New energy four-wheel drive type dynamic torque control system and method

Abstract

The invention discloses a dynamic torque control system for a new energy four-wheel drive vehicle. The dynamic torque control system comprises four modules. Wherein the future vehicle speed prediction and equivalent factor adaptive adjustment module is used for predicting a future vehicle speed and adjusting an equivalent factor based on the predicted future vehicle speed; the self-adaptive equivalent energy consumption function construction module is used for acquiring vehicle working condition information, setting point selection constraint conditions, and calculating a front-drive motor working torque, a front-drive motor rotating speed, a rear-drive motor working torque and a rear-drive motor rotating speed; constructing a self-adaptive equivalent energy consumption function based on the equivalent factor, the working torque of the front-drive motor, the rotating speed of the front-drive motor, the working torque of the rear-drive motor and the rotating speed of the rear-drive motor; the self-adaptive equivalent energy consumption function iteration module is used for setting an optimization target and carrying out iterative optimization on a self-adaptive equivalent energy consumption function; and the front and rear drive torque distribution control module is used for performing torque distribution on the front drive and the rear drive of the vehicle based on the optimization iteration result of the self-adaptive equivalent energy consumption function.

Description

Technical Field

[0001] This invention relates to the field of power control technology for new energy vehicles, specifically to a dynamic torque control system and method for new energy four-wheel drive vehicles. Background Technology

[0002] Amid the global automotive industry's shift towards new energy vehicles, four-wheel drive vehicles have become a key competitive force in the new energy vehicle market due to their superior power performance, driving stability, and adaptability to complex road conditions. Among these, the front-to-rear axle decoupled dual-motor drive architecture, with its core advantages such as structural flexibility and independent control, has gradually become the mainstream technical solution for new energy four-wheel drive vehicles. This architecture, by independently arranging drive motors on the front and rear axles and equipping each motor with a dedicated power electronic controller and reduction mechanism, forms two completely independent and controllable drive units. This allows for flexible adjustment of drive force output based on vehicle driving conditions, breaking the coupling limitations of traditional mechanical four-wheel drive systems and laying a solid foundation for improving vehicle power and handling.

[0003] However, due to differences in motor design, manufacturing processes, and the characteristics of the supporting transmission mechanisms, the universal characteristic curves and efficiency characteristic curves of the two independent drive units (front and rear) differ significantly. Specifically, at the same torque / speed operating point, the system efficiency of the front and rear drive units often shows a significant difference. This characteristic directly leads to a substantial difference in the overall energy loss of the entire electric drive system due to different front-to-rear axle torque distribution ratios, even when meeting the total drive torque required for the vehicle's current driving. In other words, the rationality of the front-to-rear axle torque distribution strategy directly determines whether the energy efficiency potential of the dual-motor drive architecture can be fully realized.

[0004] In existing technologies, the front and rear axle torque distribution of new energy four-wheel drive vehicles mostly adopts a fixed ratio distribution strategy (such as 50:50 torque sharing) or a simple distribution strategy based on vehicle dynamics stability. The former completely ignores the efficiency difference between the front and rear drive units, maintaining a fixed torque distribution ratio regardless of changes in driving conditions, resulting in the system being unable to operate in the optimal overall efficiency range for a long time, leading to serious energy waste. Although the latter can ensure the vehicle's driving stability under special conditions such as sharp turns and slippery roads, its core design goal focuses on dynamic control and does not consider energy consumption optimization as a key consideration, thus failing to fully explore the core energy-saving advantages of dual-motor four-wheel drive systems.

[0005] As new energy vehicle users increasingly demand longer driving ranges, minimizing the energy consumption of electric drive systems while ensuring driving performance, safety, and vehicle dynamics stability has become a critical technical challenge for the industry. Against this backdrop, developing advanced front-to-rear axle dynamic torque distribution strategies is crucial. By optimizing the torque sharing ratio between the front and rear drive units in real time, the entire electric drive system can operate in its most efficient range, significantly reducing overall energy consumption under complex and varied real-world driving conditions (such as urban congestion, highway cruising, and hill starts), thereby effectively improving vehicle range.

