Electric vehicle system based on combined motor driving

Abstract

The invention discloses an electric vehicle system based on combined motor driving, and relates to the technical field of multi-motor cooperative electric traction control, which comprises the following steps: collecting a traction total current vector of a driving system, abstracting a plurality of motors which are physically arranged in parallel as uniform logic power nodes, and calculating the load saturation of each logic power node; dynamically adjusting master-slave attributes and corresponding control weights of each logic power node in the logic topology matrix; and generating a phase compensation instruction for each traction inverter, and issuing the phase compensation instruction to the corresponding sub-driving unit. According to the invention, abstraction and reconfigurable characterization of a multi-motor system collaborative structure are realized; the limitation of a traditional fixed master-slave or independent control strategy is broken through; torque interference among multiple motors is effectively decoupled, current oscillation and local overload are suppressed, and the dynamic response consistency, the operation stability and the overall energy efficiency level of the system under complex working conditions are improved.

Description

Technical Field

[0001] This invention relates to the field of multi-motor cooperative electric traction control technology, and in particular to an electric vehicle system based on combined electric motor drive. Background Technology

[0002] With the deepening of the global dual-carbon strategy and the rapid development of the new energy vehicle industry, electric vehicles, as the core carrier for achieving green and low-carbon transformation in the transportation sector, are experiencing continuous increases in market penetration and technological iteration speed. Users are placing higher demands on the power performance, range, and energy efficiency of electric vehicles, especially under complex driving conditions. Achieving efficient, stable, and intelligent control of multi-motor collaborative drive systems has become a key focus of the industry's technological breakthroughs. Against this backdrop, electric drive architectures based on multi-motor combined drives are increasingly becoming an important development direction for high-end electric passenger vehicles and commercial vehicle electric drive systems due to their high redundancy, flexible torque distribution capabilities, and excellent dynamic response characteristics.

[0003] However, existing multi-motor drive systems for electric vehicles generally employ fixed master-slave control strategies or independent closed-loop control, lacking the ability to dynamically couple and perceive the real-time load status of each motor with the overall vehicle operating conditions. In scenarios such as transient acceleration, cornering, or sudden changes in road adhesion, it is difficult to accurately decouple the output torque of each motor, easily leading to uneven current distribution, local motor overload saturation, decreased system efficiency, and even deterioration in stability. Furthermore, traditional solutions typically fail to effectively analyze the transient components in the total traction current, making it impossible to construct a logical topology that reflects the cooperative relationship between multiple motors. This results in fixed control weights and insufficient phase synchronization accuracy, limiting the potential of multi-motor systems in terms of energy efficiency optimization and dynamic response. Summary of the Invention

[0004] In view of the problems existing in existing electric vehicle systems based on combined electric motor drive, this invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that the existing multi-motor drive system for electric vehicles lacks a dynamic perception and coordinated control mechanism for transient loads and operating conditions, resulting in severe torque coupling, uneven current distribution, local overload and low energy efficiency.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide an electric vehicle system based on a combined electric motor drive, which includes a traction current acquisition module for acquiring the total traction current vector of the drive system and providing data input for multi-motor cooperative control; The logical power node construction module is used to abstract multiple physically parallel motors into a unified logical power node. Based on the electrical and mechanical coupling relationship between each logical power node, a logical topology matrix is ​​constructed to characterize the cooperative structure of the multi-motor system under different operating conditions. The load saturation analysis module is used to perform joint frequency and time domain analysis on the transient components in the total traction current vector, extract the dynamic load characteristics of each logical power node, and calculate the load saturation of each logical power node. The dynamic master-slave attribute allocation module is used to dynamically adjust the master-slave attributes and corresponding control weights of each logical power node in the logical topology matrix by combining the load saturation and the current vehicle driving conditions. The phase compensation command generation module is used to generate phase compensation commands for each traction inverter based on the allocated master-slave attributes and control weights, and then send them to the corresponding sub-drive units.

[0007] As a preferred embodiment of the electric vehicle system based on combined electric motor drive according to the present invention, the traction current acquisition module includes a current sensor array submodule, a vector synthesis submodule, and a noise suppression and synchronization calibration submodule. The current sensor array submodule is used to deploy Hall current sensors on the DC bus or three-phase AC output terminal of each traction inverter to obtain the original signals of phase current or bus current of the sub-drive unit. The vector synthesis submodule is used to perform complex vector mapping on the multiple raw current signals output by the current sensor array submodule according to a unified coordinate system, and synthesize the total traction current vector that represents the total input energy flow direction of the entire drive system. The noise suppression and synchronization calibration submodule is used to perform multi-channel timing synchronization correction on the traction total current vector output by the vector synthesis submodule. It uses adaptive notch filtering and wavelet denoising algorithm to suppress high-frequency switching noise and electromagnetic interference, ensuring the synchronization and signal-to-noise ratio of current data in the time and frequency domains of each channel, and providing traction total current vector data input for multi-motor cooperative control.

