Method and system for optimizing braking strategy and speed reducer gear structure of electric vehicle

Abstract

The invention discloses a method and a system for optimizing a braking strategy and a speed reducer gear structure of an electric automobile, relates to the technical field of electric automobiles, and solves the problem that an existing braking energy recovery control strategy and a speed reducer gear are not matched with a braking energy recovery working condition. According to the embodiment of the invention, the to-be-optimized parameters influencing the braking energy recovery effect are optimized by taking the braking energy recovery control strategy as an optimization basis, so that the braking energy recovery control strategy of the electric vehicle is more matched with the braking energy recovery working condition, the distribution proportion of motor braking and mechanical braking is more reasonable, and the braking efficiency is improved. The balance between the driving comfort and the safety can be achieved; and then, performing CHTC-B urban working condition simulation based on the optimized optimal control strategy parameters to obtain a load spectrum of a speed reducer gear, optimizing structural parameters of the speed reducer gear based on the load spectrum under the braking energy recovery working condition, obtaining structural parameters better matched with the braking energy recovery working condition, and achieving the purpose of prolonging the fatigue life.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, and in particular to a braking strategy and a method and system for optimizing the gear structure of a reducer in an electric vehicle. Background Technology

[0002] Amid the global energy transition and the electrification upgrade of the automotive industry, pure electric vehicles, with their core advantages of zero emissions and low operating energy consumption, have become a key direction for driving the automotive industry towards low-carbon and high-efficiency transformation. Among them, regenerative braking technology, as a core means of improving the energy efficiency of pure electric vehicles, converts kinetic energy into electrical energy and stores it in the power battery during vehicle braking or coasting, which can significantly reduce energy loss and extend driving range. It has already been widely applied in mainstream pure electric models.

[0003] When a vehicle is in regenerative braking mode, the regenerative braking force output by the electric motor and the friction braking force of the mechanical braking system need to coordinate in real time, and the distribution ratio between the two directly determines the braking performance. Most existing technologies use manual adjustment of control parameters to control and distribute the ratio between the two in real time. However, this method can easily lead to excessive vehicle impact or prolonged braking distance, making it difficult to achieve a balance between comfort and safety.

[0004] Meanwhile, as a core load-bearing component of the power transmission system, the performance of the reducer is directly related to the reliability and lightweighting level of the entire vehicle. Current reducer gear optimization designs are mostly based on load spectra formulated for constant speed driving or conventional braking scenarios, failing to fully consider the special load characteristics under regenerative braking conditions. This results in optimized gear structures that are difficult to match the actual stress requirements under regenerative braking conditions, and thus cannot always meet the optimization goal of achieving fatigue life targets. Therefore, there is a need for a braking strategy and a method and system for optimizing the gear structure of a reducer in electric vehicles. Summary of the Invention

[0005] To address the mismatch between existing regenerative braking control strategies and the reducer gears in relation to regenerative braking conditions, this invention provides a method and system for optimizing the braking strategy and reducer gear structure of electric vehicles. This method matches the regenerative braking conditions and achieves integrated optimization of the regenerative braking control strategy and the reducer gear structure. The specific technical solution is as follows: In a first aspect, embodiments of this application provide a braking strategy and a method for optimizing the gear structure of a reducer for an electric vehicle, comprising: The structural parameters of the reducer gear, the regenerative braking control strategy, and the parameters to be optimized for the electric vehicle are obtained. The parameters to be optimized are adjustable parameters that affect the regenerative braking effect of the electric vehicle. Based on the possible values ​​of the regenerative braking control strategy and the parameters to be optimized, the parameters to be optimized are optimized to obtain the optimal control strategy parameters. Based on the optimal control strategy parameters, CHTC-B urban driving condition simulation analysis of the electric vehicle is performed to obtain the load spectrum of the reducer gear. Based on the possible values ​​of the structural parameters and the load spectrum, the structural parameters are optimized to obtain the optimal structural parameters of the reducer gear.

[0006] Among them, CHTC-B city operating condition refers to the operating condition of urban buses in China, which is specifically used for the whole vehicle testing of urban buses.

[0007] Preferably, the possible values ​​of the parameter to be optimized include the first uniform sampling point of the parameter to be optimized within the corresponding first range; the optimization of the parameter to be optimized based on the braking energy recovery control strategy and the possible values ​​of the parameter to be optimized to obtain the optimal control strategy parameters of the braking energy recovery control strategy includes: performing simulation based on the first uniform sampling point and the braking energy recovery control strategy to obtain simulation data; establishing a first surrogate model between the parameter to be optimized and the first optimization objective of the braking energy recovery control strategy based on the simulation data; and optimizing the parameter to be optimized based on the first surrogate model using a multi-objective optimization algorithm to obtain the optimal control strategy parameters.

[0008] Preferably, the first optimization objective includes minimizing the overall impact of the electric vehicle, minimizing the speed error, and maximizing the regenerative braking energy; the parameters to be optimized include the rate of change of motor torque and the rate of change of hydraulic braking torque.

[0009] Preferably, the possible values ​​of the structural parameter include the second uniform sampling point of the structural parameter within the corresponding second range; the optimization of the structural parameter based on the possible values ​​of the structural parameter and the load spectrum to obtain the optimal structural parameter of the reducer gear includes: establishing a reducer gear model based on the structural parameter; performing simulation analysis based on the load spectrum and the reducer gear model to obtain the fatigue life and volume corresponding to the second uniform sampling point; establishing a second surrogate model between the second optimization objective of the structural parameter and the reducer gear model based on the fatigue life and volume corresponding to the second uniform sampling point; and optimizing the structural parameter based on the second surrogate model using a multi-objective optimization algorithm to obtain the optimal structural parameter.