[0006] In view of this, the present invention aims to provide a method for front and rear drive torque distribution in new energy four-wheel drive vehicles. By predicting future operating conditions, the method achieves dynamic distribution of torque between the front and rear drive motors. Under the premise of fully taking into account the user's driving needs (power and handling), it achieves the optimal adaptive equivalent energy consumption target, thereby comprehensively improving the user's driving experience in terms of power and economy, and filling the gap in the existing technology in the energy consumption optimization strategy of dual-motor drive architecture. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of the aforementioned background technology and provide a dynamic torque control system and method for new energy four-wheel drive vehicles that dynamically distributes the torque of the front and rear drive motors by predicting future operating conditions, achieving the optimal adaptive equivalent energy consumption while fully considering the user's driving needs (power and handling), thereby comprehensively improving the user's power and economic driving experience.

[0008] To achieve this objective, the dynamic torque control system for new energy four-wheel drive vehicles designed in this invention includes a future vehicle speed prediction and equivalent factor adaptive adjustment module, an adaptive equivalent energy consumption function construction module, an adaptive equivalent energy consumption function iteration module, and a front and rear drive torque distribution control module.

[0009] The future vehicle speed prediction and equivalent factor adaptive adjustment module is used to predict the future vehicle speed and adjust the equivalent factor based on the predicted future vehicle speed.

[0010] The adaptive equivalent energy consumption function construction module is used to obtain vehicle operating condition information, set selection point constraints, calculate the working torque of the front drive motor, the speed of the front drive motor, the working torque of the rear drive motor, and the speed of the rear drive motor, and construct an adaptive equivalent energy consumption function based on the equivalent factor, the working torque of the front drive motor, the speed of the front drive motor, the working torque of the rear drive motor, and the speed of the rear drive motor.

[0011] The adaptive equivalent energy consumption function iteration module is used to set an optimization target and iteratively optimize the adaptive equivalent energy consumption function.

[0012] The front-wheel drive torque distribution control module is used to distribute torque between the front-wheel drive and rear-wheel drive of the vehicle based on the optimization iteration results of the adaptive equivalent energy consumption function.

[0013] Furthermore, the future vehicle speed prediction and equivalent factor adaptive adjustment module is also used to generate and optimize the future vehicle speed based on the optimization iteration results of the adaptive equivalent energy consumption function, and to adjust the equivalent factor based on the optimized future vehicle speed.

[0014] Furthermore, the method for predicting future vehicle speed and adjusting the equivalent factor based on the predicted future vehicle speed includes: using the vehicle speed of the previous m steps as the input parameters of a BP neural network, weighting the input layer, hidden layer, and output layer to calculate the predicted vehicle speed for the next n steps, and iteratively optimizing the weight parameters and bias through gradient descent backpropagation; the activation function expression for the hidden layer parameters is: The activation function expression for the output layer parameters is: Where yi, yj, and yk represent the input parameters of the input layer, the output parameters of the hidden layer, and the output parameters of the output layer, respectively; ωi,j and ωj,k represent the weights between the input layer and the hidden layer, and between the hidden layer and the output layer, respectively; f I and f O Let b represent the activation functions of the input layer and hidden layer, and the hidden layer and output layer, respectively; j and b k These represent the biases of the hidden layer and the output layer, respectively; ωi,j,ωj,k,b j and b k The iterative optimization expressions are as follows: , , , , where E is the adaptive equivalent energy consumption function.

[0015] Furthermore, acquiring vehicle operating condition information includes acquiring real-time vehicle speed V and real-time wheel-end power demand P. Setting selection point constraints includes setting the range of output torque for the front-drive motor and the range of output torque for the rear-drive motor. The constraint expressions for the output torque of the front-drive motor and the output torque of the rear-drive motor are as follows: and ,in, , , , , and These are the output torque of the front drive motor, the output torque of the rear drive motor, the minimum output torque of the front drive motor, the maximum output torque of the front drive motor, the minimum output torque of the rear drive motor, and the maximum output torque of the rear drive motor, respectively.

