Electric vehicle torque coordination control method and system based on data analysis

Abstract

The invention relates to the technical field of data processing and analysis, in particular to an electric vehicle torque coordination control method and system based on data analysis. Comprehensively analyzing the torque sudden change threat degree of the electric vehicle and the torque change track matching state of the electric vehicle when the electric vehicle runs to the road preview area range; the comprehensive analysis result is imported into an electric vehicle dynamic damage risk assessment model, and assessment of the dynamic damage risk of the electric vehicle when the electric vehicle runs to the road preview area range is achieved; and according to a dynamic damage risk assessment result, an optimal torque change scheme is selected for the torque change when the electric vehicle runs to the road preview area range, so that electromechanical collaborative optimization of the torque of the electric vehicle is realized, and the damage risk of the electric vehicle under the dynamic road working condition is reduced by coordinately controlling the torque; and the energy consumption efficiency of the automobile and the safety performance of the automobile under complex road conditions are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing and analysis technology, and in particular to a method and system for coordinated torque control of electric vehicles based on data analysis. Background Technology

[0002] With the rapid development of automotive intelligence technology, electric vehicles, as the core transportation tool in the process of automotive intelligence, have seen the refined and intelligent coordinated control of their drive systems become an important development direction. Traditional torque coordination control methods for automobiles mainly rely on preset rules and local feedback mechanisms. While this method can meet the basic performance requirements of vehicles in structured road scenarios, it falls short in complex and ever-changing real-world road conditions, especially sudden changes in road gradient, transient shifts in road curvature, or low-adhesion surfaces. The precision and intelligence of torque coordination control in these traditional methods require significant improvement. Currently, the automotive intelligence field commonly employs decoupled control architectures, processing and analyzing data related to mechanical transmission protection, motor efficiency optimization, and battery thermal management in a fragmented manner. This results in a lag in real-time torque response compared to changes in road demand during driving, potentially leading to multiple damage risks such as mechanical shock to the drivetrain and overheating of the motor windings.

[0003] In particular, in the field of torque coordination control of electric vehicles, existing methods cannot quantify the impact damage to the vehicle's transmission system caused by sudden changes in road topology in road preview scenarios in real time. Furthermore, existing technologies cannot achieve comprehensive quantification of electrical failures and mechanical damage when assessing vehicle damage under preset torque change schemes. This results in the inability to accurately assess the risk of combined vehicle damage within the road preview range, such as when emergency obstacle avoidance is required or when there are continuous curves ahead. Consequently, it is impossible to achieve targeted adjustments to the torque change scheme.

[0004] To address these issues, this application presents a data analysis-based method and system for coordinated torque control of electric vehicles. Summary of the Invention

[0005] To overcome the shortcomings and deficiencies of existing technologies, this invention provides a data analysis-based method and system for coordinated torque control of electric vehicles. By comprehensively analyzing the threat level of sudden torque changes and the matching status of torque change trajectories of electric vehicles when driving within a pre-aimed road area, the method achieves an assessment of the dynamic damage risk of electric vehicles. This allows for the selection of the optimal torque change scheme for the electric vehicle, realizing electromechanical optimization of the electric vehicle's torque. Furthermore, by coordinating torque control, the method reduces the damage risk to electric vehicles under dynamic road conditions, improving the energy efficiency and safety performance of the vehicle in complex road conditions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a torque coordination control method for electric vehicles based on data analysis, comprising the following steps:

[0008] S1. Obtain road preview data through electric vehicle sensors, and at the same time obtain the electric vehicle's electrical system operation data;

[0009] S2. Based on road preview data and electrical system operation data, construct a torque mutation threat level analysis model to analyze the torque mutation threat level of electric vehicles when driving into the road preview area;

[0010] S3. Based on road preview data and electrical system operation data, construct a torque change trajectory matching state analysis model to analyze the torque change trajectory matching state of electric vehicles when driving within the road preview area;

[0011] S4. Construct a dynamic damage risk assessment model for electric vehicles. Import the analysis results of the threat level of torque mutation and the torque change trajectory matching status analysis results of electric vehicles when driving into the road pre-aiming area into the dynamic damage risk assessment model for electric vehicles to assess the dynamic damage risk of electric vehicles when driving into the road pre-aiming area.

[0012] S5. Based on the dynamic damage risk assessment results of the electric vehicle when it travels into the road pre-aiming area, select the optimal torque change scheme for the torque change when the electric vehicle travels into the road pre-aiming area.

[0013] In a preferred embodiment of the present invention, step S2 analyzes the threat level of sudden torque change in an electric vehicle when it enters the road pre-aiming area, including the following specific steps:

[0014] S21. Extract road forecast data and electrical system operation data under the current torque change scheme;

[0015] S22. Based on the road forecast data and electrical system operation data under the current torque change scheme, construct a torque change threat level analysis model to analyze the torque change threat level of electric vehicles when driving into the road forecast area under the current torque change scheme, and obtain the analysis results of the torque change threat level of electric vehicles when driving into the road forecast area under the current torque change scheme.

