Method for testing and evaluating starting performance of electric passenger car and related equipment

Abstract

The embodiment of the invention discloses an electric passenger vehicle starting performance testing and evaluating method and related equipment, and the method comprises the steps: setting a plurality of evaluation indexes and a plurality of starting working conditions, detecting a plurality of characteristic parameters generated by starting of an electric passenger vehicle under the starting working condition for any starting working condition, and performing weighting processing according to each characteristic parameter to obtain a comprehensive score of the starting performance, and the like. According to the method, the four evaluation indexes are extracted by synchronously collecting the vehicle data, the multi-dimensional weighted comprehensive evaluation model is constructed, and the comprehensive score and the sub-item score are finally output, so that leap-type improvement of the test precision and reliability can be realized, the test comprehensiveness and scientificity are remarkably enhanced, and the test efficiency is improved. The method is widely applied to the technical field of automobile performance evaluation.

Description

Technical Field

[0001] This invention relates to the field of automotive performance evaluation technology, and in particular to a method and related equipment for testing and evaluating the starting performance of electric passenger vehicles. Background Technology

[0002] Electric vehicle start-up performance testing is a crucial step in evaluating vehicle driving quality and the effectiveness of drive system optimization. Objective and accurate evaluations of electric vehicle start-up performance provide valuable information for automotive research and development, manufacturing, maintenance, marketing, and use, directly impacting user driving experience and product market competitiveness. Currently, electric vehicle start-up performance testing technology is transitioning from a traditional gasoline vehicle framework to one adapted to electric vehicle characteristics, and from traditional subjective evaluation to objective quantitative analysis. However, existing testing systems have not yet developed a mature solution covering "multi-condition, multi-dimensional, and quantitative evaluation," and overall, there are still issues with lagging technical standards and insufficient understanding of vehicle characteristics. Specifically, current pure electric vehicle start-up performance testing technologies primarily focus on "power parameter acquisition" and "subjective evaluation," and mainly suffer from the following problems: 1. Reliance on driver subjective evaluation means that evaluation results are greatly affected by driver experience, state, and environmental factors (road conditions, weather), making them difficult to quantify and lacking repeatability; 2. Existing start-up performance testing methods largely follow the testing concepts of traditional fuel vehicles, resulting in poor adaptability to pure electric characteristics and failing to fully consider the unique characteristics of pure electric vehicles; 3. Limited and incomplete testing conditions, ignoring the impact of different battery charge states, different driving modes, and low-temperature conditions on start-up performance; 4. Outdated data acquisition and analysis methods, failing to fully utilize high-precision inertial navigation equipment and vehicle CAN bus data for fusion analysis, resulting in insufficient measurement accuracy and reliability; 5. One-sided and singular evaluation dimensions, often employing single-index evaluation or simple weighted scoring methods, lacking multi-dimensional comprehensive analysis of the entire start-up process. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, the purpose of embodiments of the present invention is to provide a method and related equipment for testing and evaluating the starting performance of electric passenger vehicles.

[0004] On one hand, embodiments of the present invention include a method for testing and evaluating the starting performance of an electric passenger vehicle, the method comprising the following steps: Set multiple evaluation indicators and multiple starting conditions; For any of the aforementioned starting conditions, multiple characteristic parameters generated by the electric passenger vehicle during starting under the aforementioned starting conditions are detected; each of the aforementioned characteristic parameters corresponds to one of the aforementioned evaluation indicators; The starting performance is comprehensively scored by weighting each of the aforementioned feature parameters.

[0005] Furthermore, the setting of multiple evaluation indicators and multiple starting conditions includes: Response delay, acceleration abruptness, impact, and acceleration smoothness are set as the evaluation indicators. The following start conditions are defined: creep start, accelerator pedal depress start, hill start, and dynamic start.

[0006] Furthermore, for any of the aforementioned starting conditions, the detection of multiple characteristic parameters generated by the electric passenger vehicle during starting under the stated starting condition includes: It traversed multiple electric passenger vehicles; For the electric passenger vehicles that are being traversed, multiple starting conditions are traversed. For the electric passenger vehicle that is traversed, a start-up is performed under the traversed start-up conditions, and multiple characteristic parameters generated by the electric passenger vehicle are detected.

[0007] Further, the step of weighting the characteristic parameters to obtain a comprehensive starting performance score includes: For any of the electric passenger vehicles and any of the starting conditions, obtain the standardized scores corresponding to each of the multiple feature parameters corresponding to the starting conditions, obtain the default weights corresponding to each of the multiple feature parameters, and determine the comprehensive single-condition starting performance score corresponding to the starting conditions based on the standardized scores and the default weights.

[0008] Further, obtaining the standardized scores corresponding to each of the multiple feature parameters corresponding to the starting condition includes: For any given feature parameter, multiple parameter intervals are defined, and each parameter interval corresponds to a corresponding linear mapping formula. Based on the parameter interval in which the feature parameter is located, the corresponding linear mapping formula is selected to map the feature parameter to the corresponding standardized score.

[0009] Further, determining the comprehensive single-condition start-up performance score corresponding to the start-up condition based on each of the standardized scores and each of the default weights includes: Set a first scoring threshold; For any of the standardized scores, when the standardized score has a first size relationship with the first score threshold, the default weight corresponding to the standardized score is determined as the calculated weight corresponding to the standardized score; when the standardized score has a second size relationship with the first score threshold, the default weight corresponding to the standardized score is adjusted to obtain the calculated weight corresponding to the standardized score. The single-condition start-up performance comprehensive score is obtained by weighting the standardized scores and the corresponding calculation weights.

[0010] Further, the step of weighting the characteristic parameters to obtain a comprehensive starting performance score includes: Obtain the comprehensive score of single-condition start-up performance for each of the start-up conditions corresponding to all the electric passenger vehicles; The multi-condition start-up performance comprehensive score is obtained by averaging the comprehensive scores of the single-condition start-up performance.

[0011] Further, the step of averaging the comprehensive scores of the single-condition start-up performance to obtain a comprehensive score of the multi-condition start-up performance includes: Set a second scoring threshold; When all the single-condition start-up performance comprehensive scores have a first size relationship with the second score threshold, the arithmetic average of all the single-condition start-up performance comprehensive scores is performed to obtain the multi-condition start-up performance comprehensive score. When a portion of the single-condition start-up performance comprehensive scores have a first magnitude relationship with the second scoring threshold, and another portion of the single-condition start-up performance comprehensive scores have a second magnitude relationship with the second scoring threshold, the single-condition start-up performance comprehensive scores with the first magnitude relationship and the single-condition start-up performance comprehensive scores with the second magnitude relationship are weighted using different weighting formulas, and the weighted single-condition start-up performance comprehensive scores are averaged to obtain the multi-condition start-up performance comprehensive score.

