Motor controller aging test method and device

Abstract

The invention provides a motor controller aging test method and device, which can accurately simulate an actual failure working condition and dynamically optimize a new strategy of aging duration so as to maximize the interception efficiency of an aging test on potential faults and realize the optimal balance of quality, cost and benefits under the constraint of limited cost. The method comprises the steps of formulating an alternating cycle working condition for an aging test based on a collected fault database of a motor controller; the historical fault database is obtained through fault data fed back by the market end motor controller, and the historical fault data comprises operation condition parameters when a fault occurs; the optimal aging duration is dynamically determined based on the alternating cycle working condition in combination with cost benefit analysis; the cost income analysis is used for evaluating the total cost under different aging durations, and the total cost is the sum of the aging test cost and the market maintenance cost estimated based on the failure probability; and performing a factory aging test on the to-be-tested motor controller according to the alternating cycle working condition and the optimal aging duration.

Description

A method and apparatus for aging test of motor controller Technical Field

[0001] This application relates to the field of motor controllers, specifically to a method and apparatus for aging tests on motor controllers. Background Technology

[0002] As the core actuator of the electric drive system in new energy vehicles, the reliability of the motor controller directly affects the safety and performance of the vehicle. The controller integrates a large number of electronic components such as power semiconductors (e.g., IGBTs), capacitors, and drive circuits, and its inherent failure rate is far higher than that of traditional mechanical components. Therefore, conducting aging tests on the motor controller before it leaves the factory has become a crucial process in the industry for intercepting early failures and improving the reliability of finished products.

[0003] Currently, the aging test methods commonly used in the industry have two main limitations: First, the aging test operating conditions are too simplified, resulting in limited interception effectiveness. Most existing methods use fixed DC bus voltage, constant load current, or simple power cycling for aging, which cannot simulate the complex and transient electro-thermal stress conditions faced by motor controllers in real vehicle operation. As a result, many potential defects sensitive to specific dynamic conditions cannot be stimulated and exposed during aging tests, thus escaping to the market and causing malfunctions after a period of user use, significantly increasing after-sales maintenance costs and brand reputation risks.

[0004] Second, the aging test time lacks scientific optimization and is economically inefficient. Currently, the determination of aging time mainly relies on experience and is usually set to a fixed value. This method has significant drawbacks: on the one hand, when production quality is consistently excellent, excessively long aging times result in huge waste of electricity, equipment occupation, and time costs; on the other hand, when production batches fluctuate and the potential defect rate increases, a fixed aging time may not be sufficient to guarantee adequate fault interception. Essentially, this is a crude management approach that results in an imbalance between cost and interception effectiveness.

[0005] While existing technologies focus on reducing the energy consumption of aging equipment through technologies such as energy feedback, they do not address the fundamental issue of how to determine the most effective operating conditions and durations. The equipment investment is large and the targeting of interception has not been improved. Summary of the Invention

[0006] This application provides a method and apparatus for aging tests of motor controllers, which can accurately simulate actual failure conditions and dynamically optimize the aging time. This new strategy aims to maximize the interception efficiency of aging tests for potential faults under limited cost constraints, and achieve the best balance between quality, cost and benefits.

[0007] The technical solution of the present invention is as follows: In a first aspect, this application provides a method for aging test of a motor controller, comprising: formulating alternating cyclic operating conditions for aging test based on a collected fault database of the motor controller; wherein the historical fault database is obtained through fault data fed back by the motor controller at the market, and the historical fault data includes operating condition parameters at the time of the fault occurrence; dynamically determining the optimal aging time based on the alternating cyclic operating conditions and in conjunction with cost-benefit analysis; wherein the cost-benefit analysis is used to evaluate the total cost under different aging times, and the total cost is the sum of the aging test cost and the market maintenance cost estimated based on the failure probability; and performing a factory aging test on the motor controller under test according to the alternating cyclic operating conditions and the optimal aging time.

[0008] By obtaining the operating parameters of motor controllers that have experienced real failures in the market, the required operating conditions for aging tests are determined. This allows the tests to accurately reproduce the actual electrothermal stress conditions that lead to failure, thus solving the problems of simplified operating conditions and limited interception effects in traditional methods. Simultaneously, by combining aging test costs with estimated market maintenance costs through cost-benefit analysis, the aging duration is dynamically optimized. This automatically adjusts the test intensity when quality fluctuates, maximizing interception efficiency within a limited cost, and overcoming the fundamental contradiction between the economics and interception effect of traditional fixed-duration methods.