[0008] As a preferred embodiment of the electric vehicle system based on combined electric motor drive according to the present invention, the logical power node construction module includes a motor topology mapping submodule, a coupling relationship modeling submodule, and a logical matrix generation submodule; The motor topology mapping submodule is used to receive the original configuration information of multiple motors output by the traction current acquisition module, and map each physically parallel motor as a logical power node entity according to its position, phase and electrical connection method on the vehicle drive axle or wheel end. The coupling relationship modeling submodule is used to establish an electro-mechanical coupling relationship mechanism that characterizes the energy interaction and motion constraints between nodes based on the mechanical transmission path between each logical power node and the electrical shared bus or power circuit structure. The electro-mechanical coupling relationship mechanism is used to quantify the mutual influence intensity of each node in torque transmission, speed synchronization and current disturbance propagation in the form of a coupling coefficient matrix. The logic matrix generation submodule is used to generate a dynamic sparse topology logic topology matrix by combining the coupling coefficient matrix output by the coupling relationship modeling submodule with the current vehicle configuration parameters and driving mode.

[0009] As a preferred embodiment of the electric vehicle system based on combined electric motor drive according to the present invention, the load saturation analysis module includes a transient component extraction submodule, a joint time-frequency feature analysis submodule, and a node load mapping submodule; The transient component extraction submodule is used to separate the high-frequency transient current component reflecting the dynamic disturbance of the system from the total traction current vector output by the traction current acquisition module through the dynamic disturbance separation operator, and to decouple the transient component according to the dynamic sparse topological logic topology matrix to obtain the transient current subvector corresponding to each logic power node. The joint time-frequency feature analysis submodule is used to synchronously perform short-time Fourier transform and continuous wavelet transform on the transient current subvectors corresponding to each logical power node, construct a time-frequency energy distribution map, extract multi-dimensional dynamic feature parameters from the time-frequency energy distribution map, and form the time-frequency domain load feature vector of each logical power node. The node load mapping submodule is used to input the time-frequency domain load feature vectors of each logical power node output by the joint time-frequency feature analysis submodule into the pre-trained nonlinear mapping model. The nonlinear mapping model is established based on the electromagnetic characteristics of the motor and the thermal-electric coupling boundary conditions, and is used to convert the dynamic feature vectors into quantitative indicators in the range of 0 to 1, that is, the load saturation of each logical power node.

[0010] As a preferred embodiment of the electric vehicle system based on combined electric motor drive according to the present invention, the dynamic master-slave attribute allocation module includes a working condition fusion identification submodule, a saturation-weight mapping submodule, and a topology role reconstruction submodule; The operating condition fusion recognition submodule is used to receive vehicle driving status signals from the vehicle controller, and combine them with drive mode commands to generate a multi-dimensional driving condition feature vector, which is used to characterize the demand characteristics of multi-motor cooperative control in the current operating scenario. The saturation-weight mapping submodule is used to jointly input the load saturation of each logical power node output by the load saturation analysis module and the multi-dimensional driving condition feature vector output by the working condition fusion identification submodule. Through the preset hierarchical decision rules, the priority score of each logical power node under the current working condition is obtained, and the corresponding initial control weight sequence is generated accordingly. The topology role reconstruction submodule is used to perform master-slave role determination based on the initial control weight sequence and under the constraints of the dynamic sparse topology logical topology matrix. It designates logical dynamic nodes with control weights higher than a set threshold as master nodes and the rest as slave nodes. It performs normalization coupling compensation processing on the control weights between master and slave nodes and outputs updated master-slave attribute identifiers and corresponding control weight sets to reconstruct the control relationships in the logical topology matrix.

[0011] As a preferred embodiment of the electric vehicle system based on combined electric motor drive according to the present invention, the phase compensation command generation module includes a master-slave phase reference determination submodule, a weighted modulation phase offset submodule, and an inverter command issuing submodule; The master-slave phase reference determination submodule is used to select the output phase of the traction inverter corresponding to the current master node as the system synchronization reference phase based on the master-slave attribute identifier output by the dynamic master-slave attribute allocation module, and broadcast it to the control channels corresponding to all slave nodes as a reference reference for phase alignment. The weighted modulation phase offset submodule is used to determine the synchronization reference phase provided by the submodule based on the control weights of each logical power node output by the dynamic master-slave attribute allocation module and the master-slave phase reference, and to obtain the phase compensation offset required for each slave node through a nonlinear phase mapping function; the nonlinear phase mapping function is used to dynamically adjust the phase lag or lead angle according to the control weight. The inverter command sending submodule is used to convert the phase compensation offsets generated by the weighted modulation phase offset submodule into PWM carrier phase adjustment commands for the corresponding traction inverters, and encapsulate and send them in real time according to the communication address of the sub-drive unit to ensure that each inverter performs output pulse width timing correction in the next control cycle.