[0010] Preferably, the second optimization objective includes minimizing fatigue damage and minimizing gear volume of the reducer gear; the structural parameters include module and helix angle.

[0011] Preferably, the method of optimizing the parameter to be optimized based on the first surrogate model using a multi-objective optimization algorithm to obtain the optimal control strategy parameter includes: using multiple multi-objective optimization algorithms to optimize the parameter to be optimized based on the first surrogate model to obtain multiple Pareto solution sets; calculating the comprehensive distance index of the multiple Pareto solution sets; and using the Top-Solving Approximation Ideal Solution Ranking Method (TOPSIS) based on the preset weights of the parameter to be optimized to obtain the optimal solution from the Pareto solution set with the smallest comprehensive distance index as the optimal control strategy parameter.

[0012] Preferably, the process of obtaining the parameter to be optimized includes: obtaining the first optimization target of the braking energy recovery control strategy; analyzing the degree of influence of the adjustable parameter on the first optimization target through sensitivity analysis, and obtaining the sensitivity ranking result; obtaining the top N adjustable parameters in the sensitivity ranking result as the parameter to be optimized, where N is a positive integer greater than or equal to 1.

[0013] In a second aspect, embodiments of this application provide a braking strategy and reducer gear structure optimization system for an electric vehicle, applied to the method described in the first aspect, the system comprising: The acquisition module is used to acquire the structural parameters of the reducer gear of the electric vehicle, the braking energy recovery control strategy, and the parameters to be optimized. The parameters to be optimized are adjustable parameters that affect the braking energy recovery effect of the electric vehicle. The first optimization module is used to optimize the parameter to be optimized based on the braking energy recovery control strategy and the possible values ​​of the parameter to be optimized, so as to obtain the optimal control strategy parameters of the braking energy recovery control strategy. The simulation module is used to perform CHTC-B urban driving condition simulation analysis of electric vehicle braking based on the optimal control strategy parameters, and to obtain the load spectrum of the reducer gear. The second optimization module is used to optimize the structural parameters based on the possible values ​​of the structural parameters and the load spectrum, so as to obtain the optimal structural parameters of the reducer gear.

[0014] Thirdly, embodiments of this application provide a computing device, including: a memory for storing a program; and a processor for loading the program to execute the method as described in the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the method described in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: by optimizing the parameters that affect the braking energy recovery effect based on the braking energy recovery control strategy, the braking energy recovery control strategy of the electric vehicle is better matched with the braking energy recovery working condition, and the distribution ratio of motor braking and mechanical braking is more reasonable, so as to achieve a balance between driving comfort and safety; then, based on the optimized optimal control strategy parameters, the CHTC-B urban working condition simulation analysis of the electric vehicle is carried out to obtain the load spectrum of the reducer gear during braking, and the structural parameters of the reducer gear are optimized based on the load spectrum under the braking energy recovery working condition to obtain structural parameters that are better matched with the braking energy recovery working condition, thereby achieving the optimization goal of fatigue life compliance. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 A schematic diagram of the structure of a braking system for a pure electric vehicle provided in an embodiment of this application; Figure 2 A framework diagram of a braking energy recovery control strategy provided in this application embodiment; Figure 3 A flowchart illustrating a braking strategy and reducer gear structure optimization method for an electric vehicle provided in this application embodiment; Figure 4 A flowchart illustrating another braking strategy and reducer gear structure optimization method for an electric vehicle provided in this application embodiment; Figure 5 A schematic diagram of a braking strategy and reducer gear structure optimization system for an electric vehicle provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0020] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0023] Please see Figure 1 , Figure 1 This is a schematic diagram of the braking system of a pure electric vehicle provided in an embodiment of this application. Figure 1 As shown, the braking system includes a battery pack, inverter, motor, vehicle controller, reducer, differential, hydraulic braking system, and mechanical brake.

[0024] The battery pack is used to power the motor and to receive and store recycled electrical energy.

[0025] The inverter is used to convert the DC power from the battery pack into AC power to supply the motor when driving it; and to rectify the AC power transmitted by the motor into DC power to be stored back in the battery during recycling.

[0026] The motor is used to convert electrical energy into mechanical energy to drive the wheels during driving; during regenerative braking, it acts as a generator to convert the mechanical energy of the wheels into electrical energy.

[0027] The vehicle controller is used to coordinate the work of various parts, such as determining when to start regenerative braking and how to distribute the braking force between mechanical braking and regenerative braking.

[0028] Among them, the reducer is used to reduce speed and increase torque; specifically, the motor has a high speed and low torque, so the reducer reduces the speed and amplifies the torque, making it more suitable for the needs of wheel travel.

[0029] The differential is used to allow the wheels on both sides to rotate at different speeds (for example, the inner wheel rotates slower and the outer wheel rotates faster when turning), ensuring driving stability.

[0030] The hydraulic braking system provides mechanical braking force. When regenerative braking is insufficient (such as in emergency braking), the mechanical brake is applied by hydraulic pressure to achieve friction braking.

[0031] Among them, the mechanical brake is the actuator that directly generates frictional braking force and is installed at the wheel.

[0032] exist Figure 1 Specific examples include energy flows from braking energy recovery, mechanical kinetic energy transfer, and electrical energy flow.

