High-rotating-speed electric steering pump system for new energy commercial vehicle

Abstract

The invention relates to the technical field of new energy commercial vehicle steering systems, in particular to a high-rotating-speed electric steering pump system for a new energy commercial vehicle, which comprises a high-rotating-speed motor, a high-precision steering pump, an intelligent control unit and a heat dissipation system, the intelligent control unit is connected with the high-rotating-speed motor, the high-precision steering pump and various sensors of the vehicle, and the heat dissipation system is used for dissipating heat of the high-rotating-speed motor and the high-precision controller. According to the high-rotating-speed electric steering pump system for the new energy commercial vehicle, through the high-rotating-speed electric steering pump system, by means of collaborative operation of a high-rotating-speed motor and a high-precision pump body, powerful flow and pressure are output, and under the extreme working conditions that the large new energy commercial vehicle climbs in a full-load mode and needs to steer greatly, the system can respond rapidly; sufficient steering assistance is provided for a driver, a steering wheel can rotate easily and smoothly, and vehicle controllability is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of steering systems for new energy commercial vehicles, and in particular to a high-speed electric steering pump system for new energy commercial vehicles. Background Art

[0002] Against the backdrop of global advocacy for energy conservation, emission reduction, and sustainable development, new energy commercial vehicles, leveraging their clean and efficient advantages, are steadily increasing their market share in logistics, transportation, urban public transportation, and other sectors. As a core component for safe driving, the steering system's performance directly impacts vehicle controllability and driver safety. Traditional fuel-powered commercial vehicles are commonly equipped with hydraulic power steering systems, which utilize the engine to drive a hydraulic pump, providing steering assistance. However, new energy commercial vehicles, powered by batteries or other alternative energy sources, eschew traditional engines. This makes engine-dependent hydraulic power steering systems incompatible, necessitating the development of new power steering solutions.

[0003] The electric steering pump systems currently on the market have numerous drawbacks. Their motor speed is limited, making it difficult for the output flow and pressure to meet the power assist requirements of large new energy commercial vehicles under complex operating conditions such as heavy loads and sharp turns. For example, when a fully loaded heavy-duty new energy truck makes a slow turn, the existing electric steering pump system provides insufficient power assist, forcing the driver to expend considerable effort turning the steering wheel, resulting in a poor control experience and potential safety hazards. Furthermore, existing systems suffer from low energy efficiency, with the motor and pump consuming excessive energy during operation. This further exacerbates range anxiety for new energy commercial vehicles, which already have limited range, and severely restricts their widespread application. Furthermore, faced with the complex and ever-changing driving environment of commercial vehicles, such as high temperatures, high humidity, and bumpy roads, existing electric steering pump systems exhibit poor reliability and stability, frequently experiencing failures, increasing vehicle maintenance costs and downtime, and resulting in significant economic losses for operators. Summary of the Invention

[0004] The object of the present invention is to provide a high-speed electric steering pump system for new energy commercial vehicles to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a high-speed electric steering pump system for new energy commercial vehicles, comprising a high-speed motor, a high-precision steering pump, an intelligent control unit and an efficient heat dissipation system. The high-speed motor is used to drive the high-precision steering pump to work. The intelligent control unit is respectively connected to the high-speed motor, the high-precision steering pump and various sensors of the vehicle, and is used to collect vehicle operation data and control the working status of the high-speed motor and the high-precision steering pump. The efficient heat dissipation system is used to dissipate heat from the high-speed motor and the high-precision controller.

[0006] Preferably, the high-speed motor adopts a new type of permanent magnet material, and the motor winding is a hollow copper wire winding.

[0007] Preferably, the permanent magnet material is a neodymium iron boron (NdFeB) permanent magnet, and the wire diameter and the hollow part diameter of the hollow copper wire winding are calculated and determined according to the rated voltage, current and resistance requirements of the motor.

[0008] Preferably, the high-precision steering pump is a vane pump, which meets the output flow and pressure requirements of the steering assist of new energy commercial vehicles by optimizing the blade width, stator inner diameter, blade inclination angle and number of blades, and high-precision processing technology and sealing technology are used between the blades and the stator and rotor.

