Integrated driving module of axial flux motor, control method and vehicle

Abstract

The invention relates to the technical field of axial flux motors and vehicle driving, and discloses an integrated driving module of an axial flux motor, a control method and a vehicle. The integrated driving module comprises an integrated electric driving design module, a magnetic field optimization and energy loss control module, a thermal management and temperature rise control module, a mechanical stress optimization and fatigue analysis module and a control method and optimization module; the control method comprises the steps that the double-stator single-rotor motor is designed, and the controller and the speed reducer are integrated; the arrangement of the magnets is optimized; establishing a heat-fluid-solid coupling model; performing structure simulation optimization based on a maximum principal stress criterion; adjusting parameters in real time according to error changes. According to the method, a mechanical stress superposition analysis and principal stress optimization method under multiple working conditions is introduced and fused, so that weak links of the structure can be identified and shape reconstruction can be carried out in an early design stage. The technical effect that the stress peak value is effectively reduced while the compact structure size is kept is achieved.

Description

Technical Field

[0001] The present application relates to the field of axial flux motors and vehicle drive technology, and specifically to an integrated drive module, control method, and vehicle for an axial flux motor. Background Art

[0002] With the growing demand for new energy vehicles, electric aircraft, and high-performance industrial equipment, axial flux motors, with their short axial length, high power density, and excellent heat dissipation, are becoming a key research direction for next-generation drive technologies. In particular, in applications requiring miniaturization and high performance, traditional radial flux motors are increasingly facing challenges with bulk and low efficiency. Axial flux topology, with its unique structural advantages, is gaining increasing attention.

[0003] In existing axial flux motor drive module technology applications, the overall machine design is usually completed by independently developing each sub-module. For example, the magnetic circuit design, cooling structure design, mechanical strength design, and control strategy development are often carried out separately by different groups. The magnetic circuit parameters are optimized using finite element software, and the cooling system simply superimposes liquid cooling or air cooling paths. The mechanical structure uses conventional static analysis for strength verification, and the control system adopts a standard current loop and acceleration loop cascade PI control method.

[0004] However, due to the independent design of each module in the existing axial flux motor drive module technology, the optimization of magnetic field distribution often ignores the structural stress concentration area, and the thermal management strategy fails to accurately match the internal loss model of the motor, resulting in the risk of efficiency loss, local overheating and even early fatigue damage under extreme operating conditions. At the same time, traditional mechanical strength design often uses static loading assumptions and lacks in-depth modeling of the dynamic stress evolution process under complex operating load overlap, which can easily lead to distortion of life prediction. Therefore, the present invention provides an integrated drive module, control method and vehicle for an axial flux motor to address the shortcomings of the existing technology. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the purpose of this application is to provide an integrated drive module, control method and vehicle for an axial flux motor, which solves the problems of modular fragmented design, insufficient performance coordination, poor adaptability to extreme working conditions and inaccurate fatigue life prediction existing in the existing axial flux motor drive module.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an integrated drive module for an axial flux motor, comprising: An integrated electric drive design module for designing an axial flux motor, controller, and reducer into a compact, integrated module, optimizing the motor's power density; The magnetic field optimization and energy loss control module is used to optimize the internal magnetic field distribution of the axial flux motor and minimize magnetic field inhomogeneity. The thermal management and temperature rise control module is used to optimize the thermal field of the axial flux motor and ensure that the motor temperature is maintained stable through an efficient heat dissipation system at high power output. Mechanical stress optimization and fatigue analysis module, used to optimize the mechanical structure of axial flux motors through finite element analysis to reduce stress concentration; The control method and optimization module is based on the distributed optimal control theory and adaptive fuzzy control algorithm to adjust the operating state of the axial flux motor in real time.

[0007] Preferably, the integrated electric drive design module includes: The motor structure unit adopts a dual-stator single-rotor structure, and the power density is improved by optimizing the layout of the rotor permanent magnets; Controller integrated unit, the controller is installed inside the motor housing and forms a heat conduction path with the housing through the metal base plate; reducer coupling unit, the reducer is directly coaxially connected to the motor output shaft through a flange.

[0008] Preferably, the magnetic field optimization and energy loss control module includes: The magnetic field modeling unit establishes a motor magnetic field simulation model centered on the optimization of magnetic permeability distribution and determines the uniformity of the magnetic field distribution through finite element analysis. The magnetic flux optimization unit optimizes the rotor magnet arrangement by the maximum power output condition. Its maximum output power objective function is: Among them, P out is the motor output power; ω is the motor angular velocity; j is the imaginary unit; E m is the induced electromotive force, R is the armature resistance, and L is the armature inductance; The effective magnetic flux control unit adjusts the armature slot parameters to increase the effective magnetic flux ratio, reduce magnetic leakage and reduce energy loss.