[0012] On the other hand, embodiments of the present invention also include a computer device, including a memory and a processor, the memory for storing at least one program, and the processor for loading at least one program to execute the electric passenger vehicle start-up performance test evaluation method in the embodiments.

[0013] On the other hand, embodiments of the present invention also include a computer program product, comprising a computer program that, when executed by a processor, implements the electric passenger vehicle start-up performance test and evaluation method in the embodiments.

[0014] The beneficial effects of the embodiments of the present invention are as follows: The electric passenger vehicle start-up performance test evaluation method in the embodiments extracts four evaluation indicators by synchronously collecting vehicle data, constructs a multi-dimensional weighted comprehensive evaluation model, and finally outputs a comprehensive score and sub-item scores, which can achieve a leapfrog improvement in test accuracy and reliability, significantly enhance the comprehensiveness and scientific nature of the test, and improve test efficiency. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the measuring equipment and apparatus for evaluating the starting performance of electric passenger vehicles, which can be applied in the embodiments. Figure 2 This is a schematic diagram illustrating the steps of the electric passenger vehicle start-up performance test and evaluation method in the embodiment; Figure 3 In the example, the response delay time T response A diagram illustrating the calculation process of the corresponding standardized score; Figure 4 In the example, b is the positive maximum value of the acceleration gradient. max A diagram illustrating the calculation process of the corresponding standardized score; Figure 5 In the example, j represents the negative maximum value of the acceleration gradient. max A diagram illustrating the calculation process of the corresponding standardized score; Figure 6 The root mean square (RMS) of acceleration in the example is shown below. ax A diagram illustrating the calculation process of the corresponding standardized score; Figure 7 This is a schematic diagram of the process for obtaining a comprehensive score of single-condition start-up performance in an embodiment. Detailed Implementation

[0016] This embodiment provides a method for testing and evaluating the starting performance of an electric passenger vehicle. This method can be applied to... Figure 1 On the measuring equipment and apparatus shown. (Refer to...) Figure 1 The measuring equipment and devices include a data acquisition system, a high-precision inertial navigation module, a single-axis accelerometer, a high-precision torque sensor, a throttle pedal controller, and other components. The working principles of these components are as follows: 1.1 Data Acquisition System The data acquisition system is responsible for connecting to various sensors and is crucial for obtaining raw data. The processor uses an STM32H743VI (480MHz), supporting 1kHz high-frequency sampling and capable of simultaneously receiving data from 24 sensors. Microchip's MCP2515 (independent CAN controller) and NXP's TJA1050 (high-speed CAN transceiver) are used to acquire vehicle CAN signals. The storage unit uses a 128GB industrial-grade SD card (100MB / s read / write speed), supporting continuous data storage for 48 hours to prevent data overflow.

[0017] 1.2 High-precision inertial navigation module The high-precision inertial navigation module is used to collect the vehicle's longitudinal, lateral, and vertical acceleration (accuracy ±0.01m / s²) and speed (accuracy ±0.01km / h). It is installed at the end of the guide rail inside the driver's seat and is connected to the data acquisition module via serial communication.

[0018] 1.3 Single-axis accelerometer.

[0019] A single-axis accelerometer, used to measure road surface excitation (accuracy ±0.01m / s²), is mounted above the lower left control arm and uses serial communication to connect to the data acquisition module.

[0020] 1.4 High-precision torque sensor A high-precision torque sensor acquires the instantaneous output torque of the motor during startup in real time (accuracy ±0.1N). (m), installed on the drive half-shaft, and connected to the data acquisition module via CAN bus.

[0021] 1.5 Accelerator Pedal Controller The accelerator pedal controller is connected in series between the vehicle's accelerator pedal and the vehicle controller via a wiring harness. The accelerator pedal controller simulates the accelerator opening and is used to precisely control the accelerator opening.

[0022] In this embodiment, measuring equipment and devices can be installed on electric passenger vehicles that require starting performance testing. When there are multiple electric passenger vehicles requiring starting performance testing, one or a small number of measuring equipment and devices can be used. The measuring equipment and devices can be installed on one electric passenger vehicle, and after performing the electric passenger vehicle starting performance test evaluation method on that vehicle, the measuring equipment and devices can be removed and installed on the next electric passenger vehicle, and then the electric passenger vehicle starting performance test evaluation method can be performed on the next electric passenger vehicle. Alternatively, one set of measuring equipment and devices can be installed on each electric passenger vehicle. In this case, the data acquisition system, high-precision inertial navigation module, single-axis acceleration sensor, high-precision torque sensor, and accelerator pedal controller installed on the electric passenger vehicle itself can be used to form the measuring equipment and devices.

[0023] In this embodiment, the working principle of the measuring device and apparatus is as follows: 2.1 Data Acquisition Sampling frequency: The data acquisition module synchronously acquires the measurement parameters shown in Table 1 at a frequency of 1kHz.

[0024] Data fusion: The "inertial navigation speed" and "vehicle CAN bus speed" are fused using the Kalman filter algorithm to eliminate errors from a single data source (such as deviations when the inertial navigation is blocked), and the fused longitudinal speed is output (accuracy ±0.05km / h).

[0025] Number of data collections: Each working condition was tested 3 times.

[0026] In this embodiment, the measurement parameters of the measuring equipment and apparatus are shown in Table 1.

[0027] Table 1 Measurement Parameters

[0028] 2.2 Measurement Conditions 2.2.1 Measurement Site Measurements should be taken on a clean, dry, level concrete or asphalt (or similar) surface with a longitudinal slope not exceeding 1%. Some tests may be conducted on ramps with a slope range of (10 ± 3)%.

[0029] 2.2.2 Environmental conditions Measurements should be performed in low-temperature, normal-temperature, and high-temperature environments respectively.

[0030] Low-temperature environments should be measured at ambient temperatures of (-10±3)℃, (-20±3)℃, and (-30±3)℃ respectively. Before measurement, the vehicle should be immersed in the environment at these temperatures for at least 12 hours.

[0031] The ambient temperature range is 5℃~35℃.

[0032] The high-temperature environment is 35-42℃.

[0033] The wind speed during measurement should not exceed 5 m / s.

[0034] 2.2.3 Vehicle Conditions The vehicle should have been driven at least 300km before measurement. The vehicle weight is the curb weight plus 180kg, with one passenger in each of the driver and front passenger seats, and the remaining load evenly distributed on the rear floor. The cold tire inflation pressure should meet the manufacturer's specifications, with an error not exceeding 10kPa.