[0009] In some embodiments, the operating condition parameters include at least one of the following: motor controller operating mode, vehicle failure mileage, DC bus voltage, motor torque, motor speed, ambient temperature, and coolant temperature.

[0010] By explicitly incorporating key parameters such as the motor controller's operating mode, vehicle failure mileage, DC bus voltage, motor torque, motor speed, ambient temperature, and coolant temperature into the range of operating condition parameters, a specific and comprehensive data foundation is provided for developing alternating cyclic operating conditions based on historical fault data. This feature enables the constructed aging conditions to accurately cover multi-dimensional stress conditions such as electrical load, thermal state, and control state at the actual failure point, thereby enhancing the ability to trigger and intercept defects caused by specific voltage stress, torque-speed combined stress, or temperature stress, and improving the effectiveness of aging tests in simulating real complex operating conditions from the data source.

[0011] In some embodiments, the step of formulating alternating cyclic operating conditions for aging tests based on the collected fault database of the motor controller includes: extracting typical failure points from each of the operating condition parameters, and combining and arranging multiple typical failure points to form various fault conditions of the motor controller.

[0012] By extracting typical failure conditions from various operating conditions and combining multiple typical failure conditions to form various fault conditions, the constructed alternating cyclic conditions can directly reproduce and combine to cover the key stress states in real failure cases. This overcomes the shortcomings of traditional fixed or simplified conditions in stimulating specific dynamic failure modes, and achieves more targeted and comprehensive stress loading for different failure mechanisms, significantly improving the efficiency of aging tests in stimulating potential defects and the coverage of interception.

[0013] In some embodiments, the step of dynamically determining the optimal aging time based on the alternating cyclic operating conditions and cost-benefit analysis includes: constructing a failure mileage statistical model for each motor controller based on historical fault data; establishing a mapping relationship between aging time and equivalent mileage based on the alternating cyclic operating conditions and the Arrhenius model; constructing an evaluation model by combining the failure mileage statistical model and the mapping relationship; the evaluation model is used to input any aging time and output the corresponding total cost evaluation value, wherein the total cost evaluation value is the sum of the estimated market maintenance cost and aging test cost based on the aging time; evaluating different aging times through the evaluation model, and determining the optimal aging time with the goal of minimizing the total cost evaluation value.

[0014] By constructing a failure mileage statistical model and an aging duration-equivalent mileage mapping relationship, and combining the two to form an evaluation model, a quantitative assessment of the market maintenance cost and aging test cost corresponding to any aging duration can be achieved. This technology transforms the decision-making of aging duration from an empirical fixed value to a dynamic optimization process based on model and data analysis. It can automatically find the optimal duration that minimizes the total cost, thereby ensuring the interception effect while accurately controlling the aging cost. This solves the problems of poor economy and unstable interception effect caused by the disconnect between aging duration and quality level and cost factors in traditional methods.

[0015] In some embodiments, the step of constructing a failure mileage statistical model of motor controllers based on historical fault data of each motor controller includes: reading vehicle failure mileage from historical fault data of each motor controller; calculating the cumulative failure rate of motor controllers based on the number of motor controllers that have failed and the total number of corresponding production batches of motor controllers; and fitting a functional relationship between the cumulative failure rate of motor controllers and vehicle failure mileage based on the cumulative failure rate of motor controllers and multiple vehicle failure mileages.

[0016] By reading vehicle failure mileage from historical fault data and calculating the cumulative failure rate by combining the number of faults with the total number of production batches, a functional relationship between the cumulative failure rate and vehicle failure mileage is fitted, thereby constructing a failure mileage statistical model that can quantitatively describe the change in failure rate with mileage. This model provides a key data foundation and predictive basis for subsequent assessment of potential market maintenance risks, enabling the decision on aging duration to be based on a scientific basis of quantitatively predicting the failure probability, and improving the accuracy and objectivity of dynamically determining the optimal aging duration.

[0017] In some embodiments, the step of establishing a mapping relationship between aging time and equivalent driving mileage based on the alternating cycle operating conditions and the Arrhenius model includes: obtaining the average junction temperature of key electronic components in the motor controller under the alternating cycle operating conditions through thermal simulation or bench testing based on the operating condition parameters corresponding to the alternating cycle operating conditions; substituting the average junction temperature and the reference average junction temperature of the key electronic components under standard operating conditions into the Arrhenius equation to calculate the aging acceleration factor of the alternating cycle operating conditions relative to the standard operating conditions; and establishing a linear proportional relationship between aging time and equivalent driving mileage based on the aging acceleration factor and the average vehicle speed under standard operating conditions.