[0009] Preferably, the thermal management and temperature rise control module includes: Heat dissipation structure unit, the cooling system is integrated into the motor stator, and the cooling channel is in direct contact with the stator core to achieve rapid heat exchange; The temperature rise analysis unit is used to establish a thermal-fluid-solid coupling simulation model and optimize the cooling path. The heat dissipation objective function is: min(∫ S h(x,y)·T(x,y)dS); Where h(x,y) is the local heat dissipation coefficient of the heat dissipation surface; T(x,y) is the local temperature distribution of the heat dissipation surface; S is the total area of ​​the heat dissipation surface; dS represents the area element in the integration area; min(·) indicates that the goal of the optimization problem is to minimize the thermal resistance effect or heat flux burden of the entire heat dissipation system; The dynamic thermal management unit uses phase change materials to assist in heat dissipation and is used to control the temperature rise of the motor under short-term high heat loads.

[0010] Preferably, the mechanical stress optimization and fatigue analysis module includes: The stress simulation unit uses the finite element method to analyze the static and dynamic stress distribution of the motor housing and rotor support structure; the stress minimization unit optimizes the mechanical structure according to the maximum principal stress criterion, and its optimization objective function is: min(∫ V σ max (x,y,z)dV); Among them, σ max (x, y, z) is the maximum principal stress at any point in the structure; V is the overall structural volume of the object under stress analysis; dV represents the volume element; min(·) indicates that the goal of the optimization problem is to minimize the thermal resistance effect or heat flux burden of the entire heat dissipation system; The fatigue life analysis unit optimizes the fatigue life of key structures through multi-condition cyclic load spectra combined with the Goodman correction criterion.

[0011] Preferably, the control method and optimization module include: State observation unit, used to estimate motor torque, current, and voltage state information using multi-sensor data fusion; Intelligent tuning unit, used to combine deep reinforcement learning algorithm to perform online update of control strategy.

[0012] Preferably, the induced electromotive force satisfies the following relationship: E m =B peak ·l eff ·ω·N; Among them, B peak is the peak value of effective magnetic flux density; l eff is the effective magnetic path length; ω is the motor angular velocity; N is the number of turns of the armature winding; E m is the induced electromotive force.

[0013] Preferably, the stress minimization unit performs stress simulation on the rotor and stator of the motor by finite element analysis method, evaluates the safety of the motor structure according to the multi-axis stress state, and calculates the equivalent stress σ using the Von-Mises stress criterion. eq , the formula is: Among them, σ1, σ2, and σ3 are the principal stress components in the motor rotor and stator structures; σ eq is the equivalent stress.

[0014] A control method for an axial flux motor is also provided, comprising the following steps: The design adopts an axial flux motor with a dual-stator and single-rotor structure, and integrates the controller and reducer into one; Optimize the magnetic flux distribution to maximize the output power of the axial flux motor, and design the magnet arrangement based on the maximum power output condition; build a thermal-fluid-solid coupling model to optimize the heat dissipation path of the axial flux motor, using a combination of coolant and phase change material cooling; perform finite element stress simulation on the stator, rotor, and housing structures, and optimize the mechanical structure based on the maximum principal stress criterion; An adaptive fuzzy control method integrated with deep learning is used to adjust the control parameters online according to the error and its rate of change to achieve dynamic optimization of the system.

[0015] Also provided is a vehicle comprising: one or more processors; a memory, connected to the processor, and configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the above-mentioned control method for an axial flux motor.

[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. By integrating mechanical stress superposition analysis under multiple operating conditions with principal stress optimization methods, this invention can identify structural weaknesses and reshape them early in the design phase. This effectively reduces peak stresses while maintaining a compact structure. Compared to existing approaches that rely on empirical assessments and post-repair structural reinforcement, this approach addresses the issues of uncontrollable local stress concentration and unpredictable fatigue life.

[0017] 2. The present invention utilizes a state estimation design that combines an extended Kalman filter with a dual redundant position sensing mechanism, enabling the motor to maintain precise control response even in the event of severe dynamic fluctuations or sensor failure, thereby achieving high-confidence identification and fault-tolerant support for the motor's critical operating states.

[0018] 3. This invention leverages a control decision network trained using a deep reinforcement learning algorithm, enabling the system to automatically optimize its current distribution and modulation strategies under diverse load conditions. This not only improves control accuracy but also energy efficiency. Compared to traditional control methods that use fixed PID parameters, this solution solves the problems of frequent manual parameter adjustments and slow response to changing operating conditions.