[0035] Reference Figure 2 The evaluation method for the starting performance of electric passenger vehicles includes the following steps: S1. Set multiple evaluation indicators and multiple starting conditions; S2. For any starting condition, detect multiple characteristic parameters generated by the electric passenger vehicle during the starting process; S3. Perform weighted processing based on each characteristic parameter to obtain a comprehensive score for initial performance.

[0036] Each feature parameter corresponds to an evaluation index; In step S1, each of the multiple evaluation indicators to be set can evaluate the starting performance of the electric passenger vehicle from a corresponding aspect. For example, in this embodiment, four evaluation indicators can be set: response delay, acceleration abruptness, impact, and acceleration smoothness. Among them, the response delay evaluation indicator can evaluate whether there is a delay in the response of the electric passenger vehicle to driving operations during start-up and its degree; the acceleration abruptness evaluation indicator can evaluate whether the electric passenger vehicle will cause abruptness to passengers during start-up and its degree; the impact evaluation indicator can evaluate whether the electric passenger vehicle will cause impact to passengers during start-up and its degree; and the acceleration smoothness evaluation indicator can evaluate whether the electric passenger vehicle will make passengers feel smooth during start-up and its degree.

[0037] In this embodiment, each evaluation index corresponds to a feature parameter, which is used to quantitatively represent the evaluation of the electric passenger vehicle's starting performance in that aspect. For example, the feature parameters corresponding to the four evaluation indices are as follows: A. Response delay, the corresponding characteristic parameter is response delay time. .

[0038] B. Abrupt acceleration, the corresponding characteristic parameter is the positive maximum value of the acceleration gradient. .

[0039] C. Impact intensity, the corresponding characteristic parameter is the negative maximum value of the acceleration gradient. .

[0040] D. Acceleration smoothness, the corresponding characteristic parameter is the root mean square of acceleration. .

[0041] In step S1, each of the multiple starting conditions to be set represents the starting condition of the electric passenger vehicle during testing and evaluation. Different starting conditions require the electric passenger vehicle to output different power, which will result in different kinematic parameters of the electric passenger vehicle as a whole, and will also cause different driving and riding experiences for the passengers in the electric passenger vehicle.

[0042] In this embodiment, the multiple starting conditions to be set include a creep start condition, an accelerator pedal depress start condition, a hill start condition, and a dynamic start condition. Specifically, the parameters of the electric passenger vehicle under these starting conditions are as follows: Crawl start-up condition: The operation process is as follows: a) The air conditioner switch is set to AUTO, and the temperature is set to 23℃; b) Quickly release the brake pedal and start without pressing the accelerator pedal.

[0043] The above operations should be performed in different driving modes.

[0044] Starting conditions when pressing the accelerator pedal: The operation process is as follows: a) The air conditioner switch is set to AUTO, and the temperature is set to 23℃; b) Quickly release the brake pedal and perform starting operations by pressing the accelerator pedal to (10±1)%, (20±2)%, (30±2)%, (40±2)%, (50±2)%, (60±2)%, (80±2)%, and 100% opening.

[0045] The above operations must be performed separately in different driving modes and at different battery levels (100%~90% SOC, 60%~50% SOC, 20%~10% SOC). In low-temperature and high-temperature environments, only the starting parameters of (10±1)%, (30±2)%, and 100% throttle opening need to be measured.

[0046] Hill start operation: The operation process is as follows: a) The vehicle is stationary on a slope with a gradient of (10±3)%. b) Set the air conditioner switch to AUTO and the temperature to 23℃; c) Quickly release the brake pedal and perform starting operations by pressing the accelerator pedal to (25±2)% and (60±2)% opening respectively.

[0047] The above operations should be performed in different driving modes, and also at different battery levels (100%–90% SOC, 60%–50% SOC, 20%–10% SOC).

[0048] Dynamic start-up conditions: The operation process is as follows: a) The air conditioner switch is set to AUTO, and the temperature is set to 23℃; b) Drive at a constant speed of 50 km / h; c) Depress the brake pedal to decelerate to 10 km / h at a deceleration rate of (-2±0.2) m / s2, then accelerate to 50 km / h at a deceleration rate of (40±2)% of the accelerator pedal opening; decelerate to 10 km / h at a deceleration rate of (-4±0.3) m / s2, then accelerate to 50 km / h at a deceleration rate of (60±2)% of the accelerator pedal opening. d) Drive at a constant speed of 50 km / h; e) Depress the brake pedal to decelerate to 5km / h at a deceleration rate of (-2±0.2)m / s2, then accelerate to 50km / h at a deceleration rate of (40±2)% of the accelerator pedal opening; decelerate to 5km / h at a deceleration rate of (-4±0.3)m / s2, then accelerate to 50km / h at a deceleration rate of (60±2)% of the accelerator pedal opening.

[0049] The above operations must be performed in different driving modes. The above operations must also be performed separately under different battery levels (100%~90% SOC, 60%~50% SOC, 20%~10% SOC). In low-temperature and high-temperature environments, only the starting position at (10±1)%, (30±2)%, and 100% throttle opening needs to be measured.

[0050] In this embodiment, each electric passenger vehicle can select a specific starting condition to start, and through... Figure 1 The measuring equipment and apparatus shown detect various characteristic parameters of the electric passenger vehicle under this starting condition. These characteristic parameters quantitatively represent the performance of the electric passenger vehicle under this starting condition in evaluation indicators such as response delay, acceleration abruptness, impact, and acceleration smoothness.

[0051] There are multiple electric passenger vehicles, such as electric passenger vehicle 1, electric passenger vehicle 2, electric passenger vehicle 3, etc. When performing step S2, which is to detect multiple characteristic parameters generated by the electric passenger vehicle during start-up under any starting condition, the following steps can be performed: S201. Traverse multiple electric passenger vehicles; S202. For the electric passenger vehicles that are being traversed, traverse multiple starting conditions; S203. For the electric passenger vehicle that has been traversed, start the vehicle under the traversed starting conditions and detect multiple characteristic parameters generated by the electric passenger vehicle.

[0052] In step S201, all electric passenger vehicles, including electric passenger vehicle 1, electric passenger vehicle 2, electric passenger vehicle 3, and so on, are traversed. Assuming that the currently traversed electric passenger vehicle is electric passenger vehicle 1, steps S202-S203 are executed for it; after executing steps S202-S203 for electric passenger vehicle 1, steps S202-S203 are then executed for electric passenger vehicle 2, and so on, until all electric passenger vehicles have been traversed.

