Method for analyzing influence of drive braking control strategy on electric drive damage

Abstract

The invention discloses a method for analyzing the influence of a drive and brake control strategy on electric drive damage, and belongs to the field of electric automobiles, and the method comprises the steps: firstly, building a vehicle longitudinal dynamic model based on the actual user operation data of 1 million kilometers in seven regions, so as to obtain the drive-brake load of an electric automobile; the effectiveness of the model is verified by combining measured data; secondly, typical characteristics such as running time, mileage and acceleration distribution of the user under different working conditions are analyzed; the difference of users in different regions is compared and analyzed, a user target suitable for road conditions is constructed, the user target is further compared with an existing standard, finally, the influence of a control strategy on drive-brake load characteristics is discussed, and the result shows that in rear drive priority, rear-deviated drive and front-rear balanced torque distribution strategies, the control strategy of the rear-deviated drive and the front-rear balanced torque distribution strategy is optimized. The front and back torque balanced distribution strategy can effectively reduce damage to mechanical parts, and the braking torque distribution threshold value has a large influence on damage to shafts and gears.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicles, and in particular relates to a method for analyzing the influence of a driving and braking control strategy on electric drive damage. Background Art

[0002] As an important branch of new energy vehicles, the operation of pure electric vehicles is completely dependent on the electric drive system to efficiently convert electrical energy into mechanical energy, thereby providing important guarantees for the vehicle's power output and driving control. Compared with traditional vehicles, electric drive systems have higher transient response characteristics and energy recovery capabilities, which makes the load changes they bear in actual use more drastic, especially under frequent start-stop and acceleration and deceleration conditions. The high transient torque output by the motor significantly increases the impact load and fatigue damage risk of components. This difference makes it impossible for the test specifications for traditional vehicles to fully reflect the actual load distribution and component damage characteristics of the electric drive system under typical conditions, limiting its applicability in the reliability design of electric vehicles. Therefore, based on the actual use conditions of electric vehicles, studying the load characteristics of the electric drive system and formulating corresponding reliability test methods are common technical issues facing the current new energy vehicle industry.

[0003] Over the years, the reliability test methods for complete vehicles and parts related to Chinese users have gradually formed a variety of effective methods based on user surveys and small sample typical working condition load data, including full life cycle reliability goals and working condition composition, the minimum sample size for typical working condition load collection, and multi-dimensional user-test-bench load spectrum damage analysis, which provides important support for complete vehicle and parts reliability testing. However, these methods are difficult to fully cover the actual user conditions. The fundamental reason is the limited data acquisition. These data are often based on specific scenarios or limited user survey results, and it is difficult to cover the diversity of different regions, road conditions and driving behaviors. Therefore, there is an urgent need to use more extensive and large sample user data as support.

[0004] Furthermore, due to the common existence of front-to-rear torque distribution and brake energy recovery in electric vehicles, these factors significantly affect the drive-brake load, and the distribution strategy of motor braking and mechanical braking greatly affects the load characteristics of related systems, which in turn leads to changes in loads and damage targets throughout the life cycle. However, there is currently a lack of in-depth discussion on the impact of the above factors on the load of related systems, resulting in a lack of effective benchmarks and references for reliability design and verification. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for analyzing the influence of driving and braking control strategies on electric drive damage, comprising:

[0006] Acquire user data based on a distributed user big data cloud platform, and build a vehicle longitudinal dynamics model based on the user data;

[0007] Constructing an electric vehicle driving-braking load model based on the vehicle longitudinal dynamics model;

[0008] Based on the stress characteristics of different components during load transfer, a damage calculation model for mechanical components of the electric drive system is established;

[0009] Based on the vehicle longitudinal dynamics model and the user data, user operating condition segmentation is performed to obtain typical features under different operating conditions;

[0010] Performing a difference analysis based on the user data to obtain a difference analysis result;

[0011] Determining the optimal front and rear torque distribution strategy and braking torque distribution threshold based on the electric vehicle drive-brake load model, the drive system mechanical component damage calculation model, the typical characteristics under different working conditions and the difference analysis results;

[0012] An optimal control strategy is determined based on the optimal front-rear torque distribution strategy and the braking torque distribution threshold.