[0019] 4. This invention tightly integrates fatigue life analysis with topology optimization, simultaneously improving the structural rigidity and service life of motor components. In actual operation, life prediction can be completed without relying on complex test and calibration cycles. Existing methods often compensate for material failure risks through post-processing, which is not only time-consuming but also prone to large errors. This invention effectively solves the problem of prediction lag and disconnection between feedback response. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a diagram of the integrated drive module architecture of an axial flux motor of the present application; Figure 2 is a flow chart of the method of this application; Figure 3 It is a schematic diagram of the vehicle of this application. DETAILED DESCRIPTION

[0021] The following is combined with Figure 1 -Attached Figure 3 , further details of this application are given.

[0022] Please see the attached Figure 1 , an embodiment of the present invention provides an integrated drive module for an axial flux motor, comprising the following modules: an integrated electric drive design module for designing the axial flux motor, a controller, and a reducer into a compact integrated module to optimize the power density of the motor; The magnetic field optimization and energy loss control module is used to optimize the internal magnetic field distribution of the axial flux motor and minimize magnetic field inhomogeneity. The thermal management and temperature rise control module is used to optimize the thermal field of the axial flux motor and ensure that the motor temperature is maintained stable through an efficient heat dissipation system at high power output. Mechanical stress optimization and fatigue analysis module, used to optimize the mechanical structure of axial flux motors through finite element analysis to reduce stress concentration; The control method and optimization module is based on the distributed optimal control theory and adaptive fuzzy control algorithm to adjust the operating state of the axial flux motor in real time.

[0023] In this embodiment, the integrated electric drive design module is used to design and package the axial flux motor, electronic control unit, and mechanical transmission components in a highly integrated manner. This module primarily comprises the axial flux motor, an integrated controller structure, and a reducer assembly coupled to the output. These three components are tightly integrated to form a platform integrating mechanical, electrical, and thermal functions.

[0024] In this embodiment, the axial flux motor employs a dual-stator, single-rotor structure. Specifically, the stators are located on either side of the motor, while the rotor is symmetrically positioned on the central axis. This structure achieves dual-sided electromagnetic force synthesis within a limited axial space, enhancing driving capability and improving magnetic flux path utilization efficiency.

[0025] Typically, the stator structure is constructed from laminated high-permeability silicon steel sheets, with a single sheet thickness of less than 0.2 mm to reduce eddy current losses. In some embodiments, the silicon steel sheets are 35WW300, which offers excellent magnetic permeability and low losses. The stator slots can be symmetrical or fan-shaped, and the winding coils utilize concentrated winding to improve the winding fill factor.

[0026] In one possible implementation, the rotor utilizes a radially magnetized permanent magnet array, preferably made of neodymium iron boron (NdFeB) with a magnetic energy product (BH_max) of at least 45 MGOe. Alternatively, the permanent magnets are embedded in the non-magnetic rotor disc slots using glue injection, with a magnet gap of less than 0.1 mm to ensure flux containment.

[0027] In this embodiment, the controller is housed within the motor housing, using a metal baseplate or ceramic copper-clad laminate for direct contact with the housing to rapidly dissipate heat. The controller's main power device is a silicon carbide MOSFET chip, with a switching frequency of no less than 20kHz, ensuring stable operation in high-temperature and high-voltage environments. The controller is directly connected to the stator windings via welding, avoiding the contact impedance issues associated with traditional connectors.

[0028] In some embodiments, the controller and motor share a common housing design, meaning the motor housing serves as both a stator support structure and a heat dissipation channel for the controller. Motor control signal lines are routed through a flexible PCB embedded within the housing, minimizing space interference and improving integration.

[0029] At the output end, the reducer in this embodiment is coupled to the motor output shaft via a coaxial flange structure, secured with evenly distributed bolts to ensure stable axial force transmission. The reducer is preferably a planetary gear mechanism with a transmission ratio range of 5:1 to 10:1, depending on the vehicle's torque requirements. Optionally, a floating pinion support structure is incorporated within the reducer to compensate for assembly errors and suppress shaft end vibration.

[0030] In order to ensure the overall assembly accuracy of the module, this embodiment adopts a segmented pre-assembly process. Specifically: First, assemble the double stators and rotors coaxially, and adjust the end cover gasket to control the air gap to about 0.3mm; Then place the controller circuit board in the slot inside the shell, and make thermal contact with the bottom shell through thermal conductive glue; Then connect the reducer to the motor output shaft by flange connection and tighten the connecting bolts; Finally, a test voltage is applied to check whether the stator winding inductance, the Hall position sensor status, and the controller PWM response are normal.