[0053] The following explanation will be based on the execution of steps S202-S203 on electric passenger vehicle 1. In step S202, the electric passenger vehicle 1 is controlled to start under all starting conditions, and step S203 is executed for each condition. During the start-up of electric passenger vehicle 1, detection is performed to obtain multiple characteristic parameters corresponding to the starting conditions.

[0054] For example, the electric passenger vehicle 1 can be controlled to start in a creeping start condition, and step S203 can be executed to detect the response delay time generated when the electric passenger vehicle 1 starts in a creeping start condition. Maximum positive value of acceleration gradient negative maximum value of acceleration gradient and root mean square of acceleration Characteristic parameters; then, control the electric passenger vehicle 1 to start when the accelerator pedal is depressed, execute step S203, and detect the response delay time generated when the electric passenger vehicle 1 starts when the accelerator pedal is depressed. Maximum positive value of acceleration gradient negative maximum value of acceleration gradient and root mean square of acceleration Characteristic parameters; then control the electric passenger vehicle 1 to start under hill start condition, execute step S203, and detect the response delay time generated when the electric passenger vehicle 1 starts under hill start condition. Maximum positive value of acceleration gradient negative maximum value of acceleration gradient and root mean square of acceleration Characteristic parameters; finally, control the electric passenger vehicle 1 to start under dynamic start-up conditions, execute step S203, and detect the response delay time generated when the electric passenger vehicle 1 starts under dynamic start-up conditions. Maximum positive value of acceleration gradient negative maximum value of acceleration gradient and root mean square of acceleration Characteristic parameters, etc.

[0055] Specifically, the measuring equipment and apparatus can accurately calculate four characteristic parameters from the collected data through the following steps: A. Response delay time T response Calculation steps: a) For the creep start measurement condition, find the time point when the brake pedal is fully released from the data and record it as t0; for other measurement conditions, find the time point when the accelerator pedal is just pressed from the data and record it as t0.

[0056] b) Use the 5-point moving average method to smooth the drive half-shaft torque data, and then find the time point from which the drive half-shaft torque first rises from zero to 10% of its maximum stable value during this start-up process, denoted as t1.

[0057] c) Calculate the response delay time T response =t1-t0, where the time is used as a characteristic parameter of the response delay.

[0058] B. Positive maximum value of acceleration gradient b max Calculation steps: a) The longitudinal acceleration is smoothed using the 5-point moving average method. The filtered longitudinal acceleration ax_SMO(5) is numerically differentiated (using the central difference method: b[i]=(ax[i+1]-ax[i-1]) / (2*Δt)) to calculate the acceleration gradient data ag(t).

[0059] b) Calculate the maximum value of ag(t) from the start of vehicle speed from 0 km / h to the acceleration cutoff (longitudinal acceleration ≤ 0.2 m / s² or vehicle speed reaches 50 km / h). This maximum value is used as the characteristic parameter of the abrupt acceleration.

[0060] C. Negative maximum value of acceleration gradient j max Calculation steps: a) Perform a 10Hz low-pass filtering on the longitudinal acceleration, and perform numerical differentiation on the filtered longitudinal acceleration ax_LP(10) (using the central difference method: b[i]=(ax[i+1]-ax[i-1]) / (2*Δt)) to calculate the acceleration gradient data ag(t).

[0061] b) Find the maximum absolute value of ag(t) when the vehicle speed is negative from 0 km / h to the acceleration cutoff (longitudinal acceleration ≤ 0.2 m / s² or vehicle speed reaches 50 km / h). This maximum value is used as the characteristic parameter of the impact.

[0062] D. Root mean square acceleration (RMS) ax Calculation steps: a) Perform a 5Hz high-pass filtering on the longitudinal acceleration and extract the longitudinal acceleration ax_HP(5) data for the entire time period from the start of vehicle speed from 0km / h to the end of acceleration (longitudinal acceleration ≤0.2m / s² or vehicle speed reaches 50km / h).

[0063] b) Calculate the root mean square of ax_HP(5) from the start of vehicle speed from 0km / h to the acceleration cutoff (longitudinal acceleration ≤0.2m / s² or vehicle speed reaches 50km / h). This root mean square is used as the characteristic parameter of acceleration smoothness.

[0064] By executing steps S201-S203, all electric passenger vehicles can be traversed, and all starting conditions can be traversed for each electric passenger vehicle, thereby detecting the characteristic parameters generated by each electric passenger vehicle under each starting condition.

[0065] In this embodiment, when performing step S3, which is to perform weighted processing based on each feature parameter to obtain the overall starting performance score, the following steps can be executed: For any starting condition of any electric passenger vehicle: S301. Obtain the standardized scores corresponding to each of the multiple feature parameters corresponding to the starting condition; S302. Obtain the default weights corresponding to each of the multiple feature parameters; S303. Determine the comprehensive score of single-condition start-up performance corresponding to the start-up condition based on each standardized score and each default weight.

[0066] The principle of steps S301-S303 is as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown. Taking the evaluation of the starting performance of electric passenger vehicle 1 under the creeping start condition as an example, the steps S301-S303 are explained.

[0067] In step S301, the response delay time T detected when the electric passenger vehicle 1 starts under the creep start condition is obtained. response The maximum positive value of the acceleration gradient b max The negative maximum value of the acceleration gradient j max and root mean square of acceleration RMS ax After obtaining the characteristic parameters, these characteristic parameters are converted into their respective standardized scores to eliminate the dimensional differences between different characteristic parameters and place them within the same range.

[0068] In step S301, the calculation process for each standardized score is as follows: A. Response delay, corresponding to response delay time T response In step S301, the response delay time T response The calculation process for the corresponding standardized score is as follows: Figure 3 As shown. (Refer to...) Figure 3 : First, preset four response delay times, which are the ideal values ​​T. ideal Satisfaction value T good Acceptable value T acceptable Unacceptable value T intolerable ; Determine the calculated response delay time T response Parameter range: If T response ≤T ideal Then its standardized score T =10; If T ideal <T response ≤T good Then its score T =9+(Tresponse -T ideal ) / (T good T ideal ); If T good <T response ≤T acceptable Then its score T =6+(T response -T good ) / (T acceptable -T good ); If T acceptable <T response ≤T intolerable Then its score T =3+(T response -T acceptable ) / (T intolerable -T acceptable ); If T response >T intolerable Then its score T =3-3*(T response -T intolerable ) / T intolerable If the result is negative, then Score T =0; All results are rounded to one decimal place.

[0069] The response delay time T can be obtained by calculating the above parameter range and linear mapping formula. response Corresponding standardized score T .

[0070] B. Abrupt acceleration, corresponding to the positive maximum value of the acceleration gradient b. max .