[0013] Preferably, the calculation expression of the vehicle longitudinal dynamics model is:

[0014]

[0015] Among them, F t is the driving force of the car; F f is the rolling resistance of the car; F w is air resistance; F i is the slope resistance; F j is the acceleration resistance; f is the rolling resistance coefficient; T is the motor torque; i g is the total transmission ratio; η is the transmission efficiency; r is the tire radius; n is the motor speed.

[0016] Preferably, the damage calculation model of the mechanical components of the electric drive system includes: a shaft damage model, a gear damage model and a bearing damage model.

[0017] Preferably, the shafting damage model is expressed as:

[0018]

[0019] Among them, N k is the torque range, n R,i is the frequency, D s For each fragment damage, N f,i N kFatigue life under load level;

[0020] The expression of the gear damage model is:

[0021]

[0022] Among them, r i is the number of revolutions under the i-th level torque load, N i is the fatigue life under the i-th load level, D g Total damage to the gear for a single run segment;

[0023] The expression of the bearing damage model is:

[0024]

[0025] Among them, L 1 is the life of the raceway, D is the total damage of the bearing, D i is the local damage under the i-th level equivalent dynamic load, N i The number of revolutions.

[0026] Preferably, the typical characteristics under different working conditions include: typical characteristics of idling and uniform speed working conditions, typical characteristics of acceleration working conditions and typical characteristics of deceleration working conditions;

[0027] The typical characteristics of the idle and uniform speed conditions include: time distribution of the idle condition, time distribution of the uniform speed condition, joint distribution of average speed and time of the uniform speed condition, and joint distribution of average speed and mileage of the uniform speed condition;

[0028] Typical characteristics of the acceleration condition include: acceleration time distribution, maximum acceleration distribution, average acceleration distribution, speed range distribution and joint distribution of speed range and average acceleration;

[0029] The typical characteristics of the deceleration working condition include: deceleration time distribution, maximum deceleration distribution, average deceleration distribution, speed range distribution and joint distribution of speed range and average deceleration.

[0030] Preferably, the process of obtaining typical characteristics under different working conditions also includes: performing statistical analysis on the constructed characteristic parameters based on the Anderson-Darling goodness of fit test method and distribution model, establishing a probability density function model and a cumulative distribution function model of each characteristic parameter, and selecting a suitable distribution model through the smaller value of the AD test statistic.

[0031] Preferably, the expression of the AD test statistic is:

[0032]

[0033] Preferably, the difference analysis is performed based on the user data, and the process of obtaining the difference analysis result includes: calculating the unit damage intensity of different users to obtain the average unit damage intensity of different users to quantify and compare the differences of users;

[0034] The calculation expression of the unit damage intensity is:

[0035]

[0036] Where Dunit_i is the unit damage intensity under the i-th segment; D i is the damage value after load counting under the i-th segment; M i is the mileage of the i-th segment.

[0037] Compared with the prior art, the present invention has the following advantages and technical effects:

[0038] Aiming at the demand for lean development of reliability of electric vehicle drive-braking system, the present invention relies on the user load big data accumulated by the research team, and realizes the conversion of user actual operation data into electric vehicle drive-braking load by constructing a vehicle longitudinal dynamics model; analyzes the typical characteristics of users under different working conditions; constructs user goals suitable for Chinese road conditions by comparing and analyzing the differences among users in different regions, further compares the user goals with existing standards, and reveals the differences between current specifications and actual usage characteristics of Chinese users; finally, discusses the influence of front and rear torque and braking torque distribution strategies on the damage of mechanical components of the electric drive system, thereby providing a basis for forward design and verification of reliability of the electric drive system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 A schematic diagram of the overall technical route of an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a vehicle longitudinal dynamics model according to an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a time domain comparison of rotation speeds according to an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of torque time domain comparison according to an embodiment of the present invention;

[0044] Figure 5 This is a torque frequency domain comparison schematic diagram of an embodiment of the present invention;

[0045] Figure 6 A torque rain flow comparison schematic diagram of an embodiment of the present invention;

[0046] Figure 7 A schematic diagram of segment division according to an embodiment of the present invention;