[0016] The formula for calculating the threat level of torque sudden change is as follows:

[0017] TW = Bw·(1-Xy);

[0018] In the formula, TW represents the threat level of sudden torque change of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme, Bw represents the threat level of torque fluctuation of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme, and Xy represents the torque control response margin of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme.

[0019] In a preferred embodiment of the present invention, the process of constructing the torque mutation threat level analysis model in step S22 includes the following specific steps:

[0020] S221. Based on the road forecast data and electrical system operation data under the current torque change scheme, analyze the torque fluctuation threat level of the electric vehicle when it travels into the road forecast area under the current torque change scheme, and obtain the analysis results of the torque fluctuation threat level of the electric vehicle when it travels into the road forecast area under the current torque change scheme.

[0021] S222. Based on the electrical system operation data under the current torque change scheme, analyze the torque control response margin of the electric vehicle when it travels within the road pre-aiming area under the current torque change scheme, and obtain the analysis results of the torque control response margin of the electric vehicle when it travels within the road pre-aiming area under the current torque change scheme.

[0022] In a preferred embodiment of the present invention, step S3 analyzes the matching state of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme, including the following specific steps:

[0023] S31. Extract road forecast data and electrical system operation data under the current torque change scheme;

[0024] S32. Based on the road preview data and electrical system operation data under the current torque change scheme, construct a torque change trajectory matching state analysis model, analyze the torque change trajectory matching state of the electric vehicle when it travels into the road preview area under the current torque change scheme, and obtain the analysis results of the torque change trajectory matching state of the electric vehicle when it travels into the road preview area under the current torque change scheme.

[0025] The formula for calculating the torque change trajectory matching state is as follows:

[0026] P = Ph·Gz;

[0027] In the formula, P represents the matching state of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme, Ph represents the smoothness of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme, and Gz represents the tracking accuracy of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme.

[0028] In a preferred embodiment of the present invention, the process of constructing the torque change trajectory matching state analysis model in step S32 includes the following specific steps:

[0029] S321. Based on the road forecast data and electrical system operation data under the current torque change scheme, analyze the smoothness of the electric vehicle torque change trajectory when it travels into the road forecast area under the current torque change scheme, and obtain the analysis results of the smoothness of the electric vehicle torque change trajectory when it travels into the road forecast area under the current torque change scheme.

[0030] The formula for calculating the smoothness of the torque change trajectory is as follows:

[0031]

[0032] In the formula, Ph represents the smoothness of the electric vehicle's torque change trajectory when it travels to the road pre-aiming area under the current torque change scheme, tc represents the pre-aiming duration in the road pre-aiming data, ac(t) represents the real-time vehicle lateral acceleration in the electrical system operation data within the time period tc before the current time, ac represents the average vehicle lateral acceleration in the electrical system operation data within the time period tc before the current time, ψ(t) represents the real-time vehicle yaw rate in the electrical system operation data within the time period tc before the current time, ψ represents the average vehicle yaw rate in the electrical system operation data within the time period tc before the current time, hq represents the real-time slip rate of the q-th tire of the vehicle at the current time in the electrical system operation data, and h represents the average real-time slip rate of all tires of the vehicle at the current time in the electrical system operation data.

[0033] S322. Based on the road preview data and electrical system operation data under the current torque change scheme, analyze the accuracy of electric vehicle torque change trajectory tracking when the vehicle is within the road preview area under the current torque change scheme, and obtain the analysis results of the accuracy of electric vehicle torque change trajectory tracking when the vehicle is within the road preview area under the current torque change scheme.

[0034] In a preferred embodiment of the present invention, step S4, which involves constructing a dynamic damage risk assessment model for electric vehicles, includes the following specific steps:

[0035] S41. Extract and analyze the threat level of sudden torque change of electric vehicles when driving into the road pre-aiming area under the current torque change scheme and the torque change trajectory matching status analysis results.

[0036] S42. Based on the analysis results of the threat level of sudden torque change of electric vehicle when driving into the road pre-aiming area and the analysis results of torque change trajectory matching status, assess the dynamic damage risk of electric vehicle when driving into the road pre-aiming area under the current torque change scheme.

[0037] The formula for assessing dynamic damage risk is as follows:

[0038]

[0039] In the formula, R represents the dynamic damage risk of the electric vehicle when it travels within the road pre-aiming area under the current torque variation scheme, Tr represents the real-time winding temperature of the electric vehicle's motor, and Tr max This is the maximum safe operating temperature for the motor.

[0040] In a preferred embodiment of the present invention, step S5, which selects the optimal torque change scheme for the torque change when the electric vehicle travels within the road pre-aiming area, includes the following specific steps:

[0041] S51. Obtain the dynamic damage risk assessment results of electric vehicles when driving into the road pre-aiming area under all torque change schemes.

[0042] S52. Based on the dynamic damage risk assessment results of the electric vehicle when it travels into the road preview area under all torque change schemes, obtain the torque change scheme that minimizes the dynamic damage risk assessment results of the electric vehicle when it travels into the road preview area as the optimal torque change scheme, and apply the optimal torque change scheme to the road driving control of the electric vehicle within the road preview area.

