An intelligent control system for power recovery of electric vehicles

Abstract

The invention discloses an intelligent control system for power recovery of electric vehicles, belonging to the technical field of automobile power control. The system monitors and compiles statistics on the speed data and steering wheel rotation data of a target vehicle, processes and analyzes the operation control status of the monitored and statistical data, supervises the speed data and active braking data of the target vehicle, and performs periodic external operation impact processing and analysis, supervises the battery data of the target vehicle, and performs periodic internal operation impact processing and analysis, periodically integrates and analyzes the power recovery risk supervision processing data of different aspects in the early stage, and adaptively and dynamically controls the real-time power recovery control of the target vehicle according to the analysis results. The invention is used to solve the technical problems of poor multi-dimensional supervision and analysis effect and poor autonomous adjustment and control effect of dynamic recovery control of electric vehicles in existing solutions.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile power control, and in particular to an intelligent control system for power recovery of an electric vehicle. Background Art

[0002] Regenerative braking in electric vehicles allows the vehicle to convert some of its kinetic energy into electrical energy during deceleration or braking and store it back in the battery; this process not only helps improve the energy efficiency of electric vehicles, but also extends the driving range on a single charge.

[0003] When implementing existing electric vehicle power recovery control schemes, most of them still remain at a single-dimensional vehicle operation data supervision and evaluation control. For example, the prior art with application number 202311450468.6 and name of an electric vehicle power recovery method and system calculates the recoverable rate of real-time brake energy recovery of the motor and compares it with the preset recoverable battery load value, thereby managing the real-time brake energy recovery of the motor by managing the recoverable rate of real-time brake energy recovery of the motor. Compared with the prediction troubles and low accuracy brought about by the existing random allocation, the electric vehicle power recovery method and system of each embodiment of the invention can achieve management of the difficulty of allocation management, operability and lateral differences of real-time brake energy recovery of the motor by managing the recoverable rate of real-time brake energy recovery of the motor, thereby improving the controllability of the wheel speed control platform, improving the power recovery efficiency, and being suitable for uninterrupted power recovery.

[0004] However, when the existing technical solutions are implemented, the external environmental factors during vehicle operation are not supervised and analyzed in a multi-dimensional integrated manner to determine whether the vehicle's power recovery meets the recovery requirements and adaptively adjust them dynamically, resulting in poor multi-dimensional supervision and analysis of dynamic recovery control of electric vehicles and poor autonomous adjustment and control effects. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent control system for electric vehicle power recovery, which is used to solve the technical problems of poor multi-dimensional supervision and analysis effect and poor autonomous adjustment and control effect of dynamic recovery control of electric vehicles in existing solutions.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] An electric vehicle power recovery intelligent control system, comprising:

[0008] A first vehicle operation state monitoring processing and analysis module is used to monitor and count the vehicle speed data and steering wheel rotation data of the target vehicle, and process and analyze the operation control state of the monitoring and statistical data to obtain first operation monitoring data;

[0009] The second vehicle operation state supervision processing and analysis module is used to supervise the speed data and active braking data of the target vehicle, and perform periodic external operation impact processing and analysis to obtain the second operation supervision data corresponding to the target vehicle in the basic supervision period;

[0010] The third vehicle operation status supervision processing and analysis module is used to supervise the battery data of the target vehicle and perform periodic internal operation impact processing and analysis to obtain the third operation supervision data corresponding to the target vehicle in the basic supervision period;

[0011] The vehicle operation power recovery status evaluation control module is used to periodically integrate and analyze the first operation supervision data, the second operation supervision data and the third operation supervision data to determine whether the real-time power recovery of the target vehicle meets the recovery requirements, and dynamically manage the real-time power recovery control of the target vehicle based on the analysis results.

[0012] Preferably, the real-time speed of the target vehicle is monitored and acquired, and the real-time speed ZY of the corresponding motor drive shaft is acquired according to the real-time speed of the target vehicle;

[0013] and, obtaining a steering angle when the steering wheel of the target vehicle is turned;

[0014] The calculated real-time speed and steering angle are analyzed through the speed operation recognition model, and the operation supervision value YJ corresponding to the target vehicle operation is output.