[0033] Among them, the green braking energy recovery energy flow refers to the fact that when the vehicle brakes, the mechanical energy of the wheels is transferred to the motor through the differential and reducer. At this time, the motor switches to generator mode and converts the mechanical energy into electrical energy. The electrical energy is then rectified into DC power by the inverter and finally stored in the battery pack, realizing the reuse of kinetic energy and improving the driving range.

[0034] Among them, the red mechanical kinetic energy transfer energy flow refers to the mechanical energy of the motor rotation when the vehicle is driving normally. After the reduction speed and torque are increased by the reducer, it is transferred to the differential and then distributed to the wheels on both sides to propel the vehicle forward.

[0035] The blue "electrical energy flow" refers to the process by which battery power is converted into AC power by an inverter to drive the motor during operation.

[0036] The vehicle controller coordinates the ratio of electric motor braking and mechanical braking based on the braking intensity (such as brake pedal depth). For example, during light braking, electric motor braking is prioritized to maximize energy recovery; during heavy braking or emergency braking, only mechanical braking is used to ensure braking safety.

[0037] Please refer to Figure 2 , Figure 2 This is a framework diagram of a braking energy recovery control strategy provided in an embodiment of this application. Figure 2 As shown, after the braking control process is initiated, the vehicle controller first determines whether the required braking intensity is large or small based on factors such as the brake pedal depth and the current vehicle speed.

[0038] When high braking intensity is required, the priority is "quick and safe stopping." In this case, the vehicle controller directly controls the braking components to enter full-power mechanical braking mode because the motor's energy recovery power is limited and cannot meet the high braking force demand. However, if the battery charge is too low and the vehicle speed is high, it indicates that the energy recovery value is high. In this case, the controller can, under the premise of ensuring safety with mechanical braking, allow the motor to participate in braking appropriately to recover energy.

[0039] When the required braking intensity is relatively low, the priority for braking is "regenerative braking." The vehicle controller can reduce engine speed to create conditions for energy recovery. However, if the battery charge is too high or the vehicle speed is too low, there is no energy recovery value, and the vehicle controller can switch to mechanical braking. Conversely, if there is energy recovery value, the vehicle controller can further determine whether the required braking force is less than the maximum braking force that the electric motor can provide. If it is less than or equal to, the control is set to pure electric motor braking mode; if it is greater, a combined electric motor and mechanical braking mode is used.

[0040] Regardless of the braking method used, the final braking force will be applied to the wheels to slow down or stop the vehicle.

[0041] As can be seen from the above, the vehicle controller needs to control the distribution ratio of motor braking and mechanical braking based on different vehicle states and braking requirements during the braking control process. How to set the control parameters so that the process of this ratio change achieves a balance between driving comfort and safety is a problem that urgently needs to be solved.

[0042] This invention provides a method and system for optimizing the braking strategy and reducer gear structure of electric vehicles, which can match the braking energy recovery working conditions and realize the integrated optimization of the braking energy recovery control strategy and the reducer gear structure.

[0043] Please see Figure 3 , Figure 3 This application provides a flowchart illustrating a method for optimizing the braking strategy and reducer gear structure of an electric vehicle; the method is applied to a computing device; such as... Figure 3 As shown, the method includes: Step 301: The calculation device obtains the structural parameters of the reducer gear of the electric vehicle, the braking energy recovery control strategy, and the parameters to be optimized.

[0044] The computing device can be a terminal or a server. Specifically, the computing device can be a personal computer, a laptop, a smartphone, a tablet, or other device with data processing capabilities.

[0045] Preferably, the electric vehicle is a pure electric vehicle.

[0046] The computing device can obtain the preset structural parameters of the reducer gear of the electric vehicle from a database or storage device, or it can obtain the transmission ratio of the electric vehicle, and then perform a preliminary design of the reducer gear based on the transmission ratio to obtain the structural parameters of the reducer gear.

[0047] Specifically, the core function of a reducer is to reduce speed and increase torque, converting the high-speed, low-torque power output from the motor into the low-speed, high-torque power required for vehicle operation. The transmission ratio is the core parameter for this function. After obtaining the transmission ratio of the target electric vehicle, the computing device can allocate this transmission ratio to a specific single-stage or two-stage gear transmission structure and calculate the core structural parameters of the gears.

[0048] For example, for a single-stage helical cylindrical gear transmission structure, the computing device can first determine the tooth ratio between the driven gear and the driving gear. This tooth ratio is consistent with the transmission ratio, and the number of teeth on the main gear is greater than a certain value to avoid insufficient strength. Then, based on the maximum torque transmitted by the gear (calculated by combining the maximum torque of the motor and the transmission ratio) and the material strength, the gear module is calculated using the strength formula. Then, parameters such as tooth width, pressure angle, tooth tip height, tooth root height, and helix angle are calculated to obtain the initial reducer gear structure parameters.

[0049] The computing device can obtain the preset braking energy recovery control strategy (hereinafter referred to as the control strategy) of the electric vehicle from the database or storage device, or it can obtain the vehicle parameters of the electric vehicle and the distribution relationship between braking intensity, motor and hydraulic braking torque. Then, based on these data, the braking energy recovery control strategy is established using MATLAB / Simulink software.