[0047] Figure 8 A schematic diagram of the cumulative frequency of user target acceleration according to an embodiment of the present invention;

[0048] Fig. 9 A schematic diagram showing the comparison between user target and standard frequency in an embodiment of the present invention;

[0049] Fig.10 A schematic diagram showing the comparison of the total damage of the motor drive and brake according to the front and rear torque distribution strategy of an embodiment of the present invention;

[0050] Fig.11 It is a schematic diagram comparing the damage of various components under the braking torque distribution strategy of an embodiment of the present invention. DETAILED DESCRIPTION

[0051] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0052] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0053] Embodiment 1

[0054] This embodiment provides a method for analyzing the impact of a driving and braking control strategy on electric drive damage, including:

[0055] This embodiment studies the driving / braking operating characteristics of electric vehicles based on user vehicle operating data. The research mainly includes five parts: user operating data collection, electric vehicle driving-braking load construction, typical feature analysis under different operating conditions, user difference analysis, and the influence of control strategy on driving-braking load characteristics. The failure of the electric drive system is mainly affected by factors such as torque, speed, current, voltage and thermal load during operation. Since the main focus of this embodiment is the mechanical components of the electric drive system (shafts, gears, bearings), the focus is on studying the influence of motor speed and torque on component reliability. The overall technical route of this embodiment is as follows: Figure 1 shown.

[0056] This embodiment uses a distributed user big data cloud platform to collect data samples, which covers the user vehicle operation data of six major regions in China, namely East China, South China, North China, Central China, Northeast China and Northwest China, including the operation data of 200 users in seven cities, namely Shanghai, Guiyang, Chongqing, Changchun, Qingdao, Xining and Yancheng, with a minimum cycle period. The collected user data includes timestamp, GPS, vehicle speed, rotation speed, torque and other information. The sampling frequency is 1Hz. In order to ensure the diversity of different regions, the number of user samples in each city is not less than 20. Abnormal data generated due to poor signal or long-term parking during data collection was eliminated; after data preprocessing, the total operation time of user data is 26041h and the total mileage is 1 million km.

[0057] As the driving and braking loads of electric vehicles, the motor speed and torque are usually collected in real time through the CAN bus. However, large-scale collection of speed and torque data of user vehicles requires a lot of time and resources, and it is difficult to ensure the effectiveness of data quality. To this end, based on the actual speed data of the user, the motor speed and torque data are quickly obtained through the vehicle longitudinal dynamics model; and further verified with the actual speed and torque data collected by the test vehicle in the time domain, frequency domain and damage domain, to ensure the accuracy of the simulation model and provide data support for further research on load characteristics under different working conditions.

[0058] The trend of the vehicle's direction of travel depends on the force acting on it in the direction of motion. The vehicle's driving force F t The torque generated by the motor is transmitted to the drive wheels through the transmission. The resistance of the car during driving mainly includes rolling resistance F f , air resistance F w , slope resistance F i and the acceleration resistance F j .

[0059] The driving resistance of a car can be expressed as:

[0060]

[0061] In the formula, F t is the vehicle driving force (N); F f is the rolling resistance of the car (N); F w is the air resistance (N); F i is the slope resistance (N); F j is the acceleration resistance (N); m is the vehicle mass; v is the vehicle speed (km / h); dv / dt is the driving acceleration (m / s 2 );A is the windward area;C D is the air resistance coefficient; α is the slope angle (°); f is the rolling resistance coefficient.

[0062] The rolling resistance coefficient on a good road surface is calculated as:

[0063]

[0064] The motor torque and speed can be calculated based on the driving force calculation formula, driving force-resistance balance equation, and reducer speed ratio formula:

[0065]

[0066] Where T is the motor torque (N·m); i g is the total transmission ratio; η is the transmission efficiency; r is the tire radius (m); n is the motor speed (RPM).

[0067] like Figure 2 As shown in Figure 1, a SUV test vehicle is taken as the research object. The speed and torque obtained by simulating the vehicle longitudinal dynamics model are compared with the actual collected data to verify the effectiveness of the model. The required dynamic model parameters are shown in Table 1.