[0018] The average junction temperature of key electronic components under alternating cyclic conditions is obtained through thermal simulation or bench testing. The aging acceleration factor is calculated using the Arrhenius equation in conjunction with the standard operating condition reference junction temperature. Based on this acceleration factor and the average vehicle speed, a linear proportional relationship is established between aging time and equivalent mileage. This technical feature transforms the abstract aging test time into an intuitive equivalent vehicle mileage, establishing a quantifiable correspondence between aging test intensity and actual reliability. This provides a unified benchmark for cost-benefit analysis based on the failure mileage statistical model, thereby supporting the scientific and accurate decision-making regarding aging time.

[0019] In some embodiments, the step of constructing an evaluation model by combining the failure mileage statistical model and the mapping relationship includes: inputting the equivalent mileage output from the mapping relationship between the aging duration and the equivalent driving mileage into the failure mileage statistical model to obtain an estimated cumulative failure rate corresponding to the equivalent driving mileage; calculating the estimated number of potential failures based on the estimated cumulative failure rate and the total number of motor controllers produced; calculating the estimated market maintenance cost based on the estimated number of potential failures and the preset single-unit maintenance cost; calculating the aging test cost based on the aging duration and the alternating cyclic operating conditions; and adding the estimated market maintenance cost and the aging test cost to obtain the total cost assessment value corresponding to the aging duration; wherein, the evaluation model is defined as a function or program module that performs the above steps to output the total cost assessment value corresponding to any input aging duration.

[0020] By inputting the equivalent driving mileage into the failure mileage statistical model to obtain the cumulative failure rate, the potential number of failures, the estimated market maintenance cost, and the aging test cost are calculated sequentially, and finally the total cost assessment value is synthesized to construct a complete assessment model. This model realizes a closed-loop quantitative assessment of the full-cycle cost caused by any aging scheme, directly linking the aging interception effect with the market quality cost, thereby transforming the decision-making process of dynamically determining the optimal aging duration into a scientific optimization problem with the goal of minimizing the total cost, fundamentally solving the inherent contradiction of the difficulty in balancing cost and effect in traditional aging tests.

[0021] In some embodiments, the steps of performing a factory aging test on the motor controller under test according to the alternating cycle conditions and the optimal aging time include: connecting the motor controller under test to the aging test bench, applying electrical stress and thermal stress according to the timing and parameters defined by the alternating cycle conditions, and continuously running the optimal aging time, thereby completing the factory aging test process based on data-driven and cost-optimized decision-making.

[0022] By connecting the motor controller under test to the test bench and applying stress according to the established alternating cyclic operating conditions, and using the optimal aging time obtained through cost optimization as the test duration, a closed-loop process from data analysis, operating condition formulation, duration optimization to physical execution is finally completed. This step directly transforms the theoretical optimization strategy derived from market data and cost models into specific and executable aging test operations, ensuring that the theoretical improvement in interception effect and cost savings can be accurately implemented and verified in the actual production process.

[0023] In some embodiments, the step of calculating the aging test cost based on the aging duration and the alternating cycle conditions includes: calculating the average power consumption during the aging process based on the load current parameters and load impedance of the alternating cycle conditions; obtaining the average thermal power consumption of the aging test equipment while maintaining the ambient temperature required for the alternating cycle conditions; calculating the total energy cost based on the average power consumption and the average thermal power consumption, combined with the duration of the aging test and the local industrial electricity price; calculating the time-related cost based on the duration of the aging test, the equipment depreciation rate, and labor input; and adding the total energy cost to the time-related cost to obtain the aging test cost.

[0024] By calculating the average power consumption based on the load parameters under alternating cyclic operating conditions, and combining the environmental temperature control heat power consumption, aging time, and electricity price to calculate the total power cost, while also considering equipment depreciation and labor input to calculate time costs, the aging test cost is accurately quantified. This step decomposes the resource consumption of the aging test into two core cost components: power and time, and performs detailed accounting, providing accurate aging test cost input for cost-benefit analysis, and ensuring the accuracy of the evaluation model's calculations and the reliability of its decisions when balancing aging investment and market risks.