[0071] In step S301, the positive maximum value of the acceleration gradient b max The calculation process for the corresponding standardized score is as follows: Figure 4 As shown. (Refer to...) Figure 4 : First, preset 5 values, which are the optimal value b. best Acceptable upper deviation value b acceptable_up Acceptable lower deviation value b acceptable_down Unacceptable upper deviation value b intolerable_up Unacceptable deviation value b intolerable_down .

[0072] Determine the positive maximum value of the calculated acceleration gradient b. max Parameter range: If b acceptable_downmax best Then its standardized score Score_b = 6 + (b max -b acceptable_down ) / (b best -b acceptable_down ); If b best ≤b max acceptable_up Then its score Score_b = 6 + (b max -b best ) / (b acceptable_up -b best ); If b intolerable_down max ≤b acceptable_down Then its score b =3+(b max -b intolerable_down ) / (b acceptable_down -b intolerable_down ); If b acceptable_up ≤b max <b_b intolerable_up Then its score b =3+(b max -b acceptable_up ) / b intolerable_up -b acceptable_up ); If b max ≤b intolerable_down Then the score b =3-3*(b intolerable_down -b max ) / b intolerable_down If the result is negative, then Score b =0; If b max ≥b intolerable_up Then the score b =3-3*(b max -b intolerable_up ) / b intolerable_up If the result is negative, then Score b =0; All results are rounded to one decimal place.

[0073] By calculating the above parameter range and linear mapping formula, the positive maximum value of the acceleration gradient b can be obtained. max Corresponding standardized score b .

[0074] C. Impact intensity, corresponding to the negative maximum value of the acceleration gradient j​​​​max .

[0075] In step S301, the negative maximum value of the acceleration gradient j max The calculation process for the corresponding standardized score is as follows: Figure 5 As shown. (Refer to...) Figure 5 : First, preset four acceleration gradients, which are the ideal values ​​j. ideal Satisfaction value j good Acceptable value j acceptable Unacceptable value j intolerable .

[0076] For j max Perform absolute value processing, j abs =|j max |, determine the processed j abs Parameter range: If j abs ≤j ideal Then its standardized score j =10; If j ideal <j abs ≤j good Then its score j =9+(j abs -j ideal ) / (j good j ideal ); If j good <j abs ≤j acceptable Then its score j =6+(j abs -j good ) / (j acceptable -j good ); If j acceptable <j abs ≤j intolerable Then its score j =3+(j abs -j acceptable ) / (j intolerable -j acceptable ); If j abs >j intolerable Then its score j =3-3*(j abs -j intolerable ) / j intolerable If the result is negative, then Score j =0; All results are rounded to one decimal place.

[0077] By calculating the above parameter range and linear mapping formula, the negative maximum value of the acceleration gradient j can be obtained. max Corresponding standardized score j .

[0078] D. Acceleration smoothness, corresponding to the root mean square (RMS) of acceleration. ax .

[0079] In step S301, the root mean square (RMS) of acceleration is... ax The calculation process for the corresponding standardized score is as follows: Figure 6 As shown. (Refer to...) Figure 6 : First, preset four root mean square acceleration values, which are the ideal values ​​(RMS). ideal Satisfaction Value (RMS) good Acceptable value (RMS) acceptable Unacceptable Value (RMS) intolerable .

[0080] Determine the root mean square (RMS) of the calculated acceleration. ax Parameter range: If RMS response ≤RMS ideal Then its standardized score RMS =10; If RMS ideal <RMS response ≤RMS good Then its score RMS =9+(RMS response -RMS ideal ) / (RMS good RMS ideal ); If RMS good <RMS response ≤RMS acceptable Then its score RMS =6+(RMS response -RMS good ) / (RMS acceptable -RMS good ); If RMS acceptable <RMS response ≤RMS intolerable Then its score RMS =3+(RMS response -RMS acceptable ) / (RMS intolerable -RMS acceptable); If RMS response >RMS intolerable Then its score RMS =3-3*(RMS response -RMS intolerable ) / RMS intolerable If the result is negative, then Score RMS =0; All results are rounded to one decimal place.

[0081] By calculating the above parameter range and linear mapping formula, the root mean square (RMS) acceleration can be obtained. ax Corresponding standardized score RMS .

[0082] In step S302, the default weights corresponding to each of the multiple feature parameters are obtained. For example, a standardized score (Score) can be set. T The corresponding default weight w T Standardized Score b The corresponding default weight w b Standardized Score j The corresponding default weight w j and standardized score RMS The corresponding default weight w RMS These default weights can each be corresponding fixed values, for example, setting w... T =w b =w j =4, w RMS =3.

[0083] In step S303, such as Figure 7 As shown, a first scoring threshold can be set, for example, a threshold value of 6. For any standardized score, if the standardized score has a first relationship with the first scoring threshold (specifically, greater than or equal to the first scoring threshold), then the default weight corresponding to this standardized score is determined as the calculated weight of the standardized score. Conversely, if the standardized score has a second relationship with the first scoring threshold (specifically, less than the first scoring threshold), then the default weight corresponding to the standardized score is adjusted to obtain the calculated weight of the standardized score. For example, it can be done according to... Figure 7 The formulas in the text, respectively, apply to the default weight w T w b w j and w RMS Adjustments are made to obtain the calculation weights w1, w2, w3, and w4.

[0084] step Figure 7According to Score T Score b Score j and Score RMS The standardized scores and their corresponding calculation weights w1, w2, w3, and w4 are used to calculate a weighted average to obtain the overall single-condition start-up performance score. single .

[0085] In this embodiment, the overall performance score for single-condition start-up is calculated. single This method can comprehensively evaluate the response delay, acceleration abruptness, impact, and acceleration smoothness of electric passenger vehicle 1 under specific starting conditions, thus providing strong data support for a comprehensive, integrated, and objective test evaluation of the starting performance of electric passenger vehicle 1 under specific starting conditions. In particular, by adjusting the weight of the standardized score when it has a primary relationship with the first scoring threshold, it can meet the sensitive needs of electric passenger vehicle design, manufacturing, use, maintenance, and promotion for different evaluation indicators. This allows for the appropriate strengthening or weakening of different evaluation indicators, avoiding the use of fixed weight combinations and improving the flexibility of the electric passenger vehicle starting performance test evaluation method.