[0031] In addition, to improve structural adaptability, in some embodiments, the controller power devices can be replaced with gallium nitride (GaN) devices to further increase the switching frequency; the reducer can also be replaced with a harmonic drive unit or a helical gearbox structure to adapt to different load conditions.

[0032] As for the magnetic field optimization and energy loss control module, in this embodiment, the magnetic field optimization and energy loss control module includes three parts: a magnetic field modeling unit, a magnetic flux optimization unit, and an effective magnetic flux control unit. The sub-modules cooperate with each other in function to form a closed-loop optimization mechanism.

[0033] In this embodiment, the magnetic field modeling unit first constructs an electromagnetic finite element simulation environment based on the three-dimensional structural model of the motor. Generally, the simulation model includes a complete geometric model of the stator core, rotor body, air gap region, and winding entities, and assigns nonlinear magnetic properties to the materials to reflect saturation effects.

[0034] In one possible implementation, magnetic field modeling uses the magnetic permeability distribution parameter as the control variable. By setting the magnetic permeability gradient area, the magnetic flux concentration effect is simulated. Combined with the multi-working magnetic field conduction state analysis, the magnetic flux distribution uniformity index under different speed and load conditions is calculated.

[0035] In this embodiment, the magnetic flux optimization unit optimizes the rotor magnet arrangement according to the maximum output power objective function, and the objective function is defined as follows: Among them, P out is the motor output power; E m is the induced electromotive force; R is the armature resistance; L is the armature inductance; ω is the motor angular velocity; j is the imaginary unit.

[0036] This formula is used to optimize the rotor permanent magnet layout for power given the stator winding structure and controller electrical parameters. Magnet arrangements are not limited to symmetrical and even distribution; unequal spacing or variable pole lengths can be used to improve magnetic flux coupling and minimize magnetic flux leakage interference between adjacent poles.

[0037] Specifically, in this embodiment, the induced electromotive force E m The calculation is based on the following physical relationship: E m =B peak ·l eff ·ω·N; Among them, B peakis the peak value of effective magnetic flux density; l eff : is the effective magnetic path length; ω is the motor angular velocity; N is the number of turns of the armature winding; E m is the induced electromotive force.

[0038] This calculation relationship is embedded in the controller scheduling module as the core quantitative indicator for dynamic power regulation. By sensing the difference between the speed ω and the actual winding induced voltage in real time, closed-loop power regulation can be achieved.

[0039] In this embodiment, the effective magnetic flux control unit is used to adjust the parameters of the armature slot structure to further improve the effective magnetic flux ratio. Generally, the slot shape is designed to be trapezoidal or composite beveled. The depth of the slot bottom is coupled with the arrangement direction of the magnetic pole pieces, which affects the magnetic flux convergence.

[0040] In specific designs, different slot width ratios and slot bottom chamfer angles can be set, and multi-parameter batch analysis can be performed using electromagnetic simulation software to identify the slot configuration that maximizes the magnetic flux fill rate. In some embodiments, the magnetic pole width is designed to be 1.1-1.3 times the stator tooth width, achieving higher magnetic flux symmetry under low-speed, high-torque conditions.

[0041] In one possible implementation, the flux optimization and induced electromotive force calculation in the module are also used to adjust the controller PWM modulation strategy and participate in the control model optimization as a feedback signal source. The specific adjustment range is based on the real-time estimated E m and its rate of change are determined.

[0042] The thermal management and temperature rise control module, a key subunit within the integrated drive system, is responsible for thermal management and temperature control of the motor system under high load and high power output conditions. This ensures that the motor's core components maintain thermal equilibrium during extended operation, preventing local overheating that could lead to performance degradation or structural damage. The thermal management and temperature rise control module comprises a heat dissipation structure unit, a temperature rise analysis unit, and a dynamic thermal management unit, all working together to form a complete closed-loop temperature control system.

[0043] The heat dissipation structure unit achieves rapid heat transfer by integrating cooling channels inside the motor stator. Generally, the cooling channels are distributed in an annular or serpentine form and are directly attached to the outer surface of the stator core to maximize the heat exchange area. In some embodiments, the cooling channels are made of high thermal conductivity aluminum alloy pipes, and the internal flow medium is preferably a water-based coolant or a fluorinated liquid, with a flow rate controlled within the range of 1.5 to 2.5 L / min to ensure that the heat in the stator winding area can be discharged in a timely manner.