[0012] As a preferred embodiment of the electric vehicle system based on combined electric motor drive according to the present invention, the sub-drive unit includes a phase command receiving sub-unit, a pulse width timing modulation sub-unit, and a power switch drive sub-unit. The phase command receiving subunit is used to receive the PWM carrier phase adjustment command issued by the phase compensation command generation module, verify, unpack and cache the PWM carrier phase adjustment command locally, and synchronize it to the internal clock reference. The pulse width timing modulation subunit is used to dynamically offset the starting phase of the local PWM carrier according to the phase adjustment amount provided by the phase instruction receiving subunit, and re-acquire the duty cycle timing in each switching cycle in combination with the upper-layer torque instruction to generate a pulse width modulation signal sequence after phase compensation. The power switch drive subunit is used to convert the phase-compensated pulse width modulation signal sequence output by the pulse width timing modulation subunit into a gate drive signal adapted to the inverter power device.

[0013] Secondly, embodiments of the present invention provide a method for an electric vehicle based on a combined electric motor drive, comprising: acquiring the total traction current vector of the drive system to provide data input for multi-motor cooperative control; abstracting multiple physically parallel electric motors into a unified logical power node, constructing a logical topology matrix based on the electrical and mechanical coupling relationship between each logical power node to characterize the cooperative structure of the multi-motor system under different operating conditions; performing joint frequency and time domain analysis on the transient components in the total traction current vector to extract the dynamic load characteristics of each logical power node and calculate the load saturation of each logical power node; dynamically adjusting the master-slave attributes and corresponding control weights of each logical power node in the logical topology matrix based on the load saturation and the current vehicle driving conditions; generating phase compensation commands for each traction inverter based on the allocated master-slave attributes and control weights, and sending them to the corresponding sub-drive units.

[0014] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the steps of the electric vehicle system based on the combined electric motor drive described above.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any of the steps of the electric vehicle system based on a combined electric motor drive described above.

[0016] The beneficial effects of this invention are as follows: By constructing logical power nodes and a dynamic sparse topological logical topology matrix, this invention achieves the abstraction and reconfigurable representation of the cooperative structure of a multi-motor system; based on the time-frequency joint analysis of the transient components in the total traction current vector, it accurately quantifies the load saturation of each logical power node, and combines the dynamic allocation of master-slave attributes and control weights under real-time driving conditions, breaking through the limitations of traditional fixed master-slave or independent control strategies; furthermore, by generating phase compensation instructions coupled with weights and accurately adjusting the PWM output timing of the sub-drive units, it effectively decouples torque interference between multiple motors, suppresses current oscillations and local overloads, and significantly improves the dynamic response consistency, operational stability and overall energy efficiency of the system under complex operating conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of an electric vehicle system based on a combined electric motor drive, provided as an embodiment of the present invention.

[0018] Figure 2 This is a flowchart of a method for an electric vehicle system based on a combined electric motor drive, provided as an embodiment of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0023] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0024] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0025] Example Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides an electric vehicle system based on a combined electric motor drive, comprising: S1: Traction current acquisition module, used to acquire the total traction current vector of the drive system, providing data input for multi-motor coordinated control.

[0026] The traction current acquisition module includes a current sensor array submodule, a vector synthesis submodule, and a noise suppression and synchronization calibration submodule. The current sensor array submodule is used to deploy Hall current sensors on the DC bus or three-phase AC output terminal of each traction inverter to obtain the raw signals of phase current or bus current of the sub-drive unit. The vector synthesis submodule is used to perform complex vector mapping on the multiple raw current signals output by the current sensor array submodule according to a unified coordinate system, and synthesize the total traction current vector that represents the total input energy flow direction of the entire drive system. The noise suppression and synchronization calibration submodule is used to perform multi-channel timing synchronization correction on the traction total current vector output by the vector synthesis submodule. It uses adaptive notch filtering and wavelet denoising algorithm to suppress high-frequency switching noise and electromagnetic interference, ensuring the synchronization and signal-to-noise ratio of current data in the time and frequency domains of each channel, and providing traction total current vector data input for multi-motor cooperative control.

[0027] Furthermore, the traction current acquisition module consists of three cooperating sub-modules to achieve high-precision, low-latency system-level current sensing. First, the current sensor array sub-module is equipped with a high-bandwidth Hall current sensor at the DC bus or three-phase AC output of each traction inverter, acquiring the raw phase current or bus current signals of each sub-drive unit to ensure coverage of electrical input information for all motor drive branches. Subsequently, the vector synthesis sub-module transforms these dispersed, multi-channel raw current signals into the same rotating coordinate system and synthesizes them into a single total traction current vector using a complex vector mapping method. This vector fully characterizes the total energy input direction and dynamic amplitude characteristics of the entire drive system at the current moment. Finally, the noise suppression and synchronization calibration submodule refines the synthesized total traction current vector: on the one hand, it eliminates sampling time offset caused by sensor response differences or communication delays through multi-channel timing synchronization correction; on the other hand, it effectively filters out electromagnetic interference and noise components introduced by the high-frequency switching action of the inverter by combining adaptive notch filtering and wavelet denoising algorithm, improving the signal-to-noise ratio while retaining the true transient dynamics, thereby providing high-fidelity and strictly synchronized total traction current vector data input for subsequent multi-motor coordinated control.