[0086] In this embodiment, the comprehensive score for single-condition start-up performance is... single It is a comprehensive performance score obtained by a specific electric passenger vehicle (e.g., electric passenger vehicle 1) under a specific starting condition (e.g., creep start condition); for the same electric passenger vehicle (e.g., electric passenger vehicle 1), since it has multiple starting conditions, each starting condition has a corresponding single-condition starting performance comprehensive score. single Therefore, the same electric passenger vehicle can correspond to multiple single-condition starting performance comprehensive scores; while in the case of multiple electric passenger vehicles (e.g., electric passenger vehicle 1, electric passenger vehicle 2, etc.), each electric passenger vehicle corresponds to one or more single-condition starting performance comprehensive scores. single So many electric passenger vehicles correspond to multiple single-condition starting performance comprehensive scores. single .

[0087] In this embodiment, the single-condition starting performance comprehensive score can be obtained for each of the starting conditions corresponding to all electric passenger vehicles. If these electric passenger vehicles have common characteristics (e.g., they are all sedan models, all models launched in a certain year, all models produced by a certain car manufacturer, or all models of the same type of vehicle), then these single-condition starting performance comprehensive scores can be averaged to obtain a multi-condition starting performance comprehensive score, which can represent the overall starting performance of these electric passenger vehicles.

[0088] In this embodiment, when averaging the comprehensive starting performance scores of each single operating condition to obtain a comprehensive starting performance score for multiple operating conditions, the following steps can be performed: S304. Set a second scoring threshold; S305. When all the single-condition start-up performance comprehensive scores have a first size relationship with the second score threshold, the arithmetic average of all the single-condition start-up performance comprehensive scores is taken to obtain the multi-condition start-up performance comprehensive score. S306. When a portion of the single-condition start-up performance comprehensive scores have a first size relationship with the second score threshold, and another portion of the single-condition start-up performance comprehensive scores have a second size relationship with the second score threshold, the single-condition start-up performance comprehensive scores with the first size relationship and the single-condition start-up performance comprehensive scores with the second size relationship are weighted using different weighting formulas, and the weighted single-condition start-up performance comprehensive scores are averaged to obtain the multi-condition start-up performance comprehensive score.

[0089] In this embodiment, it is assumed that n single-condition start-up performance comprehensive scores are obtained. single That is, it is represented as Score single_i , i=1,2,……n.

[0090] In step S304, the set second scoring threshold can be equal to the first scoring threshold, that is, both are equal to 6.

[0091] When the overall performance score for all single-condition starting conditions has a first magnitude relationship with respect to the second scoring threshold, in this embodiment, the first magnitude relationship is greater than or equal to the second scoring threshold, that is, for any i, there is a Score. single_i If the score is ≥6, then proceed to step S305 to calculate the arithmetic mean of the comprehensive scores for all single-condition start-up performance, and obtain the comprehensive score for multi-condition start-up performance. total Score total =(ΣScore single_i ) / n.

[0092] When at least a portion of the single-condition start-up performance comprehensive scores have a second magnitude relationship with respect to the second scoring threshold, in this embodiment, the second magnitude relationship is a relationship that is less than the second scoring threshold. For example, there are n1 single-condition start-up performance comprehensive scores. single_i Meet Score single_i ≥6 (first size relationship), there are n2 single-condition start-up performance comprehensive scores. single_i Meet Score single_i <6 (second size relation), n1+n2=n, then any Scoresingle_i A score ≥6 (first size relation) single_i Score up_i , will satisfy any Score single_i Score < 6 (second size relationship) single_i Score down_i For all n1 scores up_i Calculate Σ[Score] down_i (7-Score down_i ) 2 For all n² scores down_i Calculate its arithmetic mean, i.e., S1 = Σ(Score) down_i ) / n2, then according to the formula Score total ={Σ[Score down_i (7-Score down_i ) 2 ]+S1*lnn2} / {Σ[(7-Score down_i ) 2 ]+lnn2} The comprehensive score for multi-condition start-up performance was calculated. total The calculation formula actually refers to the comprehensive score of the starting performance of the n1 single-condition tests that are greater than or equal to the second scoring threshold. down_i According to the weighted formula Score down_i (7-Score down_i ) 2 Weighted averages are applied to the n² single-condition start-up performance scores that are less than the second scoring threshold. down_i The weighting is performed using the formula S1*ln2, which means that the two parts of the single-condition starting performance comprehensive score are weighted using different weighting formulas. This calculation formula is applicable when the number of the two parts of the single-condition starting performance comprehensive score is significantly unequal (e.g., n2 >> n1). By taking the logarithm of n2 to obtain the weight, it is possible to ensure that the order of magnitude of the weights obtained by the two parts of the single-condition starting performance comprehensive score are similar when there are multiple electric passenger vehicles.

[0093] For example, when conducting large-scale evaluations, the number of electric passenger vehicles participating in the evaluation may reach tens of thousands, resulting in hundreds of thousands of comprehensive scores for single-condition starting performance. Only a small number (e.g., 100 vehicles) of these electric passenger vehicles will have a comprehensive score for single-condition starting performance. down_i If the score is greater than or equal to the second scoring threshold (i.e., n1 is small and n2 is large), the weight of S1 can be obtained by taking the logarithm of n2, making the weight of S1 equal to (7-Score). down_i )2 Similar sizes help avoid calculating the overall performance score under multiple operating conditions. total distortion.

[0094] In this embodiment, the principle of executing steps S304-S306 is as follows: When all the single-condition start-up performance comprehensive scores have a first size relationship with the second scoring threshold, it indicates that the distribution of different single-condition start-up performance comprehensive scores is relatively consistent. The multi-condition start-up performance comprehensive score obtained by simple arithmetic average can represent the overall characteristics of different single-condition start-up performance comprehensive scores. When the size relationship of different single-condition start-up performance comprehensive scores with respect to the second scoring threshold is inconsistent, the single-condition start-up performance comprehensive scores of different parts are weighted using different weighting formulas. The weighted single-condition start-up performance comprehensive scores are then averaged to obtain the multi-condition start-up performance comprehensive score. This helps to reduce or even eliminate the statistical differences caused by the inconsistency of the overall characteristics of multiple single-condition start-up performance comprehensive scores, and obtains a comprehensive and objective multi-condition start-up performance comprehensive score.

[0095] In this embodiment, an innovative measurement and evaluation method, equipment, and apparatus are proposed to address the key problems and pain points in existing pure electric vehicle start-up performance testing technology. This effectively overcomes the limitations of traditional methods and brings significant technological progress and practical value.

[0096] The electric passenger vehicle start-up performance testing and evaluation method in this embodiment accurately addresses and successfully solves the five major core problems prominent in the prior art: 1. Reduce reliance on subjective evaluation: By defining and collecting multiple quantifiable objective characteristic parameters such as response delay, acceleration abruptness, impact, and acceleration smoothness, the traditional evaluation method that relies on the driver's subjective feelings is completely replaced. This eliminates the uncertainty of evaluation that varies from person to person and from environment to environment, ensuring the consistency and repeatability of test results.