[0044] Specifically, a thermal interface material (TIM) fills the gap between the stator core and the cooling channels to reduce contact thermal resistance and improve overall heat transfer efficiency. In one possible implementation, the cooling system inlet and outlet are located on opposite ends of the motor housing, creating a short, straight-through arrangement to reduce fluid pressure drop and improve system response speed.

[0045] In this embodiment, the temperature rise analysis unit is established based on a thermal-fluid-solid coupling simulation model. Generally, this simulation model comprehensively considers the internal heat sources of the motor, the coolant flow field, and the heat conduction effects of the stator and housing, using the finite volume method (FVM) to solve the temperature field distribution. In some embodiments, the model includes stator winding copper loss, core iron loss, and rotor surface eddy current loss as heat source inputs, and defines coolant heat exchange boundary conditions.

[0046] The temperature rise analysis process takes minimizing the total thermal resistance of the heat dissipation system as the optimization goal. The specific optimization function expression is as follows: min(∫ S h(x,y)·T(x,y)dS); Where h(x, y) is the local heat dissipation coefficient of the heat dissipation surface; T(x, y) is the local temperature distribution of the heat dissipation surface; S is the total area of ​​the heat dissipation surface; dS represents the area element in the integration region; and min(·) indicates that the goal of the optimization problem is to minimize the thermal resistance effect or heat flux burden of the entire heat dissipation system.

[0047] In one possible implementation, heat dissipation optimization performs parametric scanning by adjusting the cooling channel diameter, flow rate, and inlet and outlet position combinations to screen out a design solution that can keep the maximum stator temperature below 120°C under given operating conditions.

[0048] In this embodiment, the dynamic thermal management unit incorporates phase change material (PCM) to assist in heat dissipation under high heat load conditions. Typically, PCM is encapsulated between the stator core and the motor housing to form a composite heat dissipation layer. When the internal temperature of the motor rises to the phase transition point, the PCM absorbs latent heat and transforms into a liquid, effectively suppressing short-term temperature peaks.

[0049] Specifically, some embodiments utilize a paraffin-based PCM material with a melting point between 60°C and 70°C and a heat capacity greater than 150 kJ / kg. The packaging material is an aluminum foil composite structure to improve thermal response and prevent material leakage. During short bursts of high power output, the phase change layer can absorb transient heat exceeding 20 kJ, thereby slowing the temperature rise of the stator winding.

[0050] As an option, the dynamic thermal management unit can also be combined with the controller's built-in temperature sensor to monitor the temperature changes of the stator core and winding in real time. When it detects that the temperature rise rate exceeds the set threshold, it automatically increases the cooling pump speed and the coolant flow rate to enhance the heat exchange capacity.

[0051] In this embodiment, the mechanical stress optimization and fatigue analysis module is used to systematically reduce stress concentration in the mechanical structure and extend the fatigue life of core components. This module not only ensures the long-term stable operation of the motor under high load and variable load conditions, but also serves as a key support link for achieving both lightweight and high strength of the drive module. The mechanical stress optimization and fatigue analysis module mainly includes a stress simulation unit, a stress minimization unit, and a fatigue life analysis unit. Each sub-unit works in conjunction with each other to form a complete structural reliability assessment and optimization system.

[0052] In this embodiment, the stress simulation unit performs modeling and calculations based on finite element analysis (FEA). Generally, stress simulation covers key stress-bearing locations, such as the motor housing, rotor support structure, and connection flanges. During the simulation process, a multi-load superposition analysis is employed, taking into account the combined effects of static loads, deadweight loads, centrifugal loads, and thermal expansion loads.

[0053] Alternatively, the material constitutive relationship is set as an isotropic elastic body during modeling, and preliminary thermal stress boundary conditions are introduced to simulate the effects of material property changes in high-temperature environments. In some embodiments, rotational inertia loads are also introduced into the simulation model of the rotor support structure to accurately capture the stress fluctuation effects caused by speed changes.

[0054] In this embodiment, the stress minimization unit is optimized based on the maximum principal stress criterion, and the optimization objective function is set as follows: min(∫ V σ max (x,y,z)dV); Among them, σ max (x, y, z) is the maximum principal stress at any point in the structure; V is the overall structural volume of the object under analysis; dV represents the volume element; and min(·) indicates that the goal of the optimization problem is to minimize the thermal resistance effect or heat flux burden of the entire heat dissipation system.