[0050] Specifically, vehicle parameters such as basic vehicle parameters, powertrain parameters, and braking system parameters serve as the boundaries of the model design, determining the maximum braking force the vehicle can withstand and the upper limit of energy recovery by the motor. The distribution relationship between braking intensity and braking torque is the core of the control strategy, indicating how much force the motor and hydraulic system should each exert under different braking scenarios. Then, computing devices can build these parameters and control logic into a visual model in MATLAB / Simulink software and perform simulations to quickly test the strategy's effectiveness under different scenarios, and then determine the optimal control strategy based on the strategy's effectiveness.

[0051] This control strategy incorporates several adjustable parameters that affect the regenerative braking performance of the electric vehicle. The computing device can use sensitivity analysis to select the key parameters with significant impact and requiring precise control as the optimization parameters.

[0052] Understandably, the parameter to be optimized can be pre-selected and stored along with the regenerative braking control strategy so that the computing device can access it together.

[0053] Preferably, when the computing device generates the control strategy in real time, the computing device can first obtain the first optimization target of the braking energy recovery control strategy; then, through sensitivity analysis, analyze the degree of influence of the adjustable parameter on the first optimization target, and obtain the sensitivity ranking result; and obtain the top N adjustable parameters in the sensitivity ranking result as the parameter to be optimized, where N is a positive integer greater than or equal to 1.

[0054] The first optimization objective includes minimizing the overall impact of the electric vehicle to ensure smooth driving; minimizing speed error to ensure that the braking distance is not prolonged; and maximizing regenerative braking energy to ensure extended driving range.

[0055] By optimizing these parameters, we can ensure that the final strategy achieves the optimal balance between recovery efficiency, smoothness, and safety.

[0056] Step 302: The calculation device optimizes the parameter to be optimized based on the braking energy recovery control strategy and the possible values ​​of the parameter to be optimized, and obtains the optimal control strategy parameters of the braking energy recovery control strategy.

[0057] When acquiring the control strategy, the computing device simultaneously acquires a first range of adjustable parameters of the control strategy, that is, the preset upper and lower bounds of the adjustable parameters. Starting from the lower bound of the parameter to be optimized, the computing device can acquire possible values ​​of the parameter to be optimized within the first range as the first uniform sampling points with a fixed step size.

[0058] The computing device can obtain the control effect achieved by different combinations of first uniform sampling points of parameters to be optimized on the first optimization target through simulation experiments; then, the first uniform sampling point combination with the best control effect is determined as the optimal control strategy parameter.

[0059] Preferably, the computing device can perform simulation based on the first uniform sampling point and the braking energy recovery control strategy to obtain simulation data; based on the simulation data, establish a first surrogate model between the parameter to be optimized and the first optimization objective of the braking energy recovery control strategy; and optimize the parameter to be optimized based on the first surrogate model using a multi-objective optimization algorithm to obtain the optimal control strategy parameters.

[0060] The surrogate model is a simplified mathematical model used to replace complex and time-consuming real-world analysis or simulation processes. Preferably, the first surrogate model includes a response surface model.

[0061] The response surface model is a mathematical model that uses a mathematical surface to approximate the complex relationship between input variables and output results, replacing the complex vehicle visualization model in step 301.

[0062] Specifically, the computing device can use different combinations of first uniform sampling points in the vehicle visualization model to simulate and obtain the corresponding values ​​of the first optimization target; then, using the first uniform sampling point combination as the input variable and the corresponding value as the response value, it can select a quadratic polynomial form or other form of response surface model to fit the relationship and obtain the model parameters of the response surface model.

[0063] For example, the corresponding values ​​include data on the overall vehicle impact, the speed error of the regenerative braking mode, and the braking energy generated during regenerative braking.

[0064] The computing device can optimize the parameters to be optimized using one or more multi-objective optimization algorithms.

[0065] Specifically, the computing device can randomly generate multiple sets of parameters to be optimized using a multi-objective optimization algorithm, and then quickly calculate the optimization effect of the first optimization objective corresponding to each set of parameters to be optimized using the response surface model; then, Pareto sorting is performed based on the optimization effect to eliminate inferior solutions; the above process is iterated to obtain the Pareto optimal solution set.

[0066] Then, the computing device can select some key solutions from the Pareto optimal solution set and verify them on a complex and realistic vehicle visualization model. After the verification is successful, the solution with the highest optimization score after demand weighting is obtained from the Pareto optimal solution set based on actual engineering requirements, and used as the optimal control strategy parameter.

[0067] Preferably, the objective function of the first optimization objective may include: ; Where J(x) represents the impact of the entire vehicle, Ve(x) represents the speed error, and E(x) represents the braking energy generated during the regenerative braking process.

[0068] The constraint equations include: ; in, For the j-th parameter to be optimized, , These are the upper and lower bounds of the j-th parameter to be optimized, respectively.

[0069] Preferably, the parameters to be optimized include the rate of change of electric motor torque and the rate of change of hydraulic braking torque.

[0070] Preferably, the computing device can employ various multi-objective optimization algorithms to optimize the parameter to be optimized based on the first surrogate model, thereby obtaining multiple Pareto solution sets; calculate the comprehensive distance index of the multiple Pareto solution sets; and, using the Technique for Order Preference by Similarity to an IdealSolution (TOPSIS), based on the preset weights of the parameter to be optimized, obtain the optimal solution from the Pareto solution set with the smallest comprehensive distance index as the optimal control strategy parameter.

[0071] The Spacing (SP) index is used to quantify the uniformity of the distribution of solutions in the Pareto solution set. Specifically, the smaller the SP value, the closer the minimum neighbor distance of all solutions is to the average value, that is, the more uniform the distribution of solutions on the Pareto front; conversely, the larger the SP value, the more uniform the distribution, indicating that some solutions are crowded together (minimal neighbor distance) and some solutions are spaced far apart (maximum neighbor distance).