[0015] Preferably, the expression of the speed operation identification model is: ; In the formula, ZY0 is the standard speed of the motor drive shaft corresponding to the real-time vehicle speed; U1 is the standard range of the speed error of the motor drive shaft; ZJ is the yaw rate corresponding to the steering angle when the target vehicle's steering wheel is turned; ZJ0 is the standard yaw rate corresponding to the steering angle when the target vehicle's steering wheel is turned; U2 is the standard range of the wheel deflection error when the target vehicle's steering wheel is turned;

[0016] Generate an external normal operation label and prompt according to the speed operation value of 0;

[0017] Generate an external operation abnormality label and prompt according to the speed operation value of -1;

[0018] The operation supervision value obtained by calculation and the external normal operation label or the external abnormal operation label obtained by analysis are sorted and combined to obtain the first operation supervision data.

[0019] Preferably, the average speed of the target vehicle in the basic supervision cycle and the total number of active braking times of the target vehicle in the basic supervision cycle are monitored and obtained;

[0020] If the average vehicle speed is greater than the maximum average vehicle speed, a first necessary recycling tag is generated and prompted;

[0021] If the average speed is less than the maximum average speed, then the formula Calculate and obtain the external impact value YS of the target vehicle in the basic supervision cycle; where Vp and Zd are the average speed and total number of active braking of the target vehicle in the basic supervision cycle; Vb and Zb are the standard average speed and total number of standard active braking of the target vehicle in the basic supervision cycle; a is the error factor, and its value range is (0, 1); is the floor rounding function;

[0022] Perform data analysis on the external impact values ​​to determine the local external impact status corresponding to the target vehicle within its basic supervision cycle.

[0023] Preferably, if the external impact value is 1, it is determined that the local external impact of the target vehicle in the corresponding basic supervision period is abnormal, and a second recycling necessary label is generated and prompted;

[0024] If the external impact value is not 1, it is determined that the local external impact of the target vehicle within the basic supervision cycle is normal, and an operation observation label is generated and prompted;

[0025] The first necessary recycling label, the second necessary recycling label or the operation observation label obtained by the supervision are sorted and combined to obtain the second operation supervision data corresponding to the target vehicle in the basic supervision cycle.

[0026] Preferably, the real-time remaining battery power and the real-time battery temperature of the target vehicle during operation are obtained within the basic supervision cycle, and the real-time remaining battery power and the real-time battery temperature are subjected to data analysis through an internal operation identification model, and an internal impact value NY of the target vehicle in the corresponding basic supervision cycle is output;

[0027] The expression of the internal running recognition model is ; In the formula, DL and DW are the real-time remaining battery power and real-time battery temperature of the target vehicle when it is running; DL0 and DW0 are the battery limit power and battery limit temperature of the target vehicle when it is running.

[0028] Preferably, an internal impact normal label is generated and prompted according to the internal impact value having a value of 0;

[0029] Generate an internal impact abnormal label and prompt according to the internal impact value with a value of 1;

[0030] The calculated internal impact values ​​and the internal impact normal labels or the internal impact abnormal labels are sorted and combined to obtain the third operation supervision data corresponding to the target vehicle in the corresponding basic supervision cycle.

[0031] Preferably, when periodically integrating and analyzing the first operation supervision data, the second operation supervision data, and the third operation supervision data, the first operation supervision data, the second operation supervision data, and the third operation supervision data are traversed respectively, and data analysis is performed on the traversal results;

[0032] If there are no external operation abnormality labels and internal impact abnormality labels in the traversal results, it is determined that the real-time power recovery of the target vehicle meets the recovery requirements, and necessary recovery control instructions are generated. According to the necessary recovery control instructions, the target vehicle is controlled to perform real-time power recovery within the basic supervision cycle.

[0033] Preferably, if there is an external operation abnormality tag or an internal impact abnormality tag in the traversal result, it is determined that the real-time power recovery of the target vehicle does not meet the recovery requirement, and the target vehicle is controlled not to perform power recovery within the corresponding basic supervision cycle.

[0034] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0035] The present invention monitors and compiles statistics on the speed data and steering wheel rotation data of the target vehicle, and processes and analyzes the operation control status of the monitored statistical data. This can not only realize digital processing of the ground operation risk supervision status of the target vehicle during operation, but also provide reliable external operation risk supervision data support for subsequent power recovery control of the target vehicle.