[0043] Secondly, embodiments of the present invention also provide a data analysis-based electric vehicle torque coordination control system, comprising:

[0044] The data acquisition module is used to acquire road preview data through electric vehicle sensors, and at the same time acquire the electric vehicle's electrical system operation data;

[0045] The torque mutation threat level analysis module is used to build a torque mutation threat level analysis model based on road preview data and electrical system operation data, and analyze the torque mutation threat level of electric vehicles when driving into the road preview area.

[0046] The torque change trajectory matching state analysis module is used to construct a torque change trajectory matching state analysis model based on road preview data and electrical system operation data, and to analyze the torque change trajectory matching state of electric vehicles when driving within the road preview area.

[0047] The dynamic damage risk assessment module is used to construct a dynamic damage risk assessment model for electric vehicles. It imports the analysis results of the threat level of torque mutation and the torque change trajectory matching status analysis results of electric vehicles when driving into the road pre-aiming area into the dynamic damage risk assessment model of electric vehicles to assess the dynamic damage risk of electric vehicles when driving into the road pre-aiming area.

[0048] The optimal torque variation scheme selection module is used to select the optimal torque variation scheme for the electric vehicle when it is driving into the road pre-aiming area based on the dynamic damage risk assessment results of the electric vehicle when it is driving into the road pre-aiming area.

[0049] The control module is used to control the operation of the data acquisition module, the torque sudden change threat level analysis module, the torque change trajectory matching status analysis module, the dynamic damage risk assessment module, and the optimal torque change scheme selection module.

[0050] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0051] This invention comprehensively analyzes the threat level of sudden torque changes and the matching status of electric vehicle torque change trajectories when the vehicle enters the road pre-aiming area using road preview data and electrical system operation data. The comprehensive analysis results are then imported into the electric vehicle dynamic damage risk assessment model, enabling the assessment of dynamic damage risks when the vehicle enters the road pre-aiming area. Based on the dynamic damage risk assessment results, the optimal torque change scheme is selected for the torque change when the electric vehicle enters the road pre-aiming area, thereby achieving electromechanical optimization of the electric vehicle torque. By coordinating and controlling the torque, the damage risk to the electric vehicle under dynamic road conditions is reduced, improving the vehicle's energy efficiency and safety performance under complex road conditions. Attached Figure Description

[0052] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0053] Figure 1 This is a schematic diagram of the overall process of the electric vehicle torque coordination control method based on data analysis of the present invention.

[0054] Figure 2 This is a flowchart of step S2 in the data analysis-based electric vehicle torque coordination control method of the present invention.

[0055] Figure 3 This is a flowchart of step S3 in the data analysis-based electric vehicle torque coordination control method of the present invention.

[0056] Figure 4 This is a schematic diagram of the electric vehicle torque coordination control system based on data analysis according to the present invention. Detailed Implementation

[0057] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0058] Example 1

[0059] like Figure 1 As shown, this embodiment provides a data analysis-based torque coordination control method for electric vehicles, specifically including the following steps:

[0060] S1. Obtain road preview data through electric vehicle sensors, and at the same time obtain the electric vehicle's electrical system operation data;

[0061] S2. Based on road preview data and electrical system operation data, construct a torque mutation threat level analysis model to analyze the torque mutation threat level of electric vehicles when driving into the road preview area;

[0062] S3. Based on road preview data and electrical system operation data, construct a torque change trajectory matching state analysis model to analyze the torque change trajectory matching state of electric vehicles when driving within the road preview area;

[0063] S4. Construct a dynamic damage risk assessment model for electric vehicles. Import the analysis results of the threat level of torque mutation and the torque change trajectory matching status analysis results of electric vehicles when driving into the road pre-aiming area into the dynamic damage risk assessment model for electric vehicles to assess the dynamic damage risk of electric vehicles when driving into the road pre-aiming area.

[0064] S5. Based on the dynamic damage risk assessment results of the electric vehicle when it travels into the road pre-aiming area, select the optimal torque change scheme for the torque change when the electric vehicle travels into the road pre-aiming area.

[0065] In this embodiment, as Figure 2 As shown, step S2 analyzes the threat level of sudden torque change in electric vehicles when driving into the road pre-aiming area, including the following specific steps:

[0066] S21. Extract road forecast data and electrical system operation data under the current torque change scheme;

[0067] S22. Based on the road forecast data and electrical system operation data under the current torque change scheme, construct a torque change threat level analysis model to analyze the torque change threat level of electric vehicles when driving into the road forecast area under the current torque change scheme, and obtain the analysis results of the torque change threat level of electric vehicles when driving into the road forecast area under the current torque change scheme.

[0068] The formula for calculating the threat level of torque sudden change is as follows:

[0069] TW = Bw·(1-Xy);

[0070] In the formula, TW represents the threat level of sudden torque change of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme, Bw represents the threat level of torque fluctuation of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme, and Xy represents the torque control response margin of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme.

[0071] For example, the torque variation scheme is a dynamic control strategy for the motor output torque; its adjustment is not only constrained by the response capability of the electrical system, but also needs to be matched with the road condition requirements of the road pre-aiming area. Different torque variation schemes will directly affect the torque fluctuation threat level and torque control response margin of the electric vehicle. Therefore, this embodiment calculates the torque sudden change threat level of the electric vehicle by using the torque fluctuation threat level and torque control response margin of the electric vehicle when driving within the road pre-aiming area under the current torque variation scheme, which can truly reflect the torque sudden change threat level in the actual road pre-aiming scenario.

