Vehicle control unit shell random vibration reliability prediction method based on multi-physics field coupling

Abstract

The invention discloses a vehicle control unit shell random vibration reliability prediction method based on multi-physics field coupling, and the method comprises the steps: constructing a controller shell three-dimensional model, optimizing the grid transition form of the three-dimensional model through employing a variable density method, and generating a finite element model; loading the finite element model into Ansys software to create an analysis working condition, setting a boundary condition and a load, and defining material and unit attributes at the same time; carrying out free mode and constraint mode analysis on the controller, and identifying a dynamic rigidity weak region of the structure; a Harmonic Response module is used for carrying out frequency response analysis; creating a non-stationary random vibration field of the controller in nCode software, and calculating power spectrum density values in X, Y and Z directions; the frequency response result in the step (4) is imported into a Design Life module, and the power spectrum density value in the step (5) is added; dynamic response evaluation and data post-processing under random excitation are implemented; and determining a weak area of high-cycle fatigue failure of the controller under the extreme working condition and optimizing a reinforcement scheme.

Description

Technical Field

[0001] The present invention relates to a method for predicting the random vibration reliability of the housing of a vehicle controller based on multi-physical field coupling. Background Art

[0002] As the core control unit of a vehicle, the structural reliability of the pure electric vehicle controller is directly related to the stability of the drive system, the safety of energy management, and the real-time performance of fault diagnosis. Currently, the controller is generally fixed to the vehicle body chassis through a rigid bracket and needs to withstand the multi-source coupling effects of high-frequency vibration of the motor, random road excitation, and dynamic load of the battery pack in actual working conditions. Research shows that the broadband vibration energy generated by the new energy vehicle's electric drive system in the range of 20 - 2000 Hz has increased by more than 60% compared with traditional fuel vehicles, and the reduction of the shell wall thickness caused by lightweight design (generally ≤ 3 mm) significantly reduces the structural natural frequency and is prone to resonance failure. However, in the existing vibration simulation method based on the GJB150.16A - 2009 standard, the definition of the broadband random vibration spectrum still continues the steady-state environment assumption of military equipment and does not fully consider the unique transient impact conditions of electric vehicles (such as the sudden change of regenerative braking torque and the thermo-vibration coupling effect caused by fast battery charging), resulting in a deviation rate of up to 35% - 42% between the predicted position of the shell fatigue crack and the actual vehicle road test failure point.

[0003] The currently widely used vibration characteristic simulation method has three limitations: First, in the modeling process, the controller is simplified as an isotropic homogeneous body, ignoring the contact nonlinear characteristics of the PCB board and the shell bolts, resulting in a calculation error of more than 18% for the first 6 modal frequencies; Second, the vibration response analysis mostly uses the single-axis PSD spectrum dimensional superposition method, which cannot characterize the stress strengthening effect caused by the vibration phase coupling of the X / Y / Z three axes, resulting in an underestimation of the 3σ stress extreme value by about 22% - 27%; Third, the traditional method relies on the modal stress recovery technology for fatigue assessment, but does not integrate the probability distribution characteristics of the material S-N curve, resulting in a dispersion of more than 40% between the predicted shell life and the bench acceleration test data. Although this method can shorten the design cycle by 20% - 30% through topology optimization, it lacks effective discrimination ability for progressive failure modes such as local buckling of the shell and fretting wear of solder joints, and it is difficult to meet the strict requirements of intelligent electric vehicles for the controller structure. Summary of the Invention

[0004] The present invention provides a method for predicting the random vibration reliability of the housing of a vehicle controller based on multi-physical field coupling to solve the problems existing in the above-mentioned prior art. The present invention solves the problem of the structural failure of the controller shell caused by the vibration coupling effect of new energy vehicles under complex working conditions. According to the vibration durability boundary conditions, this method breaks through the technical bottlenecks of traditional single-axis linear analysis in terms of non-stationary loads, multi-physical field coupling, and the characterization of micro-crack initiation mechanisms.

[0005] The technical solutions adopted by the present invention are as follows: A method for predicting the random vibration reliability of the housing of a vehicle controller based on multi-physical field coupling, comprising the following steps: (1) Based on the topology optimization theory, a parametric model of the controller housing with gradient wall thickness characteristics is constructed in Solidworks. A second-order tetrahedron-hexahedron hybrid mesh is used, and the local area of the solder joint region is encrypted (the minimum element size is 0.2 mm) to ensure that the quality coefficient of the mesh > 0.7.

[0006] (2) Load the finite element model into Ansys software to create analysis working conditions, set boundary conditions and loads, and define material and element properties at the same time; the housing of the controller is made of ADC12 die-cast aluminum alloy.