[0025] This application also provides a motor controller aging test device, comprising: an acquisition module for acquiring historical fault data fed back by motor controllers from the market, the historical fault data including operating condition parameters at the time of the fault occurrence; an operating condition formulation module for formulating alternating cyclic operating conditions for aging tests based on the historical fault data; an optimal aging time determination module for dynamically determining the optimal aging time based on the alternating cyclic operating conditions and cost-benefit analysis; wherein the cost-benefit analysis is used to evaluate the total cost under different aging times, the total cost being the sum of the aging test cost and the market repair cost estimated based on the failure probability; and a test module for performing a factory aging test on the motor controller under test according to the alternating cyclic operating conditions and the optimal aging time. Attached Figure Description

[0026] Figure 1 is a flowchart illustrating the motor controller aging test method in an embodiment of this application; Figure 2 is a schematic diagram illustrating the motor controller aging test apparatus in an embodiment of this application. Detailed Implementation

[0027] Referring to Figure 1, an embodiment of this application provides a method for aging test of a motor controller, including: S101, formulating alternating cyclic operating conditions for aging test based on a collected fault database of the motor controller; wherein, the historical fault database is obtained through fault data fed back by motor controllers in the market, and the historical fault data includes operating condition parameters at the time of the fault; S102, dynamically determining the optimal aging time based on the alternating cyclic operating conditions and cost-benefit analysis; wherein, the cost-benefit analysis is used to evaluate the total cost under different aging times, and the total cost is the sum of the aging test cost and the market maintenance cost estimated based on the failure probability; S103, performing a factory aging test on the motor controller under test according to the alternating cyclic operating conditions and the optimal aging time.

[0028] In step S101, the market-end motor controller refers to a motor controller that has been sold and installed in a vehicle and is in actual use by the user. This contrasts with the motor controller under test on the production line or the sample controller in the R&D stage.

[0029] The generation of fault data for motor controllers relies on their built-in fault diagnosis and data logging functions. When the internal diagnostic system of the motor controller detects an error exceeding a threshold (such as overcurrent, overvoltage, overtemperature, drive failure, communication failure, etc.), it automatically records key operating parameters at the moment of the fault and within a preset time period (e.g., 5 seconds) before the fault occurs. Then, via the vehicle's CAN network and the onboard T-Box (telematics processor), the fault code and fault data snapshot are uploaded to the manufacturer's or service provider's cloud-based big data platform. When the cloud-based big data platform receives a fault code from a motor controller, it automatically triggers a data capture program to retrieve the operating parameters recorded by the motor controller before and after the fault time from the cloud database. By aggregating massive amounts of fault data reported by vehicles and their corresponding operating parameters, a fault database is formed for analysis.

[0030] In this embodiment of the application, the operating condition parameters include at least one of the following: motor controller operating mode, vehicle failure mileage, DC bus voltage, motor torque, motor speed, ambient temperature, and coolant temperature.

[0031] Based on the collected fault database of the motor controller, the steps for formulating alternating cyclic operating conditions for aging tests include: extracting typical failure points from the operating condition parameters, and combining and arranging multiple typical failure points to form various fault conditions of the motor controller.

[0032] Specifically, the fault database is cleaned and analyzed to identify common key stress conditions leading to failures. For example, statistical analysis reveals that IGBT failures are concentrated in operating conditions characterized by high DC bus voltage (e.g., ≥600V), high motor torque (near peak torque), medium to high speed, and relatively high coolant temperature (e.g., >75℃). These statistical patterns are then transformed into specific, quantifiable parameter points. For instance, a typical failure condition P1 can be defined as: {DC bus voltage: 650V, motor torque: 150% of rated torque, motor speed: 5000rpm, coolant temperature: 80℃, duration: 30 seconds}. This typical failure condition does not represent a single, isolated failure moment, but rather a typical generalization of parameters under similar failure conditions.

[0033] A single operating point represents only one type of steady-state or quasi-steady-state stress; actual failures often occur under the alternating and cyclical action of multiple stresses. Therefore, it is necessary to combine multiple typical operating points (such as P1 representing high-power impact, P2 representing high-temperature hot immersion, and P3 representing frequent start-stop) according to a certain logic and time sequence.

[0034] The programming needs to simulate the stress changes in real driving. For example, a complete alternating cycle might be designed as follows: start-up → apply P3 (frequent start-stop) cycle 5 times → transition to P1 (high power shock) and hold → transition to P2 (high temperature immersion) and hold → return to idle. This programming allows the motor controller to be subjected to the combined effects of thermal shock, electrical stress, and high temperature aging in a single test cycle.