[0072] Understandably, a solution set with a smaller SP can cover more critical intervals, allowing engineers to flexibly select solutions based on actual needs; while a solution set with a larger SP covers fewer intervals and may lack solutions for critical intervals, limiting practical applications.

[0073] Therefore, the computing device can use the Pareto solution set with the minimum SP value to find the optimal control strategy parameters.

[0074] In multi-objective optimization, there are often conflicts between the optimization objectives. That is, when one objective is better, another objective may become worse. Therefore, it is necessary to use multi-objective optimization algorithms to balance the objectives and select the best combination.

[0075] TOPSIS treats each parameter combination as an evaluation object. By calculating its similarity to the ideal optimal solution, it sorts all parameter combinations, and the parameter with the highest similarity is the optimal parameter.

[0076] The ideal optimal solution and the preset weights are both preset in advance, and can be set based on expert experience.

[0077] The computing device can calculate the distance between each evaluation object and each ideal optimal solution, and then perform weighted calculation based on preset weights to calculate the evaluation object that is closest to the ideal optimal solution, thereby obtaining the optimal control strategy parameters.

[0078] Step 303: The computing device performs CHTC-B urban operating condition simulation analysis of the electric vehicle based on the optimal control strategy parameters to obtain the load spectrum of the reducer gear.

[0079] The computing device can use simulation tools to simulate the braking energy recovery process after obtaining the optimal control strategy parameters, and obtain the actual force and speed of the reducer gear when the electric vehicle brakes during the process, that is, the torque-speed load; then, these simulation data are organized into a fatigue life loading load spectrum that can reflect the long-term force law of the gear.

[0080] Specifically, the computing device can use the optimal control strategy parameters to perform CHTC-B urban driving condition simulation analysis of electric vehicles, extract the speed-torque load of the reducer during braking energy recovery, and then compile a load spectrum based on the speed-torque load.

[0081] Specifically, for the obtained torque-speed load of the reducer gear, the calculation device can use the amplitude counting method for statistical analysis, and the calculation formula is as follows: ; in, The revolutions corresponding to the i-th level load. Where d is the rotational speed (r / min) and d is the number of time intervals. The time interval is defined as follows.

[0082] The calculation formula means that the entire working process time is divided into d small segments, and the torque-speed ratio is approximately stable within each segment, corresponding to the i-th level load; then the speed of each segment is integrated to obtain the number of revolutions in that segment; finally, the number of revolutions in all time segments corresponding to the same level load is added together to obtain the total number of revolutions for the i-th level load. .

[0083] Step 304: The calculation device optimizes the structural parameters based on the possible values ​​of the structural parameters and the load spectrum to obtain the optimal structural parameters of the reducer gear.

[0084] The computing device can obtain the optimization effect achieved by different combinations of second uniform sampling points with different structural parameters on the second optimization objective through simulation experiments; then, the combination of second uniform sampling points with the best optimization effect is determined as the optimal control strategy parameter.

[0085] The second optimization objective includes minimizing fatigue damage and minimizing the volume of the reducer gears.

[0086] Preferably, the computing device can establish a reducer gear model based on the structural parameters; perform simulation analysis based on the load spectrum and the reducer gear model to obtain the fatigue life and volume corresponding to the second uniform sampling point; establish a second surrogate model between the structural parameters and the second optimization objective of the reducer gear model based on the fatigue life and volume corresponding to the second uniform sampling point; and optimize the structural parameters based on the second surrogate model using a multi-objective optimization algorithm to obtain the optimal structural parameters.

[0087] The computing device can perform transient dynamic simulation of the reducer gear model based on the fatigue life loading load spectrum of the reducer gear, and then perform fatigue analysis based on the results of the transient simulation.

[0088] Specifically, based on this load spectrum, the computing device uses Ansys software to simulate the stress state of the gear during actual driving, and obtains data on the changes in stress and strain of each part of the gear over time; then, it uses Ncode software to calculate the estimated fatigue life of the reducer gear under stress and strain data.

[0089] For example, the computing device can combine the load spectrum and transient simulation results to perform finite element simulation of the reducer gear using Ncode software, and calculate fatigue damage using Miner's rule based on the results of the finite element simulation analysis.

[0090] The principle behind using Miner's rule to statistically analyze the cumulative amount of fatigue damage is as follows: ; in, This represents the cumulative amount of fatigue damage. denoted as the number of cycles required for the material to reach fatigue failure under the i-th level load; This represents the fatigue damage under the i-th level load in a single cycle.

[0091] Then, the computing device can convert the accumulated fatigue damage to obtain the fatigue life of the reducer gear.

[0092] After obtaining the estimated fatigue life, the computing device can determine a second range of structural parameters to ensure that the strength of the reducer gear formed by the combination of the second uniform sampling points within the second range can meet the requirements of the estimated fatigue life.

[0093] Then, the computing device can uniformly sample points within the second range to obtain the second uniform sampling points; then, based on different combinations of the second uniform sampling points, it can simulate the fatigue resistance of the corresponding reducer gear under real working conditions to obtain the corresponding fatigue life; then, based on the combination of the second uniform sampling points and combined with the classic formulas of mechanical design, it can calculate the volume of the corresponding reducer gear.

[0094] Then, the computing device can simplify the mapping relationship between structural parameters and fatigue life and volume through the second proxy model.