[0007] (3) Conduct free modal and constrained modal analyses on the controller to identify weak areas of structural dynamic stiffness; (4) Use the Harmonic Response module to perform the calculation of the frequency response function under swept-frequency excitation and conduct frequency response analysis; (5) Create a non-stationary random vibration field of the controller in nCode software and calculate the power spectral density values in the X, Y, and Z directions; (6) Import the frequency response results of step (4) into the DesignLife module and add the power spectral density values of step (5); (7) Conduct dynamic response evaluation and data post-processing under random excitation; (8) Determine the weak areas of high-cycle fatigue failure of the controller under extreme working conditions and the optimized reinforcement scheme.

[0008] Furthermore, in step (1), a three-dimensional model of the controller housing is constructed in Solidworks. A second-order tetrahedron-hexahedron hybrid mesh is used, and the local area of the solder joint region of the controller is encrypted to ensure that the quality coefficient of the mesh > 0.7.

[0009] Furthermore, in step (2), the created analysis working conditions include a) Quasi-static load condition: Apply a longitudinal equivalent load of 2.8g according to the ECE R13 braking specification and use a dynamic amplification factor of 2.8 for inertial release; b) Transient steering condition: Apply a lateral acceleration excitation of 8.6 m / s² according to the ISO 4138 standard and extract the time-varying load spectrum in combination with the ADAMS / Car multi-body dynamics model; c) Emergency braking condition: Apply a vertical impact load of 9.8 m / s² according to the MIL-STD-2101D standard and use the multi-axis synchronous excitation technology to achieve three-way coupled loading.

[0010] Further, in step (3), when performing free and constrained modal analyses on the controller, 10th-order modal parameters are extracted, and a modal effective mass matrix is constructed based on the ISO 13325 standard. When the frequency interval between any two orders of modal frequencies is less than 5% of the bandwidth and the MAC value exceeds 0.25, it is determined that there is a risk of resonance coupling. The strain energy concentration region is identified through the modal kinetic energy density cloud diagram, and when the local modal participation factor exceeds 0.35, it is marked as a dynamic stiffness weak area.

[0011] Further, in step (5), a non-stationary random vibration field of the controller is created, and the power spectral densities in the X, Y, and Z directions are respectively input. The random vibration in each direction is analyzed separately, and the power spectral density in one direction is added each time, and it is added three times.

[0012] Further, in step (7), according to the analysis results of step (6), the stress values of 1-σ, 2-σ, and 3-σ of the controller in the X, Y, and Z directions are obtained respectively, the position where the maximum stress occurs is judged, and whether the stress value is within the material yield limit is judged.

[0013] The present invention has the following beneficial effects: The present invention adopts a simulation method to pre-identify the possible structural damage areas of the controller in the vibration environment. On this basis, the structure of the controller is designed specifically, and reinforcement treatment is implemented on these potential damage areas, so as to prevent damage to this area during use. This not only optimizes the design process, shortens the design cycle, but also reduces the economic cost of research and development, providing an innovative solution for the safety protection of the high-voltage electronic control unit of intelligent electric vehicles. Description of the Drawings

[0014] Figure 1 It is a schematic diagram of the finite element model of the controller structure of the present invention.

[0015] In the figure: 1. Connector; 2. Lower cover housing; 3. PCB board; 4. Upper cover housing; 5. Plug.

[0016] Figure 2 It is a schematic diagram of the displacement frequency response results of the controller in the X, Y, and Z directions.

[0017] Figure 3 It is a schematic diagram of the stress frequency response results of the controller in the X, Y, and Z directions. Detailed Embodiments

[0018] The present invention will be further described below with reference to the accompanying drawings.

[0019] As Figures 1 to 3 , a method for predicting the random vibration reliability of the outer shell of a vehicle controller based on multi-physical field coupling according to the present invention, the specific implementation steps are as follows: (1) Apply Solidworks software through the design scheme to establish a finite element model of the controller structure; the controller structure is as Figure 1 shown, including four parts, namely joint 1, lower cover housing 2, PCB board 3, upper cover housing 4 and plug 5.

[0020] (2) Import the established model into Ansys workbench software to create different analysis conditions.

[0021] In different analysis conditions, boundary conditions and loads are set respectively, including three conditions: static load, steering and braking. The dynamic load coefficient in the static load condition is set to 2.5 times, the lateral acceleration in the steering condition is 8.6 m / s², and the dynamic load coefficient is 1.5 times. While the acceleration in the braking condition is 9.8 m / s², and the dynamic load coefficient is also 1.5 times.

[0022] (3) Define the materials and element properties in the model. The controller housing is made of aluminum alloy material. According to the material grade, the corresponding elastic modulus, Poisson's ratio and density are input. For the PCB board module inside the controller, equivalent weight treatment is carried out by the method of customizing density.

[0023] (4) Carry out free modal and constrained modal analyses on the controller to predict the areas with insufficient stiffness in the structure. By observing the first 10 modal frequencies and their vibration modes, the areas with more vibration times can be identified, and these areas are the potential problem points with insufficient stiffness.