[0036] The present invention monitors the speed data and active braking data of the target vehicle and performs periodic external operation impact processing and analysis. It can not only realize the digital processing of the traffic road condition supervision status of the target vehicle during operation, but also provide reliable other external operation risk supervision data support for the subsequent power recovery control of the target vehicle.

[0037] The present invention monitors the battery data of the target vehicle and performs periodic internal operation impact processing and analysis, which can not only realize the digital processing of the battery supervision status of the target vehicle during operation, but also provide reliable internal battery operation risk supervision data support for the subsequent power recovery control of the target vehicle.

[0038] The present invention periodically integrates and analyzes the power recovery risk supervision processing data from different aspects in the early stage, and adaptively dynamically manages the real-time power recovery control of the target vehicle according to the analysis results, thereby realizing active multi-dimensional risk supervision analysis and autonomous recovery control of electric vehicle power recovery, and improving the multi-dimensional supervision analysis effect and autonomous adjustment and control effect of dynamic recovery control of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] Figure 1 The present invention is a module block diagram of an intelligent control system for electric vehicle power recovery.

[0041] Figure 2 The present invention is a block diagram of the operation principle of an intelligent control system for electric vehicle power recovery.

[0042] Figure 3 This is a principle block diagram of the data analysis and dynamic management of the real-time power recovery control of the target vehicle in the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] like Figure 1 to Figure 2 As shown, the present invention is an electric vehicle power recovery intelligent control system, comprising a first vehicle operation state supervision processing and analysis module, a second vehicle operation state supervision processing and analysis module, a third vehicle operation state supervision processing and analysis module and a vehicle operation power recovery state evaluation control module;

[0045] The first vehicle operation status monitoring processing and analysis module is used to monitor and count the speed data and steering wheel rotation data of the target vehicle, and process and analyze the operation control status of the monitoring and statistical data to obtain the first operation supervision data; including:

[0046] Monitor and obtain the real-time speed of the target vehicle, which is specifically an electric vehicle, and obtain the real-time speed ZY of the corresponding motor drive shaft according to the real-time speed of the target vehicle; the unit is RPM; the real-time speed of the corresponding motor drive shaft obtained according to the real-time speed can be determined according to the design data of the target vehicle;

[0047] And, obtaining the steering angle of the target vehicle when the steering wheel is turned; the unit is degree; obtained by detecting through an angle detection sensor;

[0048] The calculated real-time speed and steering angle are analyzed through the speed operation recognition model, and the operation supervision value YJ corresponding to the target vehicle operation is output;

[0049] The expression of the speed operation identification model is: ; Wherein, ZY0 is the standard speed of the motor drive shaft corresponding to the real-time vehicle speed; U1 is the standard range of the speed error of the motor drive shaft; ZJ is the yaw rate corresponding to the steering angle when the steering wheel of the target vehicle is turned; ZJ0 is the standard yaw rate corresponding to the steering angle when the steering wheel of the target vehicle is turned, and the yaw rate refers to the speed at which the vehicle rotates around the vertical axis; U2 is the standard range of the wheel deflection error when the steering wheel of the target vehicle is turned, and the speed error standard range and the wheel deflection error standard range can be determined based on the operation design data of the target vehicle, or the early operation test data of the target vehicle;

[0050] It should be explained that on slippery or icy roads, in order to prevent the wheels from slipping or losing grip, the power recovery control system may limit the regenerative braking force, give priority to ensuring vehicle stability and avoid ABS (anti-lock braking system) activation, so it is necessary to actively control the vehicle's power recovery;

[0051] In an embodiment of the present invention, by processing and calculating and analyzing the real-time vehicle speed and steering angle of the target vehicle, the speed operating status of the target vehicle is determined and dynamically marked, which can provide reliable ground operation risk supervision data support for subsequent power recovery control of the target vehicle.

[0052] The speed operation value contains a value of 0 or -1;

[0053] Generate an external normal operation label and prompt according to the speed operation value of 0;

[0054] Generate an external operation abnormality label and prompt according to the speed operation value of -1;

[0055] The operation supervision value obtained by calculation and the external normal operation label or the external abnormal operation label obtained by analysis are sorted and combined to obtain first operation supervision data;

[0056] In the embodiment of the present invention, by monitoring and counting the vehicle speed data and steering wheel rotation data of the target vehicle, and processing and analyzing the operation control status of the monitored statistical data, it is possible to digitally process the ground operation risk supervision status of the target vehicle during operation, and provide reliable external operation risk supervision data support for the subsequent power recovery control of the target vehicle.