[0035] By combining different operating point sequences, test cycles with different focuses can be constructed. For example, one cycle may focus on solder fatigue caused by power cycling, while another cycle may focus on thermal expansion mismatch caused by temperature cycling. Multiple failure conditions refer to these different alternating cycle test spectra designed for different failure mechanisms.

[0036] In this embodiment of the application, step S102, which dynamically determines the optimal aging time based on the alternating cyclic operating condition and combined with cost-benefit analysis, includes: S1021, constructing a failure mileage statistical model for each motor controller based on historical fault data; S1022, establishing a mapping relationship between aging time and equivalent mileage based on the alternating cyclic operating condition and the Arrhenius model; S1023, constructing an evaluation model by combining the failure mileage statistical model and the mapping relationship; the evaluation model is used to input any aging time and output the corresponding total cost evaluation value, wherein the total cost evaluation value is the sum of the estimated market maintenance cost and aging test cost based on the aging time; S1024, evaluating different aging times through the evaluation model and determining the optimal aging time with the goal of minimizing the total cost evaluation value.

[0037] In step S1021, the key field corresponding to each failed motor controller is retrieved from the fault database—the cumulative mileage at the time of vehicle failure (i.e., vehicle failure mileage).

[0038] All motor controllers produced within a specific production batch or time period are selected as the research object, with the total number denoted as N; some controllers have failed. The collected vehicle failure mileage data, along with the current cumulative mileage of vehicles still in operation, are compiled into a standard dataset suitable for reliability analysis. Based on this standard dataset, statistical methods (such as maximum likelihood estimation) are used to fit a probability distribution model that best describes the lifetime distribution of the controllers in this batch; commonly used probability distribution models include the Weibull distribution and the log-normal distribution. Taking the Weibull distribution as an example, the fitting process will determine its shape parameter β and scale parameter η.

[0039] The completed failure mileage statistical model is essentially a cumulative failure rate function F(t). For the Weibull distribution, its expression is: In this function, t represents the input vehicle failure mileage; F(t) represents the proportion of controllers in the batch expected to have failed when the vehicle mileage reaches t. This function fully characterizes the relationship between failure probability and vehicle mileage; for example, F(100,000) can predict the theoretical cumulative failure rate when the vehicle has traveled 100,000 kilometers.

[0040] When the Arrhenius model calculates that a certain aging scheme is equivalent to driving L... equiv After kilometers, this L equiv Substitute the value into the failure mileage statistical model F(t). The calculated F(L) equiv The value represents the proportion of potential defects that can theoretically be filtered out at this aging intensity.

[0041] In summary, in this embodiment of the application, step S1021, which constructs a statistical model of the failure mileage of the motor controller based on the historical fault data of each motor controller, includes: reading the vehicle failure mileage from the historical fault data of each motor controller; calculating the cumulative failure rate of the motor controller based on the number of motor controllers that have failed and the total number of corresponding production batches of the motor controllers; and fitting a functional relationship between the cumulative failure rate of the motor controller and the vehicle failure mileage based on the cumulative failure rate of the motor controller and the failure mileage of multiple vehicles.

[0042] In step S1022, the Arrhenius model is a classical physical model describing the relationship between the rate of a chemical reaction (in analogous to the aging rate R of an electronic device) and temperature. Its basic formula is:

[0043] Where R is the aging rate, Ea is the activation energy of the failure mechanism (unit: eV), k is the Boltzmann constant, and T is the absolute temperature (unit: K).

[0044] The Arrhenius model shows that the aging rate of a device increases exponentially with its junction temperature. Therefore, by increasing the test temperature, the aging process can be accelerated, achieving the same amount of degradation as a longer actual usage time in a shorter test period.

[0045] Based on the above principles, establishing the mapping relationship requires: selecting the key electronic components in the motor controller that are most sensitive to thermal stress and whose failure dominates the overall lifespan (such as IGBT chips or diodes connected in antiparallel to them). Through thermal simulation or bench testing, obtain the average junction temperature T of the device under the stated alternating cycle conditions. test (Considering the thermal effects throughout the entire cycle); the reference average junction temperature T of this device under the standard driving conditions (such as CLTC, WLTC) of the target market's complete vehicles. field .

[0046] The acceleration factor AF is calculated using the Arrhenius equation: AF = exp[(Ea / k)*((1 / T)] field )-( 1 / T test Ea needs to be determined through prior reliability testing or by using the typical empirical value of the device. The physical meaning of the acceleration factor AF is that the amount of degradation caused to critical components by aging for 1 hour under the alternating cycle conditions is equivalent to driving for AF hours under standard road conditions.