[0097] 2. Deeply adapted to the unique characteristics of pure electric vehicles: The core feature parameters defined in this invention (such as millisecond-level response delay time and high-precision impact) are specifically designed for the characteristics of pure electric vehicles, such as fast motor torque response and zero-speed peak torque. They can accurately capture and measure the transient response characteristics of the starting process, solving the problem of poor adaptability caused by using the testing concept of fuel vehicles.

[0098] 3. Enables multi-condition, full-coverage testing: This invention provides a standardized and reusable methodological framework. By standardizing test conditions (such as preset SOC range, ambient temperature, and driving mode), this method can be conveniently applied to start-up performance testing and horizontal comparison under various operating conditions such as different battery states of charge, different driving modes, and high and low temperatures, overcoming the limitations of existing single-condition testing.

[0099] 4. Innovative Data Acquisition and Analysis Methods: A technical approach is adopted that involves parallel and synchronous acquisition and fusion processing of vehicle CAN bus data and high-precision inertial navigation (INS) data. Through high-frequency data synchronization and time alignment algorithms, the vehicle control status reflected in CAN data and the actual vehicle body motion attitude captured by INS data are integrated, greatly improving the dimensionality and accuracy of the data and providing a reliable foundation for analysis.

[0100] 5. Constructing a Multi-Dimensional Comprehensive Evaluation System: Based on multiple extracted feature parameters, this invention constructs a weighted comprehensive evaluation model that includes multiple dimensions such as response delay, acceleration abruptness, impact, and acceleration smoothness. This model can perform a three-dimensional and comprehensive analysis of the entire start-up process, outputting intuitive comprehensive scores and sub-item scores, completely changing the one-sidedness of single-indicator evaluation.

[0101] The technical summary of the electric passenger vehicle start-up performance test and evaluation method in this embodiment is as follows: a. Objective Measurement Architecture Based on Multi-Source Data Fusion: This approach employs a parallel and synchronous acquisition and fusion analysis of vehicle CAN bus data and high-precision inertial navigation (INS) data. This is not a simple data overlay, but rather ensures time-domain alignment through hardware synchronization pulses. It leverages the respective data advantages of each (CAN reflects control intent and system status, while INS reflects actual vehicle movement). From data acquisition and processing to evaluation, each step is specifically designed for the characteristics of pure electric vehicles, aiming to transform the subjective and vague starting experience into an objective and quantifiable indicator system, providing a high-precision and highly reliable data foundation for subsequent analysis.

[0102] b. High-precision multi-source data fusion acquisition technology: Utilizing high-precision sensors (torque ±0.1N) The data is fused with "m, inertial navigation speed ±0.1km / h) + vehicle CAN bus (BMS / motor controller data)" and combined with the Kalman filter algorithm to improve data accuracy (acceleration error ±0.01m / s²), thus solving the defects of "low acquisition accuracy and lack of fusion of multi-source data".

[0103] c. Multi-dimensional starting condition combination design: Innovatively set five-dimensional conditions: "Battery SOC (100%~10%) + Driving mode (Economy / Standard / Sport) + Ambient temperature (-30℃~42℃) + Initial speed (0km / h~10km / h) + Throttle load (0%~100%)", which solves the defects of existing technology "single conditions and detached from reality" and covers more than 95% of the starting scenarios of pure electric vehicles.

[0104] d. A specially defined multi-dimensional evaluation index system: It creatively proposes four precise quantitative evaluation indexes for starting performance: response delay, acceleration abruptness, impact, and acceleration smoothness. Each dimension has specific quantitative characteristic parameters.

[0105] e. Comprehensive Evaluation Model Based on Multidimensional Evaluation Indicators: A comprehensive evaluation model is established that normalizes characteristic parameters of different dimensions and performs weighted summation based on variable weights, ultimately outputting a single comprehensive score and multidimensional sub-item scores. This model transforms scattered parameters into intuitive and horizontally comparable evaluation results, greatly improving the usability and guiding value of the results.

[0106] In summary, the electric passenger vehicle start-up performance testing and evaluation method in this embodiment, by simultaneously collecting vehicle CAN bus data, high-precision inertial navigation data, and high-precision torque sensor data, extracts four key evaluation indicators: response delay, acceleration abruptness, impact, and acceleration smoothness. Based on these evaluation indicators, feature parameters are extracted, a multi-dimensional weighted comprehensive evaluation model is constructed, and finally, a comprehensive score and sub-item scores are output, achieving the following technical effects: 1. Significant improvement in testing accuracy and reliability: Based on high-precision inertial navigation and multi-source data fusion analysis, it can capture transient micro-changes in the start-up process in milliseconds. The objectivity, accuracy and repeatability of the measurement data are far superior to traditional methods, providing extremely reliable data support for research and development.

[0107] 2. Significantly enhanced comprehensiveness and scientific rigor of testing: The multi-dimensional feature parameters and comprehensive evaluation system provide a deeper and more comprehensive insight into the starting performance of pure electric vehicles, enabling clear identification of performance advantages and disadvantages, guiding R&D to make precise optimizations, and greatly improving the scientific level of the development process.

[0108] 3. Significantly improved testing efficiency and significantly reduced overall costs: This method has a standardized process, is less dependent on venues and equipment, and has a short testing time. It can perform high-frequency testing iterations within the development cycle, which accelerates the R&D verification process and saves on expensive investment in dedicated equipment and manpower costs.

[0109] 4. Extremely simple operation and control: The supporting equipment has a high degree of integration and the testing process is highly automated. It is controlled through a user-friendly human-machine interface, which greatly reduces the reliance on the professional experience of operators and makes the testing work simple and easy to operate.

[0110] 5. Wide range of applications and scalability: This solution can be used not only for performance verification and optimization in the R&D stage, but also seamlessly extended to multiple stages such as end-of-line quality inspection, after-sales fault diagnosis, and in-depth competitor analysis, comprehensively improving vehicle quality control capabilities, market competitiveness, and user experience.

[0111] In this embodiment, a computer device can be used, including a memory and a processor. The memory is used to store at least one program, and the processor is used to load at least one program to execute the electric passenger vehicle start-up performance test evaluation method, thereby obtaining the effect of the electric passenger vehicle start-up performance test evaluation method.

[0112] In this embodiment, a computer program product, including a computer program, can be used. When the computer program is executed by a processor, it implements the electric passenger vehicle start-up performance test and evaluation method in this embodiment.

