratio, or the upper limit of the slip ratio safety, serving as the upper bound of the constraint. λmax It can be a scalar or a vector for each round, and its value is dynamically determined by factors such as the current adhesion conditions, axle load Nzi, and temperature to prevent excessive slippage or lock-up. μ The coefficient of friction (adhesion coefficient) between the road surface and the tire reflects the level of road surface adhesion. The value range is usually from 0 (extremely slippery) to 1 (high adhesion) or higher. As an important constraint parameter, it affects the longitudinal force that can be withstood and the slip limit. Nzi Indicates the first i The normal load (axle load or wheel load) of a wheel, that is, the magnitude of the load acting on the wheel perpendicular to the ground, affects the mapping relationship between the tire's available friction force and slip ratio. Ttemp The parameters represent temperature-related constraints, typically temperature limits associated with motors, inverters, or brakes. These limits describe the maximum allowable sustained torque or derating threshold for regenerative power when component temperatures rise. Fx represents the target longitudinal driving force (or braking force) of the vehicle, which is the sum of driving and braking forces along the vehicle's longitudinal direction (front-to-back). It represents the traction or braking requirements that the vehicle needs to achieve at the current moment. Mz represents the target yaw moment of the vehicle, which is the desired torque value allocated to the left wheel by the optimizer, encompassing both motor braking and driving actions. TleftThis represents the target output torque (driving or braking torque) of the left wheel (or left motor), which is the desired torque value allocated to the left wheel by the optimizer, including both motor braking and driving functions; Tright This represents the target output torque of the right wheel (or right motor), which is the desired torque value allocated to the right wheel by the optimizer, encompassing both motor braking and driving functions. Bregen It indicates the amount or proportion of regenerative braking distributed by the motor, representing the absolute value or percentage of the regenerative braking force borne by the motor under braking demand, usually expressed in the form of torque, power or percentage; Bhyd It indicates the amount or proportion of hydraulic (friction) braking distribution, and represents the absolute value or proportion of braking force borne by the hydraulic braking system; left The suffix "left" indicates variables or allocations related to the left wheel / left motor. For example, Bregen_left indicates regenerative braking allocation to the left wheel. right The suffix "right" indicates a variable or allocation related to the right wheel / right motor. For example, Bhyd_right indicates the hydraulic brake allocation for the right wheel.

[0069] It should be explained that the optimization constraints include road surface adhesion, dynamic axle load, actuator thermal state, battery power limit and network delay constraints; among them, the dynamic axle load estimation is based on the IMU and the suspension / airbag pressure or sprung / unsprung acceleration to build an axle load transfer model, which updates the normal load of each wheel at a period of 10ms and feeds it back to the optimizer and the virtual electronic differential limit.

[0070] S3. Based on the optimized MPC weight matrix, solve the longitudinal driving force and target sway torque of the vehicle in the same optimization domain, and decompose the longitudinal driving force and target sway torque of the vehicle into the target torque of the left wheel motor, the target torque of the right wheel motor and the regenerative braking distribution or hydraulic braking distribution.

[0071] It should be noted that the vehicle controller has a built-in "multi-constraint adaptive coupling optimization module" that simultaneously solves for the longitudinal driving force of the entire axle and the target yaw moment within a single optimization domain, and decomposes them into the target torque of the left and right wheel motors and the regenerative / hydraulic braking distribution.

[0072] S4, based on the target vector received by the MCU with time stamp alignment, and attach a timeout or retransmission strategy to the target vector;

[0073] Specifically, step S4 includes step S41:

[0074] S41 sends the time-aligned target vector to the left and right MCUs via VCU.

[0075] S5, execute the timeout or re-trigger strategy, and perform limited slip and torque tracking based on the target torque of the left wheel motor, the target torque of the right wheel motor and the regenerative braking distribution amount or the hydraulic braking distribution amount;

[0076] Understandably, the following are performed during local execution (MCU): current-torque inner loop (1–2ms), v-ELSD limited slip and yaw torque sharing tracking; regeneration-hydraulic coordinator: priority regeneration, requesting EHB intervention when necessary; handshake with ABS to achieve emergency switching compensation and suppress switching jitter; thermal / power limits and NVH suppression (torque slope / spectrum management).

[0077] It is worth noting that the regenerative-hydraulic brake coordinator works in conjunction with ABS / ESC, including switching compensation feedforward to suppress switching jitter and maximizing the regeneration ratio within regulatory and adhesion constraints.