[0072] This embodiment quantifies the threat of torque mutations to the safety and ride comfort of electric vehicles by assessing the degree of torque mutation threat. The degree of torque mutation threat depends on both the inherent fluctuation characteristics of the torque itself and the control capability of the electric vehicle's electrical system to torque mutations. Specifically, this embodiment maps the inherent threat of torque control under the influence of road conditions in the road pre-aiming area to the degree of torque fluctuation threat; it quantifies the response capability of the electric vehicle's electrical system by assessing the torque control response margin. A higher torque control response margin indicates a stronger ability of the electrical system to correct and stabilize torque mutations, resulting in a more significant resistance to torque fluctuation threats. Therefore, this embodiment uses multiplication to achieve a coupled calculation of the degree of torque fluctuation threat after resistance through the electrical system. Furthermore, this embodiment can analyze the safety of the current torque change scheme in the road pre-aiming area based on the torque mutation threat calculation formula, determining whether the current torque change scheme has an excessively high risk of mutation; it also provides a quantitative basis for comparing different torque change schemes, thus facilitating the adjustment of the torque change scheme in step S5.

[0073] In this embodiment, the construction process of the torque mutation threat level analysis model in step S22 includes the following specific steps:

[0074] S221. Based on the road forecast data and electrical system operation data under the current torque change scheme, analyze the torque fluctuation threat level of the electric vehicle when it travels into the road forecast area under the current torque change scheme, and obtain the analysis results of the torque fluctuation threat level of the electric vehicle when it travels into the road forecast area under the current torque change scheme.

[0075] The formula for calculating the threat level of torque fluctuation is as follows:

[0076]

[0077] In the formula, Bw represents the torque fluctuation threat level of the electric vehicle when it travels within the road pre-aiming area under the current torque change scheme, N represents the number of torque sampling points within the road pre-aiming area in the road pre-aiming data, and Tmi represents the torque that the motor needs to output at the i-th torque sampling point under the current torque change scheme in the electrical system operation data. This is the average value of the motor's required output torque at all torque sampling points under the current torque variation scheme in the electrical system operation data. In this embodiment, the calculation of the motor's required output torque is an existing technology in the field of electric vehicle torque coordination control, and will not be elaborated here.

[0078] In this embodiment, the formula for calculating the number of torque sampling points within the road preview area is as follows: Where d is the aiming distance of the electric vehicle in the road aiming data, v is the real-time speed of the electric vehicle in the electrical system operation data, and fc is the torque sampling frequency of the electric vehicle in the electrical system operation data.

[0079] For example, in this embodiment, electrical system operating data in the current torque variation scheme is obtained based on the current torque variation scheme to ensure that all parameters in the torque fluctuation threat level calculation formula correspond one-to-one with the actual driving state of the vehicle. This further ensures that the torque fluctuation threat level can accurately reflect the threat of torque fluctuation to driving safety when the vehicle passes through the road pre-aiming area under the current torque variation scheme. Specifically, the torque fluctuation threat level calculation formula adopts... The deviation of the motor's output torque from its mean at each torque sampling point is quantified to describe the amplitude characteristics of torque fluctuations; and through... It eliminates the interference of torque mean difference on the threat of torque fluctuation. For example, in actual driving, small torque fluctuations under high load and similar torque fluctuations under low load can be mitigated by... Normalization processing is used to reflect the equivalent threat, enabling the generation of required motor output torque based on different torque sampling points in the road pre-aiming area, thus providing horizontal comparability when calculating torque fluctuation risk. In this embodiment, by combining the pre-aiming distance and real-time vehicle speed to calculate the time window for the vehicle to pass through the road pre-aiming area, and multiplying it with the torque sampling frequency of the electrical system, it is possible to ensure that the number of sampling points is adapted to the coupling relationship between the road pre-aiming distance and vehicle dynamic changes. For example, at high speeds, the time within the pre-aiming area is short and the vehicle speed is high, so the number of sampling points calculated in this embodiment is reduced, which can capture the macroscopic fluctuation characteristics of torque; at low speeds, the pre-aiming time window is extended, and the number of sampling points calculated in this embodiment is correspondingly increased, which can capture more subtle torque changes, thereby avoiding missed detections due to insufficient sampling, and also preventing excessive sampling from increasing computational redundancy. This embodiment quantifies the threat level of torque fluctuation by integrating the combined effects of fluctuation amplitude and fluctuation frequency, which can intuitively reflect the threat level of torque fluctuation to driving safety and ride comfort in the current torque change scheme.

[0080] S222. Based on the electrical system operation data under the current torque change scheme, analyze the torque control response margin of the electric vehicle when it travels to the road pre-aiming area under the current torque change scheme, and obtain the analysis results of the torque control response margin of the electric vehicle when it travels to the road pre-aiming area under the current torque change scheme.