[0095] Preferably, the second proxy model includes a response surface model.

[0096] Preferably, the structural parameters include the module and the helix angle.

[0097] It is understandable that the content of the second agent model is similar to that of the first agent model in step 302, and the content of obtaining the optimal structural parameters through the multi-objective optimization algorithm is similar to that of obtaining the optimal control strategy parameters through the multi-objective optimization algorithm in step 302, so it will not be repeated here.

[0098] Preferably, the computing device can directly use the multi-objective optimization algorithm corresponding to the Pareto solution set selected in step 302 to optimize the structural parameters, without having to search for the best solution from the Pareto solution set of multiple multi-objective optimization algorithms.

[0099] Preferably, the objective function of the second optimization objective may include: ; Where D(pmt) represents fatigue damage and V(pmt) represents gear volume.

[0100] The constraint equations include: In this embodiment, by using the regenerative braking control strategy as the optimization basis, the parameters that affect the regenerative braking effect are optimized, so that the regenerative braking control strategy of the electric vehicle is more matched with the regenerative braking conditions, the distribution ratio of motor braking and mechanical braking is more reasonable, and a balance can be achieved between driving comfort and safety. Then, the load spectrum of the reducer gear is generated based on the optimized optimal control strategy parameters, and the structural parameters of the reducer gear are optimized based on the load spectrum under the regenerative braking conditions, so as to obtain structural parameters that are more matched with the regenerative braking conditions and achieve the optimization goal of fatigue life compliance.

[0101] Please see Figure 4 , Figure 4 This is a flowchart illustrating another braking strategy and reducer gear structure optimization method for an electric vehicle provided in an embodiment of this application. This embodiment is executed by a computing device.

[0102] like Figure 4 As shown, this process can be divided into a strategy optimization stage and a gear structure optimization stage. Details are as follows: I. Strategy Optimization Phase 1. Based on the basic parameters of the power source and transmission ratio of the pure electric vehicle power system, as well as the required braking intensity and the distribution relationship between the motor and hydraulic braking torque, a preliminary design of the braking energy recovery control strategy for the pure electric vehicle power system is carried out.

[0103] 2. In regenerative braking mode, the electric motor provides braking force, and hydraulic braking is used to supplement the braking force when the electric motor braking is insufficient. The calculation equipment selects control strategy parameters affecting the overall vehicle impact, speed error in regenerative braking mode, and braking energy generation of the pure electric vehicle's powertrain system based on sensitivity analysis: motor torque change rate. and the rate of change of hydraulic braking torque As an optimization variable, the impact of the whole vehicle Speed ​​error in regenerative braking mode and regenerative braking energy As an optimization target.

[0104] Among them, the rate of change of motor torque If the torque is too high, it will cause the motor output torque to change rapidly, easily leading to a large impact on the vehicle. However, due to its precise torque control characteristics, it can reduce speed errors in regenerative braking mode within a reasonable range, and the rapid response of the motor allows for more timely recovery of braking energy. The hydraulic braking torque change rate... Primarily used in hydraulic braking systems, an excessive rate of change can directly exacerbate the impact on the vehicle. If poorly coordinated with electric motor braking, it may lead to a decrease in speed control accuracy. At the same time, its rate of change is also related to the generation of braking energy; a reasonable rate of change can optimize the generation of braking energy.

[0105] 3. The computing equipment uses MATLAB or Simulink software to build a complete vehicle model, including the engine, motor, transmission system, braking system, etc., and sets the driving conditions and builds a braking energy recovery control strategy based on the braking force distribution relationship; 4. Determine the control strategy parameters through experiments. and Within the range of parameters, points are uniformly selected using the optimal Latin hypercube method within the parameter range.

[0106] 5. Based on the control strategy parameter points selected in the experimental design, simulations were performed using MATLAB / Simulink. The obtained impact intensity, speed error, and braking energy data generated by the regenerative braking mode were analyzed using Design of Experiments (DOE). A surrogate model was used to establish the mapping relationship between each parameter and the optimization objective, and the coefficient of determination R-squared was employed. 2 Verify its accuracy.

[0107] 6. The computing device employs various multi-objective optimization algorithms, such as the multi-objective gray wolf algorithm and the multi-objective frost ice optimization algorithm, to perform multi-objective optimization on the surrogate model. The optimization results output by each algorithm are compared, and the algorithm with better performance is used for optimization to finally obtain the optimal control strategy parameters.

[0108] Specifically, the computing device performs quality analysis on the Pareto solution sets output by each algorithm, and uses SP (Split-Side) to quantitatively evaluate the algorithm performance. The smaller the SP, the more uniform the distribution of solutions on the Pareto front. The principle is as follows: ; in, To indicate the first The minimum Euclidean distance from one solution to all other solutions. This is the mean of all minimum distances.

[0109] Then, a comprehensive evaluation can be conducted based on actual engineering needs to select the control strategy parameters that best meet the requirements, and the optimization results can be verified.

[0110] II. Gear Structure Optimization Stage 1. Using the optimized control strategy parameters, the computer performs MATLAB / Simulink control strategy simulation to obtain the torque-speed load of the reducer gears, and compiles the fatigue life loading load spectrum of the reducer gears.

[0111] 2. The calculation equipment performs parametric modeling of the reducer gear structure parameters based on the preliminary design of the selected pure electric vehicle power system.

[0112] 3. The computing equipment performs transient dynamic simulation of the established reducer gear model based on Ansys according to the compiled fatigue life loading load spectrum of the reducer gear, and imports the results into the fatigue life analysis software Ncode for fatigue life analysis.