[0009] Preferably, the output flow calculation formula of the vane pump is Q=2bDnsinθ, where b is the blade width, D is the inner diameter of the stator, n is the pump speed, θ is the inclination angle of the blade, the surface roughness of the mating surface of the blade and the stator and rotor reaches Ra0.4μm or less, and the seal adopts a polytetrafluoroethylene (PTFE) composite sealing ring.

[0010] Preferably, the intelligent control unit is provided with a fault diagnosis algorithm model and a vehicle steering assist demand model. The fault diagnosis algorithm model is based on machine learning technology and is constructed by learning a large amount of vehicle operation data, and is used to diagnose system faults; the vehicle steering assist demand model is established according to the principles of vehicle dynamics and is used to calculate the motor speed and pump output flow required under different working conditions.

[0011] Preferably, the fault diagnosis algorithm model adopts a machine learning algorithm such as support vector machine (SVM) or neural network (NN), and pre-processes the collected data such as vehicle speed, steering wheel angle, steering torque, motor speed, pump output flow and pressure through a data cleaning algorithm before training; the vehicle steering assist demand model is F 助力 =k1v+k2θ+k3T 助力 , where F 助力 is the required steering assist, v is the vehicle speed, θ is the steering wheel angle, T 助力 is the steering torque, k1, k2, k3 are model coefficients.

[0012] Preferably, the efficient heat dissipation system includes a motor heat dissipation module and a controller heat dissipation module. The motor heat dissipation module adopts water cooling to remove the heat of the motor through coolant circulation; the controller heat dissipation module adopts air cooling to remove the heat of the controller through a fan, and the efficient heat dissipation system is provided with a heat dissipation control algorithm model, which is used to control the working state of the heat dissipation device according to the real-time temperature of the motor and controller, the ambient temperature and the system load.

[0013] Preferably, the coolant flow rate in the motor heat dissipation module is determined based on the motor heating power and the allowable temperature rise, and the calculation formula is: Where P is the motor heating power, c is the coolant specific heat capacity, and ΔT is the allowable temperature rise; the heat dissipation control algorithm model uses a method based on model predictive control (MPC) to predict the motor and controller temperatures and control the heat dissipation equipment.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. This high-speed electric steering pump system for new energy commercial vehicles, through the high-speed electric steering pump system, relies on the coordinated operation of a high-speed motor and a high-precision pump body to output strong flow and pressure. Under extreme conditions such as large new energy commercial vehicles climbing a fully loaded slope and requiring large steering, the system can respond quickly and provide the driver with sufficient steering assistance, making the steering wheel turn easily and smoothly, significantly improving vehicle controllability, effectively avoiding safety accidents caused by insufficient steering assistance, and comprehensively ensuring the travel safety of drivers and passengers.

[0016] 2. This high-speed electric steering pump system for new energy commercial vehicles intelligently adjusts motor speed and pump output flow during driving based on parameters such as real-time vehicle speed, steering wheel angle, and steering torque. When the vehicle is driving smoothly on highways, the system automatically reduces motor speed and pump output to minimize unnecessary energy consumption. In congested urban areas with frequent starts and stops, the system can also adjust promptly to ensure adequate steering assistance. Field testing has shown that compared to traditional electric steering pump systems, this system effectively extends the range of new energy commercial vehicles, reduces operating costs, and brings significant economic benefits to industries such as logistics and transportation.

[0017] 3. This high-speed electric steering pump system for new energy commercial vehicles utilizes high-precision vane pump processing technology to ensure precise fit between the vanes, stator, and rotor, reducing hydraulic oil leakage and improving the pump's volumetric and mechanical efficiency. This ensures stable operation at high speeds and high loads. Through an efficient heat dissipation system and fault diagnosis algorithm model, the heat dissipation system monitors and regulates the motor and controller temperatures in real time, maintaining them within an appropriate operating range. The fault diagnosis model analyzes vehicle operating data in real time and, upon detecting an anomaly, can quickly and accurately locate the fault, sound an alarm, and initiate protective measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a diagram of the high-speed electric power steering pump system architecture of the present invention;