[0047] Given an aging test duration t test (hours), its equivalent standard working time is: t equiv =AF*t test (Hour).

[0048] Introducing the average vehicle speed V under standard vehicle operating conditions avg (Unit: km / h). This vehicle speed is based on statistics from typical driving cycles in the target market. Finally, the aging time t is established. test Equivalent driving distance L equiv Linear mapping relationship between them: L equiv =V avg *t equiv =V avg *AF*t test This indicates that the equivalent mileage and aging time have a simple direct proportional relationship, with a proportionality coefficient of (V). avg *AF).

[0049] In summary, in this embodiment of the application, step S1022, which establishes the mapping relationship between aging time and equivalent driving mileage based on the alternating cycle operating conditions and the Arrhenius model, includes: obtaining the average junction temperature of key electronic components in the motor controller under the alternating cycle operating conditions through thermal simulation or bench testing based on the operating condition parameters corresponding to the alternating cycle operating conditions; substituting the average junction temperature and the reference average junction temperature of the key electronic components under standard operating conditions into the Arrhenius equation to calculate the aging acceleration factor of the alternating cycle operating conditions relative to the standard operating conditions; and establishing a linear proportional relationship between aging time and equivalent driving mileage based on the aging acceleration factor and the average vehicle speed under standard operating conditions.

[0050] In this embodiment of the application, step S1023, which integrates the failure mileage statistical model and the mapping relationship to construct the evaluation model, includes: inputting the equivalent mileage output from the mapping relationship between the aging time and the equivalent driving mileage into the failure mileage statistical model to obtain an estimated cumulative failure rate corresponding to the equivalent driving mileage; calculating the estimated number of potential failures based on the estimated cumulative failure rate and the total number of motor controllers produced; calculating the estimated market maintenance cost based on the estimated number of potential failures and a preset single-unit maintenance cost; calculating the aging test cost based on the aging time and the alternating cycle operating conditions; and adding the estimated market maintenance cost to the aging test cost to obtain the total cost evaluation value corresponding to the aging time. The evaluation model is defined as a function or program module that executes the above steps to output the total cost evaluation value corresponding to any input aging time.

[0051] The aging test cost consists of two main parts: energy consumption cost and time-related cost. The steps for calculating the aging test cost based on the aging duration and the alternating cyclic operating conditions include: calculating the average power consumption during the aging process based on the load current parameters and load impedance of the alternating cyclic operating conditions; specifically: calculating the effective value I(t) of the current flowing through the load (such as a three-phase inductor) and the load impedance Z in each time period based on the time sequence of the alternating cyclic operating conditions, to obtain the instantaneous power P. load (t)=3*I(t) 2 *Z. For a complete cycle T cycle Calculate the average to obtain the average power consumption P. elec_avg ; Obtain the average thermal power consumption of the aging test equipment while maintaining the ambient temperature required for the alternating cycle operating conditions; specifically: obtain the average thermal power consumption P of the heating / cooling system of the aging test equipment (such as a temperature-controlled test chamber) while maintaining the ambient temperature required for the alternating cycle operating conditions. thermal_avg Based on the average electrical power consumption and the average thermal power consumption, combined with the duration of the aging test and the local industrial electricity price, the total energy cost is calculated; the total energy cost C elec The calculation formula is: C elec =(P elec_avg +P thermal_avg )*t test *P rice_elec ; where t test P represents the duration of the aging test. rice_elec This refers to the local industrial electricity price.

[0052] Based on the duration of the aging test, equipment depreciation rate, and labor input, the time-related cost is calculated; time is associated with C. time =(Equipment depreciation per unit time + Labor cost per unit time) * ttest The total energy cost is added to the time-related cost to obtain the aging test cost, specifically: C test =C elec +C time .

[0053] This calculation process provides the evaluation model with precise input C for the cost of aging tests. test C test The duration of the aging test (t) test A linear function whose slope is determined by the specific working conditions (P) elec_avg and P thermal_avg The cost is jointly determined by local electricity prices, equipment depreciation rates, and labor costs. When the evaluation model iteratively optimizes, C... test Follow t test The property of increasing linearly with increasing estimated market maintenance costs, and the fact that these costs increase with t test The trade-off between increasing and decreasing attributes constitutes the core trade-off and is a key factor driving the system to find the optimal aging time that minimizes total cost.