[0078] Specifically, step S5 includes steps S51 to S52:

[0079] S51, monitor the link latency, packet loss rate and sensor quality when executing the timeout or retransmission policy, and determine whether the execution exceeds the limit.

[0080] S52, if so, then the left wheel motor and the right wheel motor are degraded for limited slip and the swing torque is mitigated as needed;

[0081] Understandably, when the link is abnormal or the sensing is uncertain, it automatically enters a conservative regeneration + local limited slip degradation control mode to maintain basic drivability and safety, and improve the reliability and economy of light trucks in changing operating scenarios.

[0082] In this embodiment, the control allocation includes an integrated module of left and right wheel-side motors and reducers, independent motor controllers, a vehicle controller, an electro-hydraulic braking system, and an inertial measurement unit. The vehicle controller has a "multi-constraint adaptive coupling optimization module" that simultaneously solves for the longitudinal driving force of the entire axle and the target yaw moment within a single optimization domain, and decomposes them into the target torque of the left and right wheel motors and the regenerative / hydraulic braking allocation. The left and right controllers receive the timestamped target synchronously at sub-millisecond time, and perform limited slip and torque tracking in a closed loop with a 1-2ms cycle. Its "multi-constraint adaptive coupling optimization module" is a model predictive controller. The cost function simultaneously includes stability error terms (yaw rate / side slip angle / slip rate), energy recovery terms (motor braking power integral), and ride comfort terms (torque / pressure change rate), and schedules the weights of each term online based on risk indicators.

[0083] It should be noted that in this embodiment, the communication network adopts an industrial Ethernet protocol with time synchronization and redundancy, including aligned timestamps and message receipts. When the detected delay / packet loss exceeds the threshold, degradation control is triggered, downgrading to local slip limiting + conservative regeneration and mitigating yaw moment requirements. The typical response time of the end-to-end control link is less than 50ms, the VCU optimization cycle is 10ms, the MCU inner loop is 1–2ms, and the network alignment error is less than 1ms.

[0084] In summary, the collaborative allocation method based on the wheel-side electric drive axle in the above embodiments of the present invention, because the yaw moment demand, limited slip, and regenerative braking are decoupled / coupled and solved in the same MPC, lateral stability control is no longer limited by serial triggering logic; in the braking-drive transition of icy / slippery curves, the slip ratio constraint and yaw rate error are minimized simultaneously, reducing the risk of sideslip and understeer / oversteer, resulting in a significant improvement in low-adhesion stability; adaptive weights allocate more braking demand to the motor side within the safety boundary, and because the left and right wheels independently regenerate and couple the yaw moment, "forced hydraulic intervention" is reduced, significantly improving energy recovery under typical urban distribution start-stop and downhill conditions, extending range and reducing brake pad wear. This improves energy recovery efficiency; the dual-timescale design and network synchronization keep the target-execution link latency within 50ms, and torque coordination can follow axle load transfer and attachment changes in real time, significantly reducing secondary slippage caused by "torque lag," thus enabling a fast and robust response; by incorporating heat, power, SoC, and NVH into a unified constraint, heat overflow or comfort degradation caused by short-term energy optimization is avoided, achieving more sustainable daily availability and making the global constraints more complete; when the link is abnormal or the sensing is uncertain, it automatically enters conservative regeneration + local limited slip degradation control to maintain basic drivability and safety, improve the reliability and economy of light trucks in variable operating scenarios, and enhance fault tolerance and availability.

[0085] Example 2

[0086] The second embodiment of the present invention also provides a cooperative distribution system based on a wheel-side electric drive axle. Please refer to [link to relevant documentation]. Figure 2 The figure shows a cooperative allocation system based on a wheel-side electric drive axle according to a second embodiment of the present invention. The system includes:

[0087] The acquisition module 11 is used to acquire vehicle operating conditions and perform anti-interference observation and estimation of the vehicle to identify low-level signals.

[0088] The optimization module 12 is used to calculate risk indicators and update the MPC weight matrix online based on the operating conditions and the low-level signal, and to optimize the updated MPC weight matrix.

[0089] The decomposition module 13 is used to solve the longitudinal driving force of the entire axle and the target sway torque of the vehicle in the same optimization domain based on the optimized MPC weight matrix, and decompose the longitudinal driving force of the entire axle and the target sway torque into the target torque of the left wheel motor, the target torque of the right wheel motor and the regenerative braking distribution amount or the hydraulic braking distribution amount.