[0016] Furthermore, the method for calculating the operating torque of the front-drive motor, the speed of the front-drive motor, the operating torque of the rear-drive motor, and the speed of the rear-drive motor includes: calculating the speed of the front-drive motor based on the speed ratio λ between the real-time motor speed n and the real-time vehicle speed V. and the speed of the rear drive motor ;exist to Within the range, the output torque of the front drive motor is adjusted at intervals of ΔT. Perform a point scan, calculate the real-time wheel-end demand torque based on the real-time wheel-end demand power P, and calculate the real-time wheel-end demand torque and the output torque of the front drive motor. The difference, which is the output torque of the rear drive motor. The working efficiency η of the front drive motor is obtained by interpolation. front and the working efficiency η of the rear drive motor rear According to the output torque of the front drive motor and the operating efficiency η of the front drive motor front Calculate the operating torque of the front drive motor According to the output torque of the rear drive motor and the operating efficiency η of the rear drive motor rear Calculate the operating torque of the rear drive motor .

[0017] Furthermore, the calculation expression for the adaptive equivalent energy consumption function E is as follows: Wherein, α is the equivalence factor, and the calculation expression for the equivalence factor α is: ,in, The equivalent factor at the current moment, This is the equivalent factor of the previous moment. and The constant coefficients, This represents the current remaining battery power of the vehicle. ρ represents the vehicle's remaining battery power at the previous moment, and ρ is the ratio of the standard deviation of vehicle speed to the average vehicle speed within the prediction time domain.

[0018] Furthermore, the method of setting an optimization objective and iteratively optimizing the adaptive equivalent energy consumption function includes: optimizing the equivalent factor α and the ratio ρ of the standard deviation of vehicle speed and the average vehicle speed in the prediction time domain, with the goal of minimizing the adaptive equivalent energy consumption function E.

[0019] Furthermore, a dynamic torque control method for new energy four-wheel drive vehicles based on the aforementioned dynamic torque control system for new energy four-wheel drive vehicles includes the following steps: predicting future vehicle speed and adjusting the equivalent factor based on the predicted future vehicle speed.

[0020] The system acquires vehicle operating condition information, sets selection point constraints, calculates the working torque, speed, and speed of the front drive motor, the rear drive motor, and the rear drive motor. Based on the equivalent factor, the working torque, speed, and speed of the rear drive motor, an adaptive equivalent energy consumption function is constructed.

[0021] Set an optimization objective and iteratively optimize the adaptive equivalent energy consumption function;

[0022] Based on the optimization and iteration results of the adaptive equivalent energy consumption function, torque distribution is performed between the front-wheel drive and rear-wheel drive of the vehicle.

[0023] Furthermore, a computer program product includes a computer program that, when executed by a processor, implements the steps of the method.

[0024] The beneficial effects of this invention are:

[0025] Significant energy consumption optimization and improved driving range: This invention uses a BP neural network to predict future operating conditions and dynamically adjusts the front and rear drive torque distribution ratio by combining an adaptive equivalent energy consumption function. It can accurately match the differences in efficiency characteristics between the front and rear drive motors—favoring efficient output of the front drive motor when the operating conditions are smooth (ρ<ε), and rationally distributing the rear drive force when the operating conditions are severe (ρ>ε). This avoids the energy waste of traditional fixed distribution or single stability strategies, significantly reducing the overall energy consumption of the electric drive system, effectively extending the driving range of new energy four-wheel drive vehicles, and alleviating users' range anxiety.

[0026] Power and economy in balance, driving experience enhanced: Through adaptive correction of the equivalent factor α (integrating the intensity of driving conditions ρ and SOC changes), this invention achieves dynamic adaptation of "power" and "economy"—when driving demands are high (such as frequent acceleration and deceleration, complex road conditions), it automatically increases the proportion of rear-wheel drive torque to ensure strong power output; when driving demands are moderate (such as highway cruising, constant speed in the city), it focuses on efficient front-wheel drive output to optimize energy consumption, taking into account both the user's demand for power response and the economy of daily use, thus comprehensively improving the driving experience.

[0027] Adapting to complex operating conditions and enhancing control intelligence: Compared to existing technologies that rely solely on static allocation logic based on real-time operating conditions, this invention introduces a future speed prediction and iterative optimization mechanism. This mechanism can anticipate driving intentions and changes in operating conditions in advance, proactively adjusting the torque distribution strategy rather than passively responding to real-time states. Simultaneously, through point scanning and constraint verification (motor torque limits, speed matching, etc.), it ensures that torque distribution always meets hardware safety boundaries. This enables stable and intelligent control in various scenarios, including urban congestion, highways, and inclines, improving the vehicle's adaptability to complex operating conditions and enhancing control reliability.