[0028] Furthermore, the traction current acquisition module constructs a complete data link from physical signal acquisition to high-reliability system-level current characterization through three closely connected sub-modules. Specifically, the current sensor array sub-module first deploys high-bandwidth Hall current sensors at key electrical nodes of the traction inverter corresponding to each sub-drive unit to capture the raw current signals of each motor branch in real time, ensuring that the electrical status of all drive channels is perceived without omission. Next, the vector synthesis sub-module maps these original independent raw current signals from different physical locations to a common coordinate reference system and fuses them into a unified total traction current vector using a complex vector synthesis method. This vector not only contains the amplitude and direction information of the total system input current but also retains the instantaneous change characteristics reflecting the dynamic response of the entire vehicle. Based on this... The noise suppression and synchronization calibration submodule performs dual optimization on the synthesized vector: on the one hand, it performs multi-channel timing synchronization correction to align the sampling time deviations caused by hardware delays or asynchronous communication in each channel, ensuring consistent time references; on the other hand, it adopts a composite filtering strategy combining adaptive notch filtering and wavelet denoising to accurately identify and suppress high-frequency noise and electromagnetic interference generated by high-speed switching of power devices, while preserving the true transient dynamic components contained in the current signal to the maximum extent. Finally, it outputs a high signal-to-noise ratio, strictly synchronized, and complete traction total current vector that reflects the energy flow state of the system, providing a reliable and accurate data foundation for subsequent multi-motor coordinated control.

[0029] S2: Logical Power Node Construction Module, used to abstract multiple physically parallel motors into a unified logical power node, and construct a logical topology matrix based on the electrical and mechanical coupling relationship between each logical power node to represent the cooperative structure of the multi-motor system under different operating conditions.

[0030] The logical dynamic node construction module includes a motor topology mapping submodule, a coupling relationship modeling submodule, and a logical matrix generation submodule. The motor topology mapping submodule is used to receive the original configuration information of multiple motors output by the traction current acquisition module, and map each physically parallel motor into a logical power node entity according to its position, phase and electrical connection method on the drive axle or wheel end of the vehicle. The coupling relationship modeling submodule is used to establish an electro-mechanical coupling relationship mechanism that characterizes the energy interaction and motion constraints between nodes based on the mechanical transmission path between each logical power node and the electrical shared bus or power loop structure. The electro-mechanical coupling relationship mechanism is used to quantify the mutual influence intensity of each node in torque transmission, speed synchronization and current disturbance propagation in the form of a coupling coefficient matrix. The logic matrix generation submodule is used to generate a dynamic sparse topology logic topology matrix by combining the coupling coefficient matrix output by the coupling relationship modeling submodule with the current vehicle configuration parameters and driving mode.

[0031] Furthermore, the logical power node construction module transforms multiple physically distributed motors into a computable and controllable collaborative control structure through three orderly and collaborative sub-modules. First, the motor topology mapping sub-module receives the original configuration information of the multiple motors from the traction current acquisition module. Combining this with the vehicle's mechanical layout (e.g., drive axle type, wheel end distribution), motor phase relationships, and electrical connection topology (e.g., whether they share a DC bus or are independently powered), it abstracts each physical motor into a uniquely identified logical power node entity, achieving a mapping from physical hardware to the logical control unit. Subsequently, the coupling relationship modeling sub-module, based on the actual connection methods between these logical power nodes, comprehensively analyzes their mechanical transmission paths (e.g., whether they are directly connected via a differential, half-shaft, or coaxial) and electrical shared structures (e.g., current coupling caused by a shared bus), establishing a coupling relationship mechanism that integrates electrical and mechanical characteristics. The system is designed to quantify the mutual influence of each node in torque output, speed response, and current disturbance propagation in the form of a coupling coefficient matrix. Finally, based on this coupling coefficient matrix, the logic matrix generation submodule further integrates the actual configuration parameters of the current vehicle (such as four-wheel drive / dual-axle mode switching) and the driving mode selected by the user (such as economy mode, sport mode, or off-road mode) to dynamically generate a sparse logical topology matrix with reconfigurable connections. This matrix not only accurately reflects the cooperative dependency relationship of the multi-motor system under specific operating conditions, but also provides a structured control basis for subsequent load analysis, master-slave allocation, and phase compensation, thereby achieving efficient and flexible modeling and scheduling of complex multi-motor systems.