[0068] Table 1

[0069]

[0070] Taking the speed data collected by the test vehicle as input, the speed and torque obtained by simulating the vehicle longitudinal dynamics model are compared with the actual collected data in the time domain, frequency domain, and rain flow, such as Figure 3 , 4 , 5, and 6.

[0071] The measured load data and the simulated value were counted for rain flow cycles and the damage error between the two was calculated. As shown in Table 2, the damage error was within 10%, indicating that the torque simulation result was within an acceptable error range in engineering applications.

[0072] Table 2

[0073]

[0074] The electric drive system integrates a variety of mechanical components to achieve power transmission. Different components have different stress characteristics during load transmission, so their failure mechanisms and modes are also significantly different. For example, shaft components mainly fail due to fatigue due to alternating torque; gears are prone to fatigue fracture, pitting and wear during meshing; bearings fail due to wear due to contact fatigue and deterioration of lubrication. For the above key components, establishing an accurate damage calculation model is of great significance for evaluating the reliability of electric drive systems.

[0075] Shafting damage model:

[0076] The operation of the electric drive system is accompanied by asynchronous changes in speed and torque. Different counting methods are required for damage equivalence and correlation of different components. This embodiment is aimed at shaft components, whose damage comes from alternating torque loading. The torque variation range N of the torque time history in different working condition segments is extracted by rain flow cycle counting. k With frequency n R,i , based on Miner's linear cumulative damage criterion, the damage D of each segment is calculated s , where N f,i N k Fatigue life at load level.

[0077]

[0078] Gear damage model:

[0079] For gear components, the load size is related to the input torque, and the number of load cycles is related to the input speed. Therefore, for components in the electric drive system that are affected by the combined effects of speed and torque, the speed and torque joint distribution matrix is ​​obtained by jointly counting the speed and torque time series, and the number of rotations r under the i-th level torque load is obtained. i , divided by the fatigue life N under the i-th load i The corresponding damage under the i-th level torque load is obtained, and the total damage D caused by a single running segment to the gear is g Derived from the linear cumulative damage criterion.

[0080]

[0081] Bearing damage model:

[0082] For bearing components, the load is mainly composed of the torque on the reducer input shaft helical gear and the circumferential force and radial force on the bearing helical gear. The calculation method of the bearing damage model can be based on the ISO281-2007 standard to calculate the rated dynamic load and equivalent dynamic load of the bearing to determine its basic rated life and damage.

[0083] The equivalent dynamic load of the bearing is P 1 Under the operating conditions, the life of the raceway is L 1 If N is operated under this condition 1 If P 1 The equivalent damage of the bearing under the operating conditions is: D 1 =N 1 / L 1 If the bearing is subjected to a random road load, the 1 , P 2 ,···,P n The equivalent load was N 1 , N2 ,···,N n The damage caused by the random road load to the bearing is:

[0084]

[0085] Analysis of typical characteristics under different working conditions

[0086] According to the user running data preprocessed above, the user working condition segments are divided by setting the speed and acceleration thresholds. The division basis is shown in Table 3, and the acceleration segment, deceleration segment, uniform speed segment, and idle segment are extracted respectively. Figure 7 shown.

[0087] Table 3

[0088]

[0089] Based on the above-mentioned segment division basis, the actual operating conditions are refined, and the different operating condition segments are divided. The speed and torque corresponding to each segment are obtained in combination with the vehicle longitudinal dynamics model, and a load database based on user operation data is constructed. Data statistics show that the number of acceleration segments is 8408521, the number of deceleration segments is 7178838, the number of uniform speed segments is 4428959, and the number of idling segments is 1489726, which also includes the operating time and mileage information of each segment.

[0090] The running time and mileage of each running segment are counted, and the total time and mileage proportion of each working condition segment are summarized. The acceleration segment is longer, accounting for 39% of the time, and the corresponding mileage accounts for the largest proportion, which is 43%; the idling segment represents that the car is about to stop or is moving extremely slowly, accounting for 13% of the time and only 1% of the mileage.