[0020] Figure 2 This is a structural diagram of the high-speed electric steering pump of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1-Figure 2 , the present invention provides a technical solution:

[0023] A high-speed electric steering pump system for new energy commercial vehicles includes a high-speed motor, a high-precision steering pump, an intelligent control unit, and a high-efficiency heat dissipation system. The high-speed motor drives the high-precision steering pump. The intelligent control unit is connected to the high-speed motor, the high-precision steering pump, and various vehicle sensors to collect vehicle operating data and control the operating status of the high-speed motor and high-precision steering pump. The high-efficiency heat dissipation system dissipates heat from the high-speed motor and high-precision controller. The high-speed motor uses a new type of permanent magnet material, and the motor windings are hollow copper wire windings.

[0024] High speed motor design:

[0025] Material selection: Choose permanent magnet materials with high remanence density and high coercivity, such as neodymium iron boron (NdFeB) permanent magnets, to increase the motor's power density. Accurately calculate the size and shape of the permanent magnets based on the motor's design power and speed requirements to ensure sufficient magnetic field strength at high speeds.

[0026] Winding design: Use hollow copper wire winding technology. First, calculate the wire diameter of the hollow copper wire and the diameter of the hollow part according to the rated voltage, current and required resistance value of the motor. For example, according to the resistance calculation formula (where R is the resistance, ρ is the resistivity of copper, l is the wire length, and S is the wire cross-sectional area). While ensuring the resistance meets the design requirements, maximize the wire's heat dissipation surface area. By optimizing the number of turns and winding pattern, the motor's back EMF constant can be reduced, thereby improving its speed performance.

[0027] Calculate the coolant flow requirement based on the motor's heating power and allowable temperature rise. The calculation formula is: Where P is the motor's heat output, c is the coolant's specific heat capacity, and ΔT is the allowable temperature rise. Ensure that the coolant can evenly dissipate the heat generated by the motor. Furthermore, heat dissipation ribs are installed at key heat-generating areas of the motor, such as the motor housing, to further enhance heat dissipation.

[0028] The motor output power formula is: P = Tω, where P is the motor output power, T is the motor output torque, and ω is the motor angular velocity (ω = 2πn / 60, where n is the motor speed). By increasing the speed, the motor's output torque can be reduced while maintaining the same output power, which facilitates miniaturization and lightweighting of the motor.

[0029] Back electromotive force formula: E = k e ω, where E is the back electromotive force, k e is the back electromotive force constant. Optimizing the winding design can reduce k e value, thereby generating lower back electromotive force at the same speed, which helps the motor run stably at high speed.

[0030] High-precision pump body design

[0031] Blade parameter optimization: Determine the output flow and pressure of the steering pump based on the power steering requirements of new energy commercial vehicles. By optimizing the blade width, stator inner diameter, blade tilt angle, and number of blades, ensure that the pump can output sufficient flow and pressure at high speeds. For example, if the power steering system of a new energy commercial vehicle requires the vane pump to output a flow rate Q at a speed of n = 3000 r / min, r =20L / min, system working pressure p=1Mpa.

[0032] The formula for calculating the output flow of a vane pump is Q = 2bDnsinθz, where b is the blade width, D is the stator inner diameter, n is the pump speed, θ is the blade pitch angle, and z is the number of blades. Typically, the blade pitch angle θ is around 13°, so we'll use θ = 13° here. The number of blades, z, is typically 10 or 12, so we'll use z = 10 here.

[0033] First, we can infer the relationship between the stator inner diameter D and the blade width b based on the flow formula. The formula can be transformed into Considering the volumetric efficiency η v , actual flow Q r =η v Q t , the volumetric efficiency η of a general vane pump v Between 0.85-0.95, here we take η v =0.9. Then the theoretical flow rate

[0034] Substitute n = 3000 r / min = 50 r / s, θ = 13°, z = 10 into In Chinese: sin13°≈0.225,

[0035] Assuming b = 0.02m (i.e. 20mm), then,

[0036] Calculation of vane pump output pressure and related parameters:

[0037] The output pressure p of the vane pump depends on the load and is calculated as follows: Where F is the load force and A is the effective area of ​​the vane pump. Assume that we know the system working pressure. p = 1 MPa = 1 × 10 6 Pa

[0038] The effective area a of a single blade is related to the blade width b and the effective length l of the blade when working. Assuming that the effective length l of the blade when working = 0.05m (i.e. 50mm), the effective area a of a single blade is a = b × l = 0.02 × 0.05 = 1 × 10 -3 m 2 .