[0090] The receiving module 14 is used to receive a target vector with time stamp alignment based on the MCU, and to attach a timeout or retransmission strategy to the target vector;

[0091] The execution module 15 is used to execute a timeout or re-trigger strategy, and to perform limited slip and torque tracking based on the target torque of the left wheel motor, the target torque of the right wheel motor and the regenerative braking distribution amount or the hydraulic braking distribution amount.

[0092] In some optional embodiments, the acquisition module 11 includes:

[0093] The observation unit is used to identify low-attached signals by fusing motor electrical inputs with wheel speed based on an anti-interference slip observer or an attached observer.

[0094] The estimation unit is used to estimate dynamic axle loads based on longitudinal acceleration, lateral acceleration, suspension updates of the front and rear axles, pressure updates of the front and rear axles, and left and right wheel normal loads, and to construct an axle load transfer model.

[0095] In some alternative embodiments, the optimization module 12 includes:

[0096] A constraint unit is used to minimize the updated MPC weight matrix and to constrain the MPC weight matrix. The expression for minimizing the updated MPC weight matrix is:

[0097] J=J stability +J energy +J smooth ;

[0098] In the formula, J Let represent the objective function of the overall optimization problem. J stability This represents the stability-related cost term. J energy This represents the cost associated with energy recovery. J smooth This represents the cost associated with smoothness / actuator wear.

[0099] In some alternative embodiments, the receiving module 14 includes:

[0100] The sending unit is used to send the time-aligned target vector to the left and right MCUs via the VCU.

[0101] In some alternative embodiments, the execution module 15 includes:

[0102] The monitoring unit is used to monitor the link latency, packet loss rate, and sensor quality when executing a timeout or retransmission strategy, and to determine whether the execution exceeds the limits.

[0103] The judgment unit is used to determine if the execution exceeds the limit, and then to perform limited slip degradation on the left wheel motor and the right wheel motor and to reduce the demand on the balance torque.

[0104] The collaborative distribution system based on wheel-side electric drive bridge provided in the second embodiment of the present invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0105] Example 3

[0106] The present invention also proposes a vehicle, please refer to [link / reference]. Figure 3 The vehicle shown is a third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, it implements the cooperative allocation method based on the wheel-side electric drive axle as described above.

[0107] In practical implementation, the processor 20 collects vehicle operating conditions and performs anti-interference observation and estimation on the vehicle to identify low-level signals;

[0108] The processor 20 calculates risk indicators and updates the MPC weight matrix online based on the operating conditions and the low-level signal, and optimizes the updated MPC weight matrix.

[0109] The processor 20 solves the longitudinal driving force of the entire axle and the target sway torque of the vehicle in the same optimization domain based on the optimized MPC weight matrix, and decomposes the longitudinal driving force of the entire axle and the target sway torque into the target torque of the left wheel motor, the target torque of the right wheel motor and the regenerative braking distribution amount or the hydraulic braking distribution amount.

[0110] The processor 20 receives a target vector with time stamp alignment based on the MCU and includes a timeout or retransmission strategy in the target vector;

[0111] The processor 20 executes a timeout or retransmission strategy and performs limited slip and torque tracking based on the target torque of the left wheel motor, the target torque of the right wheel motor, and the regenerative braking allocation or the hydraulic braking allocation.

[0112] In some embodiments, the processor 20 may be an electronic control unit (ECU), a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program code stored in the memory 10 or process data, such as executing access restriction programs.

[0113] The memory 10 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of the vehicle, such as the vehicle's hard disk. In other embodiments, the memory 10 may be an external storage device of the vehicle, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 10 may include both internal and external storage devices. The memory 10 can be used not only to store application software and various types of data installed in the vehicle, but also to temporarily store data that has been output or will be output.

[0114] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the vehicle. In other embodiments, the vehicle may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0115] This invention also proposes a readable storage medium storing a computer program that, when executed by a processor, implements the cooperative allocation method based on the wheel-side electric drive bridge described above.