[0054] Through the aforementioned process, the optimal aging time adapted to the alternating cyclic operating conditions is determined. Based on this, step S103 can be executed. In step S103, the motor controller under test is connected to the aging test bench, and electrical and thermal stresses are applied according to the timing and parameters defined by the alternating cyclic operating conditions. The optimal aging time is then continuously run, thereby completing the factory aging test process based on data-driven and cost-optimized decision-making.

[0055] The motor controller aging test method provided in this application integrates market-available motor controller failure data, reliability physical models, and economic analysis, achieving a fundamental transformation in aging testing from experience-driven to data and model-driven approaches, resulting in significant multi-dimensional synergistic optimization. Specifically, regarding interception effectiveness, by directly constructing an alternating cycle test spectrum based on real market failure condition data, the aging test can accurately reproduce and combine key electrical, thermal, and load stresses that lead to actual failures. This enhances the ability to trigger specific dynamic failure modes (such as power cycle fatigue and temperature shock failure), thereby improving the targeted interception coverage of potential defects and effectively preventing defective products from escaping to the market.

[0056] In terms of economic optimization, an evaluation model is constructed that integrates a failure mileage statistical model, an aging duration-equivalent mileage mapping relationship, and a refined cost model. This transforms the decision on aging duration into a scientific optimization problem aimed at minimizing total cost (aging test cost + estimated market maintenance cost). This method can dynamically calculate the optimal aging duration under current conditions based on factors such as production quality fluctuations, resource prices, and maintenance costs. This ensures effective interception while precisely controlling and minimizing aging test resource input, fundamentally resolving the cost-effectiveness imbalance caused by the traditional fixed-duration model.

[0057] In terms of the scientific nature of decision-making, this method establishes a complete and closed-loop technical chain, from market failure data to test condition settings, and then to equivalent mileage conversion and cost risk quantification. Each step is supported by clear data foundations, physical models, or statistical models, which transforms the entire aging strategy formulation process from subjective experience-based judgment to objective and quantitative engineering decisions, thereby improving the predictability, repeatability, and adaptive optimization capabilities of aging test management.

[0058] Referring to Figure 2, this application embodiment also provides a motor controller aging test device corresponding to the above-described motor main controller aging test method, comprising: an acquisition module 101, used to acquire historical fault data fed back by the motor controller at the market, the historical fault data including operating condition parameters at the time of the fault occurrence; an operating condition formulation module, used to formulate alternating cyclic operating conditions for aging tests based on the historical fault data; an optimal aging time determination module 102, used to dynamically determine the optimal aging time based on the alternating cyclic operating conditions and cost-benefit analysis; wherein, the cost-benefit analysis is used to evaluate the total cost under different aging times, the total cost being the sum of the aging test cost and the market maintenance cost estimated based on the failure probability; and a test module 103, used to perform a factory aging test on the motor controller under test according to the alternating cyclic operating conditions and the optimal aging time.

[0059] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Those skilled in the art can understand that implementing all or part of the processes of the above embodiments and making equivalent changes according to the claims of this application still fall within the scope of this application.

Claims

1. A method for aging test of a motor controller, characterized in that, include: Based on the collected fault database of motor controllers, alternating cyclic operating conditions for aging tests are formulated. The historical fault database is obtained through fault data fed back from motor controllers in the market, and this historical fault data includes operating condition parameters at the time of the fault. Based on the alternating cyclic operating conditions and combined with cost-benefit analysis, the optimal aging duration is dynamically determined. The cost-benefit analysis is used to evaluate the total cost under different aging durations, where the total cost is the sum of the aging test cost and the market repair cost estimated based on the failure probability. Based on the alternating cyclic operating conditions and the optimal aging duration, a factory aging test is performed on the motor controller under test.

2. The aging test method for the motor controller according to claim 1, characterized in that, The operating parameters include at least one of the following: motor controller operating mode, vehicle failure mileage, DC bus voltage, motor torque, motor speed, ambient temperature, and coolant temperature.

3. The aging test method for the motor controller according to claim 1 or 2, characterized in that, Based on the collected fault database of the motor controller, the steps for formulating alternating cyclic operating conditions for aging tests include: extracting typical failure points from the operating condition parameters, and combining and arranging multiple typical failure points to form various fault conditions of the motor controller.