[0039] The total effective area. A=z×a=10×1×10 -3 =0.01m 2

[0040] according to The load force F = p × A = 1 × 10 6 ×0.01=10000N

[0041] Calculation of volumetric efficiency and leakage of vane pump:

[0042] The volumetric efficiency η has been taken before v =0.9, theoretical flow rate Q t =22.22L / min, actual flow rate Q r =20L / min. Leakage ΔQ=Q t -Q r =22.22-20=2.22L / min.

[0043] Surface roughness: The surface roughness of the blades, stator and rotor mating surfaces reaches Ra0.4μm, which is a high-precision processing requirement, helping to reduce leakage and wear and improve the efficiency and life of the pump.

[0044] Seal: The seal adopts polytetrafluoroethylene (PTFE) composite sealing ring, which has good corrosion resistance, low friction coefficient and sealing performance, and can effectively prevent hydraulic oil leakage.

[0045] Some parameters of high-precision vane pump are as follows:

[0046] Stator inner diameter D = 82 mm;

[0047] Blade width b = 20 mm;

[0048] Blade tilt angle θ = 13°;

[0049] Number of leaves z = 10;

[0050] The effective length of the blade is l = 50 mm;

[0051] Theoretical flow rate Q t =22.22L / min;

[0052] Actual flow Q r =20L / min;

[0053] Leakage ΔQ = 2.22 L / min;

[0054] Working pressure p = 1 MPa;

[0055] Load force F = 10000N.

[0056] Intelligent control system

[0057] Sensor Selection and Installation: Select a high-precision vehicle speed sensor, steering wheel angle sensor, and steering torque sensor. The vehicle speed sensor is a Hall-effect sensor, installed on the vehicle's drive shaft, and detects vehicle speed by measuring wheel rotational speed. The steering wheel angle sensor is a photoelectric sensor, installed on the steering column, accurately measuring steering wheel angle. The steering torque sensor is a strain gauge sensor, installed between the steering column and the steering gear, to measure the steering torque applied by the driver to the steering wheel. Connect the signal output cables of each sensor to the corresponding input ports of the intelligent control unit.

[0058] Fault diagnosis algorithm model construction and training: A large amount of vehicle signal data from new energy commercial vehicles under normal driving and various fault conditions is collected, including parameters such as vehicle speed, steering wheel angle, steering torque, motor speed, pump output flow rate, and pressure. Data cleaning algorithms are used to preprocess the collected data to remove outliers and noise. Machine learning algorithms, such as support vector machines (SVMs) and neural networks (NNs), are then used to train the preprocessed data and build a fault diagnosis algorithm model. During the training process, model parameters are continuously adjusted to improve the model's accuracy and generalization capabilities.

[0059] Data Collection: Utilizing an onboard data acquisition system, we collect long-term operational data from new energy commercial vehicles under various operating conditions. These conditions include frequent starts and stops on congested urban roads, high-speed driving, rapid acceleration and deceleration, and climbing hills of varying gradients. Collected data includes, but is not limited to, vehicle speed signals from the speed sensor, real-time steering wheel angle feedback from the steering wheel angle sensor, driver torque applied to the steering wheel as measured by the steering torque sensor, the actual speed of the high-speed motor, the output flow and pressure of the high-precision steering pump, and parameters such as the temperature and vibration of key motor and pump components.

[0060] Outlier detection: Using the 3σ principle based on statistics. For a set of data x1, x2, ..., x n , first calculate its mean and standard deviation If a data point x j satisfy It is then judged as an outlier and removed.