[0032] Furthermore, the logical power node construction module systematically transforms multiple physically independent but functionally related motors into a single logical control model with dynamic collaborative capabilities through a three-level progressive processing flow. First, the motor topology mapping submodule obtains the original configuration information of each motor from the traction current acquisition module. Combined with the specific mechanical layout of the vehicle (e.g., front / rear / wheel-drive layout), motor installation phase, and electrical connection methods (e.g., whether a shared DC bus is used, whether an independent inverter is used for power supply), it assigns a unique logical identifier to each physical motor, abstracting it into a standardized logical power node entity, completing the initial transformation from the physical world to control logic. Based on this, the coupling relationship modeling submodule deeply analyzes the inherent relationships between these logical power nodes. On one hand, it examines their mechanical coupling paths, such as whether they are connected via a differential, drive shaft, or rigid coaxial connection, to determine the torque and speed transmission constraints. On the other hand, it examines their electrical coupling characteristics, such as the mutual penetration of current disturbances caused by a shared DC bus, and electromagnetic interference between power circuits. By integrating these two types of coupling effects, it constructs a unified model that can describe energy interaction and motion constraints. The electrical-mechanical coupling mechanism of the system is analyzed, and the mutual influence intensity of each node in torque distribution, speed synchronization, and transient current disturbance propagation is quantitatively characterized in the form of a coupling coefficient matrix. Finally, the logic matrix generation submodule takes this coupling coefficient matrix as the core input and further integrates the actual operating state of the current vehicle, including variable drive configurations (such as two-wheel drive / four-wheel drive switching, dual-axle linkage mode) and user-defined driving modes (such as energy saving, sport, snow, or off-road). This dynamically generates a sparse logical topology matrix with adaptively adjusted connections according to operating conditions. This matrix not only eliminates redundant connections with no significant coupling to improve computational efficiency, but also reflects the changes in the cooperative structure of the multi-motor system in different scenarios in real time. It provides accurate and reconfigurable topological basis for subsequent load saturation analysis, dynamic allocation of master and slave roles, and generation of phase compensation commands, thereby supporting the high-precision and robust cooperative control of the entire multi-motor drive system.

[0033] S3: Load saturation analysis module, used to perform joint frequency domain and time domain analysis on the transient components in the total traction current vector, extract the dynamic load characteristics of each logical power node, and calculate the load saturation of each logical power node.

[0034] The load saturation analysis module includes a transient component extraction submodule, a joint time-frequency feature analysis submodule, and a node load mapping submodule. The transient component extraction submodule is used to separate the high-frequency transient current component reflecting the dynamic disturbance of the system from the total traction current vector output by the traction current acquisition module through the dynamic disturbance separation operator. Based on the dynamic sparse topological logic topology matrix, the transient component is decoupled into channels to obtain the transient current subvector corresponding to each logic power node. The joint time-frequency feature analysis submodule is used to synchronously perform short-time Fourier transform and continuous wavelet transform on the transient current subvectors corresponding to each logical power node, construct a time-frequency energy distribution map, extract multi-dimensional dynamic feature parameters from the time-frequency energy distribution map, and form the time-frequency domain load feature vector of each logical power node. The node load mapping submodule is used to input the time-frequency domain load feature vectors of each logical power node output by the joint time-frequency feature analysis submodule into the pre-trained nonlinear mapping model. The nonlinear mapping model is established based on the electromagnetic characteristics of the motor and the thermal-electric coupling boundary conditions, and is used to convert the dynamic feature vectors into quantitative indicators in the range of 0 to 1, that is, the load saturation of each logical power node.

[0035] Furthermore, the formula for calculating the load saturation of each logical power node is as follows: in, Indicates the first The time-frequency domain load feature vector of each logical dynamic node This represents a nonlinear mapping function pre-trained based on the electromagnetic characteristics of the motor and the thermal-electric coupling boundary conditions. This indicates the load saturation level.

[0036] S4: Dynamic master-slave attribute allocation module, used to dynamically adjust the master-slave attributes and corresponding control weights of each logical power node in the logical topology matrix by combining load saturation and current vehicle driving conditions.

[0037] The dynamic master-slave attribute allocation module includes a working condition fusion identification submodule, a saturation-weight mapping submodule, and a topology role reconstruction submodule. The operating condition fusion recognition submodule is used to receive vehicle driving status signals from the vehicle controller, combine them with drive mode commands, and generate a multi-dimensional driving condition feature vector to characterize the current operating scenario's requirements for multi-motor collaborative control. The saturation-weight mapping submodule is used to jointly input the load saturation of each logical power node output by the load saturation analysis module and the multi-dimensional driving condition feature vector output by the working condition fusion identification submodule. Through the preset hierarchical decision rules, the priority score of each logical power node under the current working condition is obtained, and the corresponding initial control weight sequence is generated accordingly. The topology role reconstruction submodule is used to perform master-slave role determination based on the initial control weight sequence and under the constraints of the dynamic sparse topology logical topology matrix. It designates logical dynamic nodes with control weights higher than a set threshold as master nodes and the rest as slave nodes. It performs normalization coupling compensation processing on the control weights between master and slave nodes and outputs updated master-slave attribute identifiers and corresponding control weight sets, which are used to reconstruct the control relationships in the logical topology matrix.