[0057] The second vehicle operation state supervision processing and analysis module is used to supervise the speed data and active braking data of the target vehicle, and perform periodic external operation impact processing and analysis to obtain the second operation supervision data corresponding to the target vehicle in the basic supervision period; including:

[0058] Monitor and obtain the average speed of the target vehicle within the basic supervision cycle, where the unit of the basic supervision cycle is minutes, specifically 2 minutes, and the total number of active braking times of the target vehicle within the basic supervision cycle;

[0059] If the average vehicle speed is greater than the maximum average vehicle speed, a first necessary recovery tag is generated and prompted; the maximum average vehicle speed can be determined based on the power recovery vehicle speed design data of the target vehicle, or based on the previous test data of the target vehicle;

[0060] In scenarios such as highways, due to the large kinetic energy of the vehicle, the appropriate use of energy recovery can significantly save energy during deceleration; it is worth noting that the technical solution disclosed in the embodiment of the present invention is implemented on the existing test scenarios and test schemes, so the vehicle weight and road slope can be adjusted according to the actual test scheme, the vehicle weight is a non-important influencing factor, the vehicle weight can be limited to a constant, and the specific implementation application scenarios are limited to flat ground to avoid the influence of different road slopes on the processing and analysis of test data;

[0061] If the average speed is less than the maximum average speed, it can be determined based on the previous test data of the target vehicle, then the formula Calculate and obtain the external impact value YS of the target vehicle within the basic supervision cycle; where Vp and Zd are the average speed and total number of active braking of the target vehicle within the basic supervision cycle, respectively; Vb and Zb are the standard average speed and total number of standard active braking of the target vehicle within the basic supervision cycle, respectively, which are determined based on the power recovery speed design data of the target vehicle, or based on the previous test data of the target vehicle; a is an error factor, and its value range is (0, 1), and the specific value is not limited; is the floor rounding function;

[0062] It should be noted that the external impact value is used to integrate and calculate the average vehicle speed data and active braking data of the target vehicle in the basic supervision cycle to digitally represent the local external impact state corresponding to the target vehicle in the basic supervision cycle;

[0063] In addition, when the average vehicle speed is lower and the total number of active braking times is higher, it means that the target vehicle is in a congested section and has frequent starts and stops. Frequent energy recovery helps improve overall energy efficiency.

[0064] Perform data analysis on the external impact values ​​to determine the local external impact status of the target vehicle within the basic supervision cycle;

[0065] If the external impact value is 1, the local external impact of the target vehicle in the basic supervision period is determined to be abnormal, and a second necessary recycling label is generated and prompted;

[0066] If the external impact value is not 1, it is determined that the local external impact of the target vehicle within the basic supervision cycle is normal, and an operation observation label is generated and prompted;

[0067] The first necessary recycling label, the second necessary recycling label or the operation observation label obtained by the supervision are sorted and combined to obtain the second operation supervision data corresponding to the target vehicle in the basic supervision cycle;

[0068] In the embodiment of the present invention, by monitoring the speed data and active braking data of the target vehicle and performing periodic external operation impact processing and analysis, it is possible to digitally process the traffic road condition supervision status of the target vehicle during operation, and provide reliable other external operation risk supervision data support for the subsequent power recovery control of the target vehicle.

[0069] The third vehicle operation status supervision processing and analysis module is used to supervise the battery data of the target vehicle and perform periodic internal operation impact processing and analysis to obtain the third operation supervision data corresponding to the target vehicle in the basic supervision period; including:

[0070] The real-time remaining battery power and real-time battery temperature of the target vehicle during operation are obtained within the basic supervision cycle, and the real-time remaining battery power and real-time battery temperature are analyzed through the internal operation identification model to output the internal impact value NY of the target vehicle in the basic supervision cycle to which it belongs;

[0071] The expression of the internal running recognition model is ; In the formula, DL and DW are the real-time battery remaining power and real-time battery temperature of the target vehicle when it is running; DL0 and DW0 are the battery limit power and battery limit temperature of the target vehicle when it is running, which can be determined according to the power recovery battery design data of the target vehicle;

[0072] It is understandable that when the remaining battery power is too high and the real-time battery temperature is too high, it will affect the health and life of the battery. Therefore, it is necessary to limit power recovery to avoid affecting the normal use of the battery.