[0028] High compatibility and controllable implementation cost: This invention is based on the existing front and rear axle decoupled dual-motor drive architecture design, which does not require major modification to the hardware structure. Functional upgrades can be achieved only through algorithm optimization, making it compatible with most mainstream new energy four-wheel drive models. Moreover, the core BP neural network model and adaptive equivalent energy consumption function can obtain input data through the vehicle's existing sensors (vehicle speed sensor, SOC sensor, etc.), without the need for additional hardware installation, which reduces the modification cost and industrial application threshold of the technology.

[0029] SOC management coordination ensures battery life: The calculation of the equivalent factor α incorporates SOC change feedback, which can indirectly balance the battery's charging and discharging state during torque distribution. This avoids the battery being in a high-load charging and discharging state for a long time due to the pursuit of energy consumption or power, reduces battery cycle loss, indirectly extends battery life, and reduces users' later maintenance costs. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments disclosed in this invention, the accompanying drawings of the embodiments will be briefly described below. These drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0031] Figure 1 This is a schematic diagram of the BP neural network structure in this invention;

[0032] Figure 2 This is a schematic diagram of the module connection of the dynamic torque control system for new energy four-wheel drive vehicles in this invention;

[0033] Figure 3 This is a flowchart of the dynamic torque control method for new energy four-wheel drive vehicles in this invention;

[0034] Among them, 1—future vehicle speed prediction and equivalent factor adaptive adjustment module, 2—adaptive equivalent energy consumption function construction module, 3—adaptive equivalent energy consumption function iteration module, and 4—front and rear drive torque distribution control module. Detailed Implementation

[0035] The technical solutions (including preferred technical solutions) of the present invention will be further described in detail below with reference to the accompanying drawings and by way of listing some optional embodiments of the present invention. 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 are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and 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, and therefore should not be construed as a limitation of the present invention.

[0036] Example 1

[0037] like Figure 2 As shown, the present invention provides a specific embodiment of a dynamic torque control system for a new energy four-wheel drive vehicle: including a future speed prediction and equivalent factor adaptive adjustment module 1, an adaptive equivalent energy consumption function construction module 2, an adaptive equivalent energy consumption function iteration module 3, and a front and rear drive torque distribution control module 4.

[0038] The future vehicle speed prediction and equivalent factor adaptive adjustment module 1 is used to predict the future vehicle speed, adjust the equivalent factor based on the predicted future vehicle speed, and generate and optimize the future vehicle speed according to the optimization iteration results of the adaptive equivalent energy consumption function. Based on the optimized future vehicle speed, the equivalent factor is adjusted back.

[0039] The adaptive equivalent energy consumption function construction module 2 is used to obtain vehicle operating condition information, set selection point constraints, calculate the working torque of the front drive motor, the speed of the front drive motor, the working torque of the rear drive motor, and the speed of the rear drive motor, and construct an adaptive equivalent energy consumption function based on the equivalent factor, the working torque of the front drive motor, the speed of the front drive motor, the working torque of the rear drive motor, and the speed of the rear drive motor.

[0040] The adaptive equivalent energy consumption function iteration module 3 is used to set the optimization target and iteratively optimize the adaptive equivalent energy consumption function.

[0041] The front and rear drive torque distribution control module 4 is used to distribute torque between the front and rear drives of the vehicle based on the optimization iteration results of the adaptive equivalent energy consumption function.

[0042] Example 2

[0043] Based on Example 1, the present invention provides a specific embodiment of a dynamic torque control method for new energy four-wheel drive vehicles:

[0044] Prediction of future operating conditions based on BP neural network

[0045] Determine the network input and output parameters: such as Figure 1 As shown, the vehicle speed (V) selected from the previous m steps is... t-m ,…,V t-1 V t ) is used as the input to the BP neural network, with the predicted vehicle speed (V) for the next n steps. t+1 ,…,V t+n-1 V t+n As output, the vehicle speed parameter represents the future driving intention.