[0081] The formula for calculating the torque control response margin is as follows:

[0082]

[0083] In the formula, Xy is the torque control response margin of the electric vehicle when it is within the road pre-aiming area under the current torque change scheme, Tz is the real-time command torque received by the motor under the current torque change scheme in the electrical system operation data, Ts is the real-time output torque of the motor under the current torque change scheme in the electrical system operation data, Tmax is the maximum allowable torque of the motor in the electrical system operation data, ty is the response delay time of the motor in the electrical system operation data, and tw is the system stabilization time of the motor in the electrical system operation data.

[0084] For example, the torque control response margin formula provided in this embodiment is calculated based on the current torque variation scheme. The torque variation scheme directly determines the change in the real-time command torque received by the motor and also affects the real-time output torque of the motor. The deviation between the command torque and the actual output is quantified by the absolute value of Tz-Ts. The smaller the value, the stronger the electrical system's ability to reproduce the torque command. Furthermore, this embodiment also normalizes the torque deviation by dividing by Tmax, eliminating magnitude differences, so that the technical solution provided in this embodiment can be applied to torque control of most electric vehicles. Further, this embodiment also... By coupling the motor's response timeliness and system stability in the electrical system, the quantification of response performance is achieved. This embodiment also introduces an exponential function to map the above-mentioned combined effects to a range of 0 to 1: For example, when the deviation is 0 and the delay is 0, the torque control response margin of the electric vehicle under the current torque change scheme is 1 when driving within the road pre-aiming area, representing optimal margin; when the deviation or delay increases, the value of the torque control response margin decreases from 1 to 0, and its decrease rate increases with the increase of the absolute value of the independent variable, which can appropriately amplify the impact of small deviations and meet the need for rapid quantification of response margin under temporary conditions such as the road pre-aiming area. This embodiment improves the operating safety and smoothness of the electric vehicle in the road pre-aiming area by judging the response margin level of the current torque change scheme and then adjusting the torque change scheme accordingly.

[0085] In this embodiment, as Figure 3 As shown, step S3 analyzes the torque change trajectory matching state of the electric vehicle when it travels to the road pre-aiming area under the current torque change scheme, including the following specific steps:

[0086] S31. Extract road forecast data and electrical system operation data under the current torque change scheme;

[0087] S32. Based on the road preview data and electrical system operation data under the current torque change scheme, construct a torque change trajectory matching state analysis model, analyze the torque change trajectory matching state of the electric vehicle when it travels into the road preview area under the current torque change scheme, and obtain the analysis results of the torque change trajectory matching state of the electric vehicle when it travels into the road preview area under the current torque change scheme.

[0088] The formula for calculating the torque change trajectory matching state is as follows:

[0089] P = Ph·Gz;

[0090] In the formula, P represents the matching state of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme, Ph represents the smoothness of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme, and Gz represents the tracking accuracy of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme.

[0091] For example, in this embodiment, the smoothness of the torque change trajectory is used to quantify the smoothness of the torque change under the current torque change scheme. The greater the smoothness of the torque change trajectory, the longer the life of the electric vehicle's transmission system. The torque change trajectory tracking accuracy is used to quantify the closeness of the actual torque following the ideal torque trajectory. The torque change trajectory matching status is analyzed by integrating the product relationship between the smoothness of the torque change trajectory and the tracking accuracy of the torque change trajectory.

[0092] In this embodiment, the process of constructing the torque change trajectory matching state analysis model in step S32 includes the following specific steps:

[0093] S321. Based on the road forecast data and electrical system operation data under the current torque change scheme, analyze the smoothness of the electric vehicle torque change trajectory when it travels into the road forecast area under the current torque change scheme, and obtain the analysis results of the smoothness of the electric vehicle torque change trajectory when it travels into the road forecast area under the current torque change scheme.

[0094] The formula for calculating the smoothness of the torque change trajectory is as follows:

[0095]

[0096] In the formula, Ph represents the smoothness of the electric vehicle's torque change trajectory when it travels to the road pre-aiming area under the current torque change scheme, tc represents the pre-aiming duration in the road pre-aiming data, ac(t) represents the real-time vehicle lateral acceleration in the electrical system operation data within the time period tc before the current time, ac represents the average vehicle lateral acceleration in the electrical system operation data within the time period tc before the current time, ψ(t) represents the real-time vehicle yaw rate in the electrical system operation data within the time period tc before the current time, ψ represents the average vehicle yaw rate in the electrical system operation data within the time period tc before the current time, hq represents the real-time slip rate of the q-th tire of the vehicle at the current time in the electrical system operation data, and h represents the average real-time slip rate of all tires of the vehicle at the current time in the electrical system operation data.