[0055] Using the above objective function, multiple rounds of topology optimization and local geometric parameter optimization were performed. In some embodiments, a chamfered transition design is employed at the connection between the rotor end plate and the output shaft, with a chamfer radius controlled between 2.0 and 4.0 mm to effectively reduce stress concentration caused by sharp transitions. Furthermore, the bolt hole radius in the housing bolt connection area is optimized to improve local fatigue strength.

[0056] In this embodiment, the fatigue life analysis unit performs fatigue life prediction based on a multi-condition cyclic load spectrum combined with the Goodman correction criterion. Generally, a fatigue damage accumulation model under a biaxial stress state is used, taking into account the uneven distribution of stress amplitudes and cycle times between different conditions.

[0057] In order to accurately quantify the multiaxial fatigue life, the Von-Mises stress criterion is used to convert the equivalent stress. The equivalent stress σ eq The calculation formula is: Among them, σ1, σ2, σ3: are the three principal stress components in the structure; σ eq : is the equivalent stress.

[0058] In one possible implementation, fatigue life analysis models and analyzes different operating stages (such as high-speed operation, frequent start-stop, and overload impact) separately, and ultimately superimposes the damage of each stage according to the Palmgren-Miner linear damage accumulation law to determine the expected life of the overall structure.

[0059] As an option, the fatigue limit of key parts is set to a safety factor of 1.5 or above, and structural redundancy design is introduced to ensure that the module can still maintain basic functions in the event of a single point failure, thereby improving the fault tolerance of the overall system.

[0060] Regarding the control method and optimization module, in this embodiment, on the basis of achieving structural integration, magnetic field and thermal field optimization, and mechanical stress enhancement, in order to further improve the dynamic response capability and stability of the axial flux motor drive module under complex operating conditions, it undertakes the tasks of real-time observation of motor status, adaptive adjustment of control strategy, and dynamic optimization of overall performance. It is an important supporting unit for realizing intelligent drive and ensuring stable and efficient operation of the system.

[0061] The control method and optimization module mainly includes a state observation unit and an intelligent tuning unit, which work together to form a complete adaptive control closed loop.

[0062] In this embodiment, the state observation unit is used to fuse multi-source data and estimate the core state parameters of the motor in real time. Generally, the observation objects include the motor's output torque, stator current, motor terminal voltage, and related auxiliary state quantities such as stator winding temperature and rotor position change rate.

[0063] Specifically, the current and voltage sampling signals are amplified through high-frequency isolation and then fed into the state estimation module within the controller. In some embodiments, an extended Kalman filter (EKF) algorithm is used to jointly estimate rotor position and speed, improving position accuracy under low-speed and high-frequency conditions and suppressing interference from sampling noise.

[0064] In one possible implementation, the state observation unit not only performs traditional physical quantity sampling but also introduces multi-sensor redundant configuration, such as joint calculation through dual-channel Hall position sensors and inductive encoders, to improve the system's anti-fault observation capability.

[0065] In this embodiment, the intelligent tuning unit is responsible for dynamically optimizing the motor control strategy based on the data provided by the state observation unit. Generally, the control strategy is initially set by a model-based distributed optimal control method, and is adaptively adjusted during operation based on load changes and changes in its own parameters.

[0066] As an option, the intelligent tuning unit integrates a deep reinforcement learning (DRL) algorithm module to build a control strategy decision network. The training goal is to maximize the overall performance indicators of the system, including but not limited to minimizing speed tracking error, minimizing energy consumption, and minimizing temperature rise rate.

[0067] Specifically, in some embodiments, the DRL algorithm uses a temporal difference (TD) update mechanism to construct the Bellman expectation equation using the current state, action, and immediate reward, and continuously iteratively updates the control parameter weights. This approach enables the self-evolution of optimal control in a nonlinear, strongly coupled, and multi-disturbance environment.

[0068] In one possible implementation, the control instructions output by the intelligent tuning unit may include PWM duty cycle adjustment, adaptive changes to current loop PI parameters, and flux linkage observation correction compensation. The control instruction update cycle can be set to every 2ms, balancing real-time performance and computing resource utilization.

[0069] Typically, the intelligent tuning unit also has built-in fault self-healing logic. When it detects abnormal fluctuations in key control parameters (such as a sudden increase in current command exceeding the standard or an abnormal flux offset), it automatically activates a predefined fault-tolerant control strategy to maintain basic system functionality and simultaneously issues a diagnostic signal, prompting external maintenance action.

[0070] The control method of an axial flux motor described below and the integrated driving module of an axial flux motor described above may refer to each other.