[0024] (5) Carry out frequency response analysis by using the Harmonic Response module. During this process, a frequency response analysis task is created, the frequency range from 0 to 2000 Hz is defined, and 200 points are scanned by the linear method to obtain detailed analysis data.

[0025] (6) In nCode software, create a non-spatial field for the random analysis of the controller. Refer to the GJB150.16A-2009 standard and the acceleration model to calculate the power spectral density values in the X, Y, and Z directions. It should be noted that the random vibration analyses in the three directions are carried out separately, and only the power spectral density in one direction is added each time, and a total of three additions are made.

[0026] (7) Open the DesignLife module, import the frequency response results in (5), and add the power spectral density created in (6); import the frequency response results in the X, Y, and Z directions respectively, and add the PSD spectra in the corresponding directions for the analysis results in each direction.

[0027] (8) Conduct random response analysis and post-processing of the results; respectively obtain the 1-σ, 2-σ, and 3-σ stress values of the controller in the X, Y, and Z directions according to the analysis results, determine the location where the maximum stress occurs, and determine whether the stress value is within the material yield limit.

[0028] (9) According to the analysis results in step (8), the possible structural damage areas of the controller when encountering sudden vehicle conditions and bad road conditions are determined, providing a scientific basis for subsequent structural optimization and reinforcement design.

[0029] Adopt digital twin technology to synchronize simulation data and physical test results in real time through the OPC UA protocol.

[0030] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting the random vibration reliability of the shell of a vehicle controller based on multi-physical field coupling, characterized in that: It includes the following steps: (1) Construct a 3D model of the controller housing, optimize the mesh transition form of the 3D model using the variable density method, and generate a refined finite element model including threaded connection features and solder joints; (2) Load the finite element model into Ansys software to create analysis conditions, set boundary conditions and loads, and define material and element properties simultaneously; (3) Conduct free modal and constrained modal analyses on the controller to identify weak areas of structural dynamic stiffness; (4) Use the Harmonic Response module to perform frequency response analysis; (5) Create a non-stationary random vibration field of the controller in nCode software and calculate the power spectral density values in the X, Y, and Z directions; (6) Import the frequency response results of step (4) in the DesignLife module and add the power spectral density values of step (5); (7) Implement dynamic response evaluation and data post-processing under random excitation; (8) Determine the weak areas of high-cycle fatigue failure of the controller under extreme conditions and optimize the reinforcement scheme.

2. The method for predicting the random vibration reliability of the vehicle controller housing based on multi-physical field coupling according to claim 1, characterized in that: In step (1), construct a 3D model of the controller housing in Solidworks, use a second-order tetrahedron-hexahedron hybrid mesh, locally refine the solder joint area of the controller, and ensure that the quality coefficient of the mesh > 0.

7.

3. The method for predicting the random vibration reliability of the vehicle controller housing based on multi-physical field coupling according to claim 1, wherein: In step (2), the created analysis conditions include a) Quasi-static load condition: Apply a longitudinal equivalent load of 2.8g according to the ECE R13 braking specification, and use a dynamic amplification factor of 2.8 for inertial release; b) Transient steering condition: Apply a lateral acceleration excitation of 8.6 m / s² according to the ISO 4138 standard, and extract the time-varying load spectrum in combination with the ADAMS / Car multi-body dynamics model; c) Emergency braking condition: Apply a vertical impact load of 9.8 m / s² according to the MIL-STD-2101D standard, and use the multi-axis synchronous excitation technology to achieve three-way coupled loading.

4. The random vibration reliability prediction method for the vehicle controller housing based on multi-physical field coupling according to claim 1, wherein: In step (3), when conducting free modal and constrained modal analyses on the controller, extract 10-order modal parameters, construct a modal effective mass matrix based on the ISO 13325 standard. When the frequency interval between any two-order modes is less than 5% of the bandwidth and the MAC value exceeds 0.25, it is determined that there is a risk of resonance coupling; identify the strain energy concentration area through the modal kinetic energy density cloud map, and mark it as a weak area of dynamic stiffness when the local modal participation factor exceeds 0.

35.

5. The random vibration reliability prediction method for the vehicle controller housing based on multi-physical field coupling according to claim 1, characterized in that: In step (5), create a non-stationary random vibration field of the controller, input the power spectral density in the X, Y, and Z directions respectively; analyze the random vibration in each direction separately, add the power spectral density in one direction each time, and add it three times.

6. The random vibration reliability prediction method for the vehicle controller housing based on multi-physical field coupling according to claim 1, characterized in that: In step (7), according to the analysis results of step (6), obtain the stress values of 1-σ, 2-σ, and 3-σ in the X, Y, and Z directions of the controller respectively, determine the position where the maximum stress occurs, and determine whether the stress value is within the material yield limit.

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