[0046] Network layer calculation: The input layer output yi is weighted and calculated with weights ωi,j, then combined with the hidden layer bias bj, and processed by the activation function fI to obtain the hidden layer output yj. The calculation formula is as follows: The hidden layer output yj is weighted and calculated with weights ωj,k, and then combined with the output layer bias bk. This is then processed by the activation function fO to obtain the predicted vehicle speed yk (i.e., the speed in the next n steps). The calculation formula is as follows: .

[0047] Network parameter iterative optimization: Compare the predicted vehicle speed with the actual vehicle speed to determine the error value E. Backpropagation parameter update: With minimizing the error E as the objective, use gradient descent to iteratively update the weights and biases according to the learning rate η. The update formulas are as follows: Input layer - Hidden layer weights: Hidden layer - output layer weights: Hidden layer bias: Output layer bias: Repeat the iteration until the error E converges, complete the training of the BP neural network, and realize the rolling prediction of future working conditions.

[0048] Based on the optimal front-to-rear torque distribution with minimal adaptive equivalent energy consumption.

[0049] like Figure 3 As shown, basic operating condition information is obtained: real-time vehicle operating data is collected, including real-time vehicle speed V and real-time wheel-end power demand P, which serve as the basic input for torque distribution calculation.

[0050] Define torque constraint boundaries: Based on the hardware characteristics of the front and rear drive motors, define the torque limit conditions: front drive motor torque Must meet Rear drive motor torque Must meet This ensures that torque distribution meets hardware safety requirements.

[0051] Calculate the front and rear drive motor speeds: Based on the speed ratio coupling relationship between vehicle speed and motor speed, calculate the front and rear drive motor speeds separately: Front drive motor speed ( The speed ratio between the front drive motor and the wheel end, and the speed of the rear drive motor. ( (This refers to the speed ratio between the rear drive motor and the wheel end).

[0052] Torque sweep point and rear drive torque derivation: Sweep point of the output torque of the front drive motor: in [ , Within the range, points are taken sequentially with a step size ΔT to obtain the sweep torque sequence. , +△T,…, -△T, Derivation of the corresponding rear-drive output torque: Based on the balance between the required torque at the wheel end and the torque of the front and rear drives, and combined with the wheel radius R, calculate the rear-drive motor torque corresponding to each sweeping point. and verify Does it meet the constraint boundary? Eliminate sweep points that do not meet the constraints. Rear drive output torque. .

[0053] Calculation of motor efficiency and operating torque: Based on the universal characteristic curves of the front and rear drive motors, the efficiency η of the front drive motor at each effective scan point is obtained by interpolation. front and rear drive motor efficiency η front Then, combining the motor speed and efficiency, the operating torque of the front and rear drive motors is calculated separately to obtain the torque-efficiency matching data for each scanning point. The formula is as follows: , . This refers to the operating torque of the front-drive motor. This refers to the operating torque of the rear drive motor.

[0054] Construct and calculate the equivalent energy consumption function: Taking energy consumption per unit time as the optimization objective, an adaptive factor α is introduced to balance the energy consumption of the precursor and successor, and the equivalent energy consumption function is constructed. Calculate the energy consumption value E for each valid scan point.

[0055] Optimal torque distribution selection: Compare the energy consumption values ​​E of all effective scanning points, and select the scanning point with the smallest E. and As the optimal front-to-rear drive output torque, the front-to-rear drive torque ratio at this point is the optimal distribution coefficient with the lowest equivalent energy consumption.

[0056] Adaptive factor correction considering future operating condition prediction information

[0057] Calculate the operating condition change parameter ρ: based on the predicted vehicle speed (V) in the next n steps. t+1 ,…,V t+n), calculate the standard deviation Vstd and average Vave of the vehicle speed in the prediction time domain. Define the operating condition change parameter ρ = Vstd / Vave, where ρ characterizes the severity of future operating conditions, where, , .

[0058] Set the boundary of the severity of the working condition: Define the boundary value ε to determine the type of future working condition: If ρ > ε, it is determined that the working condition changes significantly (frequent acceleration and deceleration, high power demand); if ρ < ε, it is determined that the working condition changes gradually (uniform speed driving, high efficiency demand).