[0071] Please see the attached Figure 2 The present invention also provides a control method for an axial flux motor, comprising the following steps: S1. Design an axial flux motor with a dual-stator, single-rotor structure, and integrate the controller and reducer. S2. Optimize the magnetic flux distribution to maximize the output power of the axial flux motor and design the magnet arrangement based on the maximum power output condition; S3. Build a thermal-fluid-solid coupling model to optimize the heat dissipation path of the axial flux motor, using a combination of coolant and phase change material cooling; S4. Perform finite element stress simulation on the stator, rotor and housing structures, and optimize the mechanical structure based on the maximum principal stress criterion; S5. Adopting the adaptive fuzzy control method integrated with deep learning, the control parameters are adjusted online according to the error and its rate of change to achieve dynamic optimization of the system.

[0072] In step S1, a symmetrical dual-stator, single-rotor magnetic flux path design is constructed to improve the motor's output torque density and symmetry, effectively reducing end magnetic leakage and vibration. Furthermore, the controller and reducer utilize an integrated structural layout to reduce external connectors, simplify installation, optimize heat dissipation paths, and enhance overall structural compactness and system response speed.

[0073] For step S2, based on the principle of maximum energy density and uniformity of magnetic flux distribution between the stator and rotor air gaps, the shape, pole arc coefficient and arrangement of the permanent magnets are optimized. The magnet distribution structure is optimized through simulation iteration, so that the motor can run longer in the high-efficiency range, thereby maximizing the output power per unit volume.

[0074] In step S3, a complete heat-fluid-solid coupling simulation model is constructed by establishing a distribution model for heat loss within the motor, combining fluid thermodynamics with solid heat conduction equations. This model simulates the temperature distribution response under different cooling strategies. By integrating high-thermal-conductivity phase change materials (PCMs) with a closed-loop coolant channel design, peak heat flux density is rapidly reduced and thermal capacity utilization is increased.

[0075] In step S4, a complete motor structural model is built using a 3D modeling platform. Magnetic and thermal coupling loads are introduced, and finite element simulation analysis is performed under multiple operating conditions to identify structural hotspots and fatigue-sensitive areas. The structural contour is reconstructed based on the maximum principal stress criterion and equivalent stress distribution. The rib placement and connection transition geometry are optimized to improve the overall strength and fatigue life of the structure.

[0076] In step S5, a fuzzy controller based on error state feedback incorporates a deep learning strategy to self-update fuzzy rules and optimize parameter weights. During operation, the controller continuously monitors the input error and its changing trend. In combination with historical operating status data, it dynamically adjusts the output regulation factor, improving its adaptability to the system's nonlinear and time-varying characteristics and achieving efficient and robust real-time control.

[0077] The method of this embodiment can be used to execute the above system embodiment. Its principles and technical effects are similar and will not be described in detail here.

[0078] The vehicle described below and the control method of the axial flux motor described above can refer to each other.

[0079] Please see the attached Figure 3 ,The present invention also provides a vehicle comprising: one or more processors; a memory, coupled to the processor, for storing one or more programs; When one or more programs are executed by one or more processors, the one or more processors can implement the control method of an axial flux motor as described above.

[0080] The processor is used to control the overall operation of the vehicle to complete all or part of the steps of the above-mentioned control method for an axial flux motor. The memory is used to store various types of data to support the operation of the vehicle. Such data may include, for example, instructions for any application or method operated on the vehicle, as well as application-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0081] The examples of this specific embodiment are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, any equivalent changes made based on the structure, shape, and principle of this application should be included in the scope of protection of this application.

Claims

1. An integrated drive module for an axial flux motor, characterized in that: include: An integrated electric drive design module for designing an axial flux motor, controller, and reducer into a compact, integrated module, optimizing the motor's power density; The magnetic field optimization and energy loss control module is used to optimize the internal magnetic field distribution of the axial flux motor and minimize magnetic field inhomogeneity. The thermal management and temperature rise control module is used to optimize the thermal field of the axial flux motor and ensure that the motor temperature is maintained stable through an efficient heat dissipation system at high power output. The mechanical stress optimization and fatigue analysis module is used to optimize the mechanical structure of the axial flux motor through finite element analysis; the control method and optimization module, which is based on distributed optimal control theory and adaptive fuzzy control algorithm, adjusts the operating state of the axial flux motor in real time.

2. The integrated drive module of an axial flux motor according to claim 1, characterized in that: The integrated electric drive design module includes: The motor structure unit adopts a dual-stator single-rotor structure, and the power density is improved by optimizing the layout of the rotor permanent magnets; The controller is installed inside the motor housing and forms a heat conduction path with the housing through the metal substrate; Reducer coupling unit, the reducer is directly coaxially connected to the motor output shaft through a flange.