[0091] Through the damage calculation model of the mechanical components of the electric drive system, the damage of each component in different working conditions is counted, and finally the damage proportion of each working condition of the shaft system, gears, and bearings is obtained. The large torque fluctuation caused by acceleration and deceleration is the dominant working condition for shaft system failure. Continuous high-speed driving with large torque contributes more to the damage of gears and bearings. The uniform speed and idling conditions correspond to the low-speed and small torque fluctuation driving conditions, which contribute less to the damage of each component.

[0092] In order to obtain the distribution characteristics of each characteristic parameter in different working conditions of the user, this embodiment adopts the Anderson-Darling goodness of fit test method. Combined with common distribution models, including normal distribution, lognormal, extreme value distribution, etc., the constructed characteristic parameters are statistically analyzed. The probability density function model (PDF) and cumulative distribution function model (CDF) of each characteristic parameter are established, and the appropriate distribution model is selected by the smaller value of the AD test statistic. The AD test statistic is:

[0093]

[0094] Idle time distribution:

[0095] The running time of each idle segment is statistically analyzed, and the results are consistent with the log-log normal distribution, with 95% of users idling for less than 19 seconds.

[0096] Time distribution of uniform speed condition:

[0097] The running time of each uniform speed segment is statistically analyzed, and the results are consistent with the log-normal distribution, and 95% of users' idling time is less than 7s.

[0098] The joint distribution of average speed and time under uniform speed condition:

[0099] By counting the average speed and time of each uniform speed segment and performing joint distribution, it can be found that the uniform speed condition is mainly distributed in the case where the average speed is less than 85m / s and the time is less than 10s.

[0100] The joint distribution of average speed and mileage under uniform speed condition:

[0101] By counting the average vehicle speed and mileage of each uniform speed segment and performing joint distribution, it can be found that the uniform speed condition is mainly distributed in the case where the average speed is less than 85m / s and the mileage is less than 250m.

[0102] Analysis of typical characteristics of acceleration conditions:

[0103] Time distribution: The running time of each accelerated segment is statistically analyzed, and the results are in line with the log-normal distribution. The acceleration time of 95% of users is less than 10 seconds.

[0104] Maximum acceleration distribution: The maximum acceleration of each acceleration segment is statistically analyzed, and the results are in line with the log-normal distribution. The maximum acceleration of 95% of users during acceleration is less than 2.3m / s. 2 .

[0105] Average acceleration distribution: The average acceleration of each acceleration segment is statistically analyzed, and the results are consistent with the log-normal distribution. The average acceleration of 95% of users during acceleration is less than 1m / s2.

[0106] Speed ​​range distribution: The speed range of each acceleration segment is statistically analyzed, and the results are consistent with the logarithmic normal distribution. The speed range of 95% of users during acceleration is less than 42m / s.

[0107] Joint distribution of speed range and average acceleration: The average speed and mileage of each uniform speed segment are counted and jointly distributed. As the speed range increases, the average acceleration distribution tends to a low value, concentrated at 1.5m / s 2The frequency distribution is mainly in the low speed range and low acceleration area (30m / s and 1m / s 2 This reflects that the user's driving mode is mostly slow acceleration, which is consistent with the characteristics of frequent start-stop or slight acceleration in urban traffic.

[0108] Analysis of typical characteristics of deceleration conditions:

[0109] Time distribution: The running time of each deceleration segment is statistically analyzed, and the result conforms to the extreme value distribution. The deceleration time of 95% of users is less than 9.5s.

[0110] Maximum deceleration distribution: The maximum deceleration of each deceleration segment is statistically analyzed, and the results are in line with the log-normal distribution. The maximum deceleration of 95% of users is less than 3m / s2 when decelerating.

[0111] Average deceleration distribution: The average deceleration of each deceleration segment is statistically analyzed, and the results are in line with the log-normal distribution. The average deceleration of 95% of users is less than 1.3m / s2 when decelerating.

[0112] Speed ​​range distribution: The speed range of each deceleration segment was statistically analyzed, and the results were in line with the log-normal distribution. 95% of the users had a speed range of less than 48 m / s when decelerating.