[0097] For example, in this embodiment, the integral term To quantify the overall abrupt changes in vehicle dynamic response, the absolute value of the derivative of the product of lateral acceleration and yaw rate is calculated as the integral over the preview duration, and then normalized. The larger the calculated value, the more drastic the total rate change of lateral or yaw motion caused by torque change, resulting in a decrease in the smoothness of the torque change trajectory, which can intuitively quantify the deterioration of torque smoothness; the exponential term This method is used to quantify the balance of force distribution on electric vehicle tires. The standard deviation of the difference between the slip ratio and the mean of the four tires is normalized using the mean. This indicates that the smaller the difference in slip ratio, the better the exponential term. The closer the value is to 1, the smoother the torque change trajectory. By introducing an exponential term, this embodiment can demonstrate the impact of torque distribution in the current torque change scheme on the vehicle's tire load balance. Specifically, in this embodiment, when the integral term dominates and makes the smoothness of the torque change trajectory low, it indicates that the torque change has caused drastic fluctuations in the vehicle's dynamic parameters, requiring optimization of the torque change rate in the current torque change scheme. When the exponential term dominates and makes the smoothness of the torque change trajectory low, it indicates significant differences in tire slip ratios, requiring adjustment of the torque distribution in the current torque change scheme. Based on this, this embodiment can select different torque change schemes for adjustment to achieve a smoother torque change trajectory than the current torque change scheme.

[0098] S322. Based on the road preview data and electrical system operation data under the current torque change scheme, analyze the accuracy of electric vehicle torque change trajectory tracking when the vehicle is driven into the road preview area under the current torque change scheme, and obtain the analysis results of electric vehicle torque change trajectory tracking accuracy when the vehicle is driven into the road preview area under the current torque change scheme.

[0099] The formula for calculating the accuracy of torque change trajectory tracking is as follows:

[0100]

[0101] In the formula, Gz represents the accuracy of the electric vehicle's torque change trajectory tracking when it travels to the road pre-aiming area under the current torque change scheme, Tg represents the torque operating range interval value in the electrical system operating data, Tst represents the real-time output torque of the motor at time t within the time period tc before the current time in the electrical system operating data, and Tzt represents the real-time command torque received by the motor at time t within the time period tc before the current time in the electrical system operating data.

[0102] For example, the torque change trajectory tracking accuracy calculation formula provided in this embodiment is obtained by averaging and taking the square root of the sum of the squares of the deviations between the actual output torque and the commanded torque at each moment within the pre-aiming time, so that... It comprehensively reflects the overall magnitude of the torque change trajectory tracking deviation; furthermore, by dividing by the torque operating range, it achieves normalization of the overall magnitude of the torque change trajectory tracking deviation, thereby eliminating the difference in the absolute value of the deviation between different torque change schemes and making different torque change schemes horizontally comparable; when The smaller the value, the higher the accuracy of torque change trajectory tracking. The closer the value is to 1, the more accurately the actual torque tracks the commanded torque.

[0103] In this embodiment, step S4, which involves constructing a dynamic damage risk assessment model for electric vehicles, includes the following specific steps:

[0104] S41. Extract and analyze the threat level of sudden torque change of electric vehicles when driving into the road pre-aiming area under the current torque change scheme and the torque change trajectory matching status analysis results.

[0105] S42. Based on the analysis results of the threat level of sudden torque change of electric vehicle when driving into the road pre-aiming area and the analysis results of torque change trajectory matching status, assess the dynamic damage risk of electric vehicle when driving into the road pre-aiming area under the current torque change scheme.

[0106] The formula for assessing dynamic damage risk is as follows:

[0107]

[0108] In the formula, R represents the dynamic damage risk of the electric vehicle when it travels within the road pre-aiming area under the current torque variation scheme, Tr represents the real-time winding temperature of the electric vehicle's motor, and Tr max This is the maximum safe operating temperature for the motor.

[0109] For example, this embodiment focuses on the dynamic damage risk assessment of an electric vehicle when it travels within the road pre-aiming area under the current torque change scheme. The torque mutation threat level is used to reflect the linear loss of the electric vehicle's mechanical life due to torque mutation energy. The torque change trajectory matching state is used to quantify the deviation between the actual torque trajectory and the ideal torque trajectory emitted by the current torque change scheme, reflecting the adaptability of the current torque change scheme to the road segment in the road pre-aiming area. (Exponential term) Used to quantify the critical characteristics of thermal failure of motor windings, when the winding temperature approaches Tmax, the exponential term... By amplifying the risks posed by high temperatures to the actual operation of motors, we can accurately quantify the impact of accelerated failure characteristics of the thermal aging process of motor insulation on the dynamic damage risk of electric vehicles.

[0110] Specifically, this embodiment constructs a dynamic damage risk assessment model for electric vehicles by coupling analysis of the threat level of torque mutation, the matching state of torque change trajectory, and the critical characteristics of thermal failure of motor windings. This model achieves a comprehensive assessment of the dynamic damage risk of electric vehicles when driving into the road pre-aiming area under the current torque change scheme, providing a risk assessment basis for selecting the optimal torque change scheme in step S5.

[0111] In this embodiment, step S5, which selects the optimal torque change scheme for the torque change when the electric vehicle travels within the road pre-aiming area, includes the following specific steps:

[0112] S51. Obtain the dynamic damage risk assessment results of electric vehicles when driving into the road pre-aiming area under all torque change schemes.

[0113] S52. Based on the dynamic damage risk assessment results of the electric vehicle when it travels into the road preview area under all torque change schemes, obtain the torque change scheme that minimizes the dynamic damage risk assessment results of the electric vehicle when it travels into the road preview area as the optimal torque change scheme, and apply the optimal torque change scheme to the road driving control of the electric vehicle within the road preview area.