[0073] Internal impact values ​​contain values ​​of 0 or 1;

[0074] Generate an internal impact normal label and prompt according to the internal impact value of 0;

[0075] Generate an internal impact abnormal label and prompt according to the internal impact value with a value of 1;

[0076] The calculated internal impact value and the internal impact normal label or the internal impact abnormal label are sorted and combined to obtain the third operation supervision data corresponding to the target vehicle in the corresponding basic supervision cycle;

[0077] In the embodiment of the present invention, by monitoring the battery data of the target vehicle and performing periodic internal operation impact processing and analysis, it is possible to digitally process the battery monitoring status of the target vehicle during operation, and provide reliable internal battery operation risk monitoring data support for subsequent power recovery control of the target vehicle.

[0078] The vehicle operation power recovery state evaluation control module is used to periodically integrate and analyze the first operation supervision data, the second operation supervision data and the third operation supervision data to determine whether the real-time power recovery of the target vehicle meets the recovery requirements, and dynamically control the real-time power recovery control of the target vehicle according to the analysis results; including:

[0079] When periodically integrating and analyzing the first operation supervision data, the second operation supervision data, and the third operation supervision data, the first operation supervision data, the second operation supervision data, and the third operation supervision data are traversed respectively, and data analysis is performed on the traversal results;

[0080] like Figure 3 As shown, if there are no external operation abnormality labels and internal impact abnormality labels in the traversal results, it is determined that the real-time power recovery of the target vehicle meets the recovery requirements, and a necessary recovery control instruction is generated. According to the necessary recovery control instruction, the target vehicle is controlled to perform real-time power recovery within the basic supervision cycle.

[0081] If there are external operation abnormality tags or internal impact abnormality tags in the traversal results, it is determined that the real-time power recovery of the target vehicle does not meet the recovery requirements, and the target vehicle is controlled not to perform power recovery within the basic supervision cycle.

[0082] Different from the existing technical solutions which still stay at the processing, analysis and management of power recovery data, without actively supervising and analyzing the implementation risks of electric vehicle power recovery from different dimensions, and dynamically managing power recovery, to avoid the negative impact of inappropriate power recovery on the electric vehicle's power system; in the embodiment of the present invention, by periodically integrating and analyzing the power recovery risk supervision processing data from different aspects in the early stage, and adaptively dynamically controlling the real-time power recovery control of the target vehicle based on the analysis results, active multi-dimensional risk supervision analysis and autonomous recovery control of electric vehicle power recovery are achieved, thereby improving the multi-dimensional supervision analysis effect and autonomous adjustment and control effect of dynamic recovery control of electric vehicles.

[0083] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0084] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0086] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An intelligent control system for power recovery of electric vehicles, characterized in that: include: A first vehicle operation state monitoring processing and analysis module is used to monitor and count the vehicle speed data and steering wheel rotation data of the target vehicle, and process and analyze the operation control state of the monitoring and statistical data to obtain first operation monitoring data; The second vehicle operation state supervision processing and analysis module is used to supervise the speed data and active braking data of the target vehicle, and perform periodic external operation impact processing and analysis to obtain the second operation supervision data corresponding to the target vehicle in the basic supervision period; Among them, the average speed of the target vehicle during the basic supervision cycle and the total number of active braking times of the target vehicle during the basic supervision cycle are monitored; If the average vehicle speed is greater than the maximum average vehicle speed, a first necessary recycling tag is generated and prompted; If the average speed is less than the maximum average speed, then the formula Calculate and obtain the external impact value YS of the target vehicle in the basic supervision cycle; where Vp and Zd are the average speed and total number of active braking of the target vehicle in the basic supervision cycle; Vb and Zb are the standard average speed and total number of standard active braking of the target vehicle in the basic supervision cycle; a is the error factor, and its value range is (0, 1); is the floor rounding function; Perform data analysis on the external impact value, and sort and combine the second necessary recycling label or operation observation label obtained by the analysis with the first necessary recycling label to obtain the second operation supervision data corresponding to the target vehicle within the basic supervision cycle; The third vehicle operation status supervision processing and analysis module is used to supervise the battery data of the target vehicle and perform periodic internal operation impact processing and analysis to obtain the third operation supervision data corresponding to the target vehicle in the basic supervision period; The vehicle operation power recovery status evaluation control module is used to periodically integrate and analyze the first operation supervision data, the second operation supervision data and the third operation supervision data to determine whether the real-time power recovery of the target vehicle meets the recovery requirements, and dynamically manage the real-time power recovery control of the target vehicle based on the analysis results.