[0113] Joint distribution of speed range and average deceleration: The average speed and mileage of each deceleration segment are counted and jointly distributed. During the deceleration process, most of the deceleration occurs when the speed range is 45m / s and the average deceleration is 1.5m / s. 2 This indicates that users tend to prefer smoother braking operations in daily driving; from the perspective of braking energy recovery, this smooth braking behavior helps to improve energy recovery efficiency and provides a reference and basis for optimizing braking control strategies.

[0114] Analysis of typical characteristics of deceleration conditions:

[0115] Time distribution: The running time of each deceleration segment is statistically analyzed, and the result conforms to the extreme value distribution. The deceleration time of 95% of users is less than 9.5s.

[0116] Maximum deceleration distribution: The maximum deceleration of each deceleration segment is statistically analyzed, and the results are in line with the log-normal distribution. The maximum deceleration of 95% of users is less than 3m / s when decelerating. 2 .

[0117] Average deceleration distribution: The average deceleration of each deceleration segment is statistically analyzed, and the results are in line with the log-normal distribution. The average deceleration of 95% of users is less than 1.3m / s. 2 .

[0118] Speed ​​range distribution: The speed range of each deceleration segment was statistically analyzed, and the results were in line with the log-normal distribution. 95% of the users had a speed range of less than 48 m / s when decelerating.

[0119] The speed range and average deceleration are jointly distributed. The average speed and mileage of each deceleration segment are counted and jointly distributed. During the deceleration process, most of the deceleration occurs when the speed range is 45m / s and the average deceleration is 1.5m / s. 2 This indicates that users tend to prefer smoother braking operations in daily driving; from the perspective of braking energy recovery, this smooth braking behavior helps to improve energy recovery efficiency and provides a reference and basis for optimizing braking control strategies.

[0120] User Difference Analysis: Due to the significant differences in mileage, operating time, load frequency, and damage contribution of different users, it is difficult to analyze the differences between users. This embodiment calculates the unit damage intensity of different users to obtain the average unit damage intensity of different users, so as to quantify and compare the differences among users.

[0121] The unit damage intensity is the damage intensity value D of each segment per unit mileage. unit_i .

[0122]

[0123] Dunit_i is the unit damage intensity in the i-th segment; D i is the damage value after load counting under the i-th segment; M i is the mileage of the i-th segment.

[0124] Regional difference analysis: Driving behavior is one of the important factors that lead to high unit damage intensity of shafts, gears, and bearings. Some drivers frequently accelerate and decelerate, which leads to large fluctuations in motor torque. By calculating the unit damage intensity of shafts, gears, and bearings in the drive / brake section of each user, the average, maximum, and minimum values ​​of the unit damage intensity of users in different cities are counted.

[0125] The unit damage intensity of users in different cities shows obvious regional differences. Among them, the user conditions in Yancheng and Changchun show higher unit damage intensity of components, while those in Shanghai are relatively low. This phenomenon is mainly closely related to the city's traffic environment and driving behavior; the road infrastructure in Yancheng and Changchun is relatively new and the traffic conditions are relatively smooth, so the proportion of high-torque drive conditions is relatively high, resulting in higher component damage intensity. The population in Shanghai is dense, there are many traffic lights, traffic jams occur frequently, and there are fewer high-torque drive conditions, so the overall unit damage intensity of components is relatively low.

[0126] To further explain this difference, we counted the cumulative acceleration frequency of representative users in different cities. The driving behavior of Shanghai users was generally stable, while the driving behavior of Yancheng and Changchun users was relatively intense.

[0127] Analysis of differences between user goals and standards: In order to further reveal the differences between users and standards, the acceleration frequencies of 200 users' driving / braking segments were statistically extrapolated to a full life cycle of 300,000 km, and the frequency counts of acceleration in each interval group were divided according to the intervals to obtain the acceleration interval frequency of the user's goal as follows: Figure 8 shown.

[0128] Compare the user's target braking intensity frequency with the existing AKB-I, AKB-II, and AKB-2016 standards. Fig. 9 shown.