[0038] Furthermore, the dynamic master-slave attribute allocation module achieves intelligent and real-time reconfiguration of the control roles of the multi-motor system through three closely connected sub-modules. First, the driving condition fusion and identification sub-module acquires vehicle driving status signals, including vehicle speed, acceleration, steering angle, gradient, and road adhesion estimation, from the vehicle controller. Combined with the currently active driving mode command (such as economy, sport, off-road, or obstacle avoidance mode), this heterogeneous information is fused into a structured multi-dimensional driving condition feature vector to accurately characterize the performance requirements of the current driving scenario for multi-motor collaborative output. Subsequently, the saturation-weight mapping sub-module jointly analyzes this driving condition feature vector with the load saturation of each logical power node provided by the load saturation analysis module. Based on preset hierarchical decision rules (e.g., high-load nodes are deweighted during steady-state cruise, and low-load nodes are weighted during rapid acceleration), a priority score reflecting the current adaptability of each logical power node is calculated. Based on this, an initial control weight sequence is generated, ensuring that the weight allocation considers both the motor's own load state and the vehicle's operational requirements. Finally, within the feasible coupling relationship defined by the dynamic sparse topology logical topology matrix, the topology role reconstruction submodule processes the initial control weight sequence: nodes with weights higher than a preset threshold are identified as master nodes, undertaking the functions of dominant torque output and phase reference, while the rest act as slave nodes following and cooperating. At the same time, the control weights between master and slave nodes are normalized and coupled (e.g., suppressing slave node response lag caused by excessive weight concentration), and finally outputs updated master and slave attribute identifiers and a refined control weight set, which are used to reconstruct the control dependencies in the logical topology matrix in real time, thereby supporting the system to achieve flexible, stable and efficient master-slave cooperative control under different operating conditions.

[0039] S5: Phase compensation command generation module, used to generate phase compensation commands for each traction inverter based on the allocated master-slave attributes and control weights, and send them to the corresponding sub-drive units.

[0040] The phase compensation command generation module includes a master-slave phase reference determination submodule, a weighted modulation phase offset submodule, and an inverter command issuing submodule. The master-slave phase reference determination submodule is used to select the output phase of the traction inverter corresponding to the current master node as the system synchronization reference phase based on the master-slave attribute identifier output by the dynamic master-slave attribute allocation module. This phase is then broadcast to the control channels corresponding to all slave nodes as a reference for phase alignment. The weighted modulation phase offset submodule is used to determine the synchronization reference phase provided by the submodule based on the control weights of each logical power node output by the dynamic master-slave attribute allocation module and the master-slave phase reference. The phase compensation offset required for each slave node is obtained through a nonlinear phase mapping function. The nonlinear phase mapping function is used to dynamically adjust the phase lag or lead angle according to the control weight. The inverter command sending submodule is used to convert the phase compensation offsets generated by the weighted modulation phase offset submodule into the PWM carrier phase adjustment commands of the corresponding traction inverters, and encapsulate and send them in real time according to the communication address of the sub-drive unit to ensure that each inverter performs output pulse width timing correction in the next control cycle.

[0041] Furthermore, the phase compensation command generation module achieves refined, weight-aware, and coordinated control of the output phase of the multi-motor system through three collaborative sub-modules. First, the master-slave phase reference determination sub-module extracts the real-time output phase from the traction inverter corresponding to the logical power node currently designated as the master node, based on the master-slave attribute identifiers provided by the dynamic master-slave attribute allocation module, and establishes it as the synchronization reference phase for the entire system. This reference phase is then broadcast to the control channels corresponding to all slave nodes as a unified reference for subsequent phase alignment. Next, the weighted modulation phase offset sub-module, based on this synchronization reference phase and combined with the control weight values ​​corresponding to each slave node, dynamically calculates the required phase compensation offset for each slave node through a preset nonlinear phase mapping function. The design of this mapping function ensures that the slave node with the higher control weight has a phase closer to the target phase. The master node has a small offset, while the slave nodes with lower weights are allowed to have larger phase lags or moderate leads. This achieves flexible decoupling and disturbance suppression of torque output while ensuring the stability of the master control. Finally, the inverter command sending submodule converts the phase compensation offsets calculated above into specific executable PWM carrier phase adjustment commands, encapsulates them according to the unique communication address of each sub-drive unit, and sends them in real time through the high-speed control bus. This ensures that in the next control cycle, each traction inverter can accurately adjust the start time of its pulse width modulation signal to complete the coordinated correction of the output current phase, and finally achieve high-precision phase synchronization and torque coordination of multiple motors under complex operating conditions.

[0042] Preferably, the sub-driving unit includes a phase command receiving sub-unit, a pulse width timing modulation sub-unit, and a power switch driving sub-unit; The phase command receiving subunit is used to receive the PWM carrier phase adjustment command issued by the phase compensation command generation module, verify, unpack and cache the PWM carrier phase adjustment command locally, and synchronize it to the internal clock reference. The pulse width timing modulation subunit is used to dynamically offset the starting phase of the local PWM carrier according to the phase adjustment amount provided by the phase command receiving subunit, and re-acquire the duty cycle timing of each switching cycle in combination with the upper-layer torque command to generate a pulse width modulation signal sequence after phase compensation. The power switch drive subunit is used to convert the phase-compensated pulse width modulation signal sequence output by the pulse width timing modulation subunit into a gate drive signal adapted to the inverter power device.