4. The aging test method for the motor controller according to claim 3, characterized in that, Based on the alternating cyclic operating conditions and combined with cost-benefit analysis, the steps for dynamically determining the optimal aging duration include: constructing a failure mileage statistical model for each motor controller based on historical fault data; establishing a mapping relationship between aging duration and equivalent mileage based on the alternating cyclic operating conditions and the Arrhenius model; constructing an evaluation model by combining the failure mileage statistical model and the mapping relationship; the evaluation model is used to input any aging duration and output the corresponding total cost evaluation value, which is the sum of the estimated market maintenance cost and aging test cost based on the aging duration; evaluating different aging durations through the evaluation model, and determining the optimal aging duration with the goal of minimizing the total cost evaluation value.

5. The aging test method for the motor controller according to claim 4, characterized in that, The steps for constructing a statistical model of motor controller failure mileage based on historical fault data of each motor controller include: reading vehicle failure mileage from historical fault data of each motor controller; calculating the cumulative failure rate of the motor controller based on the number of motor controllers that have failed and the total number of corresponding production batches of motor controllers; and fitting a functional relationship between the cumulative failure rate of the motor controller and vehicle failure mileage based on the cumulative failure rate of the motor controller and multiple vehicle failure mileages.

6. The aging test method for the motor controller according to claim 4, characterized in that, The steps for establishing the mapping relationship between aging time and equivalent driving mileage based on the alternating cycle operating conditions and the Arrhenius model include: obtaining the average junction temperature of key electronic components in the motor controller under the alternating cycle operating conditions through thermal simulation or bench testing based on the operating condition parameters corresponding to the alternating cycle operating conditions; substituting the average junction temperature and the reference average junction temperature of the key electronic components under standard operating conditions into the Arrhenius equation to calculate the aging acceleration factor of the alternating cycle operating conditions relative to the standard operating conditions; and establishing a linear proportional relationship between aging time and equivalent driving mileage based on the aging acceleration factor and the average vehicle speed under standard operating conditions.

7. The aging test method for the motor controller according to claim 4, characterized in that, The steps for constructing an evaluation model, combining the failure mileage statistical model and the mapping relationship, include: inputting the equivalent mileage output from the mapping relationship between the aging duration and the equivalent mileage into the failure mileage statistical model to obtain an estimated cumulative failure rate corresponding to the equivalent mileage; calculating the estimated number of potential failures based on the estimated cumulative failure rate and the total number of motor controllers produced; calculating the estimated market maintenance cost based on the estimated number of potential failures and the preset single-unit maintenance cost; calculating the aging test cost based on the aging duration and the alternating cyclic operating conditions; and adding the estimated market maintenance cost to the aging test cost to obtain the total cost assessment value corresponding to the aging duration. The evaluation model is defined as a function or program module that executes the above steps to output the total cost assessment value corresponding to any input aging duration.

8. The aging test method for the motor controller according to claim 7, characterized in that, The steps for calculating the aging test cost based on the aging duration and the alternating cycle conditions include: calculating the average power consumption during the aging process based on the load current parameters and load impedance of the alternating cycle conditions; obtaining the average thermal power consumption of the aging test equipment while maintaining the ambient temperature required for the alternating cycle conditions; calculating the total energy cost based on the average power consumption and the average thermal power consumption, combined with the duration of the aging test and the local industrial electricity price; calculating the time-related cost based on the duration of the aging test, the equipment depreciation rate, and labor input; and adding the total energy cost to the time-related cost to obtain the aging test cost.

9. The aging test method for a motor controller according to claim 1 or 4, characterized in that, Based on the alternating cycle conditions and the optimal aging time, the steps for performing a factory aging test on the motor controller under test include: connecting the motor controller under test to the aging test bench, applying electrical and thermal stress according to the timing and parameters defined by the alternating cycle conditions, and continuously running the optimal aging time, thereby completing the factory aging test process based on data-driven and cost-optimized decision-making.

10. An aging test apparatus for a motor controller, characterized in that, include: The operating condition formulation module is used to formulate alternating cyclic operating conditions for aging tests based on the collected fault database of motor controllers. The historical fault database is obtained through fault data fed back from motor controllers in the market, and the historical fault data includes operating condition parameters at the time of the fault. The optimal aging duration determination module is used to dynamically determine the optimal aging duration based on the alternating cyclic operating conditions and cost-benefit analysis. The cost-benefit analysis is used to evaluate the total cost under different aging durations, and the total cost is the sum of the aging test cost and the market maintenance cost estimated based on the failure probability. The testing module is used to perform factory aging tests on the motor controller under test according to the alternating cyclic operating conditions and the optimal aging duration.

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