[0114] Example 2

[0115] like Figure 4 As shown, this embodiment provides a data analysis-based electric vehicle torque coordination control system, including:

[0116] The data acquisition module is used to acquire road preview data through electric vehicle sensors, and at the same time acquire the electric vehicle's electrical system operation data;

[0117] The torque mutation threat level analysis module is used to build a torque mutation threat level analysis model based on road preview data and electrical system operation data, and analyze the torque mutation threat level of electric vehicles when driving into the road preview area.

[0118] The torque change trajectory matching state analysis module is used to construct a torque change trajectory matching state analysis model based on road preview data and electrical system operation data, and to analyze the torque change trajectory matching state of electric vehicles when driving within the road preview area.

[0119] The dynamic damage risk assessment module is used to construct a dynamic damage risk assessment model for electric vehicles. It imports the analysis results of the threat level of torque mutation and the torque change trajectory matching status analysis results of electric vehicles when driving into the road pre-aiming area into the dynamic damage risk assessment model of electric vehicles to assess the dynamic damage risk of electric vehicles when driving into the road pre-aiming area.

[0120] The optimal torque variation scheme selection module is used to select the optimal torque variation scheme for the electric vehicle when it is driving into the road pre-aiming area based on the dynamic damage risk assessment results of the electric vehicle when it is driving into the road pre-aiming area.

[0121] The control module is used to control the operation of the data acquisition module, the torque sudden change threat level analysis module, the torque change trajectory matching status analysis module, the dynamic damage risk assessment module, and the optimal torque change scheme selection module.

[0122] The steps for implementing the corresponding functions of each parameter and each unit module in the data analysis-based electric vehicle torque coordination control system of the present invention described above can be referred to the parameters and steps in the embodiments of the data analysis-based electric vehicle torque coordination control method above, and will not be repeated here.

[0123] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0124] The systems, media, and methods provided in the embodiments of the present invention are in one-to-one correspondence. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0125] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0128] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0129] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0130] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0131] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0132] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A data analysis-based torque coordination control method for electric vehicles, characterized in that, Includes the following steps: S1. Obtain road preview data through electric vehicle sensors, and at the same time obtain the electric vehicle's electrical system operation data; S2. Based on road preview data and electrical system operation data, construct a torque mutation threat level analysis model to analyze the torque mutation threat level of electric vehicles when driving into the road preview area; S3. Based on road preview data and electrical system operation data, construct a torque change trajectory matching state analysis model to analyze the torque change trajectory matching state of electric vehicles when driving within the road preview area; S4. Construct a dynamic damage risk assessment model for electric vehicles. Import the analysis results of the threat level of torque mutation and the torque change trajectory matching status analysis results of electric vehicles when driving into the road pre-aiming area into the dynamic damage risk assessment model for electric vehicles to assess the dynamic damage risk of electric vehicles when driving into the road pre-aiming area. S5. Based on the dynamic damage risk assessment results of the electric vehicle when it travels into the road pre-aiming area, select the optimal torque change scheme for the torque change when the electric vehicle travels into the road pre-aiming area.

2. The electric vehicle torque coordination control method based on data analysis according to claim 1, characterized in that, Step S2 analyzes the threat level of sudden torque change in electric vehicles when they enter the road pre-aiming area, including the following specific steps: S21. Extract road forecast data and electrical system operation data under the current torque change scheme; S22. Based on the road forecast data and electrical system operation data under the current torque change scheme, construct a torque change threat level analysis model to analyze the torque change threat level of electric vehicles when driving into the road forecast area under the current torque change scheme, and obtain the analysis results of the torque change threat level of electric vehicles when driving into the road forecast area under the current torque change scheme. The formula for calculating the threat level of torque sudden change is as follows: TW = Bw·(1-Xy); In the formula, TW represents the threat level of sudden torque change of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme, Bw represents the threat level of torque fluctuation of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme, and Xy represents the torque control response margin of the electric vehicle when it travels into the road pre-aiming area under the current torque change scheme.

3. The electric vehicle torque coordination control method based on data analysis according to claim 2, characterized in that, The process of constructing the torque mutation threat level analysis model in step S22 includes the following specific steps: S221. Based on the road forecast data and electrical system operation data under the current torque change scheme, analyze the torque fluctuation threat level of the electric vehicle when it travels into the road forecast area under the current torque change scheme, and obtain the analysis results of the torque fluctuation threat level of the electric vehicle when it travels into the road forecast area under the current torque change scheme. S222. Based on the electrical system operation data under the current torque change scheme, analyze the torque control response margin of the electric vehicle when it travels within the road pre-aiming area under the current torque change scheme, and obtain the analysis results of the torque control response margin of the electric vehicle when it travels within the road pre-aiming area under the current torque change scheme.