[0113] 4. The computational equipment analyzes the fatigue damage of each gear stage and optimizes the structural parameters of the stage with the most significant fatigue damage. Specifically, the computational equipment can select the gear module. and helix angle As optimization variables, the range of gear structure parameters of the reducer is determined, and the optimal Latin hypercube sampling method is used to uniformly sample points for each parameter within its parameter range.

[0114] 5. The computational equipment obtains the fatigue life corresponding to each parameter combination based on fatigue analysis, and simultaneously calculates the gear volume for each parameter combination. DOE analysis is performed, and a response surface model is used to establish the mapping relationship between each parameter and the optimization objective. Ri is then used. 2 Verify its accuracy.

[0115] 6. The computing device uses the multi-objective optimization algorithm corresponding to the Pareto solution set finally selected in the strategy optimization stage to optimize the structural parameters and finally obtain the optimal reducer gear structural parameters.

[0116] This application uses the rate of change of motor torque and hydraulic braking torque as optimization variables, with the optimization objectives of minimizing vehicle impact, minimizing vehicle speed control error, and maximizing regenerative braking energy. An optimal Latin hypercube design test scheme is employed, and a response surface model is established. Then, a multi-objective optimization algorithm is used to obtain the optimal braking control strategy parameters. Next, based on the optimized control strategy parameters, a reducer gear load spectrum is generated. Combining Ansys transient dynamics simulation and Ncode fatigue analysis technology, the reducer gear structural parameters are further optimized with the objectives of minimizing gear fatigue damage and volume. This application achieves an effective balance between driving comfort and braking safety, significantly improves braking energy recovery efficiency, extends the service life of reducer gears, and provides a systematic technical solution for the integrated optimization design of pure electric vehicles.

[0117] The method provided in the embodiments of this application has been described above. The system provided in the embodiments of this application will be described below.

[0118] Please see Figure 5 , Figure 5 This application provides a schematic diagram of the structure of an electric vehicle braking strategy and reducer gear structure optimization system, as shown in the embodiment of the present application. Figure 5 As shown, the system 50 includes: The acquisition module 501 is used to acquire the structural parameters of the reducer gear of the electric vehicle, the braking energy recovery control strategy and the parameters to be optimized. The parameters to be optimized are adjustable parameters that affect the braking energy recovery effect of the electric vehicle. The first optimization module 502 is used to optimize the parameter to be optimized based on the braking energy recovery control strategy and the possible values ​​of the parameter to be optimized, so as to obtain the optimal control strategy parameters of the braking energy recovery control strategy. Simulation module 503 is used to perform simulation analysis of braking based on the optimal control strategy parameters to obtain the load spectrum of the reducer gear; The second optimization module 504 is used to optimize the structural parameters based on the possible values ​​of the structural parameters and the load spectrum, so as to obtain the optimal structural parameters of the reducer gear.

[0119] Preferably, the possible values ​​of the parameter to be optimized include the first uniform sampling point of the parameter to be optimized within the corresponding first range; the first optimization module 502 is specifically used to perform simulation based on the first uniform sampling point and the braking energy recovery control strategy to obtain simulation data; based on the simulation data, establish a first surrogate model between the parameter to be optimized and the first optimization objective of the braking energy recovery control strategy; and optimize the parameter to be optimized based on the first surrogate model using a multi-objective optimization algorithm to obtain the optimal control strategy parameter.

[0120] Preferably, the first optimization objective includes minimizing impact, minimizing speed error, and maximizing regenerative braking energy; the parameters to be optimized include the rate of change of electric motor torque and the rate of change of hydraulic braking torque.

[0121] Preferably, the possible values ​​of the structural parameter include the second uniform sampling point within the corresponding second range; the second optimization module 504 is specifically used to establish a reducer gear model based on the structural parameter; perform simulation analysis based on the load spectrum and the reducer gear model to obtain the fatigue life and volume corresponding to the second uniform sampling point; establish a second surrogate model between the second optimization objective of the structural parameter and the reducer gear model based on the fatigue life and volume corresponding to the second uniform sampling point; and optimize the structural parameter based on the second surrogate model using a multi-objective optimization algorithm to obtain the optimal structural parameter.

[0122] Preferably, the second optimization objective includes minimizing fatigue damage and minimizing gear volume; the structural parameters include module and helix angle.

[0123] Preferably, the first optimization module 502 is specifically used to employ multiple multi-objective optimization algorithms to optimize the parameter to be optimized based on the first surrogate model, thereby obtaining multiple Pareto solution sets; calculate the comprehensive distance index of the multiple Pareto solution sets; and, based on the preset weight of the parameter to be optimized, obtain the optimal solution from the Pareto solution set with the smallest comprehensive distance index as the optimal control strategy parameter by using the Top-Ideal Solution Ranking Method (TOPSIS).

[0124] Preferably, the process of obtaining the parameter to be optimized includes: obtaining the first optimization target of the braking energy recovery control strategy; analyzing the degree of influence of the adjustable parameter on the first optimization target through sensitivity analysis, and obtaining the sensitivity ranking result; obtaining the top N adjustable parameters in the sensitivity ranking result as the parameter to be optimized, where N is a positive integer greater than or equal to 1.

[0125] The braking strategy and reducer gear structure optimization system for electric vehicles provided in this application embodiment can be understood by referring to the relevant content in the foregoing method embodiment section, and will not be repeated here.