[0129] from Fig. 9 It can be seen that the cumulative frequency of AKB-I and AKB-II in the high braking intensity part is relatively high, indicating that these two specifications cover the high braking intensity conditions more fully. However, they show obvious deficiencies in the low braking intensity frequency. In contrast, although AKB-2016 has increased the operating condition level and expanded the coverage, it still differs from the actual user goals, especially in the low and medium load parts. The frequency is low. This difference shows that the existing reliability specifications tend to focus on high-load and high-intensity conditions, and may ignore the small and medium load conditions that users frequently experience in actual driving. This causes the reliability test results to deviate from the user's actual usage conditions.

[0130] In summary, the significant difference between the existing reliability specifications in braking frequency distribution and the actual operating conditions of Chinese users further highlights the importance of optimizing current test specifications based on big data actually used by users.

[0131] The influence of control strategy on drive-brake load characteristics:

[0132] Front and rear torque distribution strategy: Different front and rear torque distribution strategies not only determine the power output characteristics and driving stability of the vehicle, but also affect the reliability of various components of the vehicle during long-term use. In order to explore the impact of the front and rear torque distribution strategy on the drive-brake load characteristics, the differences in component damage in the drive-brake conditions of the electric drive system under the three strategies of rear-drive priority, rear-drive bias, and balanced distribution were compared.

[0133] pass Fig.10It can be seen that among the three torque distribution strategies, the strategy of balanced front and rear torque distribution causes the least damage to the electric drive system components during long-term use of the vehicle, with the damage to the shaft, gear, and bearing being only 13%, 10%, and 32% of that of the rear-drive priority strategy, respectively. Therefore, this reduction in damage level indicates that it is expected to significantly extend the service life of components, especially the life of shafts and gears, which can be increased by 10 times compared to the rear-drive priority strategy. This result provides data support for the selection of torque distribution strategies, thereby providing new ideas for extending the life of key components.

[0134] In the automobile braking system, when the deceleration is less than a certain threshold, the vehicle only relies on regenerative braking torque for braking, making full use of the energy recovery of the motor. When the deceleration is greater than the threshold, the motor will output the maximum regenerative braking torque. If it still cannot meet the braking demand, the remaining braking torque will be provided by mechanical braking.

[0135] Fig.11 The damage of electric drive system components under different braking torque distribution strategies is shown. As the threshold increases, the participation of mechanical braking gradually decreases, the proportion of braking energy recovery increases, and the negative torque demand of the motor increases accordingly, resulting in an increase in the damage value of the electric drive components. The results show that the damage of shafts and gears is more sensitive to the change of threshold, while the bearing damage is relatively gentle with the increase of threshold. This trend provides an important reference for optimizing the braking torque distribution strategy to improve the reliability of the braking system, indicating that the relationship between the threshold and the damage of each key component must be fully considered in the design to reduce component damage and extend service life.

[0136] This embodiment has the following beneficial effects:

[0137] In this embodiment, based on actual user operation data, a study on the driving-braking load characteristics of electric vehicles is carried out, and the main conclusions are as follows.

[0138] Based on the user's operating data, the driving and braking loads of the electric drive system are constructed through the vehicle longitudinal dynamics model, and its effectiveness is verified in combination with the measured data to achieve the conversion from the user's actual usage scenario to the target vehicle model's operating load.

[0139] Through the overall analysis of user operating conditions, the results show that the acceleration and deceleration sections contribute significantly to the vehicle's time and mileage; the damage analysis results show that repeated acceleration and deceleration causing large torque fluctuations is the dominant operating condition for shaft failure, and continuous high-speed driving with large torque contributes significantly to gear and bearing damage.

[0140] The user's uniform speed condition is mainly concentrated in the range of average speed less than 85m / s and time less than 10 seconds; the user's acceleration condition is mostly slow acceleration; the deceleration condition is mainly distributed in the speed range of 45m / s and the average deceleration of 1.5m / s2 within the range; it provides a reference for optimizing braking control strategies and improving energy recovery efficiency.

[0141] From the perspective of the differences among users in different regions, the driving and braking characteristics of users in different regions were compared. The results showed that the component damage intensity under the working conditions of users in Yancheng and Changchun was higher, while that in Shanghai was lower. The differences between user goals and existing standards were further analyzed, and the results showed that there were significant differences in the frequency of the two in the medium and low load parts.