2. The electric vehicle power recovery intelligent control system according to claim 1, characterized in that: Monitor and obtain the real-time speed of the target vehicle, and obtain the standard speed ZY0 of the corresponding tire according to the real-time speed of the target vehicle; and, obtaining a steering angle when the steering wheel of the target vehicle is turned; The calculated real-time vehicle speed and steering angle are analyzed through the speed operation recognition model, and the operation supervision value YJ corresponding to the target vehicle operation is output.

3. The electric vehicle power recovery intelligent control system according to claim 2, characterized in that: The expression of the speed operation identification model is: ; Where ZY is the actual tire speed; U1 is the wheel slip error range of the target vehicle; ZJ is the yaw rate corresponding to the steering angle when the target vehicle's steering wheel is turned; ZJ0 is the standard yaw rate corresponding to the steering angle when the target vehicle's steering wheel is turned; U2 is the wheel deflection error range when the steering wheel of the target vehicle is turned; Generate an external normal operation label and prompt according to the speed operation value of 0; Generate an external operation abnormality label and prompt according to the speed operation value of -1; The operation supervision value obtained by calculation and the external normal operation label or the external abnormal operation label obtained by analysis are sorted and combined to obtain the first operation supervision data.

4. The electric vehicle power recovery intelligent control system according to claim 1, characterized in that: If the external impact value is 1, the local external impact of the target vehicle in the basic supervision period is determined to be abnormal, and a second necessary recycling label is generated and prompted; If the external impact value is not 1, it is determined that the local external impact of the target vehicle within the basic supervision cycle is normal, and an operation observation label is generated and prompted.

5. The electric vehicle power recovery intelligent control system according to claim 4, characterized in that: The real-time remaining battery power and real-time battery temperature of the target vehicle during operation are obtained within the basic supervision cycle, and the real-time remaining battery power and real-time battery temperature are analyzed through the internal operation identification model to output the internal impact value NY of the target vehicle in the basic supervision cycle to which it belongs; The expression of the internal running recognition model is ; In the formula, DL and DW are the real-time remaining battery power and real-time battery temperature of the target vehicle when it is running; DL0 and DW0 are the battery limit power and battery limit temperature of the target vehicle when it is running.

6. The electric vehicle power recovery intelligent control system according to claim 5, characterized in that: Generate an internal impact normal label and prompt according to the internal impact value of 0; Generate an internal impact abnormal label and prompt according to the internal impact value with a value of 1; The calculated internal impact value and the internal impact normal label or the internal impact abnormal label are sorted and combined to obtain the third operation supervision data corresponding to the target vehicle in the basic supervision cycle.

7. The electric vehicle power recovery intelligent control system according to claim 6, characterized in that: When periodically integrating and analyzing the first operation supervision data, the second operation supervision data, and the third operation supervision data, the first operation supervision data, the second operation supervision data, and the third operation supervision data are traversed respectively, and data analysis is performed on the traversal results; If there are no external operation abnormality labels and internal impact abnormality labels in the traversal results, it is determined that the real-time power recovery of the target vehicle meets the recovery requirements, and necessary recovery control instructions are generated. According to the necessary recovery control instructions, the target vehicle is controlled to perform real-time power recovery within the basic supervision cycle.

8. The electric vehicle power recovery intelligent control system according to claim 7, characterized in that: If there are external operation abnormality tags or internal impact abnormality tags in the traversal results, it is determined that the real-time power recovery of the target vehicle does not meet the recovery requirements, and the target vehicle is controlled not to perform power recovery within the basic supervision cycle.

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