[0043] In a preferred embodiment, an electric vehicle method based on combined electric motor drive includes: acquiring the total traction current vector of the drive system to provide data input for multi-motor cooperative control; abstracting multiple physically parallel electric motors into unified logical power nodes; constructing a logical topology matrix based on the electrical and mechanical coupling relationships between the logical power nodes to characterize the cooperative structure of the multi-motor system under different operating conditions; performing joint frequency and time domain analysis on the transient components in the total traction current vector to extract the dynamic load characteristics of each logical power node and calculate the load saturation of each logical power node; dynamically adjusting the master-slave attributes and corresponding control weights of each logical power node in the logical topology matrix based on the load saturation and the current vehicle driving conditions; and generating phase compensation commands for each traction inverter based on the allocated master-slave attributes and control weights, and sending them to the corresponding sub-drive units.

[0044] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0045] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with an external terminal; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen may be an LCD screen or an e-ink screen. The input device may be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0046] In summary, this invention achieves the abstraction and reconfigurable representation of the collaborative structure of a multi-motor system by constructing logical power nodes and a dynamic sparse topological logical topology matrix. Based on the time-frequency joint analysis of the transient components in the total traction current vector, it accurately quantifies the load saturation of each logical power node and, combined with the dynamic allocation of master-slave attributes and control weights under real-time driving conditions, overcomes the limitations of traditional fixed master-slave or independent control strategies. Furthermore, by generating phase compensation instructions coupled with weights and precisely adjusting the PWM output timing of the sub-drive units, it effectively decouples torque interference between multiple motors, suppresses current oscillations and local overloads, and significantly improves the dynamic response consistency, operational stability, and overall energy efficiency of the system under complex operating conditions.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An electric vehicle system based on a combined electric motor drive, characterized in that: include, The traction current acquisition module is used to acquire the total traction current vector of the drive system, providing data input for multi-motor coordinated control; The logical power node construction module is used to abstract multiple physically parallel motors into a unified logical power node. Based on the electrical and mechanical coupling relationship between each logical power node, a logical topology matrix is ​​constructed to characterize the cooperative structure of the multi-motor system under different operating conditions. The load saturation analysis module is used to perform joint frequency and time domain analysis on the transient components in the total traction current vector, extract the dynamic load characteristics of each logical power node, and calculate the load saturation of each logical power node. The dynamic master-slave attribute allocation module is used to dynamically adjust the master-slave attributes and corresponding control weights of each logical power node in the logical topology matrix by combining the load saturation and the current vehicle driving conditions. The phase compensation command generation module is used to generate phase compensation commands for each traction inverter based on the allocated master-slave attributes and control weights, and then send them to the corresponding sub-drive units.

2. The electric vehicle system based on combined electric motor drive as described in claim 1, characterized in that: The traction current acquisition module includes a current sensor array submodule, a vector synthesis submodule, and a noise suppression and synchronization calibration submodule. The current sensor array submodule is used to deploy Hall current sensors on the DC bus or three-phase AC output terminal of each traction inverter to obtain the original signals of phase current or bus current of the sub-drive unit. The vector synthesis submodule is used to perform complex vector mapping on the multiple raw current signals output by the current sensor array submodule according to a unified coordinate system, and synthesize the total traction current vector that represents the total input energy flow direction of the entire drive system. The noise suppression and synchronization calibration submodule is used to perform multi-channel timing synchronization correction on the traction total current vector output by the vector synthesis submodule. It uses adaptive notch filtering and wavelet denoising algorithm to suppress high-frequency switching noise and electromagnetic interference, ensuring the synchronization and signal-to-noise ratio of current data in the time and frequency domains of each channel, and providing traction total current vector data input for multi-motor cooperative control.

3. The electric vehicle system based on combined electric motor drive as described in claim 2, characterized in that: The logical dynamic node construction module includes a motor topology mapping submodule, a coupling relationship modeling submodule, and a logical matrix generation submodule; The motor topology mapping submodule is used to receive the original configuration information of multiple motors output by the traction current acquisition module, and map each physically parallel motor as a logical power node entity according to its position, phase and electrical connection method on the vehicle drive axle or wheel end. The coupling relationship modeling submodule is used to establish an electro-mechanical coupling relationship mechanism that characterizes the energy interaction and motion constraints between nodes based on the mechanical transmission path between each logical power node and the electrical shared bus or power circuit structure. The electro-mechanical coupling relationship mechanism is used to quantify the mutual influence intensity of each node in torque transmission, speed synchronization and current disturbance propagation in the form of a coupling coefficient matrix. The logic matrix generation submodule is used to generate a dynamic sparse topology logic topology matrix by combining the coupling coefficient matrix output by the coupling relationship modeling submodule with the current vehicle configuration parameters and driving mode.