[0116] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0117] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0118] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A cooperative distribution method based on a wheel-side electric drive axle, characterized in that, The method comprises: Collecting vehicle working conditions and performing anti-interference observation and estimation on the vehicle to identify low adhesion signals, which specifically comprises: Fusing motor electricity and wheel speed based on an anti-interference slip observer or an adhesion observer to identify low adhesion signals; Performing dynamic axle load estimation based on longitudinal acceleration, lateral acceleration, suspension updated front and rear axles, pressure updated front and rear axles, and left and right wheel normal loads, and constructing an axle load transfer model; Calculating a risk index and online updating an MPC weight matrix based on the working conditions and the low adhesion signals, and optimizing the updated MPC weight matrix, which specifically comprises: Minimizing the updated MPC weight matrix and constraining the MPC weight matrix, and the expression of the minimized updated MPC weight matrix is: J=J stability +J energy +J smooth ； wherein J a cost function representing the overall optimization problem, J stability a cost function representing the stability related cost term, J energy a cost function representing the energy recovery related cost term, J smooth a cost function representing the smoothness / actuator wear related cost term; Solving the whole axle longitudinal driving force and the target heave moment of the vehicle in the same optimization domain based on the optimized MPC weight matrix, and decomposing the whole axle longitudinal driving force and the target heave moment into left wheel motor target torque, right wheel motor target torque, and regenerative braking distribution or hydraulic braking distribution; Receiving a target vector aligned with a time scale based on MCU, and attaching a timeout or retransmission strategy to the target vector; Performing the timeout or retransmission strategy, and performing slip limiting and torque tracking based on the left wheel motor target torque, the right wheel motor target torque, and the regenerative braking distribution or the hydraulic braking distribution.

2. The collaborative distribution method based on the electric wheel-side drive axle according to claim 1, characterized in that, The working conditions include VCU reading vehicle speed, steering wheel angle, pedal opening, IMU, wheel speed, axle pressure proxy, sprung mass proxy, temperature, and SOC.

3. The collaborative distribution method based on the electric wheel-side drive axle according to claim 1, characterized in that, The step of receiving a target vector aligned with a time scale based on MCU comprises: Downlinking the target vector aligned with the time scale to the left MCU and the right MCU through VCU.

4. The collaborative distribution method based on the electric wheel-side drive axle according to claim 1, characterized in that, After the step of performing slip limiting and torque tracking based on the left wheel motor target torque, the right wheel motor target torque, and the regenerative braking distribution or the hydraulic braking distribution, the method further comprises: Monitoring link delay, packet loss rate, and sensing quality of performing the timeout or retransmission strategy, and determining whether the performance is out of limit; If yes, performing slip limiting degradation on the left wheel motor and the right wheel motor, and performing demand relaxation on the heave moment.

5. A cooperative distribution system based on a wheel-side electric drive axle, characterized in that The system comprises: A collection module for collecting vehicle working conditions and performing anti-interference observation and estimation on the vehicle to identify low adhesion signals; The collection module comprises: An observation unit for fusing motor electricity and wheel speed based on an anti-interference slip observer or an adhesion observer to identify low adhesion signals; An estimation unit for performing dynamic axle load estimation based on longitudinal acceleration, lateral acceleration, suspension updated front and rear axles, pressure updated front and rear axles, and left and right wheel normal loads, and constructing an axle load transfer model; An optimization module for calculating a risk index and online updating an MPC weight matrix based on the working conditions and the low adhesion signals, and optimizing the updated MPC weight matrix; The optimization module comprises: A constraint unit for minimizing the updated MPC weight matrix and constraining the MPC weight matrix, and the expression of the minimized updated MPC weight matrix is: J=J stability +J energy +J smooth ； wherein J represents an objective function of the overall optimization problem, J stability represents a stability-related cost term, J energy represents an energy recovery-related cost term, J smooth represents a smoothness / actuator wear-related cost term; a decomposition module configured to solve the whole axle longitudinal driving force and the target pitch moment of the vehicle in the same optimization domain based on the optimized MPC weight matrix, and decompose the whole axle longitudinal driving force and the target pitch moment into a left wheel motor target torque, a right wheel motor target torque, and a regenerative braking distribution or a hydraulic braking distribution; a receiving module configured to receive the target vector aligned with the MCU receiving time scale, and attach a timeout or retransmission strategy to the target vector; an executing module configured to execute the timeout or retransmission strategy, and perform slip limiting and torque tracking based on the left wheel motor target torque, the right wheel motor target torque, and the regenerative braking distribution or the hydraulic braking distribution.

6. A readable storage medium, having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the cooperative distribution method based on the wheel-side electric drive axle according to any one of claims 1 to 4.

7. A vehicle comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the cooperative distribution method based on the wheel-side electric drive axle according to any one of claims 1 to 4.

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