[0126] like Figure 6 As shown, Figure 6 This is a schematic diagram of a possible logical structure of a computing device provided in an embodiment of this application. The computing device 60 includes a processor 601, a communication interface 602, a memory 603, and a bus 604. The processor 601, the communication interface 602, and the memory 603 are interconnected via the bus 604. In an embodiment of this application, the processor 601 is used to control and manage the operation of the computing device 60. For example, the processor 601 is used to execute... Figure 1 The steps in the embodiments and / or other processes used in the techniques described herein. Communication interface 602 is used to support communication by computing device 60. Memory 603 is used to store program code and data of computing device 60.

[0127] The processor 601 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. The bus 604 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0128] In another embodiment of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the above-described... Figure 1 The method described in the embodiments.

[0129] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0130] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0131] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A braking strategy and reducer gear structure optimization method for electric vehicles, characterized in that, The method includes: The structural parameters of the reducer gear, the braking energy recovery control strategy, and the parameters to be optimized of the electric vehicle are obtained. The parameters to be optimized are adjustable parameters that affect the braking energy recovery effect of the electric vehicle. Based on the braking energy recovery control strategy and the possible values ​​of the parameters to be optimized, the parameters to be optimized are optimized to obtain the optimal control strategy parameters of the braking energy recovery control strategy. Based on the optimal control strategy parameters, CHTC-B urban driving condition simulation analysis of the electric vehicle was performed to obtain the load spectrum of the reducer gear. Based on the possible values ​​of the structural parameters and the load spectrum, the structural parameters are optimized to obtain the optimal structural parameters of the reducer gear.

2. The method according to claim 1, characterized in that, The possible values ​​of the parameter to be optimized include the first uniform sampling point of the parameter to be optimized within the corresponding first range; the optimization of the parameter to be optimized based on the braking energy recovery control strategy and the possible values ​​of the parameter to be optimized to obtain the optimal control strategy parameters of the braking energy recovery control strategy includes: Simulations were performed based on the first uniform sampling points and the braking energy recovery control strategy to obtain simulation data. Based on the simulation data, a first proxy model is established between the parameters to be optimized and the first optimization objective of the braking energy recovery control strategy. The optimal control strategy parameters are obtained by optimizing the parameters to be optimized based on the first agent model using a multi-objective optimization algorithm.

3. The method according to claim 2, characterized in that, The first optimization objective includes minimizing the overall impact of the electric vehicle, minimizing the speed error, and maximizing the regenerative braking energy; the parameters to be optimized include the rate of change of motor torque and the rate of change of hydraulic braking torque.

4. The method according to any one of claims 1-3, characterized in that, The possible values ​​of the structural parameters include the second uniform sampling points of the structural parameters within the corresponding second range; The process of optimizing the structural parameters based on their possible values ​​and the load spectrum to obtain the optimal structural parameters for the reducer gear includes: A reducer gear model is established based on the aforementioned structural parameters; Based on the load spectrum and the reducer gear model, simulation analysis is performed to obtain the fatigue life and volume corresponding to the second uniform sampling point; Based on the fatigue life and volume corresponding to the second uniform sampling point, a second proxy model is established between the structural parameters and the second optimization objective of the reducer gear model; The optimal structural parameters are obtained by optimizing the structural parameters based on the second surrogate model using a multi-objective optimization algorithm.

5. The method according to claim 4, characterized in that, The second optimization objective includes minimizing fatigue damage and minimizing gear volume of the reducer gear; the structural parameters include module and helix angle.

6. The method according to claim 2, characterized in that, The step of optimizing the parameters to be optimized based on the first surrogate model using a multi-objective optimization algorithm to obtain the optimal control strategy parameters includes: By employing various multi-objective optimization algorithms, the parameters to be optimized are optimized based on the first surrogate model to obtain multiple Pareto solution sets. Calculate the overall spacing index of the multiple Pareto solution sets; By using the Top-Solution Approximation Method (TOPSIS), based on the preset weights of the parameters to be optimized, the optimal solution is obtained from the Pareto solution set with the smallest comprehensive distance index as the optimal control strategy parameter.

7. The method according to claim 1, characterized in that, The process of obtaining the parameters to be optimized includes: Obtain the first optimization objective of the braking energy recovery control strategy; Sensitivity analysis is used to analyze the degree of influence of the adjustable parameters on the first optimization objective, and the sensitivity ranking results are obtained. The top N adjustable parameters in the sensitivity ranking result are obtained as the parameters to be optimized, where N is a positive integer greater than or equal to 1.

8. A braking strategy and reducer gear structure optimization system for an electric vehicle, characterized in that, The system, applied to the method of any one of claims 1-7, comprises: The acquisition module is used to acquire the structural parameters of the reducer gear of the electric vehicle, the braking energy recovery control strategy, and the parameters to be optimized. The parameters to be optimized are adjustable parameters that affect the braking energy recovery effect of the electric vehicle. The first optimization module is used to optimize the parameter to be optimized based on the braking energy recovery control strategy and the possible values ​​of the parameter to be optimized, so as to obtain the optimal control strategy parameters of the braking energy recovery control strategy. The simulation module is used to perform CHTC-B urban driving condition simulation analysis of the electric vehicle based on the optimal control strategy parameters, and to obtain the load spectrum of the reducer gear. The second optimization module is used to optimize the structural parameters based on the possible values ​​of the structural parameters and the load spectrum to obtain the optimal structural parameters of the reducer gear.

9. A computing device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method of any one of claims 1-7.

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