4. The electric vehicle system based on combined electric motor drive as described in claim 3, characterized in that: The load saturation analysis module includes a transient component extraction submodule, a joint time-frequency feature analysis submodule, and a node load mapping submodule; The transient component extraction submodule is used to separate the high-frequency transient current component reflecting the dynamic disturbance of the system from the total traction current vector output by the traction current acquisition module through the dynamic disturbance separation operator, and to decouple the transient component according to the dynamic sparse topological logic topology matrix to obtain the transient current subvector corresponding to each logic power node. The joint time-frequency feature analysis submodule is used to synchronously perform short-time Fourier transform and continuous wavelet transform on the transient current subvectors corresponding to each logical power node, construct a time-frequency energy distribution map, extract multi-dimensional dynamic feature parameters from the time-frequency energy distribution map, and form the time-frequency domain load feature vector of each logical power node. The node load mapping submodule is used to input the time-frequency domain load feature vectors of each logical power node output by the joint time-frequency feature analysis submodule into the pre-trained nonlinear mapping model. The nonlinear mapping model is established based on the electromagnetic characteristics of the motor and the thermal-electric coupling boundary conditions, and is used to convert the dynamic feature vectors into quantitative indicators in the range of 0 to 1, that is, the load saturation of each logical power node.

5. The electric vehicle system based on combined electric motor drive as described in claim 4, characterized in that: The dynamic master-slave attribute allocation module includes a working condition fusion identification submodule, a saturation-weight mapping submodule, and a topology role reconstruction submodule; The operating condition fusion recognition submodule is used to receive vehicle driving status signals from the vehicle controller, and combine them with drive mode commands to generate a multi-dimensional driving condition feature vector, which is used to characterize the demand characteristics of multi-motor cooperative control in the current operating scenario. The saturation-weight mapping submodule is used to jointly input the load saturation of each logical power node output by the load saturation analysis module and the multi-dimensional driving condition feature vector output by the working condition fusion identification submodule. Through the preset hierarchical decision rules, the priority score of each logical power node under the current working condition is obtained, and the corresponding initial control weight sequence is generated accordingly. The topology role reconstruction submodule is used to perform master-slave role determination based on the initial control weight sequence and under the constraint of the dynamic sparse topology logic topology matrix. Logical dynamic nodes with control weights higher than a set threshold are designated as master nodes, and the rest are slave nodes. Normalize the coupling compensation process for the control weights between master and slave nodes, and output the updated master and slave attribute identifiers and the corresponding control weight sets, which are used to reconstruct the control relationships in the logical topology matrix.

6. The electric vehicle system based on combined electric motor drive as described in claim 5, characterized in that: The phase compensation command generation module includes a master-slave phase reference determination submodule, a weighted modulation phase offset submodule, and an inverter command issuing submodule. The master-slave phase reference determination submodule is used to select the output phase of the traction inverter corresponding to the current master node as the system synchronization reference phase based on the master-slave attribute identifier output by the dynamic master-slave attribute allocation module, and broadcast it to the control channels corresponding to all slave nodes as a reference reference for phase alignment. The weighted modulation phase offset submodule is used to determine the synchronization reference phase provided by the submodule based on the control weights of each logical power node output by the dynamic master-slave attribute allocation module and the master-slave phase reference, and to obtain the phase compensation offset required for each slave node through a nonlinear phase mapping function; the nonlinear phase mapping function is used to dynamically adjust the phase lag or lead angle according to the control weight. The inverter command sending submodule is used to convert the phase compensation offsets generated by the weighted modulation phase offset submodule into PWM carrier phase adjustment commands for the corresponding traction inverters, and encapsulate and send them in real time according to the communication address of the sub-drive unit to ensure that each inverter performs output pulse width timing correction in the next control cycle.

7. The electric vehicle system based on combined electric motor drive as described in claim 6, characterized in that: The sub-driving unit includes a phase command receiving sub-unit, a pulse width timing modulation sub-unit, and a power switch driving sub-unit; The phase command receiving subunit is used to receive the PWM carrier phase adjustment command issued by the phase compensation command generation module, verify, unpack and cache the PWM carrier phase adjustment command locally, and synchronize it to the internal clock reference. The pulse width timing modulation subunit is used to dynamically offset the starting phase of the local PWM carrier according to the phase adjustment amount provided by the phase instruction receiving subunit, and re-acquire the duty cycle timing in each switching cycle in combination with the upper-layer torque instruction to generate a pulse width modulation signal sequence after phase compensation. The power switch drive subunit is used to convert the phase-compensated pulse width modulation signal sequence output by the pulse width timing modulation subunit into a gate drive signal adapted to the inverter power device.

8. A method for driving an electric vehicle based on a combined electric motor, based on the electric vehicle system based on a combined electric motor as described in any one of claims 1 to 7, characterized in that: include, The total traction current vector of the drive system is acquired to provide data input for multi-motor coordinated control. Multiple physically parallel motors are abstracted into a unified logical power node. A logical topology matrix is ​​constructed based on the electrical and mechanical coupling relationship between each logical power node to characterize the cooperative structure of the multi-motor system under different operating conditions. The transient components in the total traction current vector are analyzed in both the frequency and time domains to extract the dynamic load characteristics of each logical power node and calculate the load saturation of each logical power node. By combining load saturation and current vehicle driving conditions, the master-slave attributes and corresponding control weights of each logical power node in the logical topology matrix are dynamically adjusted. Based on the assigned master-slave attributes and control weights, phase compensation commands are generated for each traction inverter and sent to the corresponding sub-drive units.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the electric vehicle system based on combined electric motor drive as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the electric vehicle system based on combined electric motor drive as described in any one of claims 1 to 7.

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