[0059] Adaptive factor α correction: Combining the operating condition judgment results with the change in SOC (remaining vehicle charge), a correction formula for the equivalent factor α is set: (HV and HSOC are calibration constants). When ρ > ε (high power demand): After correction, α decreases, reducing the weight of front-drive torque and favoring the rear-drive motor to output more power, ensuring power performance. When ρ < ε (high efficiency demand): After correction, α increases, increasing the weight of front-drive torque and favoring the efficient output of the front-drive motor to optimize energy consumption. At the same time, through SOC change feedback, it avoids overcharging and discharging of the battery due to solely pursuing power or efficiency, achieving a balance of multiple objectives.

[0060] Example 3

[0061] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the dynamic torque control method for new energy four-wheel drive vehicles described in Embodiment 2.

[0062] Example 4

[0063] This invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other through the communication bus. The memory is used to store computer programs. When the processor executes the program stored in the memory, it implements the dynamic torque control method for new energy four-wheel drive vehicles described in Embodiment 2.

[0064] The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0065] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0066] Memory can be volatile memory, such as random-access memory (RAM); memory can also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. Memory can be a combination of the above-mentioned types of memory.

[0067] Example 5

[0068] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, provides the dynamic torque control method for a new energy four-wheel drive vehicle as described in Example 2.

[0069] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0070] It should be noted that the description of the above technical solutions is exemplary, and this specification can be embodied in different forms and should not be construed as limiting it to the technical solutions set forth herein. Rather, providing these descriptions will ensure that the disclosure of this invention is thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Furthermore, the technical solutions of this invention are defined only by the scope of the claims. When using the terms "comprising," "having," and "including" as described in this specification, there may also be another part or other parts; the terms used are generally singular but may also represent plural forms. Finally, it should be pointed out that the above embodiments are merely representative examples of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention should be considered to fall within the protection scope of the present invention.

Claims

1. A new energy four-wheel drive vehicle dynamic torque control system, characterized in that: It comprises a future vehicle speed prediction and equivalent factor adaptive adjustment module (1), an adaptive equivalent energy consumption function construction module (2), an adaptive equivalent energy consumption function iteration module (3) and a front and rear drive torque distribution control module (4). The future vehicle speed prediction and equivalent factor adaptive adjustment module (1) is used for predicting a future vehicle speed and adjusting an equivalent factor based on the predicted future vehicle speed. The adaptive equivalent energy consumption function construction module (2) is used for obtaining vehicle working condition information, setting a selected point constraint condition, calculating front drive motor working torque, front drive motor speed, rear drive motor working torque and rear drive motor speed, and constructing an adaptive equivalent energy consumption function based on the equivalent factor, the front drive motor working torque, the front drive motor speed, the rear drive motor working torque and the rear drive motor speed. The adaptive equivalent energy consumption function iteration module (3) is used for setting an optimization target and iteratively optimizing the adaptive equivalent energy consumption function. The front and rear drive torque distribution control module (4) is used for distributing torque of the front drive and the rear drive of the vehicle based on the optimization iteration result of the adaptive equivalent energy consumption function.

2. The new energy four-wheel drive vehicle dynamic torque control system of claim 1, wherein: The future vehicle speed prediction and equivalent factor adaptive adjustment module (1) is further used for generating and optimizing a future vehicle speed according to the optimization iteration result of the adaptive equivalent energy consumption function, and adjusting the equivalent factor based on the future vehicle speed generated after optimization.

3. The new energy four-wheel drive vehicle dynamic torque control system of claim 2, wherein: The method for adjusting the equivalent factor based on the predicted future vehicle speed comprises: taking the historical vehicle speed of m steps as the input parameter of the BP neural network, performing full connection and weighted calculation of the input layer, the hidden layer and the output layer to output the future n-step predicted vehicle speed, and performing gradient descent back propagation iteration to optimize the weight parameter and the bias amount; the activation function expression of the hidden layer parameter is: The activation function expression of the output layer parameter is: Wherein, yi, yj and yk represent the input parameter of the input layer, the output parameter of the hidden layer and the output parameter of the output layer respectively; ωi,j and ωj,k represent the weight between the input layer and the hidden layer and between the hidden layer and the output layer respectively; f I and f O represent the activation function of the input layer and the hidden layer and of the hidden layer and the output layer respectively; b j and b k represent the bias amount of the hidden layer and the output layer respectively; the iteration optimization expressions of ωi,j, ωj,k, b j and b k are respectively: , , , Wherein, E is the adaptive equivalent energy consumption function.