[0061] Noise filtering: Use moving average filtering. For time series data y1,y2,…,y m , set the filter window size to k (usually an odd number), then the filtered data point y′ j for (when or When the data is processed, boundary processing is performed to remove high-frequency noise interference in the data.

[0062] Feature engineering: Extract features that can effectively characterize the system's operating status from the cleaned data. For example, to calculate the fluctuation coefficient of the motor speed, the formula is where σ n is the standard deviation of the motor speed, is the average value of the motor speed; for the output pressure of the steering pump, calculate the pressure change rate, that is, where p t and p t +Δt is the pump output pressure at time t and time t+Δt respectively. These features can highlight the differences between the system under normal and fault conditions.

[0063] Neural network-based model: If a multilayer perceptron (MLP) is used to build a fault diagnosis model, the model structure includes an input layer, multiple hidden layers, and an output layer. The number of input layer nodes is determined by the number of selected features. Assuming that N features are extracted, the input layer has N nodes. The number of hidden layer nodes is determined by the empirical formula Estimation (n i( n is the number of input layer nodes, n0 is the number of output layer nodes, and α is a tuning constant, typically between 1 and 10). The number of output layer nodes corresponds to the number of fault types. For example, if a system has M common fault types, then the output layer has M nodes. Each layer is connected via a weight matrix W and a bias vector b. The output of each neuron is transformed using an activation function (such as the ReLU function, f(x) = max(0, x)).

[0064] Model based on support vector machine (SVM): For linearly separable problems, a linear SVM model is constructed, whose decision function is f(x)=sgn(ω T x+b), where ω is the weight vector, b is the bias, and x is the input feature vector. For nonlinear separable problems, kernel functions (such as radial basis kernel functions) are introduced. Where γ is the kernel function parameter), the data is mapped to a high-dimensional space to construct a nonlinear SVM model.

[0065] Model training: The processed data is divided into a training set and a test set according to a certain ratio (e.g. 70% for training and 30% for testing). During the training process, the stochastic gradient descent (SGD) algorithm is used to update the weights and biases of the neural network model. The mean square error (MSE) is used as the loss function. For the samples in the training set (x (i) y (j) ), i=1,…,n (n is the number of training samples), the MSE loss function is in is the model's predicted output. By iteratively calculating gradients and updating parameters, the loss function is gradually reduced. For the SVM model, a quadratic programming algorithm is used to find the optimal w and b to maximize the classification margin.

[0066] Model evaluation and optimization: Use the test set to evaluate the trained model using indicators such as accuracy, recall, and F1 value. Recall F1 value Where TP is the number of true positives, FP is the number of false positives, and FN is the number of false negatives. If the model evaluation metrics do not meet expectations, adjust the model parameters (such as the number of hidden layer nodes and learning rate of the neural network, the kernel function parameters of the SVM, etc.), or re-engineer the features by adding or replacing features, and then retrain and evaluate until the model performance meets the requirements.

[0067] Control Strategy Implementation: A control program is written in the intelligent control unit. Based on the collected vehicle speed, steering wheel angle, and steering torque signals, combined with the vehicle's dynamic model, it calculates the required motor speed and pump output flow rate for the current operating conditions. For example, when the vehicle is traveling at low speed and with a large steering angle, the preset control strategy increases the motor speed and pump output flow rate to provide sufficient steering assistance. At high speeds, the motor speed and pump output flow rate are reduced to minimize energy consumption. The control program also monitors the output of the fault diagnosis algorithm model in real time. If a system fault is detected, appropriate protective measures are immediately implemented, such as limiting the motor speed and issuing an alarm.

[0068] Cooling module design: The cooling control algorithm calculates the optimal cooling power required for the current operating conditions based on parameters such as the real-time motor and controller temperatures, ambient temperature, and system load. For example, when the motor temperature approaches a high-temperature threshold, the model controls the coolant pump to increase speed and flow. If the controller temperature is too high, the model activates additional cooling fans and adjusts fan speed to enhance cooling.

[0069] Heat dissipation power formula: P 散热 =hΔT, where P 散热 Where h is the heat dissipation coefficient, and ΔT is the temperature difference between the heat dissipating object and the surrounding environment. In the motor heat dissipation module, increasing the heat dissipation coefficient h between the coolant and the motor increases the heat dissipation power and reduces the motor temperature.