4. The electric vehicle torque coordination control method based on data analysis according to claim 3, characterized in that, Step S3 analyzes the torque change trajectory matching status of the electric vehicle when it travels within the road pre-aiming area under the current torque change scheme, including the following specific steps: S31. Extract road forecast data and electrical system operation data under the current torque change scheme; S32. Based on the road preview data and electrical system operation data under the current torque change scheme, construct a torque change trajectory matching state analysis model, analyze the torque change trajectory matching state of the electric vehicle when it travels into the road preview area under the current torque change scheme, and obtain the analysis results of the torque change trajectory matching state of the electric vehicle when it travels into the road preview area under the current torque change scheme. The formula for calculating the torque change trajectory matching state is as follows: P = Ph·Gz; In the formula, P represents the matching state of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme, Ph represents the smoothness of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme, and Gz represents the tracking accuracy of the electric vehicle's torque change trajectory when it travels into the road pre-aiming area under the current torque change scheme.

5. The electric vehicle torque coordination control method based on data analysis according to claim 4, characterized in that, The process of constructing the torque change trajectory matching state analysis model in step S32 includes the following specific steps: S321. Based on the road forecast data and electrical system operation data under the current torque change scheme, analyze the smoothness of the electric vehicle torque change trajectory when it travels into the road forecast area under the current torque change scheme, and obtain the analysis results of the smoothness of the electric vehicle torque change trajectory when it travels into the road forecast area under the current torque change scheme. The formula for calculating the smoothness of the torque change trajectory is as follows: In the formula, Ph represents the smoothness of the electric vehicle's torque change trajectory when it reaches the road pre-aiming area under the current torque change scheme; tc represents the pre-aiming duration in the road pre-aiming data; ac(t) represents the real-time vehicle lateral acceleration within the time period tc before the current time in the electrical system operation data; ac represents the average vehicle lateral acceleration within the time period tc before the current time in the electrical system operation data; ψ(t) represents the real-time vehicle yaw rate within the time period tc before the current time in the electrical system operation data; ψ represents the average vehicle yaw rate within the time period tc before the current time in the electrical system operation data; and hq represents the real-time slip rate of the q-th tire of the vehicle at the current time in the electrical system operation data. This represents the average real-time slip ratio of all tires of the vehicle at the current moment, based on the electrical system operation data. S322. Based on the road preview data and electrical system operation data under the current torque change scheme, analyze the accuracy of electric vehicle torque change trajectory tracking when the vehicle is within the road preview area under the current torque change scheme, and obtain the analysis results of the accuracy of electric vehicle torque change trajectory tracking when the vehicle is within the road preview area under the current torque change scheme.

6. The electric vehicle torque coordination control method based on data analysis according to claim 5, characterized in that, The step S4, which involves constructing a dynamic damage risk assessment model for electric vehicles, includes the following specific steps: S41. Extract and analyze the threat level of sudden torque change of electric vehicles when driving into the road pre-aiming area under the current torque change scheme and the torque change trajectory matching status analysis results. S42. Based on the analysis results of the threat level of sudden torque change of electric vehicle when driving into the road pre-aiming area and the analysis results of torque change trajectory matching status, assess the dynamic damage risk of electric vehicle when driving into the road pre-aiming area under the current torque change scheme. The formula for assessing dynamic damage risk is as follows: In the formula, R represents the dynamic damage risk of the electric vehicle when it travels within the road pre-aiming area under the current torque variation scheme, Tr represents the real-time winding temperature of the electric vehicle's motor, and Tr max This is the maximum safe operating temperature for the motor.

7. The electric vehicle torque coordination control method based on data analysis according to claim 6, characterized in that, Step S5 involves selecting the optimal torque change scheme for the electric vehicle when it travels within the pre-aimed area of ​​the road, including the following specific steps: S51. Obtain the dynamic damage risk assessment results of electric vehicles when driving into the road pre-aiming area under all torque change schemes. S52. Based on the dynamic damage risk assessment results of the electric vehicle when it travels into the road preview area under all torque change schemes, obtain the torque change scheme that minimizes the dynamic damage risk assessment results of the electric vehicle when it travels into the road preview area as the optimal torque change scheme, and apply the optimal torque change scheme to the road driving control of the electric vehicle within the road preview area.

8. A data-analysis-based electric vehicle torque coordination control system, implemented based on any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to acquire road preview data through electric vehicle sensors, and at the same time acquire the electric vehicle's electrical system operation data; The torque mutation threat level analysis module is used to build a torque mutation threat level analysis model based on road preview data and electrical system operation data, and analyze the torque mutation threat level of electric vehicles when driving into the road preview area. The torque change trajectory matching state analysis module is used to construct a torque change trajectory matching state analysis model based on road preview data and electrical system operation data, and to analyze the torque change trajectory matching state of electric vehicles when driving within the road preview area. The dynamic damage risk assessment module is used to construct a dynamic damage risk assessment model for electric vehicles. It imports the analysis results of the threat level of torque mutation and the torque change trajectory matching status analysis results of electric vehicles when driving into the road pre-aiming area into the dynamic damage risk assessment model of electric vehicles to assess the dynamic damage risk of electric vehicles when driving into the road pre-aiming area. The optimal torque variation scheme selection module is used to select the optimal torque variation scheme for the electric vehicle when it is driving into the road pre-aiming area based on the dynamic damage risk assessment results of the electric vehicle when it is driving into the road pre-aiming area. The control module is used to control the operation of the data acquisition module, the torque mutation threat level analysis module, the torque change trajectory matching status analysis module, the dynamic damage risk assessment module, and the optimal torque change scheme selection module.

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