[0142] To explore the influence of control strategies on drive-brake loads, three strategies were compared: rear-drive priority, partial rear-drive, and front-rear balanced distribution. The results show that front-rear balanced distribution can significantly reduce the damage to electric drive system components, and the damage to shafts and gears is sensitive to changes in the brake torque distribution threshold, while the bearing damage changes more slowly with the increase of the threshold.

[0143] The relevant research results provide reference and basis for formulating electric vehicle reliability design and evaluation verification that is more in line with Chinese user conditions.

[0144] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for analyzing the influence of driving and braking control strategy on electric drive damage, characterized in that: include: Acquire user data based on a distributed user big data cloud platform, and build a vehicle longitudinal dynamics model based on the user data; Constructing an electric vehicle driving-braking load model based on the vehicle longitudinal dynamics model; Based on the stress characteristics of different components during load transfer, a damage calculation model for mechanical components of the electric drive system is established; Based on the vehicle longitudinal dynamics model and the user data, user operating condition segmentation is performed to obtain typical features under different operating conditions; Performing a difference analysis based on the user data to obtain a difference analysis result; Determining the optimal front and rear torque distribution strategy and braking torque distribution threshold based on the electric vehicle drive-brake load model, the drive system mechanical component damage calculation model, the typical characteristics under different working conditions and the difference analysis results; An optimal control strategy is determined based on the optimal front-rear torque distribution strategy and the braking torque distribution threshold.

2. The method according to claim 1, characterized in that The calculation expression of the vehicle longitudinal dynamics model is: Among them, F t is the driving force of the car; F f is the rolling resistance of the car; F w is air resistance; F i is the slope resistance; F j is the acceleration resistance; f is the rolling resistance coefficient; T is the motor torque; i g is the total transmission ratio; η is the transmission efficiency; r is the tire radius; n is the motor speed.

3. The method according to claim 1, characterized in that The damage calculation model of the mechanical components of the electric drive system includes: a shaft damage model, a gear damage model and a bearing damage model.

4. The method according to claim 3, characterized in that The expression of the shaft damage model is: Among them, N k is the torque range, n R,i is the frequency, D s For each fragment damage, N f,i N k Fatigue life under load level; The expression of the gear damage model is: Among them, r i is the number of revolutions under the i-th level torque load, N i is the fatigue life under the i-th load level, D g Total damage to the gear for a single run segment; The expression of the bearing damage model is: Among them, L1 is the life of the raceway, D is the total damage of the bearing, and D i is the local damage under the i-th level equivalent dynamic load, N i The number of operating revolutions.

5. The method according to claim 1, characterized in that The typical characteristics under different working conditions include: typical characteristics of idling and uniform speed working conditions, typical characteristics of acceleration working conditions, and typical characteristics of deceleration working conditions; The typical characteristics of the idle and uniform speed conditions include: time distribution of the idle condition, time distribution of the uniform speed condition, joint distribution of average speed and time of the uniform speed condition, and joint distribution of average speed and mileage of the uniform speed condition; Typical characteristics of the acceleration condition include: acceleration time distribution, maximum acceleration distribution, average acceleration distribution, speed range distribution and joint distribution of speed range and average acceleration; The typical characteristics of the deceleration working condition include: deceleration time distribution, maximum deceleration distribution, average deceleration distribution, speed range distribution and joint distribution of speed range and average deceleration.

6. The method according to claim 1, characterized in that The process of obtaining typical characteristics under different working conditions also includes: performing statistical analysis on the constructed characteristic parameters based on the Anderson-Darling goodness of fit test method and the distribution model, establishing a probability density function model and a cumulative distribution function model for each characteristic parameter, and selecting a suitable distribution model through the smaller value of the AD test statistic.

7. The method according to claim 6, characterized in that The expression of the AD test statistic is:

8. The method according to claim 1, characterized in that The process of performing difference analysis based on the user data and obtaining the difference analysis result includes: calculating the unit damage intensity of different users to obtain the average unit damage intensity of different users to quantify and compare the differences of users; The calculation expression of the unit damage intensity is: Where Dunit_i is the unit damage intensity under the i-th segment; D i is the damage value after load counting under the i-th segment; M i is the mileage of the i-th segment.

Images