[0070] Temperature prediction formula in the heat dissipation control algorithm model: The temperature of the motor and controller is predicted using the model predictive control (MPC) method. Assume that the temperature change model of the motor or controller is T k+1 =f(T k ,Q k ,P k ), where T k+1 is the temperature at the next moment, T k is the current temperature, Q k is the heat dissipation power at the current moment, P k is the current heating power, and f is the temperature change function. Based on this prediction formula, the heat dissipation control algorithm model adjusts the working state of the heat dissipation device in advance to ensure that the system temperature is always within the appropriate range.

[0071] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0072] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-speed electric steering pump system for new energy commercial vehicles, comprising a high-speed motor, a high-precision steering pump, an intelligent control unit, and an efficient heat dissipation system, characterized by: The high-speed motor is used to drive the high-precision steering pump. The intelligent control unit is respectively connected to the high-speed motor, the high-precision steering pump and various sensors of the vehicle, and is used to collect vehicle operation data and control the working status of the high-speed motor and the high-precision steering pump. The high-efficiency heat dissipation system is used to dissipate heat from the high-speed motor and the high-precision controller.

2. A high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The high-speed motor adopts a new type of permanent magnet material, and the motor winding is a hollow copper wire winding.

3. The high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The permanent magnet material is a neodymium iron boron (NdFeB) permanent magnet, and the wire diameter and the hollow part diameter of the hollow copper wire winding are calculated and determined according to the rated voltage, current and resistance requirements of the motor.

4. The high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The high-precision steering pump is a vane pump. By optimizing the blade width, stator inner diameter, blade inclination angle and number of blades, it meets the output flow and pressure requirements of the steering assist of new energy commercial vehicles. High-precision processing and sealing technology are used between the blades and the stator and rotor.

5. The high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The output flow calculation formula of the vane pump is Q=2bDnsinθ, where b is the blade width, D is the inner diameter of the stator, n is the pump speed, and θ is the inclination angle of the blade. The surface roughness of the mating surface between the blade and the stator and rotor reaches Ra0.4μm or less, and the seal adopts a polytetrafluoroethylene (PTFE) composite sealing ring.

6. The high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The intelligent control unit is equipped with a fault diagnosis algorithm model and a vehicle steering power demand model. The fault diagnosis algorithm model is based on machine learning technology and is constructed by learning a large amount of vehicle operation data to diagnose system faults; the vehicle steering power demand model is established according to the principles of vehicle dynamics to calculate the required motor speed and pump output flow under different working conditions.

7. The high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The fault diagnosis algorithm model adopts machine learning algorithms such as support vector machine (SVM) or neural network (NN), and pre-processes the collected data such as vehicle speed, steering wheel angle, steering torque, motor speed, pump output flow and pressure through data cleaning algorithm before training; the vehicle steering power requirement model is F 助力 =k1v+k2θ+k3T 助力 , where F 助力 is the required steering assist, v is the vehicle speed, θ is the steering wheel angle, T 助力 is the steering torque, k1, k2, k3 are model coefficients.

8. The high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The efficient heat dissipation system includes a motor heat dissipation module and a controller heat dissipation module. The motor heat dissipation module adopts water cooling to remove the heat of the motor through coolant circulation; the controller heat dissipation module adopts air cooling to remove the heat of the controller through a fan. The efficient heat dissipation system is provided with a heat dissipation control algorithm model, which is used to control the working state of the heat dissipation device according to the real-time temperature of the motor and controller, the ambient temperature and the system load.

9. The high-speed electric power steering pump system for new energy commercial vehicles according to claim 1, characterized in that: The coolant flow rate in the motor heat dissipation module is determined based on the motor heating power and the allowable temperature rise. The calculation formula is: Where P is the motor heating power, c is the coolant specific heat capacity, and ΔT is the allowable temperature rise; the heat dissipation control algorithm model uses a method based on model predictive control (MPC) to predict the motor and controller temperatures and control the heat dissipation equipment.

Images