3. The integrated drive module of an axial flux motor according to claim 1, characterized in that: The magnetic field optimization and energy loss control module includes: The magnetic field modeling unit establishes a motor magnetic field simulation model centered on the optimization of magnetic permeability distribution and determines the uniformity of the magnetic field distribution through finite element analysis. The magnetic flux optimization unit optimizes the rotor magnet arrangement by the maximum power output condition. Its maximum output power objective function is: Among them, P out is the motor output power; ω is the motor angular velocity; j is the imaginary unit; E m is the induced electromotive force, R is the armature resistance, and L is the armature inductance; The effective magnetic flux control unit adjusts the armature slot parameters to increase the effective magnetic flux ratio, reduce magnetic leakage and reduce energy loss.

4. The integrated drive module of an axial flux motor according to claim 1, characterized in that: The thermal management and temperature rise control module includes: Heat dissipation structure unit, the cooling system is integrated into the motor stator, and the cooling channel is in direct contact with the stator core; The temperature rise analysis unit is used to establish a thermal-fluid-solid coupling simulation model and optimize the cooling path. The heat dissipation objective function is: min(∫ S h(x,y)·T(x,y)dS); Where h(x,y) is the local heat dissipation coefficient of the heat dissipation surface; T(x,y) is the local temperature distribution of the heat dissipation surface; S is the total area of ​​the heat dissipation surface; dS represents the area element in the integration area; min(·) indicates that the goal of the optimization problem is to minimize the thermal resistance effect or heat flux burden of the entire heat dissipation system; The dynamic thermal management unit uses phase change materials to assist in heat dissipation and is used to control the temperature rise of the motor under short-term high heat loads.

5. The integrated drive module of an axial flux motor according to claim 1, characterized in that: The mechanical stress optimization and fatigue analysis module includes: Stress simulation unit, using finite element method to analyze the static and dynamic stress distribution of motor housing and rotor support structure; The stress minimization unit optimizes the mechanical structure according to the maximum principal stress criterion, and its optimization objective function is: min(∫ V s max (x,y,z)dV); Among them, σ max (x, y, z) is the maximum principal stress at any point in the structure; V is the overall structural volume of the object under stress analysis; dV represents the volume element; min(·) indicates that the goal of the optimization problem is to minimize the thermal resistance effect or heat flux burden of the entire heat dissipation system; The fatigue life analysis unit optimizes the fatigue life of key structures through multi-condition cyclic load spectra combined with the Goodman correction criterion.

6. The integrated drive module of an axial flux motor according to claim 1, characterized in that: The control method and optimization module include: State observation unit, used to estimate motor torque, current, and voltage state information using multi-sensor data fusion; Intelligent tuning unit, used to combine deep reinforcement learning algorithm to perform online update of control strategy.

7. The integrated drive module of an axial flux motor according to claim 3, characterized in that: The induced electromotive force satisfies the following relationship: E m =B peak ·l eff ·ω·N; Among them, B peak is the peak value of effective magnetic flux density; l eff is the effective magnetic path length; ω is the motor angular velocity; N is the number of turns of the armature winding; E m is the induced electromotive force.

8. The integrated drive module of an axial flux motor according to claim 5, characterized in that: The stress minimization unit performs stress simulation on the rotor and stator of the motor by finite element analysis method, evaluates the safety of the motor structure according to the multi-axis stress state, and calculates the equivalent stress σ using the Von-Mises stress criterion. eq , the formula is: Among them, σ1, σ2, and σ3 are the principal stress components in the motor rotor and stator structures; σ eq is the equivalent stress.

9. A control method for an axial flux motor, applied to an integrated drive module of an axial flux motor according to claims 1-8, characterized in that: The following steps are involved: An axial flux motor with a dual-stator, single-rotor structure was designed, integrating the controller and reducer. The magnetic flux distribution was optimized to maximize the motor's output power, and the magnet layout was designed based on maximum power output. A thermal-fluid-solid coupling model was constructed to optimize the motor's heat dissipation path, using a combination of coolant and phase change material cooling. Perform finite element stress simulation on the stator, rotor and housing structures, and optimize the mechanical structure based on the maximum principal stress criterion; An adaptive fuzzy control method integrated with deep learning is used to adjust the control parameters online according to the error and its rate of change to achieve dynamic optimization of the system.

10. A vehicle comprising: one or more processors; a memory, connected to the processor, and configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the control method of an axial flux motor as described in claim 9 above.

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