[0113] It should be noted that, unless otherwise specified, when a feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. Furthermore, the descriptions of "upper," "lower," "left," and "right" used in this disclosure are only relative to the relative positional relationships of the components of this disclosure in the accompanying drawings. The singular forms "a" and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. Moreover, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this embodiment specification is only for describing specific embodiments and is not intended to limit the embodiments of the invention. The term "and / or" as used in this embodiment includes any combination of one or more of the associated listed items.

[0114] It should be understood that although the terms first, second, third, etc., may be used to describe various elements in this disclosure, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, a first element may also be referred to as a second element without departing from the scope of this disclosure, and similarly, a second element may also be referred to as a first element. The use of any and all instances or exemplary language (“e.g.,” “such as,” etc.) provided in this embodiment is intended only to better illustrate embodiments of the invention and, unless otherwise required, does not impose a limitation on the scope of embodiments of the invention.

[0115] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques—including a non-transitory computer-readable storage medium configured with a computer program, wherein such a storage medium causes the computer to operate in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

[0116] Furthermore, the procedures described in this embodiment can be performed in any suitable order unless otherwise indicated by this embodiment or otherwise obviously contradict the context. The procedures (or variations and / or combinations thereof) described in this embodiment can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. A computer program includes a plurality of instructions executable by one or more processors.

[0117] Furthermore, the method can be implemented in any suitable type of computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices, etc. Aspects of embodiments of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention of this embodiment includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps above in conjunction with a microprocessor or other data processor. Embodiments of the invention also include the computer itself when programmed according to the methods and techniques of embodiments of the invention.

[0118] A computer program can be applied to input data to perform the functions of this embodiment, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including a specific visual depiction of physical and tangible objects generated on the display.

[0119] The above are merely preferred embodiments of the present invention. The embodiments of the present invention are not limited to the above-described implementations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the embodiments of the present invention, as long as they achieve the same technical effects, should be included within the scope of protection of the embodiments of the present invention. Within the scope of protection of the embodiments of the present invention, the technical solutions and / or implementation methods can have various modifications and variations.

Claims

1. A method for testing and evaluating the starting performance of an electric passenger vehicle, characterized in that, The method for testing and evaluating the starting performance of electric passenger vehicles includes: Set multiple evaluation indicators and multiple starting conditions; For any of the aforementioned starting conditions, multiple characteristic parameters generated by the electric passenger vehicle during starting under the aforementioned starting conditions are detected; each of the aforementioned characteristic parameters corresponds to one of the aforementioned evaluation indicators; The starting performance is comprehensively scored by weighting each of the aforementioned feature parameters.

2. The method for testing and evaluating the starting performance of electric passenger vehicles according to claim 1, characterized in that, The setting of multiple evaluation indicators and multiple starting conditions includes: Response delay, acceleration abruptness, impact, and acceleration smoothness are set as the evaluation indicators. The following start conditions are defined: creep start, accelerator pedal depress start, hill start, and dynamic start.

3. The method for testing and evaluating the starting performance of electric passenger vehicles according to claim 1, characterized in that, For any of the aforementioned starting conditions, multiple characteristic parameters generated by the electric passenger vehicle during startup under those conditions are detected, including: It traversed multiple electric passenger vehicles; For the electric passenger vehicle being traversed, multiple starting conditions are traversed. For the electric passenger vehicle that is traversed, a start-up is performed under the traversed start-up conditions, and multiple characteristic parameters generated by the electric passenger vehicle are detected.

4. The method for testing and evaluating the starting performance of electric passenger vehicles according to claim 1, characterized in that, The step of weighting the characteristic parameters to obtain a comprehensive starting performance score includes: For any of the electric passenger vehicles and any of the starting conditions, obtain the standardized scores corresponding to each of the multiple feature parameters corresponding to the starting conditions, obtain the default weights corresponding to each of the multiple feature parameters, and determine the comprehensive single-condition starting performance score corresponding to the starting conditions based on the standardized scores and the default weights.

5. The method for testing and evaluating the starting performance of electric passenger vehicles according to claim 4, characterized in that, The step of obtaining the standardized scores corresponding to each of the multiple feature parameters corresponding to the starting condition includes: For any given feature parameter, multiple parameter intervals are defined, and each parameter interval corresponds to a corresponding linear mapping formula. Based on the parameter interval in which the feature parameter is located, the corresponding linear mapping formula is selected to map the feature parameter to the corresponding standardized score.

6. The method for testing and evaluating the starting performance of an electric passenger vehicle according to claim 4, characterized in that, The determination of the comprehensive single-condition start-up performance score corresponding to the start-up condition based on the standardized scores and default weights includes: Set a first scoring threshold; For any of the standardized scores, when the standardized score has a first size relationship with the first score threshold, the default weight corresponding to the standardized score is determined as the calculated weight corresponding to the standardized score; when the standardized score has a second size relationship with the first score threshold, the default weight corresponding to the standardized score is adjusted to obtain the calculated weight corresponding to the standardized score. The single-condition start-up performance comprehensive score is obtained by weighting the standardized scores and the corresponding calculation weights.

7. The method for testing and evaluating the starting performance of an electric passenger vehicle according to any one of claims 4-6, characterized in that, The step of weighting the characteristic parameters to obtain a comprehensive starting performance score includes: Obtain the comprehensive score of single-condition start-up performance for each of the start-up conditions corresponding to all the electric passenger vehicles; The multi-condition start-up performance comprehensive score is obtained by averaging the comprehensive scores of the single-condition start-up performance.

8. The method for testing and evaluating the starting performance of an electric passenger vehicle according to claim 7, characterized in that, The step of averaging the comprehensive scores of the single-condition start-up performance to obtain a comprehensive score of the multi-condition start-up performance includes: Set a second scoring threshold; When all the single-condition start-up performance comprehensive scores have a first size relationship with the second score threshold, the arithmetic average of all the single-condition start-up performance comprehensive scores is performed to obtain the multi-condition start-up performance comprehensive score. When a portion of the single-condition start-up performance comprehensive scores have a first magnitude relationship with the second scoring threshold, and another portion of the single-condition start-up performance comprehensive scores have a second magnitude relationship with the second scoring threshold, the single-condition start-up performance comprehensive scores with the first magnitude relationship and the single-condition start-up performance comprehensive scores with the second magnitude relationship are weighted using different weighting formulas, and the weighted single-condition start-up performance comprehensive scores are averaged to obtain the multi-condition start-up performance comprehensive score.

9. A computer device, characterized in that, It includes a memory and a processor, the memory being used to store at least one program, and the processor being used to load at least one program to execute the electric passenger vehicle start-up performance test evaluation method according to any one of claims 1-8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the electric passenger vehicle start-up performance test and evaluation method according to any one of claims 1-8.

Images