4. The new energy four-wheel drive vehicle dynamic torque control system of claim 1, wherein: The acquiring vehicle working condition information comprises acquiring real-time vehicle speed V and real-time wheel end demand power P, the setting selected point constraint condition comprises setting a range of front drive motor output torque and a range of rear drive motor output torque, and constraint condition expressions of the front drive motor output torque and the rear drive motor output torque are respectively: and , , , , , and are respectively front drive motor output torque, rear drive motor output torque, front drive motor output torque minimum value, front drive motor output torque maximum value, rear drive motor output torque minimum value and rear drive motor output torque maximum value.

5. The new energy four-wheel drive vehicle dynamic torque control system of claim 4, characterized in that: The method for calculating the operating torque of the front drive motor, the speed of the front drive motor, the operating torque of the rear drive motor, and the speed of the rear drive motor includes: calculating the speed of the front drive motor based on the speed ratio λ between the real-time motor speed n and the real-time vehicle speed V. and the speed of the rear drive motor ;exist to Within the range, the output torque of the front drive motor is adjusted at intervals of ΔT. Perform a point scan, calculate the real-time wheel-end demand torque based on the real-time wheel-end demand power P, and calculate the real-time wheel-end demand torque and the output torque of the front drive motor. The difference, which is the output torque of the rear drive motor. The working efficiency η of the front drive motor is obtained by interpolation. front and the working efficiency η of the rear drive motor rear According to the output torque of the front drive motor and the operating efficiency η of the front drive motor front Calculate the operating torque of the front drive motor According to the output torque of the rear drive motor and the operating efficiency η of the rear drive motor rear Calculate the operating torque of the rear drive motor .

6. The new energy four-wheel drive vehicle dynamic torque control system of claim 5, wherein: The calculation expression of the adaptive equivalent energy consumption function E is: Wherein, a is the equivalent factor, and the calculation expression of the equivalent factor a is: Wherein, is the equivalent factor at the current moment, is the equivalent factor at the last moment, and is a constant coefficient, is the current vehicle remaining power, is the vehicle remaining power at the last moment, and p is the ratio of the standard deviation of the vehicle speed to the average value of the vehicle speed in the prediction time domain.

7. The new energy four-wheel drive vehicle dynamic torque control system of claim 6, characterized in that: The method of setting an optimization target and iteratively optimizing the adaptive equivalent energy consumption function comprises: optimizing the equivalent factor α and the ratio ρ of the standard deviation and the average value of the vehicle speed in the predicted time domain with the minimization of the adaptive equivalent energy consumption function E as the target.

8. A new energy four-wheel drive vehicle dynamic torque control method based on the new energy four-wheel drive vehicle dynamic torque control system according to any one of claims 1-7, characterized in that: It comprises the following steps: predicting a future vehicle speed and adjusting an equivalent factor based on the predicted future vehicle speed; obtaining vehicle working condition information, setting a selected point constraint condition, calculating front drive motor working torque, front drive motor speed, rear drive motor working torque and rear drive motor speed, and constructing an adaptive equivalent energy consumption function based on the equivalent factor, the front drive motor working torque, the front drive motor speed, the rear drive motor working torque and the rear drive motor speed; setting an optimization target and iteratively optimizing the adaptive equivalent energy consumption function; distributing torque of the front drive and the rear drive of the vehicle based on the optimization iteration result of the adaptive equivalent energy consumption function.

9. The new energy four-wheel drive vehicle dynamic torque control method of the new energy four-wheel drive vehicle dynamic torque control system according to claim 8, characterized in that: It further comprises the following steps: generating and optimizing a future vehicle speed according to the optimization iteration result of the adaptive equivalent energy consumption function, and adjusting the equivalent factor based on the future vehicle speed generated after optimization.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to realize the steps of the method in claim 8 or 9.

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